diff --git a/_quarto.yml b/_quarto.yml index a9305e4e..4ce30d30 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -6,6 +6,11 @@ project: navigate: true render: - "*.qmd" + - "contents/*.qmd" + - "contents/*/*.qmd" + - "contents/*/*/*.qmd" + - "contents/*/*/*/*.qmd" + - "contents/*/*/*/*/*.qmd" # contents/labs////*.qmd title-prefix: "" @@ -44,6 +49,8 @@ book: cover-image: cover-image-transparent.png cover-image-alt: "Cover image." + bread-crumbs: true + sidebar: collapse-level: 2 border: true @@ -113,21 +120,34 @@ book: chapters: - contents/conclusion/conclusion.qmd - text: "---" + # LABS + - part: LABS + # nicla vision + - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd + chapters: + - contents/labs/arduino/nicla_vision/setup/setup.qmd + - contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd + - contents/labs/arduino/nicla_vision/object_detection/object_detection.qmd + - contents/labs/arduino/nicla_vision/kws/kws.qmd + - contents/labs/arduino/nicla_vision/motion_classification/motion_classification.qmd + # xiao sense + - part: contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd + chapters: + - contents/labs/seeed/xiao_esp32s3/setup/setup.qmd + - contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd + - contents/labs/seeed/xiao_esp32s3/object_detection/object_detection.qmd + - contents/labs/seeed/xiao_esp32s3/kws/kws.qmd + - contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd + - part: contents/labs/shared/shared.qmd + chapters: + - contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd + - contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd + - text: "---" + # REFERENCES - part: REFERENCES chapters: - references.qmd - text: "---" - - part: contents/labs.qmd - chapters: - - contents/niclav_sys/niclav_sys.qmd - - contents/image_classification/image_classification.qmd - - contents/object_detection_fomo/object_detection_fomo.qmd - - contents/kws_feature_eng/kws_feature_eng.qmd - - contents/kws_nicla/kws_nicla.qmd - - contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd - - contents/motion_classify_ad/motion_classify_ad.qmd - - text: "---" - appendices: - contents/tools.qmd - contents/zoo_datasets.qmd @@ -144,23 +164,17 @@ filters: - custom_callout.lua bibliography: + # main - contents/introduction/introduction.bib - contents/ai_for_good/ai_for_good.bib - contents/benchmarking/benchmarking.bib - contents/data_engineering/data_engineering.bib - contents/dl_primer/dl_primer.bib - - contents/dsp_spectral_features_block/dsp_spectral_features_block.bib - contents/efficient_ai/efficient_ai.bib - contents/ml_systems/ml_systems.bib - contents/frameworks/frameworks.bib - contents/generative_ai/generative_ai.bib - contents/hw_acceleration/hw_acceleration.bib - - contents/image_classification/image_classification.bib - - contents/kws_feature_eng/kws_feature_eng.bib - - contents/kws_nicla/kws_nicla.bib - - contents/motion_classify_ad/motion_classify_ad.bib - - contents/niclav_sys/niclav_sys.bib - - contents/object_detection_fomo/object_detection_fomo.bib - contents/ondevice_learning/ondevice_learning.bib - contents/ops/ops.bib - contents/optimizations/optimizations.bib diff --git a/contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd b/contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd index 5b7daade..f0f5cc76 100644 --- a/contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd +++ b/contents/dsp_spectral_features_block/dsp_spectral_features_block.qmd @@ -2,7 +2,7 @@ bibliography: dsp_spectral_features_block.bib --- -# DSP - Spectral Features {.unnumbered} +# DSP Spectral Features {.unnumbered} ![*DALL·E 3 Prompt: 1950s style cartoon illustration of a Latin male and female scientist in a vibration research room. The man is using a calculus ruler to examine ancient circuitry. The woman is at a computer with complex vibration graphs. The wooden table has boards with sensors, prominently an accelerometer. A classic, rounded-back computer shows the Arduino IDE with code for LED pin assignments and machine learning algorithms for movement detection. The Serial Monitor displays FFT, classification, wavelets, and DSPs. Vintage lamps, tools, and charts with FFT and Wavelets graphs complete the scene.*](images/jpg/dsp_ini.jpg){fig-align="center" width="6.5in"} diff --git a/contents/labs.qmd b/contents/labs.qmd deleted file mode 100644 index 3798952a..00000000 --- a/contents/labs.qmd +++ /dev/null @@ -1,7 +0,0 @@ -# HANDS-ON LABS - -The following labs provide a unique opportunity to gain hands-on experience deploying TinyML models onto real embedded devices. In contrast to working with large models that require data center-scale resources, these labs allow you to interact directly with the hardware and software, giving you a tangible understanding of the challenges and opportunities in embedded AI. - -From setting up the [Nicla Vision](https://store.arduino.cc/products/nicla-vision) board to implementing computer vision, audio processing, and motion classification tasks using tools like [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers) and Arduino firmware, you'll develop practical skills in deploying efficient AI models on resource-constrained devices. By completing these labs, you'll appreciate the beauty of TinyML—the ability to hold cutting-edge AI technology in the palm of your hand. This hands-on perspective is invaluable for understanding the end-to-end workflow of embedded AI systems and will prepare you for real-world applications where model efficiency, robustness, and responsiveness are paramount. In the future, we plan to add a few other platforms. Please stay tuned! - -These lab exercises are the contributions of [Marcelo Rovai](https://github.com/Mjrovai). \ No newline at end of file diff --git a/contents/kws_feature_eng/kws_feature_eng.bib b/contents/labs/arduino/nicla_vision/image_classification/image_classification.bib similarity index 100% rename from contents/kws_feature_eng/kws_feature_eng.bib rename to contents/labs/arduino/nicla_vision/image_classification/image_classification.bib diff --git a/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd b/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd new file mode 100644 index 00000000..6cc27c7e --- /dev/null +++ b/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd @@ -0,0 +1,518 @@ +--- +bibliography: image_classification.bib +--- + +# Image Classification {.unnumbered} + +![*DALL·E 3 Prompt: Cartoon in a 1950s style featuring a compact electronic device with a camera module placed on a wooden table. The screen displays blue robots on one side and green periquitos on the other. LED lights on the device indicate classifications, while characters in retro clothing observe with interest.*](images/jpg/img_class_ini.jpg){fig-align="center" width="6.5in"} + +## Introduction + +As we initiate our studies into embedded machine learning or TinyML, it's impossible to overlook the transformative impact of Computer Vision (CV) and Artificial Intelligence (AI) in our lives. These two intertwined disciplines redefine what machines can perceive and accomplish, from autonomous vehicles and robotics to healthcare and surveillance. + +More and more, we are facing an artificial intelligence (AI) revolution where, as stated by Gartner, **Edge AI** has a very high impact potential, and **it is for now**! + +![](images/jpg/image2.jpg){fig-align="center" width="4.729166666666667in"} + +In the "bullseye" of the Radar is the *Edge Computer Vision*, and when we talk about Machine Learning (ML) applied to vision, the first thing that comes to mind is **Image Classification**, a kind of ML "Hello World"! + +This exercise will explore a computer vision project utilizing Convolutional Neural Networks (CNNs) for real-time image classification. Leveraging TensorFlow's robust ecosystem, we'll implement a pre-trained MobileNet model and adapt it for edge deployment. The focus will be on optimizing the model to run efficiently on resource-constrained hardware without sacrificing accuracy. + +We'll employ techniques like quantization and pruning to reduce the computational load. By the end of this tutorial, you'll have a working prototype capable of classifying images in real-time, all running on a low-power embedded system based on the Arduino Nicla Vision board. + +## Computer Vision + +At its core, computer vision aims to enable machines to interpret and make decisions based on visual data from the world, essentially mimicking the capability of the human optical system. Conversely, AI is a broader field encompassing machine learning, natural language processing, and robotics, among other technologies. When you bring AI algorithms into computer vision projects, you supercharge the system's ability to understand, interpret, and react to visual stimuli. + +When discussing Computer Vision projects applied to embedded devices, the most common applications that come to mind are *Image Classification* and *Object Detection*. + +![](images/jpg/image15.jpg){fig-align="center" width="6.5in"} + +Both models can be implemented on tiny devices like the Arduino Nicla Vision and used on real projects. In this chapter, we will cover Image Classification. + +## Image Classification Project Goal + +The first step in any ML project is to define the goal. In this case, it is to detect and classify two specific objects present in one image. For this project, we will use two small toys: a *robot* and a small Brazilian parrot (named *Periquito*). Also, we will collect images of a *background* where those two objects are absent. + +![](images/jpg/image36.jpg){fig-align="center" width="6.5in"} + +## Data Collection + +Once you have defined your Machine Learning project goal, the next and most crucial step is the dataset collection. You can use the Edge Impulse Studio, the OpenMV IDE we installed, or even your phone for the image capture. Here, we will use the OpenMV IDE for that. + +### Collecting Dataset with OpenMV IDE + +First, create in your computer a folder where your data will be saved, for example, "data." Next, on the OpenMV IDE, go to `Tools > Dataset Editor` and select `New Dataset` to start the dataset collection: + +![](images/png/image29.png){fig-align="center" width="6.291666666666667in"} + +The IDE will ask you to open the file where your data will be saved and choose the "data" folder that was created. Note that new icons will appear on the Left panel. + +![](images/png/image46.png){fig-align="center" width="0.9583333333333334in"} + +Using the upper icon (1), enter with the first class name, for example, "periquito": + +![](images/png/image22.png){fig-align="center" width="3.25in"} + +Running the `dataset_capture_script.py` and clicking on the camera icon (2), will start capturing images: + +![](images/png/image43.png){fig-align="center" width="6.5in"} + +Repeat the same procedure with the other classes + +![](images/jpg/image6.jpg){fig-align="center" width="6.5in"} + +> We suggest around 60 images from each category. Try to capture different angles, backgrounds, and light conditions. + +The stored images use a QVGA frame size of 320x240 and the RGB565 (color pixel format). + +After capturing your dataset, close the Dataset Editor Tool on the `Tools > Dataset Editor`. + +On your computer, you will end with a dataset that contains three classes: *periquito,* *robot*, and *background*. + +![](images/png/image20.png){fig-align="center" width="6.5in"} + +You should return to *Edge Impulse Studio* and upload the dataset to your project. + +## Training the model with Edge Impulse Studio + +We will use the Edge Impulse Studio for training our model. Enter your account credentials and create a new project: + +![](images/png/image45.png){fig-align="center" width="6.5in"} + +> Here, you can clone a similar project: [NICLA-Vision_Image_Classification](https://studio.edgeimpulse.com/public/273858/latest). + +## Dataset + +Using the EI Studio (or *Studio*), we will go over four main steps to have our model ready for use on the Nicla Vision board: Dataset, Impulse, Tests, and Deploy (on the Edge Device, in this case, the NiclaV). + +![](images/jpg/image41.jpg){fig-align="center" width="6.5in"} + +Regarding the Dataset, it is essential to point out that our Original Dataset, captured with the OpenMV IDE, will be split into *Training*, *Validation*, and *Test*. The Test Set will be divided from the beginning, and a part will reserved to be used only in the Test phase after training. The Validation Set will be used during training. + +![](images/jpg/image7.jpg){fig-align="center" width="6.5in"} + +On Studio, go to the Data acquisition tab, and on the UPLOAD DATA section, upload the chosen categories files from your computer: + +![](images/png/image39.png){fig-align="center" width="6.5in"} + +Leave to the Studio the splitting of the original dataset into *train and test* and choose the label about that specific data: + +![](images/png/image30.png){fig-align="center" width="6.5in"} + +Repeat the procedure for all three classes. At the end, you should see your "raw data" in the Studio: + +![](images/png/image11.png){fig-align="center" width="6.5in"} + +The Studio allows you to explore your data, showing a complete view of all the data in your project. You can clear, inspect, or change labels by clicking on individual data items. In our case, a very simple project, the data seems OK. + +![](images/png/image44.png){fig-align="center" width="6.5in"} + +## The Impulse Design + +In this phase, we should define how to: + +- Pre-process our data, which consists of resizing the individual images and determining the `color depth` to use (be it RGB or Grayscale) and + +- Specify a Model, in this case, it will be the `Transfer Learning (Images)` to fine-tune a pre-trained MobileNet V2 image classification model on our data. This method performs well even with relatively small image datasets (around 150 images in our case). + +![](images/jpg/image23.jpg){fig-align="center" width="6.5in"} + +Transfer Learning with MobileNet offers a streamlined approach to model training, which is especially beneficial for resource-constrained environments and projects with limited labeled data. MobileNet, known for its lightweight architecture, is a pre-trained model that has already learned valuable features from a large dataset (ImageNet). + +![](images/jpg/image9.jpg){fig-align="center" width="6.5in"} + +By leveraging these learned features, you can train a new model for your specific task with fewer data and computational resources and yet achieve competitive accuracy. + +![](images/jpg/image32.jpg){fig-align="center" width="6.5in"} + +This approach significantly reduces training time and computational cost, making it ideal for quick prototyping and deployment on embedded devices where efficiency is paramount. + +Go to the Impulse Design Tab and create the *impulse*, defining an image size of 96x96 and squashing them (squared form, without cropping). Select Image and Transfer Learning blocks. Save the Impulse. + +![](images/png/image16.png){fig-align="center" width="6.5in"} + +### Image Pre-Processing + +All the input QVGA/RGB565 images will be converted to 27,640 features (96x96x3). + +![](images/png/image17.png){fig-align="center" width="6.5in"} + +Press \[Save parameters\] and Generate all features: + +![](images/png/image5.png){fig-align="center" width="6.5in"} + +### Model Design + +In 2007, Google introduced [[MobileNetV1]{.underline}](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html), a family of general-purpose computer vision neural networks designed with mobile devices in mind to support classification, detection, and more. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of various use cases. in 2018, Google launched [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381). + +MobileNet V1 and MobileNet V2 aim at mobile efficiency and embedded vision applications but differ in architectural complexity and performance. While both use depthwise separable convolutions to reduce the computational cost, MobileNet V2 introduces Inverted Residual Blocks and Linear Bottlenecks to enhance performance. These new features allow V2 to capture more complex features using fewer parameters, making it computationally more efficient and generally more accurate than its predecessor. Additionally, V2 employs a non-linear activation in the intermediate expansion layer. It still uses a linear activation for the bottleneck layer, a design choice found to preserve important information through the network. MobileNet V2 offers an optimized architecture for higher accuracy and efficiency and will be used in this project. + +Although the base MobileNet architecture is already tiny and has low latency, many times, a specific use case or application may require the model to be even smaller and faster. MobileNets introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller, less computationally expensive models. The role of the width multiplier α is that of thinning a network uniformly at each layer. + +Edge Impulse Studio can use both MobileNetV1 (96x96 images) and V2 (96x96 or 160x160 images), with several different **α** values (from 0.05 to 1.0). For example, you will get the highest accuracy with V2, 160x160 images, and α=1.0. Of course, there is a trade-off. The higher the accuracy, the more memory (around 1.3MB RAM and 2.6MB ROM) will be needed to run the model, implying more latency. The smaller footprint will be obtained at the other extreme with MobileNetV1 and α=0.10 (around 53.2K RAM and 101K ROM). + +![](images/jpg/image27.jpg){fig-align="center" width="6.5in"} + +We will use **MobileNetV2 96x96 0.1** for this project, with an estimated memory cost of 265.3 KB in RAM. This model should be OK for the Nicla Vision with 1MB of SRAM. On the Transfer Learning Tab, select this model: + +![](images/png/image24.png){fig-align="center" width="6.5in"} + +## Model Training + +Another valuable technique to be used with Deep Learning is **Data Augmentation**. Data augmentation is a method to improve the accuracy of machine learning models by creating additional artificial data. A data augmentation system makes small, random changes to your training data during the training process (such as flipping, cropping, or rotating the images). + +Looking under the hood, here you can see how Edge Impulse implements a data Augmentation policy on your data: + +``` python +# Implements the data augmentation policy +def augment_image(image, label): + # Flips the image randomly + image = tf.image.random_flip_left_right(image) + + # Increase the image size, then randomly crop it down to + # the original dimensions + resize_factor = random.uniform(1, 1.2) + new_height = math.floor(resize_factor * INPUT_SHAPE[0]) + new_width = math.floor(resize_factor * INPUT_SHAPE[1]) + image = tf.image.resize_with_crop_or_pad(image, new_height, new_width) + image = tf.image.random_crop(image, size=INPUT_SHAPE) + + # Vary the brightness of the image + image = tf.image.random_brightness(image, max_delta=0.2) + + return image, label +``` + +Exposure to these variations during training can help prevent your model from taking shortcuts by "memorizing" superficial clues in your training data, meaning it may better reflect the deep underlying patterns in your dataset. + +The final layer of our model will have 12 neurons with a 15% dropout for overfitting prevention. Here is the Training result: + +![](images/jpg/image31.jpg){fig-align="center" width="6.5in"} + +The result is excellent, with 77ms of latency, which should result in 13fps (frames per second) during inference. + +## Model Testing + +![](images/jpg/image10.jpg){fig-align="center" width="6.5in"} + +Now, you should take the data set aside at the start of the project and run the trained model using it as input: + +![](images/png/image34.png){fig-align="center" width="3.1041666666666665in"} + +The result is, again, excellent. + +![](images/png/image12.png){fig-align="center" width="6.5in"} + +## Deploying the model + +At this point, we can deploy the trained model as.tflite and use the OpenMV IDE to run it using MicroPython, or we can deploy it as a C/C++ or an Arduino library. + +![](images/jpg/image28.jpg){fig-align="center" width="6.5in"} + +### Arduino Library + +First, Let's deploy it as an Arduino Library: + +![](images/png/image48.png){fig-align="center" width="6.5in"} + +You should install the library as.zip on the Arduino IDE and run the sketch *nicla_vision_camera.ino* available in Examples under your library name. + +> Note that Arduino Nicla Vision has, by default, 512KB of RAM allocated for the M7 core and an additional 244KB on the M4 address space. In the code, this allocation was changed to 288 kB to guarantee that the model will run on the device (`malloc_addblock((void*)0x30000000, 288 * 1024);`). + +The result is good, with 86ms of measured latency. + +![](images/jpg/image25.jpg){fig-align="center" width="6.5in"} + +Here is a short video showing the inference results: {{< video https://youtu.be/bZPZZJblU-o width="480" height="270" center >}} + +### OpenMV + +It is possible to deploy the trained model to be used with OpenMV in two ways: as a library and as a firmware. + +Three files are generated as a library: the trained.tflite model, a list with labels, and a simple MicroPython script that can make inferences using the model. + +![](images/png/image26.png){fig-align="center" width="6.5in"} + +Running this model as a *.tflite* directly in the Nicla was impossible. So, we can sacrifice the accuracy using a smaller model or deploy the model as an OpenMV Firmware (FW). Choosing FW, the Edge Impulse Studio generates optimized models, libraries, and frameworks needed to make the inference. Let's explore this option. + +Select `OpenMV Firmware` on the `Deploy Tab` and press `[Build]`. + +![](images/png/image3.png){fig-align="center" width="6.5in"} + +On your computer, you will find a ZIP file. Open it: + +![](images/png/image33.png){fig-align="center" width="6.5in"} + +Use the Bootloader tool on the OpenMV IDE to load the FW on your board: + +![](images/jpg/image35.jpg){fig-align="center" width="6.5in"} + +Select the appropriate file (.bin for Nicla-Vision): + +![](images/png/image8.png){fig-align="center" width="6.5in"} + +After the download is finished, press OK: + +![](images/png/image40.png){fig-align="center" width="3.875in"} + +If a message says that the FW is outdated, DO NOT UPGRADE. Select \[NO\]. + +![](images/png/image42.png){fig-align="center" width="4.572916666666667in"} + +Now, open the script **ei_image_classification.py** that was downloaded from the Studio and the.bin file for the Nicla. + +![](images/png/image14.png){fig-align="center" width="6.5in"} + +Run it. Pointing the camera to the objects we want to classify, the inference result will be displayed on the Serial Terminal. + +![](images/png/image37.png){fig-align="center" width="6.5in"} + +#### Changing the Code to add labels + +The code provided by Edge Impulse can be modified so that we can see, for test reasons, the inference result directly on the image displayed on the OpenMV IDE. + +[[Upload the code from GitHub,]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification.py) or modify it as below: + +``` python +# Marcelo Rovai - NICLA Vision - Image Classification +# Adapted from Edge Impulse - OpenMV Image Classification Example +# @24Aug23 + +import sensor, image, time, os, tf, uos, gc + +sensor.reset() # Reset and initialize the sensor. +sensor.set_pixformat(sensor.RGB565) # Set pxl fmt to RGB565 (or GRAYSCALE) +sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) +sensor.set_windowing((240, 240)) # Set 240x240 window. +sensor.skip_frames(time=2000) # Let the camera adjust. + +net = None +labels = None + +try: + # Load built in model + labels, net = tf.load_builtin_model('trained') +except Exception as e: + raise Exception(e) + +clock = time.clock() +while(True): + clock.tick() # Starts tracking elapsed time. + + img = sensor.snapshot() + + # default settings just do one detection + for obj in net.classify(img, + min_scale=1.0, + scale_mul=0.8, + x_overlap=0.5, + y_overlap=0.5): + fps = clock.fps() + lat = clock.avg() + + print("**********\nPrediction:") + img.draw_rectangle(obj.rect()) + # This combines the labels and confidence values into a list of tuples + predictions_list = list(zip(labels, obj.output())) + + max_val = predictions_list[0][1] + max_lbl = 'background' + for i in range(len(predictions_list)): + val = predictions_list[i][1] + lbl = predictions_list[i][0] + + if val > max_val: + max_val = val + max_lbl = lbl + + # Print label with the highest probability + if max_val < 0.5: + max_lbl = 'uncertain' + print("{} with a prob of {:.2f}".format(max_lbl, max_val)) + print("FPS: {:.2f} fps ==> latency: {:.0f} ms".format(fps, lat)) + + # Draw label with highest probability to image viewer + img.draw_string( + 10, 10, + max_lbl + "\n{:.2f}".format(max_val), + mono_space = False, + scale=2 + ) +``` + +Here you can see the result: + +![](images/jpg/image47.jpg){fig-align="center" width="6.5in"} + +Note that the latency (136 ms) is almost double of what we got directly with the Arduino IDE. This is because we are using the IDE as an interface and also the time to wait for the camera to be ready. If we start the clock just before the inference: + +![](images/jpg/image13.jpg){fig-align="center" width="6.5in"} + +The latency will drop to only 71 ms. + +![](images/jpg/image1.jpg){fig-align="center" width="3.5520833333333335in"} + +> The NiclaV runs about half as fast when connected to the IDE. The FPS should increase once disconnected. + +#### Post-Processing with LEDs + +When working with embedded machine learning, we are looking for devices that can continually proceed with the inference and result, taking some action directly on the physical world and not displaying the result on a connected computer. To simulate this, we will light up a different LED for each possible inference result. + +![](images/jpg/image38.jpg){fig-align="center" width="6.5in"} + +To accomplish that, we should [[upload the code from GitHub]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification_LED.py) or change the last code to include the LEDs: + +``` python +# Marcelo Rovai - NICLA Vision - Image Classification with LEDs +# Adapted from Edge Impulse - OpenMV Image Classification Example +# @24Aug23 + +import sensor, image, time, os, tf, uos, gc, pyb + +ledRed = pyb.LED(1) +ledGre = pyb.LED(2) +ledBlu = pyb.LED(3) + +sensor.reset() # Reset and initialize the sensor. +sensor.set_pixformat(sensor.RGB565) # Set pixl fmt to RGB565 (or GRAYSCALE) +sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) +sensor.set_windowing((240, 240)) # Set 240x240 window. +sensor.skip_frames(time=2000) # Let the camera adjust. + +net = None +labels = None + +ledRed.off() +ledGre.off() +ledBlu.off() + +try: + # Load built in model + labels, net = tf.load_builtin_model('trained') +except Exception as e: + raise Exception(e) + +clock = time.clock() + + +def setLEDs(max_lbl): + + if max_lbl == 'uncertain': + ledRed.on() + ledGre.off() + ledBlu.off() + + if max_lbl == 'periquito': + ledRed.off() + ledGre.on() + ledBlu.off() + + if max_lbl == 'robot': + ledRed.off() + ledGre.off() + ledBlu.on() + + if max_lbl == 'background': + ledRed.off() + ledGre.off() + ledBlu.off() + + +while(True): + img = sensor.snapshot() + clock.tick() # Starts tracking elapsed time. + + # default settings just do one detection. + for obj in net.classify(img, + min_scale=1.0, + scale_mul=0.8, + x_overlap=0.5, + y_overlap=0.5): + fps = clock.fps() + lat = clock.avg() + + print("**********\nPrediction:") + img.draw_rectangle(obj.rect()) + # This combines the labels and confidence values into a list of tuples + predictions_list = list(zip(labels, obj.output())) + + max_val = predictions_list[0][1] + max_lbl = 'background' + for i in range(len(predictions_list)): + val = predictions_list[i][1] + lbl = predictions_list[i][0] + + if val > max_val: + max_val = val + max_lbl = lbl + + # Print label and turn on LED with the highest probability + if max_val < 0.8: + max_lbl = 'uncertain' + + setLEDs(max_lbl) + + print("{} with a prob of {:.2f}".format(max_lbl, max_val)) + print("FPS: {:.2f} fps ==> latency: {:.0f} ms".format(fps, lat)) + + # Draw label with highest probability to image viewer + img.draw_string( + 10, 10, + max_lbl + "\n{:.2f}".format(max_val), + mono_space = False, + scale=2 + ) +``` + +Now, each time that a class scores a result greater than 0.8, the correspondent LED will be lit: + +- Led Red 0n: Uncertain (no class is over 0.8) + +- Led Green 0n: Periquito \> 0.8 + +- Led Blue 0n: Robot \> 0.8 + +- All LEDs Off: Background \> 0.8 + +Here is the result: + +![](images/jpg/image18.jpg){fig-align="center" width="6.5in"} + +In more detail + +![](images/jpg/image21.jpg){fig-align="center" width="6.5in"} + +## Image Classification (non-official) Benchmark + +Several development boards can be used for embedded machine learning (TinyML), and the most common ones for Computer Vision applications (consuming low energy), are the ESP32 CAM, the Seeed XIAO ESP32S3 Sense, the Arduino Nicla Vison, and the Arduino Portenta. + +![](images/jpg/image19.jpg){fig-align="center" width="6.5in"} + +Catching the opportunity, the same trained model was deployed on the ESP-CAM, the XIAO, and the Portenta (in this one, the model was trained again, using grayscaled images to be compatible with its camera). Here is the result, deploying the models as Arduino's Library: + +![](images/jpg/image4.jpg){fig-align="center" width="6.5in"} + +## Conclusion + +Before we finish, consider that Computer Vision is more than just image classification. For example, you can develop Edge Machine Learning projects around vision in several areas, such as: + +- **Autonomous Vehicles:** Use sensor fusion, lidar data, and computer vision algorithms to navigate and make decisions. + +- **Healthcare:** Automated diagnosis of diseases through MRI, X-ray, and CT scan image analysis + +- **Retail:** Automated checkout systems that identify products as they pass through a scanner. + +- **Security and Surveillance:** Facial recognition, anomaly detection, and object tracking in real-time video feeds. + +- **Augmented Reality:** Object detection and classification to overlay digital information in the real world. + +- **Industrial Automation:** Visual inspection of products, predictive maintenance, and robot and drone guidance. + +- **Agriculture:** Drone-based crop monitoring and automated harvesting. + +- **Natural Language Processing:** Image captioning and visual question answering. + +- **Gesture Recognition:** For gaming, sign language translation, and human-machine interaction. + +- **Content Recommendation:** Image-based recommendation systems in e-commerce. diff --git a/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image1.jpg b/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image1.jpg new file mode 100644 index 00000000..48985805 Binary files /dev/null and b/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image1.jpg differ diff --git a/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image10.jpg b/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image10.jpg new file mode 100644 index 00000000..8cf6eb84 Binary files /dev/null and b/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image10.jpg differ diff --git a/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image13.jpg b/contents/labs/arduino/nicla_vision/image_classification/images/jpg/image13.jpg new file mode 100644 index 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b/contents/labs/arduino/nicla_vision/motion_classification/motion_classification.qmd @@ -1,5 +1,5 @@ --- -bibliography: motion_classify_ad.bib +bibliography: motion_classification.bib --- # Motion Classification and Anomaly Detection {.unnumbered} diff --git a/contents/labs/arduino/nicla_vision/nicla_vision.qmd b/contents/labs/arduino/nicla_vision/nicla_vision.qmd new file mode 100644 index 00000000..07cc8738 --- /dev/null +++ b/contents/labs/arduino/nicla_vision/nicla_vision.qmd @@ -0,0 +1,24 @@ +# Nicla Vision {.unnumbered} + +These labs provide a unique opportunity to gain practical experience with machine learning (ML) systems. Unlike working with large models requiring data center-scale resources, these exercises allow you to directly interact with hardware and software using TinyML. This hands-on approach gives you a tangible understanding of the challenges and opportunities in deploying AI, albeit at a tiny scale. However, the principles are largely the same as what you would encounter when working with larger systems. + +![Nicla Vision. Credit: Arduino](./images/jpg/nicla_vision_quarter.jpeg){#fig-nicla_vision height=3in} + + +## Pre-requisites + +- **Nicla Vision Board**: Ensure you have the Nicla Vision board. +- **USB Cable**: For connecting the board to your computer. +- **Network**: With internet access for downloading necessary software. + +## Setup + +- [Setup Nicla Vision](./setup/setup.qmd) + +## Exercises + +| **Modality** | **Task** | **Description** | **Link** | +|--------------|--------------|-----------------|----------| +| Vision | Image Classification | Learn to classify images | [Link](./image_classification/image_classification.qmd) | +| Vision | Object Detection | Implement object detection | [Link](./object_detection/object_detection.qmd) | +| IMU | Motion Classification and Anomaly Detection | Classify motion data and detect anomalies | [Link](./motion_classification/motion_classification.qmd) | \ No newline at end of file diff --git a/contents/object_detection_fomo/images/jpg/cv_obj_detect.jpg 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NICLA VISION board, equipped with a variety of sensors including a camera, is the focal point on an old-fashioned desk. In the background, a computer screen with rounded edges displays the Arduino IDE. The code seen is related to LED configurations and machine learning voice command detection. Outputs on the Serial Monitor explicitly display the words 'yes' and 'no'.*](images/jpg/nicla_sys_ini.jpg){fig-align="center" width="6.5in"} diff --git a/contents/labs/labs.qmd b/contents/labs/labs.qmd new file mode 100644 index 00000000..21a74203 --- /dev/null +++ b/contents/labs/labs.qmd @@ -0,0 +1,24 @@ +# Overview {.unnumbered} + +The following labs offer a unique chance to gain hands-on experience with machine learning (ML) systems by deploying TinyML models onto real embedded devices. Instead of working with large models that need data center-scale resources, you'll interact directly with both hardware and software. These exercises cover different sensor modalities, giving you exposure to a variety of applications. This approach helps you understand the real-world challenges and opportunities in deploying AI on real systems. + +## Supported Devices + +| Device/Board | Installaion & Setup | Keyword Spotting (KWS) | Image Classification | Object Detection | Motion Detection | +| --------------------------------- | ------------------------------- | --------------------------------------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | +| [Nicla Vision](./arduino/nicla_vision/nicla_vision.qmd) | [Link](./arduino/nicla_vision/setup/setup.qmd) | [Link](./arduino/nicla_vision/kws/kws.qmd) | [Link](./arduino/nicla_vision/image_classification/image_classification.qmd) | [Link](./arduino/nicla_vision/object_detection/object_detection.qmd) | [Link](./arduino/nicla_vision/motion_classification/motion_classification.qmd) | +| [XIAO ESP32S3](./seeed/xiao_esp32s3/xiao_esp32s3.qmd) | [Link](./seeed/xiao_esp32s3/setup/setup.qmd) | [Link](./seeed/xiao_esp32s3/kws/kws.qmd) | [Link](./seeed/xiao_esp32s3/image_classification/image_classification.qmd) | Coming soon. | [Link](./seeed/xiao_esp32s3/motion_classification/motion_classification.qmd) | + +## Lab Structure + +Each lab follows a similar structure: + +#. Introduction to the application and its real-world significance +#. Step-by-step instructions to set up the hardware and software environment +#. Detailed guidance on deploying the pre-trained TinyML model +#. Exercises to modify and experiment with the model and its parameters +#. Discussion on the results and potential improvements + +## Troubleshooting and Support + +If you encounter any issues during the labs, please refer to the troubleshooting guides and FAQs provided with each lab. 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #ffffff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box 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.value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return 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00000000..d9fd98f0 --- /dev/null +++ b/contents/labs/labs_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/contents/labs/labs_files/libs/quarto-html/tippy.css b/contents/labs/labs_files/libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/contents/labs/labs_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.bib similarity index 100% rename from contents/object_detection_fomo/object_detection_fomo.bib rename to contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.bib diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd new file mode 100644 index 00000000..523d9a68 --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd @@ -0,0 +1,328 @@ +# Image Classification {.unnumbered} + +![*Image by Marcelo Rovai*](./images/png/ini.png){fig-align="center" width="6.5in"} + +## Introduction + +More and more, we are facing an artificial intelligence (AI) revolution where, as stated by Gartner, **Edge AI** has a very high impact potential, and **it is for now**! + +![](https://hackster.imgix.net/uploads/attachments/1587506/image_EZKT6sirt5.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +At the forefront of the Emerging Technologies Radar is the universal language of Edge Computer Vision. When we delve into Machine Learning (ML) applied to vision, the first concept that greets us is Image Classification, a kind of ML' Hello World ' that is both simple and profound! + +The Seeed Studio XIAO ESP32S3 Sense is a powerful tool that combines camera and SD card support. With its embedded ML computing power and photography capability, it is an excellent starting point for exploring TinyML vision AI. + +## A TinyML Image Classification Project - Fruits versus Veggies + +![](./images/png/vegetables.png) + +The whole idea of our project will be to train a model and proceed with inference on the XIAO ESP32S3 Sense. For training, we should find some data **(in fact, tons of data!**). + +*But first of all, we need a goal! What do we want to classify?* + +With TinyML, a set of techniques associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory). We will differentiate **apples** from **bananas** and **potatoes** (you can try other categories)**.** + +So, let's find a specific dataset that includes images from those categories. Kaggle is a good start: + +https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition + +This dataset contains images of the following food items: + +- **Fruits** - *banana, apple*, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango. +- **Vegetables** - cucumber, carrot, capsicum, onion, *potato,* lemon, tomato, radish, beetroot, cabbage, lettuce, spinach, soybean, cauliflower, bell pepper, chili pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant. + +Each category is split into the **train** (100 images), **test** (10 images), and **validation** (10 images). + +- Download the dataset from the Kaggle website and put it on your computer. + +> Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the codes discussed in the setup lab. + +## Training the model with Edge Impulse Studio + +We will use the Edge Impulse Studio to train our model. As you may know, [Edge Impulse](https://www.edgeimpulse.com/) is a leading development platform for machine learning on edge devices. + +Enter your account credentials (or create a free account) at Edge Impulse. Next, create a new project: + +![](https://hackster.imgix.net/uploads/attachments/1587543/image_MDgkE355g3.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +### Data Acquisition + +Next, on the `UPLOAD DATA` section, upload from your computer the files from chosen categories: + +![](https://hackster.imgix.net/uploads/attachments/1587488/image_brdDCN6bc5.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +It would be best if you now had your training dataset split into three classes of data: + +![](https://hackster.imgix.net/uploads/attachments/1587489/image_QyxusuY3DM.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> You can upload extra data for further model testing or split the training data. I will leave it as it is to use the most data possible. + +### Impulse Design + +> An impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data. + +Classifying images is the most common use of deep learning, but a lot of data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousands of images to "teach or model" to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We call this technique "Transfer Learning" (TL). + +![](https://hackster.imgix.net/uploads/attachments/1587490/tl_fuVIsKd7YV.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +With TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case). + +So, starting from the raw images, we will resize them (96x96) pixels and feed them to our Transfer Learning block: + +![](https://hackster.imgix.net/uploads/attachments/1587491/image_QhTt0Av8u3.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +#### Pre-processing (Feature Generation) + +Besides resizing the images, we can change them to Grayscale or keep the actual RGB color depth. Let's start selecting `Grayscale`. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference. + +![](https://hackster.imgix.net/uploads/attachments/1587492/image_eqGdUoXrMb.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Remember to `[Save parameters]`. This will generate the features to be used in training. + +#### Model Design + +**Transfer Learning** + +In 2007, Google introduced [MobileNetV1,](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html) a family of general-purpose computer vision neural networks designed with mobile devices in mind to support classification, detection, and more. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of various use cases. + +Although the base MobileNet architecture is already tiny and has low latency, many times, a specific use case or application may require the model to be smaller and faster. MobileNet introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller, less computationally expensive models. The role of the width multiplier α is to thin a network uniformly at each layer. + +Edge Impulse Studio has **MobileNet V1 (96x96 images)** and **V2 (96x96 and 160x160 images)** available, with several different **α** values (from 0.05 to 1.0). For example, you will get the highest accuracy with V2, 160x160 images, and α=1.0. Of course, there is a trade-off. The higher the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be needed to run the model, implying more latency. + +The smaller footprint will be obtained at another extreme with **MobileNet V1** and α=0.10 (around 53.2K RAM and 101K ROM). + +For this first pass, we will use **MobileNet V1** and α=0.10. + +### Training + +**Data Augmentation** + +Another necessary technique to use with deep learning is **data augmentation**. Data augmentation is a method that can help improve the accuracy of machine learning models, creating additional artificial data. A data augmentation system makes small, random changes to your training data during the training process (such as flipping, cropping, or rotating the images). + +Under the rood, here you can see how Edge Impulse implements a data Augmentation policy on your data: + +```cpp +# Implements the data augmentation policy +def augment_image(image, label): + # Flips the image randomly + image = tf.image.random_flip_left_right(image) + + # Increase the image size, then randomly crop it down to + # the original dimensions + resize_factor = random.uniform(1, 1.2) + new_height = math.floor(resize_factor * INPUT_SHAPE[0]) + new_width = math.floor(resize_factor * INPUT_SHAPE[1]) + image = tf.image.resize_with_crop_or_pad(image, new_height, new_width) + image = tf.image.random_crop(image, size=INPUT_SHAPE) + + # Vary the brightness of the image + image = tf.image.random_brightness(image, max_delta=0.2) + + return image, label +``` + +Exposure to these variations during training can help prevent your model from taking shortcuts by "memorizing" superficial clues in your training data, meaning it may better reflect the deep underlying patterns in your dataset. + +The final layer of our model will have 16 neurons with a 10% dropout for overfitting prevention. Here is the Training output: + +![](./images/png/train.png) + +The result could be better. The model reached around 77% accuracy, but the amount of RAM expected to be used during the inference is relatively tiny (about 60 KBytes), which is very good. + +### Deployment + +The trained model will be deployed as a .zip Arduino library: + +![](./images/png/depl.png) + +Open your Arduino IDE, and under **Sketch,** go to **Include Library** and **add.ZIP Library.** Please select the file you download from Edge Impulse Studio, and that's it! + +![](./images/png/arduino_zip.png) + +Under the **Examples** tab on Arduino IDE, you should find a sketch code under your project name. + +![](./images/png/sketch.png) + +Open the Static Buffer example: + +![](./images/png/static_buffer.png) + +You can see that the first line of code is exactly the calling of a library with all the necessary stuff for running inference on your device. + +```cpp +#include +``` + +Of course, this is a generic code (a "template") that only gets one sample of raw data (stored on the variable: features = {} and runs the classifier, doing the inference. The result is shown on the Serial Monitor. + +We should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes. + +![](./images/png/deploy_block.png) + +Returning to your project (Tab Image), copy one of the Raw Data Sample: + +![](./images/png/get_test_data.png) + +9, 216 features will be copied to the clipboard. This is the input tensor (a flattened image of 96x96x1), in this case, bananas. Past this Input tensor on`features[] = {0xb2d77b, 0xb5d687, 0xd8e8c0, 0xeaecba, 0xc2cf67, ...}` + +![](./images/png/features.png) + +Edge Impulse included the [library ESP NN](https://github.com/espressif/esp-nn) in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (running at Arduino IDE). + +When running the inference, you should get the highest score for "banana." + +![](./images/png/inference1.png) + +Great news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. + +Now, we should incorporate the camera and classify images in real time. + +Go to the Arduino IDE Examples and download from your project the sketch `esp32_camera`: + +![](https://hackster.imgix.net/uploads/attachments/1587604/image_hjX5k8gTl8.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +You should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75: + +```cpp +#define PWDN_GPIO_NUM -1 +#define RESET_GPIO_NUM -1 +#define XCLK_GPIO_NUM 10 +#define SIOD_GPIO_NUM 40 +#define SIOC_GPIO_NUM 39 +#define Y9_GPIO_NUM 48 +#define Y8_GPIO_NUM 11 +#define Y7_GPIO_NUM 12 +#define Y6_GPIO_NUM 14 +#define Y5_GPIO_NUM 16 +#define Y4_GPIO_NUM 18 +#define Y3_GPIO_NUM 17 +#define Y2_GPIO_NUM 15 +#define VSYNC_GPIO_NUM 38 +#define HREF_GPIO_NUM 47 +#define PCLK_GPIO_NUM 13 +``` + +Here you can see the resulting code: + +![](./images/png/camera_set.png) + +The modified sketch can be downloaded from GitHub: [xiao_esp32s3_camera](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_camera). + +> Note that you can optionally keep the pins as a .h file as we did in the Setup Lab. + +Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor. + +## Testing the Model (Inference) + +![](./images/png/inf_banana.jpg){fig-align="center" width="6.5in"} + +Getting a photo with the camera, the classification result will appear on the Serial Monitor: + +![](./images/png/inf_banana.png) + +Other tests: + +![](images/png/inferencia2_apple.png) + + +![](images/jpeg/inferencia_potato.jpg) + +## Testing with a Bigger Model + +Now, let's go to the other side of the model size. Let's select a MobilinetV2 96x96 0.35, having as input RGB images. + +![](./images/png/train_2.png) + +Even with a bigger model, the accuracy could be better, and the amount of memory necessary to run the model increases five times, with latency increasing seven times. + +> Note that the performance here is estimated with a smaller device, the ESP-EYE. The actual inference with the ESP32S3 should be better. + +To improve our model, we will need to train more images. + +Even though our model did not improve accuracy, let's test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch. + +Let's redeploy the model. If the EON Compiler is enabled when you generate the library, the total memory needed for inference should be reduced, but it does not influence accuracy. + +> ⚠️ **Attention** - The Xiao ESP32S3 with PSRAM enable has enought memory to run the inference, even in such bigger model. Keep the EON Compiler **NOT ENABLED**. + +![](./images/png/deploy_2.png) + +Doing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the latency was 219ms, which is great for such a bigger model. + +![](./images/png/inf_2.png) + +For the test, we can train the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the result in accuracy was higher. + +![](https://hackster.imgix.net/uploads/attachments/1591705/image_lwYLKM696A.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> Note that the estimated latency for an Arduino Portenta (ou Nicla), running with a clock of 480MHz is 45ms. + +Deploying the model, we got an inference of only 135ms, remembering that the XIAO runs with half of the clock used by the Portenta/Nicla (240MHz): + +![](https://hackster.imgix.net/uploads/attachments/1591706/image_dAfOl9Tguz.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +## Running inference on the SenseCraft-Web-Toolkit + +One significant limitation of viewing inference on Arduino IDE is that we can not see what the camera focuses on. A good alternative is the **SenseCraft-Web-Toolkit**, a visual model deployment tool provided by [SSCMA ](https://sensecraftma.seeed.cc/)(Seeed SenseCraft Model Assistant). This tool allows you to deploy models to various platforms easily through simple operations. The tool offers a user-friendly interface and does not require any coding. + +Follow the following steps to start the SenseCraft-Web-Toolkit: + +1. Open the [SenseCraft-Web-Toolkit website.](https://seeed-studio.github.io/SenseCraft-Web-Toolkit/#/setup/process) +2. Connect the XIAO to your computer: + +- Having the XIAO connected, select it as below: + +![](./images/jpeg/senseCraft-1.jpg) + +- Select the device/Port and press `[Connect]`: + + ![](./images/jpeg/senseCraft-2.jpg) + +> You can try several Computer Vision models previously uploaded by Seeed Studio. Try them and have fun! + +In our case, we will use the blue button at the bottom of the page: `[Upload Custom AI Model]`. + +But first, we must download from Edge Impulse Studio our **quantized .tflite** model. + +3. Go to your project at Edge Impulse Studio, or clone this one: + +- [XIAO-ESP32S3-CAM-Fruits-vs-Veggies-v1-ESP-NN](https://studio.edgeimpulse.com/public/228516/live) + +4. On the `Dashboard`, download the model ("block output"): `Transfer learning model - TensorFlow Lite (int8 quantized).` + +![](./images/jpeg/senseCraft-4.jpg) + +5. On SenseCraft-Web-Toolkit, use the blue button at the bottom of the page: `[Upload Custom AI Model]`. A window will pop up. Enter the Model file that you downloaded to your computer from Edge Impulse Studio, choose a Model Name, and enter with labels (ID: Object): + +![](./images/jpeg/senseCraft-3.jpg) + +> Note that you should use the labels trained on EI Studio, entering them in alphabetic order (in our case: apple, banana, potato). + +After a few seconds (or minutes), the model will be uploaded to your device, and the camera image will appear in real-time on the Preview Sector: + +![](./images/jpeg/senseCraft-apple.jpg) + +The Classification result will be at the top of the image. You can also select the Confidence of your inference cursor `Confidence`. + +Clicking on the top button (Device Log), you can open a Serial Monitor to follow the inference, the same that we have done with the Arduino IDE: + +![](./images/jpeg/senseCraft-apple-2.jpg) + +On Device Log, you will get information as: + +![](./images/jpeg//senseCraft-log.jpg) + +- Preprocess time (image capture and Crop): 4ms; +- Inference time (model latency): 106ms, +- Postprocess time (display of the image and inclusion of data): 0ms. +- Output tensor (classes), for example: [[89,0]]; where 0 is Apple (and 1is banana and 2 is potato) + +Here are other screenshots: + +![](./images/jpeg//inference.jpg) + +## Conclusion + +The XIAO ESP32S3 Sense is very flexible, inexpensive, and easy to program. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication. + +You will find the last version of the codes on the GitHub repository: [XIAO-ESP32S3-Sense.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inference.jpg b/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inference.jpg new file mode 100644 index 00000000..883926f9 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inferencia_potato.jpg b/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inferencia_potato.jpg new file mode 100644 index 00000000..0e9ed165 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/image_classification/images/jpeg/inferencia_potato.jpg differ diff --git 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b/contents/labs/seeed/xiao_esp32s3/kws/kws.bib new file mode 100644 index 00000000..00614696 --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/kws/kws.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/labs/seeed/xiao_esp32s3/kws/kws.qmd b/contents/labs/seeed/xiao_esp32s3/kws/kws.qmd new file mode 100644 index 00000000..e25749df --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/kws/kws.qmd @@ -0,0 +1,628 @@ + + +# Keyword Spotting (KWS) {.unnumbered} + +![*Image by Marcelo Rovai*](images/jpeg/kws_ini.jpg){fig-align="center" width="6.5in"} + +## Introduction + +Keyword Spotting (KWS) is integral to many voice recognition systems, enabling devices to respond to specific words or phrases. While this technology underpins popular devices like Google Assistant or Amazon Alexa, it's equally applicable and achievable on smaller, low-power devices. This lab will guide you through implementing a KWS system using TinyML on the XIAO ESP32S3 microcontroller board. + +The XIAO ESP32S3, equipped with Espressif's ESP32-S3 chip, is a compact and potent microcontroller offering a dual-core Xtensa LX7 processor, integrated Wi-Fi, and Bluetooth. Its balance of computational power, energy efficiency, and versatile connectivity make it a fantastic platform for TinyML applications. Also, with its expansion board, we will have access to the "sense" part of the device, which has a 1600x1200 OV2640 camera, an SD card slot, and a **digital microphone**. The integrated microphone and the SD card will be essential in this project. + +We will utilize the [Edge Impulse Studio](https://www.edgeimpulse.com/), a powerful, user-friendly platform that simplifies creating and deploying machine learning models onto edge devices. We'll train a KWS model step-by-step, optimizing and deploying it onto the XIAO ESP32S3 Sense. + +Our model will be designed to recognize keywords that can trigger device wake-up or specific actions (in the case of "YES"), bringing your projects to life with voice-activated commands. + +Leveraging our experience with TensorFlow Lite for Microcontrollers (the engine "under the hood" on the EI Studio), we'll create a KWS system capable of real-time machine learning on the device. + +As we progress through the lab, we'll break down each process stage - from data collection and preparation to model training and deployment - to provide a comprehensive understanding of implementing a KWS system on a microcontroller. + +### How does a voice assistant work? + +Keyword Spotting (KWS) is critical to many voice assistants, enabling devices to respond to specific words or phrases. To start, it is essential to realize that Voice Assistants on the market, like Google Home or Amazon Echo-Dot, only react to humans when they are “waked up” by particular keywords such as “ Hey Google” on the first one and “Alexa” on the second. + +![](https://hackster.imgix.net/uploads/attachments/1594299/1_3n44ykL_GNR5jQSwrUSKWA.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +In other words, recognizing voice commands is based on a multi-stage model or Cascade Detection. + +![](https://hackster.imgix.net/uploads/attachments/1594300/image_Zd5vTdG9RB.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Stage 1:** A smaller microprocessor inside the Echo Dot or Google Home **continuously** listens to the sound, waiting for the keyword to be spotted. For such detection, a TinyML model at the edge is used (KWS application). + +**Stage 2:** Only when triggered by the KWS application on Stage 1 is the data sent to the cloud and processed on a larger model. + +The video below shows an example where I emulate a Google Assistant on a Raspberry Pi (Stage 2), having an Arduino Nano 33 BLE as the tinyML device (Stage 1). + + + +> If you want to go deeper on the full project, please see my tutorial: [Building an Intelligent Voice Assistant From Scratch](https://www.hackster.io/mjrobot/building-an-intelligent-voice-assistant-from-scratch-2199c3). + +In this lab, we will focus on Stage 1 (KWS or Keyword Spotting), where we will use the XIAO ESP2S3 Sense, which has a digital microphone for spotting the keyword. + +### The KWS Project + +The below diagram will give an idea of how the final KWS application should work (during inference): + +![](https://hackster.imgix.net/uploads/attachments/1594331/image_buEZet7Pje.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Our KWS application will recognize four classes of sound: + +- **YES** (Keyword 1) +- **NO** (Keyword 2) +- **NOISE** (no keywords spoken, only background noise is present) +- **UNKNOW** (a mix of different words than YES and NO) + +> Optionally for real-world projects, it is always advised to include different words than keywords, such as "Noise" (or Background) and "Unknow." + +### The Machine Learning workflow + +The main component of the KWS application is its model. So, we must train such a model with our specific keywords, noise, and other words (the "unknown"): + +![](https://hackster.imgix.net/uploads/attachments/1594302/image_VjDpbeenv9.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Dataset + +The critical component of Machine Learning Workflow is the **dataset**. Once we have decided on specific keywords (*YES* and NO), we can take advantage of the dataset developed by Pete Warden, ["Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)." This dataset has 35 keywords (with +1,000 samples each), such as yes, no, stop, and go. In other words, we can get 1,500 samples of *yes* and *no*. + +You can download a small portion of the dataset from Edge Studio ([Keyword spotting pre-built dataset](https://docs.edgeimpulse.com/docs/pre-built-datasets/keyword-spotting)), which includes samples from the four classes we will use in this project: yes, no, noise, and background. For this, follow the steps below: + +- Download the [keywords dataset.](https://cdn.edgeimpulse.com/datasets/keywords2.zip) +- Unzip the file in a location of your choice. + +Although we have a lot of data from Pete's dataset, collecting some words spoken by us is advised. When working with accelerometers, creating a dataset with data captured by the same type of sensor was essential. In the case of *sound*, it is different because what we will classify is, in reality, *audio* data. + +> The key difference between sound and audio is their form of energy. Sound is mechanical wave energy (longitudinal sound waves) that propagate through a medium causing variations in pressure within the medium. Audio is made of electrical energy (analog or digital signals) that represent sound electrically. + +The sound waves should be converted to audio data when we speak a keyword. The conversion should be done by sampling the signal generated by the microphone in 16KHz with a 16-bit depth. + +So, any device that can generate audio data with this basic specification (16Khz/16bits) will work fine. As a device, we can use the proper XIAO ESP32S3 Sense, a computer, or even your mobile phone. + +![](https://hackster.imgix.net/uploads/attachments/1594337/sound-audio_lOADMI6ern.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Capturing online Audio Data with Edge Impulse and a smartphone** + +In the lab Motion Classification and Anomaly Detection, we connect our device directly to Edge Impulse Studio for data capturing (having a sampling frequency of 50Hz to 100Hz). For such low frequency, we could use the EI CLI function *Data Forwarder,* but according to Jan Jongboom, Edge Impulse CTO, *audio (*16KHz) *goes too fast for the data forwarder to be captured.* So, once we have the digital data captured by the microphone, we can turn *it into a WAV file* to be sent to the Studio via Data Uploader (same as we will do with Pete's dataset)*.* + +> If we want to collect audio data directly on the Studio, we can use any smartphone connected online with it. We will not explore this option here, but you can easily follow EI [documentation](https://docs.edgeimpulse.com/docs/development-platforms/using-your-mobile-phone). + +### Capturing (offline) Audio Data with the XIAO ESP32S3 Sense + +The built-in microphone is the [MSM261D3526H1CPM](https://files.seeedstudio.com/wiki/XIAO-BLE/mic-MSM261D3526H1CPM-ENG.pdf), a PDM digital output MEMS microphone with Multi-modes. Internally, it is connected to the ESP32S3 via an I2S bus using pins IO41 (Clock) and IO41 (Data). + +![](https://hackster.imgix.net/uploads/attachments/1594599/pasted_graphic_62_RRD6zoEXwv.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**What is I2S?** + +I2S, or Inter-IC Sound, is a standard protocol for transmitting digital audio from one device to another. It was initially developed by Philips Semiconductor (now NXP Semiconductors). It is commonly used in audio devices such as digital signal processors, digital audio processors, and, more recently, microcontrollers with digital audio capabilities (our case here). + +The I2S protocol consists of at least three lines: + +![](https://hackster.imgix.net/uploads/attachments/1594628/image_8CRJmXD9Fr.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**1. Bit (or Serial) clock line (BCLK or CLK)**: This line toggles to indicate the start of a new bit of data (pin IO42). + +**2. Word select line (WS)**: This line toggles to indicate the start of a new word (left channel or right channel). The Word select clock (WS) frequency defines the sample rate. In our case, L/R on the microphone is set to ground, meaning that we will use only the left channel (mono). + +**3. Data line (SD)**: This line carries the audio data (pin IO41) + +In an I2S data stream, the data is sent as a sequence of frames, each containing a left-channel word and a right-channel word. This makes I2S particularly suited for transmitting stereo audio data. However, it can also be used for mono or multichannel audio with additional data lines. + +Let's start understanding how to capture raw data using the microphone. Go to the [GitHub project ](https://github.com/Mjrovai/XIAO-ESP32S3-Sense)and download the sketch: [XIAOEsp2s3_Mic_Test](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/XiaoEsp32s3_Mic_Test): + +``` +/* + XIAO ESP32S3 Simple Mic Test +*/ + +#include + +void setup() { + Serial.begin(115200); + while (!Serial) { + } + + // start I2S at 16 kHz with 16-bits per sample + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, 16000, 16)) { + Serial.println("Failed to initialize I2S!"); + while (1); // do nothing + } +} + +void loop() { + // read a sample + int sample = I2S.read(); + + if (sample && sample != -1 && sample != 1) { + Serial.println(sample); + } +} +``` + +This code is a simple microphone test for the XIAO ESP32S3 using the I2S (Inter-IC Sound) interface. It sets up the I2S interface to capture audio data at a sample rate of 16 kHz with 16 bits per sample and then continuously reads samples from the microphone and prints them to the serial monitor. + +Let's dig into the code's main parts: + +- Include the I2S library: This library provides functions to configure and use the [I2S interface](https://espressif-docs.readthedocs-hosted.com/projects/arduino-esp32/en/latest/api/i2s.html), which is a standard for connecting digital audio devices. +- I2S.setAllPins(-1, 42, 41, -1, -1): This sets up the I2S pins. The parameters are (-1, 42, 41, -1, -1), where the second parameter (42) is the PIN for the I2S clock (CLK), and the third parameter (41) is the PIN for the I2S data (DATA) line. The other parameters are set to -1, meaning those pins are not used. +- I2S.begin(PDM_MONO_MODE, 16000, 16): This initializes the I2S interface in Pulse Density Modulation (PDM) mono mode, with a sample rate of 16 kHz and 16 bits per sample. If the initialization fails, an error message is printed, and the program halts. +- int sample = I2S.read(): This reads an audio sample from the I2S interface. + +If the sample is valid, it is printed on the Serial Monitor and Plotter. + +Below is a test "whispering" in two different tones. + +![](https://hackster.imgix.net/uploads/attachments/1594603/plotter_zIdxqUxqkY.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +### Save recorded sound samples (dataset) as .wav audio files to a microSD card. + +Let's use the onboard SD Card reader to save .wav audio files; we must habilitate the XIAO PSRAM first. + +> ESP32-S3 has only a few hundred kilobytes of internal RAM on the MCU chip. It can be insufficient for some purposes so that ESP32-S3 can use up to 16 MB of external PSRAM (Psuedostatic RAM) connected in parallel with the SPI flash chip. The external memory is incorporated in the memory map and, with certain restrictions, is usable in the same way as internal data RAM. + +For a start, Insert the SD Card on the XIAO as shown in the photo below (the SD Card should be formatted to FAT32). + +![](https://hackster.imgix.net/uploads/attachments/1594791/image_qIPJ5vK4IA.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Turn the PSRAM function of the ESP-32 chip on (Arduino IDE): Tools>PSRAM: "OPI PSRAM”>OPI PSRAM + +![](https://hackster.imgix.net/uploads/attachments/1594639/image_Zo8usTd0A2.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +- Download the sketch [Wav_Record_dataset](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Wav_Record_dataset),[ ](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Wav_Record_dataset)which you can find on the project's GitHub. + +This code records audio using the I2S interface of the Seeed XIAO ESP32S3 Sense board, saves the recording as a.wav file on an SD card, and allows for control of the recording process through commands sent from the serial monitor. The name of the audio file is customizable (it should be the class labels to be used with the training), and multiple recordings can be made, each saved in a new file. The code also includes functionality to increase the volume of the recordings. + +Let's break down the most essential parts of it: + +``` +#include +#include "FS.h" +#include "SD.h" +#include "SPI.h" +``` + +Those are the necessary libraries for the program. I2S.h allows for audio input, FS.h provides file system handling capabilities, SD.h enables the program to interact with an SD card, and SPI.h handles the SPI communication with the SD card. + +``` +#define RECORD_TIME 10 +#define SAMPLE_RATE 16000U +#define SAMPLE_BITS 16 +#define WAV_HEADER_SIZE 44 +#define VOLUME_GAIN 2 +``` + +Here, various constants are defined for the program. + +- **RECORD_TIME** specifies the length of the audio recording in seconds. +- **SAMPLE_RATE** and **SAMPLE_BITS** define the audio quality of the recording. +- **WAV_HEADER_SIZE** specifies the size of the .wav file header. +- **VOLUME_GAIN** is used to increase the volume of the recording. + +``` +int fileNumber = 1; +String baseFileName; +bool isRecording = false; +``` + +These variables keep track of the current file number (to create unique file names), the base file name, and whether the system is currently recording. + +``` +void setup() { + Serial.begin(115200); + while (!Serial); + + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, SAMPLE_RATE, SAMPLE_BITS)) { + Serial.println("Failed to initialize I2S!"); + while (1); + } + + if(!SD.begin(21)){ + Serial.println("Failed to mount SD Card!"); + while (1); + } + Serial.printf("Enter with the label name\n"); +} +``` + +The setup function initializes the serial communication, I2S interface for audio input, and SD card interface. If the I2S did not initialize or the SD card fails to mount, it will print an error message and halt execution. + +``` +void loop() { + if (Serial.available() > 0) { + String command = Serial.readStringUntil('\n'); + command.trim(); + if (command == "rec") { + isRecording = true; + } else { + baseFileName = command; + fileNumber = 1; //reset file number each time a new basefile name is set + Serial.printf("Send rec for starting recording label \n"); + } + } + if (isRecording && baseFileName != "") { + String fileName = "/" + baseFileName + "." + String(fileNumber) + ".wav"; + fileNumber++; + record_wav(fileName); + delay(1000); // delay to avoid recording multiple files at once + isRecording = false; + } +} +``` + +In the main loop, the program waits for a command from the serial monitor. If the command is rec, the program starts recording. Otherwise, the command is assumed to be the base name for the .wav files. If it's currently recording and a base file name is set, it records the audio and saves it as a.wav file. The file names are generated by appending the file number to the base file name. + +``` +void record_wav(String fileName) +{ + ... + + File file = SD.open(fileName.c_str(), FILE_WRITE); + ... + rec_buffer = (uint8_t *)ps_malloc(record_size); + ... + + esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, + rec_buffer, + record_size, + &sample_size, + portMAX_DELAY); + ... +} +``` + +This function records audio and saves it as a.wav file with the given name. It starts by initializing the sample_size and record_size variables. record_size is calculated based on the sample rate, size, and desired recording time. Let's dig into the essential sections; + +``` +File file = SD.open(fileName.c_str(), FILE_WRITE); +// Write the header to the WAV file +uint8_t wav_header[WAV_HEADER_SIZE]; +generate_wav_header(wav_header, record_size, SAMPLE_RATE); +file.write(wav_header, WAV_HEADER_SIZE); +``` + +This section of the code opens the file on the SD card for writing and then generates the .wav file header using the generate_wav_header function. It then writes the header to the file. + +``` +// PSRAM malloc for recording +rec_buffer = (uint8_t *)ps_malloc(record_size); +if (rec_buffer == NULL) { + Serial.printf("malloc failed!\n"); + while(1) ; +} +Serial.printf("Buffer: %d bytes\n", ESP.getPsramSize() - ESP.getFreePsram()); +``` + +The ps_malloc function allocates memory in the PSRAM for the recording. If the allocation fails (i.e., rec_buffer is NULL), it prints an error message and halts execution. + +``` +// Start recording +esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, + rec_buffer, + record_size, + &sample_size, + portMAX_DELAY); +if (sample_size == 0) { + Serial.printf("Record Failed!\n"); +} else { + Serial.printf("Record %d bytes\n", sample_size); + } +``` + +The i2s_read function reads audio data from the microphone into rec_buffer. It prints an error message if no data is read (sample_size is 0). + +``` +// Increase volume +for (uint32_t i = 0; i < sample_size; i += SAMPLE_BITS/8) { + (*(uint16_t *)(rec_buffer+i)) <<= VOLUME_GAIN; +} +``` + +This section of the code increases the recording volume by shifting the sample values by VOLUME_GAIN. + +``` +// Write data to the WAV file +Serial.printf("Writing to the file ...\n"); +if (file.write(rec_buffer, record_size) != record_size) + Serial.printf("Write file Failed!\n"); + +free(rec_buffer); +file.close(); +Serial.printf("Recording complete: \n"); +Serial.printf("Send rec for a new sample or enter a new label\n\n"); +``` + +Finally, the audio data is written to the .wav file. If the write operation fails, it prints an error message. After writing, the memory allocated for rec_buffer is freed, and the file is closed. The function finishes by printing a completion message and prompting the user to send a new command. + +``` +void generate_wav_header(uint8_t *wav_header, + uint32_t wav_size, + uint32_t sample_rate) +{ + ... + memcpy(wav_header, set_wav_header, sizeof(set_wav_header)); +} +``` + +The generate_wav_header function creates a.wav file header based on the parameters (wav_size and sample_rate). It generates an array of bytes according to the .wav file format, which includes fields for the file size, audio format, number of channels, sample rate, byte rate, block alignment, bits per sample, and data size. The generated header is then copied into the wav_header array passed to the function. + +Now, upload the code to the XIAO and get samples from the keywords (yes and no). You can also capture noise and other words. + +The Serial monitor will prompt you to receive the label to be recorded. + +![](https://hackster.imgix.net/uploads/attachments/1594657/pasted_graphic_x87Mi3IFkT.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Send the label (for example, yes). The program will wait for another command: rec + +![](https://hackster.imgix.net/uploads/attachments/1594659/pasted_graphic_2_ONWtwJmxr6.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +And the program will start recording new samples every time a command rec is sent. The files will be saved as yes.1.wav, yes.2.wav, yes.3.wav, etc., until a new label (for example, no) is sent. In this case, you should send the command rec for each new sample, which will be saved as no.1.wav, no.2.wav, no.3.wav, etc. + +![](https://hackster.imgix.net/uploads/attachments/1594661/pasted_graphic_4_8cwca5pRTa.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Ultimately, we will get the saved files on the SD card. + +![](https://hackster.imgix.net/uploads/attachments/1594663/image_Cos4bNiaDF.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The files are ready to be uploaded to Edge Impulse Studio + +### Capturing (offline) Audio Data Apps + +Alternatively, you can also use your PC or smartphone to capture audio data with a sampling frequency 16KHz and a bit depth of 16 Bits. A good app for that is [*Voice Recorder Pro*](https://www.bejbej.ca/app/voicerecordpro) [(](https://www.bejbej.ca/app/voicerecordpro)IOS). You should save your records as .wav files and send them to your computer. + +![](https://hackster.imgix.net/uploads/attachments/1594808/image_pNmXUg1ux5.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Note that any app, such as [Audacity](https://www.audacityteam.org/), can be used for audio recording or even your computer[.](https://www.audacityteam.org/) + +## Training model with Edge Impulse Studio + +### Uploading the Data + +When the raw dataset is defined and collected (Pete's dataset + recorded keywords), we should initiate a new project at Edge Impulse Studio: + +![](https://hackster.imgix.net/uploads/attachments/1594809/pasted_graphic_44_AxzJtW0fRQ.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Once the project is created, select the Upload Existing Data tool in the Data Acquisition section. Choose the files to be uploaded: + +![](https://hackster.imgix.net/uploads/attachments/1594810/pasted_graphic_48_JAwBsZY3lh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +And upload them to the Studio (You can automatically split data in train/test). Repete to all classes and all raw data. + +![](https://hackster.imgix.net/uploads/attachments/1594813/pasted_graphic_46_Zyg8bVdDuG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The samples will now appear in the Data acquisition section. + +![](https://hackster.imgix.net/uploads/attachments/1594834/pasted_graphic_49_OaHcAmQTRg.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +All data on Pete's dataset have a 1s length, but the samples recorded in the previous section have 10s and must be split into 1s samples to be compatible. + +Click on three dots after the sample name and select Split sample. + +![](https://hackster.imgix.net/uploads/attachments/1594836/image_gE0k6Mevup.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Once inside the tool, split the data into 1-second records. If necessary, add or remove segments: + +![](https://hackster.imgix.net/uploads/attachments/1594852/image_4Ii4Ng4m2f.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This procedure should be repeated for all samples. + +> Note: For longer audio files (minutes), first, split into 10-second segments and after that, use the tool again to get the final 1-second splits. + +Suppose we do not split data automatically in train/test during upload. In that case, we can do it manually (using the three dots menu, moving samples individually) or using Perform Train / Test Split on Dashboard - Danger Zone. + +> We can optionally check all datasets using the tab Data Explorer. + +### Creating Impulse (Pre-Process / Model definition) + +*An* **impulse** *takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.* + +![](https://hackster.imgix.net/uploads/attachments/1594912/pasted_graphic_51_BoV3CAx2lS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +First, we will take the data points with a 1-second window, augmenting the data, sliding that window each 500ms. Note that the option zero-pad data is set. It is essential to fill with zeros samples smaller than 1 second (in some cases, I reduced the 1000 ms window on the split tool to avoid noises and spikes). + +Each 1-second audio sample should be pre-processed and converted to an image (for example, 13 x 49 x 1). We will use MFCC, which extracts features from audio signals using [Mel Frequency Cepstral Coefficients](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), which are great for the human voice. + +![](https://hackster.imgix.net/uploads/attachments/1595150/image_uk5EiFvTHh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Next, we select KERAS for classification and build our model from scratch by doing Image Classification using Convolution Neural Network). + +### Pre-Processing (MFCC) + +The next step is to create the images to be trained in the next phase: + +We can keep the default parameter values or take advantage of the DSP Autotuneparameters option, which we will do. + +![](https://hackster.imgix.net/uploads/attachments/1595153/image_qLl1o4Ruj5.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The result will not spend much memory to pre-process data (only 16KB). Still, the estimated processing time is high, 675 ms for an Espressif ESP-EYE (the closest reference available), with a 240KHz clock (same as our device), but with a smaller CPU ( XTensa LX6, versus the LX7 on the ESP32S). The real inference time should be smaller. + +Suppose we need to reduce the inference time later. In that case, we should return to the pre-processing stage and, for example, reduce the FFT length to 256, change the Number of coefficients, or another parameter. + +For now, let's keep the parameters defined by the Autotuning tool. Save parameters and generate the features. + +![](https://hackster.imgix.net/uploads/attachments/1595159/pasted_graphic_54_ejdOEShDDa.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> If you want to go further with converting temporal serial data into images using FFT, Spectrogram, etc., you can play with this CoLab: [Audio Raw Data Analysis.](https://colab.research.google.com/github/Mjrovai/UNIFEI-IESTI01-TinyML-2022.1/blob/main/00_Curse_Folder/2_Applications_Deploy/Class_24/IESTI01_Audio_Raw_Data_Analisys.ipynb) + +### Model Design and Training + +We will use a Convolution Neural Network (CNN) model. The basic architecture is defined with two blocks of Conv1D + MaxPooling (with 8 and 16 neurons, respectively) and a 0.25 Dropout. And on the last layer, after Flattening four neurons, one for each class: + +![](https://hackster.imgix.net/uploads/attachments/1595163/image_tLZhhkaWgS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +As hyper-parameters, we will have a Learning Rate of 0.005 and a model that will be trained by 100 epochs. We will also include data augmentation, as some noise. The result seems OK: + +![](https://hackster.imgix.net/uploads/attachments/1595165/image_iJtkzDOJ11.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +If you want to understand what is happening "under the hood, " you can download the dataset and run a Jupyter Notebook playing with the code. For example, you can analyze the accuracy by each epoch: + +![](https://hackster.imgix.net/uploads/attachments/1595193/image_wi6KMb5EcS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This CoLab Notebook can explain how you can go further: [KWS Classifier Project - Looking “Under the hood](https://colab.research.google.com/github/Mjrovai/XIAO-ESP32S3-Sense/blob/main/KWS Training/xiao_esp32s3_keyword_spotting_project_nn_classifier.ipynb).” + +## Testing + +Testing the model with the data put apart before training (Test Data), we got an accuracy of approximately 87%. + +![](https://hackster.imgix.net/uploads/attachments/1595225/pasted_graphic_58_TmPGA8iljK.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Inspecting the F1 score, we can see that for YES. We got 0.95, an excellent result once we used this keyword to "trigger" our postprocessing stage (turn on the built-in LED). Even for NO, we got 0.90. The worst result is for unknown, what is OK. + +We can proceed with the project, but it is possible to perform Live Classification using a smartphone before deployment on our device. Go to the Live Classification section and click on Connect a Development board: + +![](https://hackster.imgix.net/uploads/attachments/1595226/image_7MfzDDxs1C.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Point your phone to the barcode and select the link. + +![](https://hackster.imgix.net/uploads/attachments/1595229/image_dGusVuQ6HI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Your phone will be connected to the Studio. Select the option Classification on the app, and when it is running, start testing your keywords, confirming that the model is working with live and real data: + +![](https://hackster.imgix.net/uploads/attachments/1595228/image_jVLeBB4tbk.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Deploy and Inference + +The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option Arduino Library, and at the bottom, choose Quantized (Int8) and press the button Build. + +![](https://hackster.imgix.net/uploads/attachments/1595230/pasted_graphic_59_SdCzZ80grw.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now it is time for a real test. We will make inferences wholly disconnected from the Studio. Let's change one of the ESP32 code examples created when you deploy the Arduino Library. + +In your Arduino IDE, go to the File/Examples tab look for your project, and select esp32/esp32_microphone: + +![](https://hackster.imgix.net/uploads/attachments/1595434/image_o2IC7U796n.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This code was created for the ESP-EYE built-in microphone, which should be adapted for our device. + +Start changing the libraries to handle the I2S bus: + +![](https://hackster.imgix.net/uploads/attachments/1595435/image_APjcWclO6P.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +#include +#define SAMPLE_RATE 16000U +#define SAMPLE_BITS 16 +``` + +Initialize the IS2 microphone at setup(), including the lines: + +``` +void setup() +{ +... + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, SAMPLE_RATE, SAMPLE_BITS)) { + Serial.println("Failed to initialize I2S!"); + while (1) ; +... +} +``` + +On the static void capture_samples(void* arg) function, replace the line 153 that reads data from I2S mic: + +![](https://hackster.imgix.net/uploads/attachments/1595437/image_lQtCch3Ptw.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +/* read data at once from i2s */ +esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, + (void*)sampleBuffer, + i2s_bytes_to_read, + &bytes_read, 100); +``` + +On function static bool microphone_inference_start(uint32_t n_samples), we should comment or delete lines 198 to 200, where the microphone initialization function is called. This is unnecessary because the I2S microphone was already initialized during the setup(). + +![](https://hackster.imgix.net/uploads/attachments/1595444/image_8G6p7WF9ga.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Finally, on static void microphone_inference_end(void) function, replace line 243: + +![](https://hackster.imgix.net/uploads/attachments/1595438/image_jjY4COA0DE.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +static void microphone_inference_end(void) +{ + free(sampleBuffer); + ei_free(inference.buffer); +} +``` + +You can find the complete code on the [project's GitHub](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_microphone). Upload the sketch to your board and test some real inferences: + +![](https://hackster.imgix.net/uploads/attachments/1595484/image_iPcCPucH2k.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Postprocessing + +Now that we know the model is working by detecting our keywords, let's modify the code to see the internal LED going on every time a YES is detected. + +You should initialize the LED: + +``` +#define LED_BUILT_IN 21 +... +void setup() +{ +... + pinMode(LED_BUILT_IN, OUTPUT); // Set the pin as output + digitalWrite(LED_BUILT_IN, HIGH); //Turn off +... +} +``` + +And change the // print the predictions portion of the previous code (on loop(): + +``` +int pred_index = 0; // Initialize pred_index +float pred_value = 0; // Initialize pred_value + +// print the predictions +ei_printf("Predictions "); +ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)", + result.timing.dsp, result.timing.classification, result.timing.anomaly); +ei_printf(": \n"); +for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) { + ei_printf(" %s: ", result.classification[ix].label); + ei_printf_float(result.classification[ix].value); + ei_printf("\n"); + + if (result.classification[ix].value > pred_value){ + pred_index = ix; + pred_value = result.classification[ix].value; + } +} + +// show the inference result on LED +if (pred_index == 3){ + digitalWrite(LED_BUILT_IN, LOW); //Turn on +} +else{ + digitalWrite(LED_BUILT_IN, HIGH); //Turn off +} +``` + +You can find the complete code on the [project's GitHub.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_microphone_led) Upload the sketch to your board and test some real inferences: + +![](https://hackster.imgix.net/uploads/attachments/1595542/image_UTzc7GrWWp.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The idea is that the LED will be ON whenever the keyword YES is detected. In the same way, instead of turning on an LED, this could be a "trigger" for an external device, as we saw in the introduction. + + + +## Conclusion + +The Seeed XIAO ESP32S3 Sense is a *giant tiny device*! However, it is powerful, trustworthy, not expensive, low power, and has suitable sensors to be used on the most common embedded machine learning applications such as vision and sound. Even though Edge Impulse does not officially support XIAO ESP32S3 Sense (yet!), we realized that using the Studio for training and deployment is straightforward. + +> On my [GitHub repository](https://github.com/Mjrovai/XIAO-ESP32S3-Sense), you will find the last version all the codes used on this project and the previous ones of the XIAO ESP32S3 series. + +Before we finish, consider that Sound Classification is more than just voice. For example, you can develop TinyML projects around sound in several areas, such as: + +- **Security** (Broken Glass detection) +- **Industry** (Anomaly Detection) +- **Medical** (Snore, Toss, Pulmonary diseases) +- **Nature** (Beehive control, insect sound) \ No newline at end of file diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/anomaly-inference.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/anomaly-inference.jpg new file mode 100644 index 00000000..8988ae0b Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/anomaly-inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/idle-inference.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/idle-inference.jpg new file mode 100644 index 00000000..241157c6 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/idle-inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/ini.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/ini.jpg new file mode 100644 index 00000000..48790e11 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/ini.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/lift-inference.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/lift-inference.jpg new file mode 100644 index 00000000..b4e64874 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/lift-inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/maritime-inference.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/maritime-inference.jpg new file mode 100644 index 00000000..a25ddde1 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/maritime-inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/terrestrial-inference.jpg b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/terrestrial-inference.jpg new file mode 100644 index 00000000..6800d71e Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/jpeg/terrestrial-inference.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.bib b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.bib new file mode 100644 index 00000000..00614696 --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd new file mode 100644 index 00000000..b5ab567a --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd @@ -0,0 +1,486 @@ +# Motion Classification and Anomaly Detection {.unnumbered} + +![*DALL·E prompt - 1950s style cartoon illustration set in a vintage audio lab. Scientists, dressed in classic attire with white lab coats, are intently analyzing audio data on large chalkboards. The boards display intricate FFT (Fast Fourier Transform) graphs and time-domain curves. Antique audio equipment is scattered around, but the data representations are clear and detailed, indicating their focus on audio analysis.*](./images/jpeg/ini.jpg){fig-align="center" width="6.5in"} + +## Introduction + +The XIAO ESP32S3 Sense, with its built-in camera and mic, is a versatile device. But what if you need to add another type of sensor, such as an IMU? No problem! One of the standout features of the XIAO ESP32S3 is its multiple pins that can be used as an I2C bus (SDA/SCL pins), making it a suitable platform for sensor integration. + +![](https://hackster.imgix.net/uploads/attachments/1590599/image_GstFLMyDUy.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Installing the IMU + +When selecting your IMU, the market offers a wide range of devices, each with unique features and capabilities. You could choose, for example, the ADXL362 (3-axis), MAX21100 (6-axis), MPU6050 (6-axis), LIS3DHTR (3-axis), or the LCM20600Seeed Grove— (6-axis), which is part of the IMU 9DOF (lcm20600+AK09918). This variety allows you to tailor your choice to your project's specific needs. + +For this project, we will use an IMU, the MPU6050 (or 6500), a low-cost (less than 2.00 USD) 6-axis Accelerometer/Gyroscope unit. + +> At the end of the lab, we will also comment on using the LCM20600. + +The [MPU-6500](https://invensense.tdk.com/download-pdf/mpu-6500-datasheet/) is a 6-axis Motion Tracking device that combines a 3-axis gyroscope, 3-axis accelerometer, and a Digital Motion ProcessorTM (DMP) in a small 3x3x0.9mm package. It also features a 4096-byte FIFO that can lower the traffic on the serial bus interface and reduce power consumption by allowing the system processor to burst read sensor data and then go into a low-power mode. + +With its dedicated I2C sensor bus, the MPU-6500 directly accepts inputs from external I2C devices. MPU-6500, with its 6-axis integration, on-chip DMP, and run-time calibration firmware, enables manufacturers to eliminate the costly and complex selection, qualification, and system-level integration of discrete devices, guaranteeing optimal motion performance for consumers. MPU-6500 is also designed to interface with multiple non-inertial digital sensors, such as pressure sensors, on its auxiliary I2C port. + +![](https://hackster.imgix.net/uploads/attachments/1590608/image_ZFuJgZIdRi.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Usually, the libraries available are for MPU6050, but they work for both devices. + +**Connecting the HW** + +Connect the IMU to the XIAO according to the below diagram: + +- MPU6050 **SCL** --> XIAO **D5** +- MPU6050 **SDA** --> XIAO **D4** +- MPU6050 **VCC** --> XIAO **3.3V** +- MPU6050 **GND** --> XIAO **GND** + +![](https://hackster.imgix.net/uploads/attachments/1590645/drawing_Vp4G8xChAB.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Install the Library** + +Go to Arduino Library Manager and type MPU6050. Install the latest version. + +![](https://hackster.imgix.net/uploads/attachments/1590642/pasted_graphic_16_CH1rHB6s2M.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Download the sketch [MPU6050_Acc_Data_Acquisition.in](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/MPU6050_Acc_Data_Acquisition): + +``` +/* + * Based on I2C device class (I2Cdev) Arduino sketch for MPU6050 class + by Jeff Rowberg + * and Edge Impulse Data Forwarder Exampe (Arduino) + - https://docs.edgeimpulse.com/docs/cli-data-forwarder + * + * Developed by M.Rovai @11May23 + */ + +#include "I2Cdev.h" +#include "MPU6050.h" +#include "Wire.h" + +#define FREQUENCY_HZ 50 +#define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) +#define ACC_RANGE 1 // 0: -/+2G; 1: +/-4G + +// convert factor g to m/s2 ==> [-32768, +32767] ==> [-2g, +2g] +#define CONVERT_G_TO_MS2 (9.81/(16384.0/(1.+ACC_RANGE))) + +static unsigned long last_interval_ms = 0; + +MPU6050 imu; +int16_t ax, ay, az; + +void setup() { + + Serial.begin(115200); + + + // initialize device + Serial.println("Initializing I2C devices..."); + Wire.begin(); + imu.initialize(); + delay(10); + +// // verify connection +// if (imu.testConnection()) { +// Serial.println("IMU connected"); +// } +// else { +// Serial.println("IMU Error"); +// } + delay(300); + + //Set MCU 6050 OffSet Calibration + imu.setXAccelOffset(-4732); + imu.setYAccelOffset(4703); + imu.setZAccelOffset(8867); + imu.setXGyroOffset(61); + imu.setYGyroOffset(-73); + imu.setZGyroOffset(35); + + /* Set full-scale accelerometer range. + * 0 = +/- 2g + * 1 = +/- 4g + * 2 = +/- 8g + * 3 = +/- 16g + */ + imu.setFullScaleAccelRange(ACC_RANGE); +} + +void loop() { + + if (millis() > last_interval_ms + INTERVAL_MS) { + last_interval_ms = millis(); + + // read raw accel/gyro measurements from device + imu.getAcceleration(&ax, &ay, &az); + + // converting to m/s2 + float ax_m_s2 = ax * CONVERT_G_TO_MS2; + float ay_m_s2 = ay * CONVERT_G_TO_MS2; + float az_m_s2 = az * CONVERT_G_TO_MS2; + + Serial.print(ax_m_s2); + Serial.print("\t"); + Serial.print(ay_m_s2); + Serial.print("\t"); + Serial.println(az_m_s2); + } +} +``` + +**Some comments about the code:** + +Note that the values generated by the accelerometer and gyroscope have a range: [-32768, +32767], so for example, if the default accelerometer range is used, the range in Gs should be: [-2g, +2g]. So, "1G" means 16384. + +For conversion to m/s2, for example, you can define the following: + +``` +#define CONVERT_G_TO_MS2 (9.81/16384.0) +``` + +In the code, I left an option (ACC_RANGE) to be set to 0 (+/-2G) or 1 (+/- 4G). We will use +/-4G; that should be enough for us. In this case. + +We will capture the accelerometer data on a frequency of 50Hz, and the acceleration data will be sent to the Serial Port as meters per squared second (m/s2). + +When you ran the code with the IMU resting over your table, the accelerometer data shown on the Serial Monitor should be around 0.00, 0.00, and 9.81. If the values are a lot different, you should calibrate the IMU. + +The MCU6050 can be calibrated using the sketch: [mcu6050-calibration.ino](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/mcu6050-calibration). + +Run the code. The following will be displayed on the Serial Monitor: + +![](https://hackster.imgix.net/uploads/attachments/1590654/pasted_graphic_19_FhU4qX0dLU.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Send any character (in the above example, "x"), and the calibration should start. + +> Note that A message MPU6050 connection failed. Ignore this message. For some reason, imu.testConnection() is not returning a correct result. + +In the end, you will receive the offset values to be used on all your sketches: + +![](https://hackster.imgix.net/uploads/attachments/1590656/pasted_graphic_20_Tui5mRNqOL.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Take the values and use them on the setup: + +``` +//Set MCU 6050 OffSet Calibration +imu.setXAccelOffset(-4732); +imu.setYAccelOffset(4703); +imu.setZAccelOffset(8867); +imu.setXGyroOffset(61); +imu.setYGyroOffset(-73); +imu.setZGyroOffset(35); +``` + +Now, run the sketch [MPU6050_Acc_Data_Acquisition.in:](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/MPU6050_Acc_Data_Acquisition) + +Once you run the above sketch, open the Serial Monitor: + +![](https://hackster.imgix.net/uploads/attachments/1590659/pasted_graphic_21_DTRap3UbE7.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Or check the Plotter: + +![](https://hackster.imgix.net/uploads/attachments/1590660/pasted_graphic_23_hM0BpXdmeI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Move your device in the three axes. You should see the variation on Plotter: + +![](https://hackster.imgix.net/uploads/attachments/1590661/pasted_graphic_22_qOS34YmKic.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## The TinyML Motion Classification Project + +For our lab, we will simulate mechanical stresses in transport. Our problem will be to classify four classes of movement: + +- **Maritime** (pallets in boats) +- **Terrestrial** (palettes in a Truck or Train) +- **Lift** (Palettes being handled by Fork-Lift) +- **Idle** (Palettes in Storage houses) + +So, to start, we should collect data. Then, accelerometers will provide the data on the palette (or container). + +![](https://hackster.imgix.net/uploads/attachments/1590536/data1_sg5MS6KfkM.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +From the above images, we can see that primarily horizontal movements should be associated with the "Terrestrial class, " Vertical movements with the "Lift Class, " no activity with the "Idle class, " and movement on all three axes to [Maritime class.](https://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.htm) + +## Connecting the device to Edge Impulse + +For data collection, we should first connect our device to the Edge Impulse Studio, which will also be used for data pre-processing, model training, testing, and deployment. + +> Follow the instructions [here ](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation)to install the [Node.js ](https://nodejs.org/en/)and Edge Impulse CLI on your computer. + +Once the XIAO ESP32S3 is not a fully supported development board by Edge Impulse, we should, for example, use the [CLI Data Forwarder t](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder)o capture data from our sensor and send it to the Studio, as shown in this diagram: + +![](https://hackster.imgix.net/uploads/attachments/1590537/image_PHK0GELEYh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> You can alternately capture your data "offline," store them on an SD card or send them to your computer via Bluetooth or Wi-Fi. In this [video](https://youtu.be/2KBPq_826WM), you can learn alternative ways to send data to the Edge Impulse Studio. + +Connect your device to the serial port and run the previous code to capture IMU (Accelerometer) data, "printing them" on the serial. This will allow the Edge Impulse Studio to "capture" them. + +Go to the Edge Impulse page and create a project. + +![](https://hackster.imgix.net/uploads/attachments/1590663/image_xUyC0uWhnG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> The maximum length for an Arduino library name is **63 characters**. Note that the Studio will name the final library using your project name and include “_inference” to it. The name I chose initially did not work when I tried to deploy the Arduino library because it resulted in 64 characters. So, I need to change it by taking out the “anomaly detection” part. + +Start the [CLI Data Forwarder ](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder)on your terminal, entering (if it is the first time) the following command: + +$ edge-impulse-data-forwarder --clean + +``` +$ edge-impulse-data-forwarder --clean +``` + +Next, enter your EI credentials and choose your project, variables, and device names: + +![](https://hackster.imgix.net/uploads/attachments/1590664/image_qkRsm7A981.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Go to your EI Project and verify if the device is connected (the dot should be green): + +![](https://hackster.imgix.net/uploads/attachments/1590667/image_a5J303wHbE.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Data Collection + +As discussed before, we should capture data from all four Transportation Classes. Imagine that you have a container with a built-in accelerometer: + +![](https://hackster.imgix.net/uploads/attachments/1591091/boat_aOqDzqArqs.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now imagine your container is on a boat, facing an angry ocean, on a truck, etc.: + +- **Maritime** (pallets in boats) + - Move the XIAO in all directions, simulating an undulatory boat movement. + +- **Terrestrial** (palettes in a Truck or Train) + - Move the XIAO over a horizontal line. + +- **Lift** (Palettes being handled by + - Move the XIAO over a vertical line. + +- **Idle** (Palettes in Storage houses) + - Leave the XIAO over the table. + +![](https://hackster.imgix.net/uploads/attachments/1590677/idle_OiZWwciVVh.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +Below is one sample (raw data) of 10 seconds: + +![](https://hackster.imgix.net/uploads/attachments/1590541/image_E3mFL7tvSh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +You can capture, for example, 2 minutes (twelve samples of 10 seconds each) for the four classes. Using the "3 dots" after each one of the samples, select 2, moving them for the Test set (or use the automatic Train/Test Split tool on the Danger Zone of Dashboard tab). Below, you can see the result datasets: + +![](https://hackster.imgix.net/uploads/attachments/1590679/image_WB3eKzzN6R.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Data Pre-Processing + +The raw data type captured by the accelerometer is a "time series" and should be converted to "tabular data". We can do this conversion using a sliding window over the sample data. For example, in the below figure, + +![](https://hackster.imgix.net/uploads/attachments/1590693/image_KQNIPcxqXV.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +We can see 10 seconds of accelerometer data captured with a sample rate (SR) of 50Hz. A 2-second window will capture 300 data points (3 axis x 2 seconds x 50 samples). We will slide this window each 200ms, creating a larger dataset where each instance has 300 raw features. + +> You should use the best SR for your case, considering Nyquist's theorem, which states that a periodic signal must be sampled at more than twice the signal's highest frequency component. + +Data preprocessing is a challenging area for embedded machine learning. Still, Edge Impulse helps overcome this with its digital signal processing (DSP) preprocessing step and, more specifically, the Spectral Features. + +On the Studio, this dataset will be the input of a Spectral Analysis block, which is excellent for analyzing repetitive motion, such as data from accelerometers. This block will perform a DSP (Digital Signal Processing), extracting features such as "FFT" or "Wavelets". In the most common case, FFT, the **Time Domain Statistical features** per axis/channel are: + +- RMS +- Skewness +- Kurtosis + +And the **Frequency Domain Spectral features** per axis/channel are: + +- Spectral Power +- Skewness +- Kurtosis + +For example, for an FFT length of 32 points, the Spectral Analysis Block's resulting output will be 21 features per axis (a total of 63 features). + +Those 63 features will be the Input Tensor of a Neural Network Classifier and the Anomaly Detection model (K-Means). + +> You can learn more by digging into the lab *DSP - Spectral Features* + +## Model Design + +Our classifier will be a Dense Neural Network (DNN) that will have 63 neurons on its input layer, two hidden layers with 20 and 10 neurons, and an output layer with four neurons (one per each class), as shown here: + +![](https://hackster.imgix.net/uploads/attachments/1590702/image_ojSbkXrKse.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Impulse Design + +An impulse takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data. + +We also take advantage of a second model, the K-means, that can be used for Anomaly Detection. If we imagine that we could have our known classes as clusters, any sample that could not fit on that could be an outlier, an anomaly (for example, a container rolling out of a ship on the ocean). + +![](https://hackster.imgix.net/uploads/attachments/1590547/image_pFnNVK4Wjc.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Imagine our XIAO rolling or moving upside-down, on a movement complement different from the one trained + +![](https://hackster.imgix.net/uploads/attachments/1590548/image_iW1ygppsHi.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Below is our final Impulse design: + +![](https://hackster.imgix.net/uploads/attachments/1590696/image_W8xMffuTwP.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Generating features + +At this point in our project, we have defined the pre-processing method and the model designed. Now, it is time to have the job done. First, let's take the raw data (time-series type) and convert it to tabular data. Go to the Spectral Features tab and select Save Parameters: + +![](https://hackster.imgix.net/uploads/attachments/1590697/image_bsHjHtleGs.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +At the top menu, select the Generate Features option and the Generate Features button. Each 2-second window data will be converted into one data point of 63 features. + +> The Feature Explorer will show those data in 2D using [UMAP.](https://umap-learn.readthedocs.io/en/latest/) Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE but also for general non-linear dimension reduction. + +The visualization allows one to verify that the classes present an excellent separation, which indicates that the classifier should work well. + +![](https://hackster.imgix.net/uploads/attachments/1590706/image_fyynJu1laN.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Optionally, you can analyze the relative importance of each feature for one class compared with other classes. + +## Training + +Our model has four layers, as shown below: + +![](https://hackster.imgix.net/uploads/attachments/1590707/image_0M4u1e4dJI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +As hyperparameters, we will use a Learning Rate of 0.005 and 20% of data for validation for 30 epochs. After training, we can see that the accuracy is 97%. + +![](https://hackster.imgix.net/uploads/attachments/1590709/image_cCscB5HMw9.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +For anomaly detection, we should choose the suggested features that are precisely the most important in feature extraction. The number of clusters will be 32, as suggested by the Studio: + +![](https://hackster.imgix.net/uploads/attachments/1590710/image_8IOqOw1yoX.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Testing + +Using 20% of the data left behind during the data capture phase, we can verify how our model will behave with unknown data; if not 100% (what is expected), the result was not that good (8%), mainly due to the terrestrial class. Once we have four classes (which output should add 1.0), we can set up a lower threshold for a class to be considered valid (for example, 0.4): + +![](https://hackster.imgix.net/uploads/attachments/1590714/image_ecSV5fIlPu.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now, the Test accuracy will go up to 97%. + +![](https://hackster.imgix.net/uploads/attachments/1590715/image_TnLYYt60Vc.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +You should also use your device (which is still connected to the Studio) and perform some Live Classification. + +> Be aware that here you will capture real data with your device and upload it to the Studio, where an inference will be taken using the trained model (But the model is NOT in your device). + +## Deploy + +Now it is time for magic˜! The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option Arduino Library, and at the bottom, choose Quantized (Int8) and Build. A Zip file will be created and downloaded to your computer. + +![](https://hackster.imgix.net/uploads/attachments/1590716/image_d5jrYgBErG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +On your Arduino IDE, go to the Sketch tab, select the option Add.ZIP Library, and Choose the.zip file downloaded by the Studio: + +![](https://hackster.imgix.net/uploads/attachments/1590717/image_6w7t1NYsBV.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Inference + +Now, it is time for a real test. We will make inferences that are wholly disconnected from the Studio. Let's change one of the code examples created when you deploy the Arduino Library. + +In your Arduino IDE, go to the File/Examples tab and look for your project, and on examples, select nano_ble_sense_accelerometer: + +![](https://hackster.imgix.net/uploads/attachments/1590718/image_M3k3wqDRto.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Of course, this is not your board, but we can have the code working with only a few changes. + +For example, at the beginning of the code, you have the library related to Arduino Sense IMU: + +``` +/* Includes --------------------------------------------------------------- */ +#include +#include +``` + +Change the "includes" portion with the code related to the IMU: + +``` +#include +#include "I2Cdev.h" +#include "MPU6050.h" +#include "Wire.h" +``` + +Change the Constant Defines + +``` +/* Constant defines ------------------------------------------------------- */ +MPU6050 imu; +int16_t ax, ay, az; + +#define ACC_RANGE 1 // 0: -/+2G; 1: +/-4G +#define CONVERT_G_TO_MS2 (9.81/(16384/(1.+ACC_RANGE))) +#define MAX_ACCEPTED_RANGE (2*9.81)+(2*9.81)*ACC_RANGE +``` + +On the setup function, initiate the IMU set the off-set values and range: + +``` +// initialize device +Serial.println("Initializing I2C devices..."); +Wire.begin(); +imu.initialize(); +delay(10); + +//Set MCU 6050 OffSet Calibration +imu.setXAccelOffset(-4732); +imu.setYAccelOffset(4703); +imu.setZAccelOffset(8867); +imu.setXGyroOffset(61); +imu.setYGyroOffset(-73); +imu.setZGyroOffset(35); + +imu.setFullScaleAccelRange(ACC_RANGE); +``` + +At the loop function, the buffers buffer[ix], buffer[ix + 1], and buffer[ix + 2] will receive the 3-axis data captured by the accelerometer. On the original code, you have the line: + +``` +IMU.readAcceleration(buffer[ix], buffer[ix + 1], buffer[ix + 2]); +``` + +Change it with this block of code: + +``` +imu.getAcceleration(&ax, &ay, &az); +buffer[ix + 0] = ax; +buffer[ix + 1] = ay; +buffer[ix + 2] = az; +``` + +You should change the order of the following two blocks of code. First, you make the conversion to raw data to "Meters per squared second (ms2)", followed by the test regarding the maximum acceptance range (that here is in ms2, but on Arduino, was in Gs): + +``` +buffer[ix + 0] *= CONVERT_G_TO_MS2; +buffer[ix + 1] *= CONVERT_G_TO_MS2; +buffer[ix + 2] *= CONVERT_G_TO_MS2; + +for (int i = 0; i < 3; i++) { + if (fabs(buffer[ix + i]) > MAX_ACCEPTED_RANGE) { + buffer[ix + i] = ei_get_sign(buffer[ix + i]) * MAX_ACCEPTED_RANGE; + } +} +``` + +And that is it! You can now upload the code to your device and proceed with the inferences. The complete code is available on the [project's GitHub](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU). + +Now you should try your movements, seeing the result of the inference of each class on the images: + +![](images/jpeg/idle-inference.jpg) + +![](images/jpeg/terrestrial-inference.jpg) + +![](images/jpeg/lift-inference.jpg) + +![](images/jpeg/maritime-inference.jpg) + +And, of course, some "anomaly", for example, putting the XIAO upside-down. The anomaly score will be over 1: + +![](images/jpeg/anomaly-inference.jpg) + +## Conclusion. + +Regarding the IMU, this project used the low-cost MPU6050 but could also use other IMUs, for example, the LCM20600 (6-axis), which is part of the [Seeed Grove - IMU 9DOF (lcm20600+AK09918)](https://wiki.seeedstudio.com/Grove-IMU_9DOF-lcm20600+AK09918/). You can take advantage of this sensor, which has integrated a Grove connector, which can be helpful in the case you use the [XIAO with an extension board](https://wiki.seeedstudio.com/Seeeduino-XIAO-Expansion-Board/), as shown below: + +![](https://hackster.imgix.net/uploads/attachments/1591025/grove-icm2060-small_plZuu0oQ5W.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +You can follow the instructions [here](https://wiki.seeedstudio.com/Grove-IMU_9DOF-lcm20600+AK09918/#specification) to connect the IMU with the MCU. Only note that for using the Grove ICM20600 Accelerometer, it is essential to update the files **I2Cdev.cpp** and **I2Cdev.h** that you will download from the [library provided by Seeed Studio](https://github.com/Seeed-Studio/Seeed_ICM20600_AK09918). For that, replace both files from this [link](https://github.com/jrowberg/i2cdevlib/tree/master/Arduino/I2Cdev). You can find a sketch for testing the IMU on the GitHub project: [accelerometer_test.ino](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/accelerometer_test). + +> On the projet's GitHub repository, you will find the last version of all codes and other docs: [XIAO-ESP32S3 - IMU](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU). \ No newline at end of file diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/monitor.png b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/monitor.png new file mode 100644 index 00000000..178b6101 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/monitor.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-1.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-1.jpg new file mode 100644 index 00000000..99b92c72 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-1.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-2.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-2.jpg new file mode 100644 index 00000000..472e4f44 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-2.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-3.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-3.jpg new file mode 100644 index 00000000..5ddb1fe3 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-3.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-4.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-4.jpg new file mode 100644 index 00000000..e969c7c8 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/sense-craft-4.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-1.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-1.jpg new file mode 100644 index 00000000..07382818 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-1.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-2.jpg b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-2.jpg new file mode 100644 index 00000000..7a9b9b98 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/jpeg/senseCraft-2.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/images/png/obj_detec_ini.png b/contents/labs/seeed/xiao_esp32s3/object_detection/images/png/obj_detec_ini.png new file mode 100644 index 00000000..b976c7ee Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/object_detection/images/png/obj_detec_ini.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/object_detection/object_detection.qmd b/contents/labs/seeed/xiao_esp32s3/object_detection/object_detection.qmd new file mode 100644 index 00000000..56b441f9 --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/object_detection/object_detection.qmd @@ -0,0 +1,348 @@ +# Object Detection {.unnumbered} + +![*DALL·E prompt - Cartoon styled after 1950s animations, showing a detailed board with sensors, particularly a camera, on a table with patterned cloth. Behind the board, a computer with a large back showcases the Arduino IDE. The IDE's content hints at LED pin assignments and machine learning inference for detecting spoken commands. The Serial Monitor, in a distinct window, reveals outputs for the commands 'yes' and 'no'.*](./images/png/obj_detec_ini.png){fig-align="center" width="6.5in"} + +## Introduction + +In the last section regarding Computer Vision (CV) and the XIAO ESP32S3, *Image Classification,* we learned how to set up and classify images with this remarkable development board. Continuing our CV journey, we will explore **Object Detection** on microcontrollers. + +### Object Detection versus Image Classification + +The main task with Image Classification models is to identify the most probable object category present on an image, for example, to classify between a cat or a dog, dominant "objects" in an image: + +![](https://hackster.imgix.net/uploads/attachments/1654476/img_class_Oafs1LJbVZ.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +But what happens if there is no dominant category in the image? + +![](https://hackster.imgix.net/uploads/attachments/1654477/img_3_03NVYn1A61.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +An image classification model identifies the above image utterly wrong as an "ashcan," possibly due to the color tonalities. + +> The model used in the previous images is MobileNet, which is trained with a large dataset, *ImageNet*, running on a Raspberry Pi. + +To solve this issue, we need another type of model, where not only **multiple categories** (or labels) can be found but also **where** the objects are located on a given image. + +As we can imagine, such models are much more complicated and bigger, for example, the **MobileNetV2 SSD FPN-Lite 320x320, trained with the COCO dataset.** This pre-trained object detection model is designed to locate up to 10 objects within an image, outputting a bounding box for each object detected. The below image is the result of such a model running on a Raspberry Pi: + +![](https://hackster.imgix.net/uploads/attachments/1654478/img_4_Z4otzrJp6I.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Those models used for object detection (such as the MobileNet SSD or YOLO) usually have several MB in size, which is OK for use with Raspberry Pi but unsuitable for use with embedded devices, where the RAM usually has, at most, a few MB as in the case of the XIAO ESP32S3. + +### An Innovative Solution for Object Detection: FOMO + +[Edge Impulse launched in 2022, **FOMO** (Faster Objects, More Objects),](https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices) a novel solution to perform object detection on embedded devices, such as the Nicla Vision and Portenta (Cortex M7), on Cortex M4F CPUs (Arduino Nano33 and OpenMV M4 series) as well the Espressif ESP32 devices (ESP-CAM, ESP-EYE and XIAO ESP32S3 Sense). + +In this Hands-On project, we will explore Object Detection using FOMO. + +> To understand more about FOMO, you can go into the [official FOMO announcement](https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects) by Edge Impulse, where Louis Moreau and Mat Kelcey explain in detail how it works. + +## The Object Detection Project Goal + +All Machine Learning projects need to start with a detailed goal. Let's assume we are in an industrial or rural facility and must sort and count **oranges (fruits)** and particular **frogs (bugs)**. + +![](https://hackster.imgix.net/uploads/attachments/1654479/oranges-frogs_nHEaTqne53.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +In other words, we should perform a multi-label classification, where each image can have three classes: + +- Background (No objects) +- Fruit +- Bug + +Here are some not labeled image samples that we should use to detect the objects (fruits and bugs): + +![](https://hackster.imgix.net/uploads/attachments/1654480/objects_QYBPGKlycG.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +We are interested in which object is in the image, its location (centroid), and how many we can find on it. The object's size is not detected with FOMO, as with MobileNet SSD or YOLO, where the Bounding Box is one of the model outputs. + +We will develop the project using the XIAO ESP32S3 for image capture and model inference. The ML project will be developed using the Edge Impulse Studio. But before starting the object detection project in the Studio, let's create a *raw dataset* (not labeled) with images that contain the objects to be detected. + +## Data Collection + +You can capture images using the XIAO, your phone, or other devices. Here, we will use the XIAO with code from the Arduino IDE ESP32 library. + +### Collecting Dataset with the XIAO ESP32S3 + +Open the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On `File > Examples > ESP32 > Camera`, select `CameraWebServer`. + +On the BOARDS MANAGER panel, confirm that you have installed the latest "stable" package. + +> ⚠️ **Attention** +> +> Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead. + +You also should comment on all cameras' models, except the XIAO model pins: + +`#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM` + +And on `Tools`, enable the PSRAM. Enter your wifi credentials and upload the code to the device: + +![](https://hackster.imgix.net/uploads/attachments/1654482/ide_UM8udFSg8J.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +If the code is executed correctly, you should see the address on the Serial Monitor: + +![](https://hackster.imgix.net/uploads/attachments/1654483/serial_monitor_0sYoddSZfP.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Copy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select `[START STREAM]`. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the \[Save\] button. + +![](https://hackster.imgix.net/uploads/attachments/1654484/setup-img-collection_wSKNMRCSX5.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Edge impulse suggests that the objects should be similar in size and not overlapping for better performance. This is OK in an industrial facility, where the camera should be fixed, keeping the same distance from the objects to be detected. Despite that, we will also try using mixed sizes and positions to see the result. + +> We do not need to create separate folders for our images because each contains multiple labels. + +We suggest using around 50 images to mix the objects and vary the number of each appearing on the scene. Try to capture different angles, backgrounds, and light conditions. + +> The stored images use a QVGA frame size of 320x240 and RGB565 (color pixel format). + +After capturing your dataset, `[Stop Stream]` and move your images to a folder. + +## Edge Impulse Studio + +### Setup the project + +Go to [Edge Impulse Studio,](https://www.edgeimpulse.com/) enter your credentials at **Login** (or create an account), and start a new project. + +![](https://hackster.imgix.net/uploads/attachments/1654488/img_6_USMrnsGavw.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> Here, you can clone the project developed for this hands-on: [XIAO-ESP32S3-Sense-Object_Detection](https://studio.edgeimpulse.com/public/315759/latest) + +On your Project Dashboard, go down and on **Project info** and select **Bounding boxes (object detection)** and **Espressif ESP-EYE** (most similar to our board) as your Target Device: + +![](https://hackster.imgix.net/uploads/attachments/1654490/img_7_QXn8PxtWMa.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +### Uploading the unlabeled data + +On Studio, go to the `Data acquisition` tab, and on the `UPLOAD DATA` section, upload files captured as a folder from your computer. + +![](https://hackster.imgix.net/uploads/attachments/1654491/img_8_5hY40TOZKY.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> You can leave for the Studio to split your data automatically between Train and Test or do it manually. We will upload all of them as training. + +![](https://hackster.imgix.net/uploads/attachments/1654492/img_9_evgYUfkKcp.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +All the not-labeled images (47) were uploaded but must be labeled appropriately before being used as a project dataset. The Studio has a tool for that purpose, which you can find in the link Labeling queue (47). + +There are two ways you can use to perform AI-assisted labeling on the Edge Impulse Studio (free version): + +- Using yolov5 +- Tracking objects between frames + +> Edge Impulse launched an [auto-labeling feature](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/auto-labeler) for Enterprise customers, easing labeling tasks in object detection projects. + +Ordinary objects can quickly be identified and labeled using an existing library of pre-trained object detection models from YOLOv5 (trained with the COCO dataset). But since, in our case, the objects are not part of COCO datasets, we should select the option of tracking objects. With this option, once you draw bounding boxes and label the images in one frame, the objects will be tracked automatically from frame to frame, *partially* labeling the new ones (not all are correctly labeled). + +> You can use the [EI uploader](https://docs.edgeimpulse.com/docs/tools/edge-impulse-cli/cli-uploader#bounding-boxes) to import your data if you already have a labeled dataset containing bounding boxes. + +### Labeling the Dataset + +Starting with the first image of your unlabeled data, use your mouse to drag a box around an object to add a label. Then click **Save labels** to advance to the next item. + +![](https://hackster.imgix.net/uploads/attachments/1654493/img_10_guoeW66Fee.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Continue with this process until the queue is empty. At the end, all images should have the objects labeled as those samples below: + +![](https://hackster.imgix.net/uploads/attachments/1654502/img_11_J1KJZAc2T7.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Next, review the labeled samples on the `Data acquisition` tab. If one of the labels is wrong, you can edit it using the *three dots* menu after the sample name: + +![](https://hackster.imgix.net/uploads/attachments/1654512/img_12_szymDAiZSt.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +You will be guided to replace the wrong label and correct the dataset. + +![](https://hackster.imgix.net/uploads/attachments/1654516/img_13_PO2Q1FA0Sv.jpg?auto=compress%2Cformat&w=1280&h=960&fit=max) + +### Balancing the dataset and split Train/Test + +After labeling all data, it was realized that the class fruit had many more samples than the bug. So, 11 new and additional bug images were collected (ending with 58 images). After labeling them, it is time to select some images and move them to the test dataset. You can do it using the three-dot menu after the image name. I selected six images, representing 13% of the total dataset. + +![](https://hackster.imgix.net/uploads/attachments/1654521/move_to_test_zAWSz4v3Qf.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +## The Impulse Design + +In this phase, you should define how to: + +- **Pre-processing** consists of resizing the individual images from 320 x 240 to 96 x 96 and squashing them (squared form, without cropping). Afterward, the images are converted from RGB to Grayscale. +- **Design a Model,** in this case, "Object Detection." + +![](https://hackster.imgix.net/uploads/attachments/1654524/img_14_5LM3MnENo8.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +### Preprocessing all dataset + +In this section, select **Color depth** as Grayscale, suitable for use with FOMO models and Save parameters. + +![](https://hackster.imgix.net/uploads/attachments/1654526/img_15_RNibQ5TKZd.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +The Studio moves automatically to the next section, Generate features, where all samples will be pre-processed, resulting in a dataset with individual 96x96x1 images or 9,216 features. + +![](https://hackster.imgix.net/uploads/attachments/1654529/img_16_7WukfTFmf6.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +The feature explorer shows that all samples evidence a good separation after the feature generation. + +> Some samples seem to be in the wrong space, but clicking on them confirms the correct labeling. + +## Model Design, Training, and Test + +We will use FOMO, an object detection model based on MobileNetV2 (alpha 0.35) designed to coarsely segment an image into a grid of **background** vs **objects of interest** (here, *boxes* and *wheels*). + +FOMO is an innovative machine learning model for object detection, which can use up to 30 times less energy and memory than traditional models like Mobilenet SSD and YOLOv5. FOMO can operate on microcontrollers with less than 200 KB of RAM. The main reason this is possible is that while other models calculate the object's size by drawing a square around it (bounding box), FOMO ignores the size of the image, providing only the information about where the object is located in the image through its centroid coordinates. + +**How FOMO works?** + +FOMO takes the image in grayscale and divides it into blocks of pixels using a factor of 8. For the input of 96x96, the grid would be 12x12 (96/8=12). Next, FOMO will run a classifier through each pixel block to calculate the probability that there is a box or a wheel in each of them and, subsequently, determine the regions that have the highest probability of containing the object (If a pixel block has no objects, it will be classified as *background*). From the overlap of the final region, the FOMO provides the coordinates (related to the image dimensions) of the centroid of this region. + +![](https://hackster.imgix.net/uploads/attachments/1654531/img_17_L59gC89Uju.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +For training, we should select a pre-trained model. Let's use the **FOMO (Faster Objects, More Objects) MobileNetV2 0.35.** This model uses around 250KB of RAM and 80KB of ROM (Flash), which suits well with our board. + +![](https://hackster.imgix.net/uploads/attachments/1654532/img_18_LSDsmljicI.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Regarding the training hyper-parameters, the model will be trained with: + +- Epochs: 60 +- Batch size: 32 +- Learning Rate: 0.001. + +For validation during training, 20% of the dataset (*validation_dataset*) will be spared. For the remaining 80% (*train_dataset*), we will apply Data Augmentation, which will randomly flip, change the size and brightness of the image, and crop them, artificially increasing the number of samples on the dataset for training. + +As a result, the model ends with an overall F1 score of 85%, similar to the result when using the test data (83%). + +> Note that FOMO automatically added a 3rd label background to the two previously defined (*box* and *wheel*). + +![](https://hackster.imgix.net/uploads/attachments/1654533/img_19_s2e9Is84y2.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> In object detection tasks, accuracy is generally not the primary [evaluation metric.](https://learnopencv.com/mean-average-precision-map-object-detection-model-evaluation-metric/) Object detection involves classifying objects and providing bounding boxes around them, making it a more complex problem than simple classification. The issue is that we do not have the bounding box, only the centroids. In short, using accuracy as a metric could be misleading and may not provide a complete understanding of how well the model is performing. Because of that, we will use the F1 score. + +### Test model with "Live Classification" + +Once our model is trained, we can test it using the Live Classification tool. On the correspondent section, click on Connect a development board icon (a small MCU) and scan the QR code with your phone. + +![](https://hackster.imgix.net/uploads/attachments/1654534/img_20_ntLrthagWX.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Once connected, you can use the smartphone to capture actual images to be tested by the trained model on Edge Impulse Studio. + +![](https://hackster.imgix.net/uploads/attachments/1654535/img_21_h8Xe7I1W11.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +One thing to be noted is that the model can produce false positives and negatives. This can be minimized by defining a proper Confidence Threshold (use the Three dots menu for the setup). Try with 0.8 or more. + +## Deploying the Model (Arduino IDE) + +Select the Arduino Library and Quantized (int8) model, enable the EON Compiler on the Deploy Tab, and press \[Build\]. + +![](https://hackster.imgix.net/uploads/attachments/1654537/img_22_Xu9uwecZuV.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Open your Arduino IDE, and under Sketch, go to Include Library and add.ZIP Library. Select the file you download from Edge Impulse Studio, and that's it! + +![](https://hackster.imgix.net/uploads/attachments/1654538/img_24_bokujC4nFg.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Under the Examples tab on Arduino IDE, you should find a sketch code (`esp32 > esp32_camera`) under your project name. + +![](https://hackster.imgix.net/uploads/attachments/1654539/img_23_gm9v86mJkL.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +You should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75: + +```cpp +#define PWDN_GPIO_NUM -1 +#define RESET_GPIO_NUM -1 +#define XCLK_GPIO_NUM 10 +#define SIOD_GPIO_NUM 40 +#define SIOC_GPIO_NUM 39 +#define Y9_GPIO_NUM 48 +#define Y8_GPIO_NUM 11 +#define Y7_GPIO_NUM 12 +#define Y6_GPIO_NUM 14 +#define Y5_GPIO_NUM 16 +#define Y4_GPIO_NUM 18 +#define Y3_GPIO_NUM 17 +#define Y2_GPIO_NUM 15 +#define VSYNC_GPIO_NUM 38 +#define HREF_GPIO_NUM 47 +#define PCLK_GPIO_NUM 13 +``` + +Here you can see the resulting code: + +![](https://hackster.imgix.net/uploads/attachments/1654540/img_25_3uwrBVZ83q.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start detecting fruits and bugs. You can check the result on Serial Monitor. + +**Background** + +![](https://hackster.imgix.net/uploads/attachments/1654541/inference-back_Zi8gtT7YY6.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +**Fruits** + +![](https://hackster.imgix.net/uploads/attachments/1654542/fruits-inference_RxYagWYKOc.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +**Bugs** + +![](https://hackster.imgix.net/uploads/attachments/1654543/bugs-inference_fXpzxJOZRj.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Note that the model latency is 143ms, and the frame rate per second is around 7 fps (similar to what we got with the Image Classification project). This happens because FOMO is cleverly built over a CNN model, not with an object detection model like the SSD MobileNet. For example, when running a MobileNetV2 SSD FPN-Lite 320x320 model on a Raspberry Pi 4, the latency is around five times higher (around 1.5 fps). + +## Deploying the Model (SenseCraft-Web-Toolkit) + +As discussed in the Image Classification chapter, verifying inference with Image models on Arduino IDE is very challenging because we can not see what the camera focuses on. Again, let's use the **SenseCraft-Web Toolkit**. + +Follow the following steps to start the SenseCraft-Web-Toolkit: + +1. Open the [SenseCraft-Web-Toolkit website.](https://seeed-studio.github.io/SenseCraft-Web-Toolkit/#/setup/process) +2. Connect the XIAO to your computer: + +- Having the XIAO connected, select it as below: + +![](./images/jpeg/senseCraft-1.jpg) + +- Select the device/Port and press `[Connect]`: + +![](./images/jpeg/senseCraft-2.jpg) + +> You can try several Computer Vision models previously uploaded by Seeed Studio. Try them and have fun! + +In our case, we will use the blue button at the bottom of the page: `[Upload Custom AI Model]`. + +But first, we must download from Edge Impulse Studio our **quantized .tflite** model. + +3. Go to your project at Edge Impulse Studio, or clone this one: + +- [XIAO-ESP32S3-CAM-Fruits-vs-Veggies-v1-ESP-NN](https://studio.edgeimpulse.com/public/228516/live) + +4. On `Dashboard`, download the model ("block output"): `Object Detection model - TensorFlow Lite (int8 quantized)` + +![](./images/jpeg/sense-craft-1.jpg) + +5. On SenseCraft-Web-Toolkit, use the blue button at the bottom of the page: `[Upload Custom AI Model]`. A window will pop up. Enter the Model file that you downloaded to your computer from Edge Impulse Studio, choose a Model Name, and enter with labels (ID: Object): + +![](./images/jpeg/sense-craft-2.jpg) + +> Note that you should use the labels trained on EI Studio and enter them in alphabetic order (in our case, background, bug, fruit). + +After a few seconds (or minutes), the model will be uploaded to your device, and the camera image will appear in real-time on the Preview Sector: + +![](./images/jpeg/sense-craft-3.jpg) + +The detected objects will be marked (the centroid). You can select the Confidence of your inference cursor `Confidence`. and `IoU`, which is used to assess the accuracy of predicted bounding boxes compared to truth bounding boxes + +Clicking on the top button (Device Log), you can open a Serial Monitor to follow the inference, as we did with the Arduino IDE. + +![](./images/jpeg/monitor.png) + +On Device Log, you will get information as: + +- Preprocess time (image capture and Crop): 3 ms; +- Inference time (model latency): 115 ms, +- Postprocess time (display of the image and marking objects): 1 ms. +- Output tensor (boxes), for example, one of the boxes: [[30,150, 20, 20,97, 2]]; where 30,150, 20, 20 are the coordinates of the box (around the centroid); 97 is the inference result, and 2 is the class (in this case 2: fruit) + +> Note that in the above example, we got 5 boxes because none of the fruits got 3 centroids. One solution will be post-processing, where we can aggregate close centroids in one. + +Here are other screenshots: + +![](./images/jpeg/sense-craft-4.jpg) + +## Conclusion + +FOMO is a significant leap in the image processing space, as Louis Moreau and Mat Kelcey put it during its launch in 2022: + +> FOMO is a ground-breaking algorithm that brings real-time object detection, tracking, and counting to microcontrollers for the first time. + +Multiple possibilities exist for exploring object detection (and, more precisely, counting them) on embedded devices. diff --git a/contents/labs/seeed/xiao_esp32s3/setup/images/jpeg/img_cap.jpg b/contents/labs/seeed/xiao_esp32s3/setup/images/jpeg/img_cap.jpg new file mode 100644 index 00000000..5926a61d Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/setup/images/jpeg/img_cap.jpg differ diff --git a/contents/labs/seeed/xiao_esp32s3/setup/images/jpeg/serial_monitor.png 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+![*DALL·E prompt - 1950s cartoon-style drawing of a XIAO ESP32S3 board with a distinctive camera module, as shown in the image provided. The board is placed on a classic lab table with various sensors, including a microphone. Behind the board, a vintage computer screen displays the Arduino IDE in muted colors, with code focusing on LED pin setups and machine learning inference for voice commands. The Serial Monitor on the IDE showcases outputs detecting voice commands like 'yes' and 'no'. The scene merges the retro charm of mid-century labs with modern electronics.*](./images/jpeg/xiao_setup.jpg){fig-align="center" width="6.5in"} + +## Introduction + +The [XIAO ESP32S3 Sense](https://www.seeedstudio.com/XIAO-ESP32S3-Sense-p-5639.html) is Seeed Studio's affordable development board, which integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board is a great tool to start with TinyML (intelligent voice and vision AI). + +![](./images/png/xiao.png){fig-align="center" width="6.5in"} + +**XIAO ESP32S3 Sense Main Features** + +- **Powerful MCU Board**: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino / MicroPython supported +- **Advanced Functionality**: Detachable OV2640 camera sensor for 1600 * 1200 resolution, compatible with OV5640 camera sensor, integrating an additional digital microphone +- **Elaborate Power Design**: Lithium battery charge management capability offers four power consumption models, which allows for deep sleep mode with power consumption as low as 14μA +- **Great Memory for more Possibilities**: Offer 8MB PSRAM and 8MB FLASH, supporting SD card slot for external 32GB FAT memory +- **Outstanding RF performance**: Support 2.4GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna +- **Thumb-sized Compact Design**: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices + +![](./images/png/xiao_pins.png){fig-align="center" width="6.5in"} + + +Below is the general board pinout: + +![](./images/png/xiao_esp32c3_sense_pin-out.png){fig-align="center" width="6.5in"} + +> For more details, please refer to the Seeed Studio WiKi page:
+> https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/ + +## Installing the XIAO ESP32S3 Sense on Arduino IDE + +On Arduino IDE, navigate to **File > Preferences**, and fill in the URL: + +[*https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json*](https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json) + +on the field ==> **Additional Boards Manager URLs** + +![](./images/png/board_manag.png) + +Next, open boards manager. Go to **Tools** > **Board** > **Boards Manager...** and enter with *esp32.* Select and install the most updated and stable package (avoid *alpha* versions) : + +![](./images/png/board_manag2.png) + +> ⚠️ **Attention** +> +> Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead. + +On **Tools**, select the Board (**XIAO ESP32S3**): + +![](./images/png/tools_board.png) + +Last but not least, choose the **Port** where the ESP32S3 is connected. + +That is it! The device should be OK. Let's do some tests. + +## Testing the board with BLINK + +The XIAO ESP32S3 Sense has a built-in LED that is connected to GPIO21. So, you can run the blink sketch as it is (using the `LED_BUILTIN` Arduino constant) or by changing the Blink sketch accordingly: + +```cpp +#define LED_BUILT_IN 21 + +void setup() { + pinMode(LED_BUILT_IN, OUTPUT); // Set the pin as output +} + +// Remember that the pin work with inverted logic +// LOW to Turn on and HIGH to turn off +void loop() { + digitalWrite(LED_BUILT_IN, LOW); //Turn on + delay (1000); //Wait 1 sec + digitalWrite(LED_BUILT_IN, HIGH); //Turn off + delay (1000); //Wait 1 sec +} +``` + +> Note that the pins work with inverted logic: LOW to Turn on and HIGH to turn off. + +![](./images/png/blink.png) + +## Connecting Sense module (Expansion Board) + +When purchased, the expansion board is separated from the main board, but installing the expansion board is very simple. You need to align the connector on the expansion board with the B2B connector on the XIAO ESP32S3, press it hard, and when you hear a "click," the installation is complete. + +As commented in the introduction, the expansion board, or the "sense" part of the device, has a 1600x1200 OV2640 camera, an SD card slot, and a digital microphone. + +## Microphone Test + +Let's start with sound detection. Go to the [GitHub project](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) and download the sketch: [XIAOEsp2s3_Mic_Test](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/XiaoEsp32s3_Mic_Test) and run it on the Arduino IDE: + +![](./images/png/sound_test.png) + +When producing sound, you can verify it on the Serial Plotter. + +**Save recorded sound (.wav audio files) to a microSD card.** + +Now, the onboard SD Card reader can save .wav audio files. To do that, we need to habilitate the XIAO PSRAM. + +> ESP32-S3 has only a few hundred kilobytes of internal RAM on the MCU chip. This can be insufficient for some purposes, so up to 16 MB of external PSRAM (pseudo-static RAM) can be connected with the SPI flash chip. The external memory is incorporated in the memory map and, with certain restrictions, is usable in the same way as internal data RAM. + +For a start, Insert the SD Card on the XIAO as shown in the photo below (the SD Card should be formatted to **FAT32**). + +![](./images/png/sdcard.png) + +- Download the sketch [Wav_Record](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/Wav_Record), which you can find on GitHub. +- To execute the code (Wav Record), it is necessary to use the PSRAM function of the ESP-32 chip, so turn it on before uploading.: Tools\>PSRAM: "OPI PSRAM"\>OPI PSRAM + +![](./images/png/psram.png) + +- Run the code `Wav_Record.ino` +- This program is executed only once after the user **turns on the serial monitor. It records for 20 seconds and saves the recording file to a microSD card as "arduino_rec.wav." +- When the "." is output every 1 second in the serial monitor, the program execution is finished, and you can play the recorded sound file with the help of a card reader. + +![](./images/png/rec.png) + +The sound quality is excellent! + +> The explanation of how the code works is beyond the scope of this tutorial, but you can find an excellent description on the [wiki](https://wiki.seeedstudio.com/xiao_esp32s3_sense_mic#save-recorded-sound-to-microsd-card) page. + +## Testing the Camera + +To test the camera, you should download the folder [take_photos_command](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/take_photos_command) from GitHub. The folder contains the sketch (.ino) and two .h files with camera details. + +- Run the code: `take_photos_command.ino`. Open the Serial Monitor and send the command `capture` to capture and save the image on the SD Card: + +> Verify that `[Both NL & CR]` are selected on Serial Monitor. + +![](./images/png/pic_capture.png) + +Here is an example of a taken photo: + +![](./images/png/image_test.png) + +## Testing WiFi + +One of the XIAO ESP32S3's differentiators is its WiFi capability. So, let's test its radio by scanning the Wi-Fi networks around it. You can do this by running one of the code examples on the board. + +Go to Arduino IDE Examples and look for **WiFI ==\> WiFIScan** + +You should see the Wi-Fi networks (SSIDs and RSSIs) within your device's range on the serial monitor. Here is what I got in the lab: + +![](./images/png/wifi.png) + +**Simple WiFi Server (Turning LED ON/OFF)** + +Let's test the device's capability to behave as a WiFi Server. We will host a simple page on the device that sends commands to turn the XIAO built-in LED ON and OFF. + +Like before, go to GitHub to download the folder using the sketch [SimpleWiFiServer](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/SimpleWiFiServer). + +Before running the sketch, you should enter your network credentials: + +```cpp +const char* ssid = "Your credentials here"; +const char* password = "Your credentials here"; +``` + +You can monitor how your server is working with the Serial Monitor. + +![](./images/png/wifi-2.png) + +Take the IP address and enter it on your browser: + +![](./images/png/app.png) + +You will see a page with links that can turn the built-in LED of your XIAO ON and OFF. + +**Streaming video to Web** + +Now that you know that you can send commands from the webpage to your device, let's do the reverse. Let's take the image captured by the camera and stream it to a webpage: + +Download from GitHub the [folder](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Streeming_Video) that contains the code: XIAO-ESP32S3-Streeming_Video.ino. + +> Remember that the folder contains the.ino file and a couple of .h files necessary to handle the camera. + +Enter your credentials and run the sketch. On the Serial monitor, you can find the page address to enter in your browser: + +![](./images/png/wifi3.png) + +Open the page on your browser (wait a few seconds to start the streaming). That's it. + +![](./images/png/image_web.png) + +Streamlining what your camera is "seen" can be important when you position it to capture a dataset for an ML project (for example, using the code "take_phots_commands.ino". + +Of course, we can do both things simultaneously: show what the camera sees on the page and send a command to capture and save the image on the SD card. For that, you can use the code Camera_HTTP_Server_STA, which can be downloaded from GitHub. + +![](./images/png/app2.png) + +The program will do the following tasks: + +- Set the camera to JPEG output mode. +- Create a web page (for example ==\> http://192.168.4.119//). The correct address will be displayed on the Serial Monitor. +- If server.on ("/capture", HTTP_GET, serverCapture), the program takes a photo and sends it to the Web. +- It is possible to rotate the image on webPage using the button \[ROTATE\] +- The command \[CAPTURE\] only will preview the image on the webpage, showing its size on the Serial Monitor +- The `[SAVE]` command will save an image on the SD Card and show the image on the browser. +- Saved images will follow a sequential naming (image1.jpg, image2.jpg. + +![](./images/png/terminal.png) + +> This program can capture an image dataset with an image classification project. + +Inspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial [ESP32-CAM Take Photo and Display in Web Server](https://randomnerdtutorials.com/esp32-cam-take-photo-display-web-server/), which I invite all of you to visit. + +**Using the CameraWebServer** + +In the Arduino IDE, go to `File > Examples > ESP32 > Camera`, and select `CameraWebServer` + +You also should comment on all cameras' models, except the XIAO model pins: + +`#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM` + +Do not forget the `Tools` to enable the PSRAM. + +Enter your wifi credentials and upload the code to the device: + +![](./images/jpeg/webCap1.jpg) + +If the code is executed correctly, you should see the address on the Serial Monitor: + +![](./images/jpeg/serial_monitor.png) + +Copy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select `[START STREAM]`. Wait for a few seconds/minutes, depending on your connection. Using the `[Save]` button, you can save an image to your computer download area. + +![](./images/jpeg/img_cap.jpg) + +That's it! You can save the images directly on your computer for use on projects. + +## Conclusion + +The XIAO ESP32S3 Sense is flexible, inexpensive, and easy to program. With 8 MB of RAM, memory is not an issue, and the device can handle many post-processing tasks, including communication. + +You will find the last version of the codes on the GitHub repository: [XIAO-ESP32S3-Sense.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) diff --git a/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd b/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd new file mode 100644 index 00000000..40cda16a --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd @@ -0,0 +1,25 @@ +# XIAO ESP32S3 {.unnumbered} + +These labs provide a unique opportunity to gain practical experience with machine learning (ML) systems. Unlike working with large models requiring data center-scale resources, these exercises allow you to directly interact with hardware and software using TinyML. This hands-on approach gives you a tangible understanding of the challenges and opportunities in deploying AI, albeit at a tiny scale. However, the principles are largely the same as what you would encounter when working with larger systems. + +![XIAO ESP32S3 Sense. Credit: SEEED Studio](./images/jpeg/xiao_esp32s3_decked.jpeg){#fig-xiao_esp32s3 height=3in} + +## Pre-requisites + +- **XIAO ESP32S3 Sense Board**: Ensure you have the XIAO ESP32S3 Sense Board. +- **USB-C Cable**: This is for connecting the board to your computer. +- **Network**: With internet access for downloading necessary software. +- **SD Card and an SD card Adapter**: This saves audio and images (optional). + +## Setup + +- [Setup XIAO ESP32S3](./setup/setup.qmd) + +## Exercises + +| **Modality** | **Task** | **Description** | **Link** | +| ------------ | ------------------------------------------- | ----------------------------------------- | --------------------------------------- | +| Vision | Image Classification | Learn to classify images | [Link](./image_classification/image_classification.qmd) | +| Vision | Object Detection | Implement object detection | [Link](./object_detection/object_detection.qmd) | +| Sound | Keyword Spotting (KWS) | Explore voice recognition systems | [Link](./kws/kws.qmd) | +| IMU | Motion Classification and Anomaly Detection | Classify motion data and detect anomalies | [Link](./motion_classification/motion_classification.qmd) | diff --git a/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.bib b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.bib new file mode 100644 index 00000000..00614696 --- /dev/null +++ b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd new file mode 100644 index 00000000..5b7daade --- /dev/null +++ b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd @@ -0,0 +1,619 @@ +--- +bibliography: dsp_spectral_features_block.bib +--- + +# DSP - Spectral Features {.unnumbered} + +![*DALL·E 3 Prompt: 1950s style cartoon illustration of a Latin male and female scientist in a vibration research room. The man is using a calculus ruler to examine ancient circuitry. The woman is at a computer with complex vibration graphs. The wooden table has boards with sensors, prominently an accelerometer. A classic, rounded-back computer shows the Arduino IDE with code for LED pin assignments and machine learning algorithms for movement detection. The Serial Monitor displays FFT, classification, wavelets, and DSPs. Vintage lamps, tools, and charts with FFT and Wavelets graphs complete the scene.*](images/jpg/dsp_ini.jpg){fig-align="center" width="6.5in"} + +## Introduction + +TinyML projects related to motion (or vibration) involve data from IMUs (usually **accelerometers** and **Gyroscopes**). These time-series type datasets should be preprocessed before inputting them into a Machine Learning model training, which is a challenging area for embedded machine learning. Still, Edge Impulse helps overcome this complexity with its digital signal processing (DSP) preprocessing step and, more specifically, the [Spectral Features Block](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features) for Inertial sensors. + +But how does it work under the hood? Let's dig into it. + +## Extracting Features Review + +Extracting features from a dataset captured with inertial sensors, such as accelerometers, involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. Here's a high-level overview of the process: + +**Data collection:** First, we need to gather data from the accelerometers. Depending on the application, data may be collected at different sampling rates. It's essential to ensure that the sampling rate is high enough to capture the relevant dynamics of the studied motion (the sampling rate should be at least double the maximum relevant frequency present in the signal). + +**Data preprocessing:** Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can help clean and standardize the data, making it more suitable for feature extraction. + +> The Studio does not perform normalization or standardization, so sometimes, when working with Sensor Fusion, it could be necessary to perform this step before uploading data to the Studio. This is particularly crucial in sensor fusion projects, as seen in this tutorial, [Sensor Data Fusion with Spresense and CommonSense](https://docs.edgeimpulse.com/experts/air-quality-and-environmental-projects/environmental-sensor-fusion-commonsense). + +**Segmentation:** Depending on the nature of the data and the application, dividing the data into smaller segments or **windows** may be necessary. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window span**) choice depend on the application and the frequency of the events of interest. As a rule of thumb, we should try to capture a couple of "data cycles." + +**Feature extraction:** Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include: + +- **Time-domain** features describe the data's [statistical properties](https://www.mdpi.com/1424-8220/22/5/2012) within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate. +- **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the [Fast Fourier Transform (FFT)](https://en.wikipedia.org/wiki/Fast_Fourier_transform). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy. +- **Time-frequency** domain features combine the time and frequency domain information, such as the [Short-Time Fourier Transform (STFT)](https://en.wikipedia.org/wiki/Short-time_Fourier_transform) or the [Discrete Wavelet Transform (DWT)](https://en.wikipedia.org/wiki/Discrete_wavelet_transform). They can provide a more detailed understanding of how the signal's frequency content changes over time. + +In many cases, the number of extracted features can be large, which may lead to overfitting or increased computational complexity. Feature selection techniques, such as mutual information, correlation-based methods, or principal component analysis (PCA), can help identify the most relevant features for a given application and reduce the dimensionality of the dataset. The Studio can help with such feature-relevant calculations. + +Let's explore in more detail a typical TinyML Motion Classification project covered in this series of Hands-Ons. + +## A TinyML Motion Classification project + +![](images/jpg/spectral_block.jpeg){fig-align="center" width="6.5in"} + +In the hands-on project, *Motion Classification and Anomaly Detection*, we simulated mechanical stresses in transport, where our problem was to classify four classes of movement: + +- **Maritime** (pallets in boats) +- **Terrestrial** (pallets in a Truck or Train) +- **Lift** (pallets being handled by Fork-Lift) +- **Idle** (pallets in Storage houses) + +The accelerometers provided the data on the pallet (or container). + +![](images/png/case_study.png){fig-align="center" width="6.5in"} + +Below is one sample (raw data) of 10 seconds, captured with a sampling frequency of 50Hz: + +![](images/png/data_sample.png){fig-align="center" width="6.5in"} + +> The result is similar when this analysis is done over another dataset with the same principle, using a different sampling frequency, 62.5Hz instead of 50Hz. + +## Data Pre-Processing + +The raw data captured by the accelerometer (a "time series" data) should be converted to "tabular data" using one of the typical Feature Extraction methods described in the last section. + +We should segment the data using a sliding window over the sample data for feature extraction. The project captured accelerometer data every 10 seconds with a sample rate of 62.5 Hz. A 2-second window captures 375 data points (3 axis x 2 seconds x 62.5 samples). The window is slid every 80ms, creating a larger dataset where each instance has 375 "raw features." + +![](images/png/v1.png){fig-align="center" width="6.5in"} + +On the Studio, the previous version (V1) of the **Spectral Analysis Block** extracted as time-domain features only the RMS, and for the frequency-domain, the peaks and frequency (using FFT) and the power characteristics (PSD) of the signal over time resulting in a fixed tabular dataset of 33 features (11 per each axis), + +![](images/png/v1_features.png){fig-align="center" width="6.5in"} + +Those 33 features were the Input tensor of a Neural Network Classifier. + +In 2022, Edge Impulse released version 2 of the Spectral Analysis block, which we will explore here. + +### Edge Impulse - Spectral Analysis Block V.2 under the hood + +In Version 2, Time Domain Statistical features per axis/channel are: + +- RMS +- Skewness +- Kurtosis + +And the Frequency Domain Spectral features per axis/channel are: + +- Spectral Power +- Skewness (in the next version) +- Kurtosis (in the next version) + +In this [link,](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features) we can have more details about the feature extraction. + +> Clone the [public project](https://studio.edgeimpulse.com/public/198358/latest). You can also follow the explanation, playing with the code using my Google CoLab Notebook: [Edge Impulse Spectral Analysis Block Notebook](https://colab.research.google.com/github/Mjrovai/TinyML4D/blob/main/SciTinyM-2023/Edge_Impulse-Spectral_Analysis_Block/Edge_Impulse_Spectral_Analysis_Block_V3.ipynb). + +Start importing the libraries: + +``` python +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import math +from scipy.stats import skew, kurtosis +from scipy import signal +from scipy.signal import welch +from scipy.stats import entropy +from sklearn import preprocessing +import pywt + +plt.rcParams['figure.figsize'] = (12, 6) +plt.rcParams['lines.linewidth'] = 3 +``` + +From the studied project, let's choose a data sample from accelerometers as below: + +- Window size of 2 seconds: `[2,000]` ms +- Sample frequency: `[62.5]` Hz +- We will choose the `[None]` filter (for simplicity) and a +- FFT length: `[16]`. + +``` python +f = 62.5 # Hertz +wind_sec = 2 # seconds +FFT_Lenght = 16 +axis = ['accX', 'accY', 'accZ'] +n_sensors = len(axis) +``` + +![](images/png/impulse.png){fig-align="center" width="5.6in"} + +Selecting the *Raw Features* on the Studio Spectral Analysis tab, we can copy all 375 data points of a particular 2-second window to the clipboard. + +![](images/png/features.png){fig-align="center" width="6.5in"} + +Paste the data points to a new variable *data*: + +``` python +data=[-5.6330, 0.2376, 9.8701, -5.9442, 0.4830, 9.8701, -5.4217, ...] +No_raw_features = len(data) +N = int(No_raw_features/n_sensors) +``` + +The total raw features are 375, but we will work with each axis individually, where N= 125 (number of samples per axis). + +We aim to understand how Edge Impulse gets the processed features. + +![](images/png/process_features.png){fig-align="center" width="4.57in"} + +So, you should also past the processed features on a variable (to compare the calculated features in Python with the ones provided by the Studio) : + +``` python +features = [2.7322, -0.0978, -0.3813, 2.3980, 3.8924, 24.6841, 9.6303, ...] +N_feat = len(features) +N_feat_axis = int(N_feat/n_sensors) +``` + +The total number of processed features is 39, which means 13 features/axis. + +Looking at those 13 features closely, we will find 3 for the time domain (RMS, Skewness, and Kurtosis): + +- `[rms] [skew] [kurtosis]` + +and 10 for the frequency domain (we will return to this later). + +- `[spectral skew][spectral kurtosis][Spectral Power 1] ... [Spectral Power 8]` + +**Splitting raw data per sensor** + +The data has samples from all axes; let's split and plot them separately: + +``` python +def plot_data(sensors, axis, title): + [plt.plot(x, label=y) for x,y in zip(sensors, axis)] + plt.legend(loc='lower right') + plt.title(title) + plt.xlabel('#Sample') + plt.ylabel('Value') + plt.box(False) + plt.grid() + plt.show() + +accX = data[0::3] +accY = data[1::3] +accZ = data[2::3] +sensors = [accX, accY, accZ] +plot_data(sensors, axis, 'Raw Features') +``` + +![](images/png/sample.png){fig-align="center" width="6.5in"} + +**Subtracting the mean** + +Next, we should subtract the mean from the *data*. Subtracting the mean from a data set is a common data pre-processing step in statistics and machine learning. The purpose of subtracting the mean from the data is to center the data around zero. This is important because it can reveal patterns and relationships that might be hidden if the data is not centered. + +Here are some specific reasons why subtracting the mean can be helpful: + +- It simplifies analysis: By centering the data, the mean becomes zero, making some calculations simpler and easier to interpret. +- It removes bias: If the data is biased, subtracting the mean can remove it and allow for a more accurate analysis. +- It can reveal patterns: Centering the data can help uncover patterns that might be hidden if the data is not centered. For example, centering the data can help you identify trends over time if you analyze a time series dataset. +- It can improve performance: In some machine learning algorithms, centering the data can improve performance by reducing the influence of outliers and making the data more easily comparable. Overall, subtracting the mean is a simple but powerful technique that can be used to improve the analysis and interpretation of data. + +``` python +dtmean = [(sum(x)/len(x)) for x in sensors] +[print('mean_'+x+'= ', round(y, 4)) for x,y in zip(axis, dtmean)][0] + +accX = [(x - dtmean[0]) for x in accX] +accY = [(x - dtmean[1]) for x in accY] +accZ = [(x - dtmean[2]) for x in accZ] +sensors = [accX, accY, accZ] + +plot_data(sensors, axis, 'Raw Features - Subctract the Mean') +``` + +![](images/png/sample_no_mean.png){fig-align="center" width="6.5in"} + +## Time Domain Statistical features + +**RMS Calculation** + +The RMS value of a set of values (or a continuous-time waveform) is the square root of the arithmetic mean of the squares of the values or the square of the function that defines the continuous waveform. In physics, the RMS value of an electrical current is defined as the "value of the direct current that dissipates the same power in a resistor." + +In the case of a set of n values {𝑥1, 𝑥2, ..., 𝑥𝑛}, the RMS is: + +![](images/png/rms.png){fig-align="center"} + +> NOTE that the RMS value is different for the original raw data, and after subtracting the mean + +``` py +# Using numpy and standartized data (subtracting mean) +rms = [np.sqrt(np.mean(np.square(x))) for x in sensors] +``` + +We can compare the calculated RMS values here with the ones presented by Edge Impulse: + +``` python +[print('rms_'+x+'= ', round(y, 4)) for x,y in zip(axis, rms)][0] +print("\nCompare with Edge Impulse result features") +print(features[0:N_feat:N_feat_axis]) +``` + +`rms_accX= 2.7322` + +`rms_accY= 0.7833` + +`rms_accZ= 0.1383` + +Compared with Edge Impulse result features: + +`[2.7322, 0.7833, 0.1383]` + +**Skewness and kurtosis calculation** + +In statistics, skewness and kurtosis are two ways to measure the **shape of a distribution**. + +Here, we can see the sensor values distribution: + +``` python +fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(13, 4)) +sns.kdeplot(accX, fill=True, ax=axes[0]) +sns.kdeplot(accY, fill=True, ax=axes[1]) +sns.kdeplot(accZ, fill=True, ax=axes[2]) +axes[0].set_title('accX') +axes[1].set_title('accY') +axes[2].set_title('accZ') +plt.suptitle('IMU Sensors distribution', fontsize=16, y=1.02) +plt.show() +``` + +![](images/png/skew.png){fig-align="center" width="6.5in"} + +[**Skewness**](https://en.wikipedia.org/wiki/Skewness) is a measure of the asymmetry of a distribution. This value can be positive or negative. + +![](images/png/skew_2.png){fig-align="center" width="4.65in"} + +- A negative skew indicates that the tail is on the left side of the distribution, which extends towards more negative values. +- A positive skew indicates that the tail is on the right side of the distribution, which extends towards more positive values. +- A zero value indicates no skewness in the distribution at all, meaning the distribution is perfectly symmetrical. + +``` python +skew = [skew(x, bias=False) for x in sensors] +[print('skew_'+x+'= ', round(y, 4)) for x,y in zip(axis, skew)][0] +print("\nCompare with Edge Impulse result features") +features[1:N_feat:N_feat_axis] +``` + +`skew_accX= -0.099` + +`skew_accY= 0.1756` + +`skew_accZ= 6.9463` + +Compared with Edge Impulse result features: + +`[-0.0978, 0.1735, 6.8629]` + +[**Kurtosis**](https://en.wikipedia.org/wiki/Kurtosis) is a measure of whether or not a distribution is heavy-tailed or light-tailed relative to a normal distribution. + +![](images/png/kurto.png){fig-align="center"} + +- The kurtosis of a normal distribution is zero. +- If a given distribution has a negative kurtosis, it is said to be playkurtic, which means it tends to produce fewer and less extreme outliers than the normal distribution. +- If a given distribution has a positive kurtosis , it is said to be leptokurtic, which means it tends to produce more outliers than the normal distribution. + +``` python +kurt = [kurtosis(x, bias=False) for x in sensors] +[print('kurt_'+x+'= ', round(y, 4)) for x,y in zip(axis, kurt)][0] +print("\nCompare with Edge Impulse result features") +features[2:N_feat:N_feat_axis] +``` + +`kurt_accX= -0.3475` + +`kurt_accY= 1.2673` + +`kurt_accZ= 68.1123` + +Compared with Edge Impulse result features: + +`[-0.3813, 1.1696, 65.3726]` + +## Spectral features + +The filtered signal is passed to the Spectral power section, which computes the **FFT** to generate the spectral features. + +Since the sampled window is usually larger than the FFT size, the window will be broken into frames (or "sub-windows"), and the FFT is calculated over each frame. + +**FFT length** - The FFT size. This determines the number of FFT bins and the resolution of frequency peaks that can be separated. A low number means more signals will average together in the same FFT bin, but it also reduces the number of features and model size. A high number will separate more signals into separate bins, generating a larger model. + +- The total number of Spectral Power features will vary depending on how you set the filter and FFT parameters. With No filtering, the number of features is 1/2 of the FFT Length. + +**Spectral Power - Welch's method** + +We should use [Welch's method](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html) to split the signal on the frequency domain in bins and calculate the power spectrum for each bin. This method divides the signal into overlapping segments, applies a window function to each segment, computes the periodogram of each segment using DFT, and averages them to obtain a smoother estimate of the power spectrum. + +``` python +# Function used by Edge Impulse instead of scipy.signal.welch(). +def welch_max_hold(fx, sampling_freq, nfft, n_overlap): + n_overlap = int(n_overlap) + spec_powers = [0 for _ in range(nfft//2+1)] + ix = 0 + while ix <= len(fx): + # Slicing truncates if end_idx > len, and rfft will auto-zero pad + fft_out = np.abs(np.fft.rfft(fx[ix:ix+nfft], nfft)) + spec_powers = np.maximum(spec_powers, fft_out**2/nfft) + ix = ix + (nfft-n_overlap) + return np.fft.rfftfreq(nfft, 1/sampling_freq), spec_powers +``` + +Applying the above function to 3 signals: + +``` python +fax,Pax = welch_max_hold(accX, fs, FFT_Lenght, 0) +fay,Pay = welch_max_hold(accY, fs, FFT_Lenght, 0) +faz,Paz = welch_max_hold(accZ, fs, FFT_Lenght, 0) +specs = [Pax, Pay, Paz ] +``` + +We can plot the Power Spectrum P(f): + +``` python +plt.plot(fax,Pax, label='accX') +plt.plot(fay,Pay, label='accY') +plt.plot(faz,Paz, label='accZ') +plt.legend(loc='upper right') +plt.xlabel('Frequency (Hz)') +#plt.ylabel('PSD [V**2/Hz]') +plt.ylabel('Power') +plt.title('Power spectrum P(f) using Welch's method') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/png/fft.png){fig-align="center" width="6.5in"} + +Besides the Power Spectrum, we can also include the skewness and kurtosis of the features in the frequency domain (should be available on a new version): + +``` python +spec_skew = [skew(x, bias=False) for x in specs] +spec_kurtosis = [kurtosis(x, bias=False) for x in specs] +``` + +Let's now list all Spectral features per axis and compare them with EI: + +``` python +print("EI Processed Spectral features (accX): ") +print(features[3:N_feat_axis][0:]) +print("\nCalculated features:") +print (round(spec_skew[0],4)) +print (round(spec_kurtosis[0],4)) +[print(round(x, 4)) for x in Pax[1:]][0] +``` + +EI Processed Spectral features (accX): + +2.398, 3.8924, 24.6841, 9.6303, 8.4867, 7.7793, 2.9963, 5.6242, 3.4198, 4.2735 + +Calculated features: + +2.9069 8.5569 24.6844 9.6304 8.4865 7.7794 2.9964 5.6242 3.4198 4.2736 + +``` python +print("EI Processed Spectral features (accY): ") +print(features[16:26][0:]) #13: 3+N_feat_axis; 26 = 2x N_feat_axis +print("\nCalculated features:") +print (round(spec_skew[1],4)) +print (round(spec_kurtosis[1],4)) +[print(round(x, 4)) for x in Pay[1:]][0] +``` + +EI Processed Spectral features (accY): + +0.9426, -0.8039, 5.429, 0.999, 1.0315, 0.9459, 1.8117, 0.9088, 1.3302, 3.112 + +Calculated features: + +1.1426 -0.3886 5.4289 0.999 1.0315 0.9458 1.8116 0.9088 1.3301 3.1121 + +``` python +print("EI Processed Spectral features (accZ): ") +print(features[29:][0:]) #29: 3+(2*N_feat_axis); +print("\nCalculated features:") +print (round(spec_skew[2],4)) +print (round(spec_kurtosis[2],4)) +[print(round(x, 4)) for x in Paz[1:]][0] +``` + +EI Processed Spectral features (accZ): + +0.3117, -1.3812, 0.0606, 0.057, 0.0567, 0.0976, 0.194, 0.2574, 0.2083, 0.166 + +Calculated features: + +0.3781 -1.4874 0.0606 0.057 0.0567 0.0976 0.194 0.2574 0.2083 0.166 + +## Time-frequency domain + +### Wavelets + +[Wavelet](https://en.wikipedia.org/wiki/Wavelet) is a powerful technique for analyzing signals with transient features or abrupt changes, such as spikes or edges, which are difficult to interpret with traditional Fourier-based methods. + +Wavelet transforms work by breaking down a signal into different frequency components and analyzing them individually. The transformation is achieved by convolving the signal with a **wavelet function**, a small waveform centered at a specific time and frequency. This process effectively decomposes the signal into different frequency bands, each of which can be analyzed separately. + +One of the critical benefits of wavelet transforms is that they allow for time-frequency analysis, which means that they can reveal the frequency content of a signal as it changes over time. This makes them particularly useful for analyzing non-stationary signals, which vary over time. + +Wavelets have many practical applications, including signal and image compression, denoising, feature extraction, and image processing. + +Let's select Wavelet on the Spectral Features block in the same project: + +- Type: Wavelet +- Wavelet Decomposition Level: 1 +- Wavelet: bior1.3 + +![](images/png/fft_result.png){fig-align="center"} + +**The Wavelet Function** + +``` python +wavelet_name='bior1.3' +num_layer = 1 + +wavelet = pywt.Wavelet(wavelet_name) +[phi_d,psi_d,phi_r,psi_r,x] = wavelet.wavefun(level=5) +plt.plot(x, psi_d, color='red') +plt.title('Wavelet Function') +plt.ylabel('Value') +plt.xlabel('Time') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/png/wav.png){fig-align="center" width="6.5in"} + +As we did before, let's copy and past the Processed Features: + +![](images/png/wav_processed.png){fig-align="center" width="6.5in"} + +``` python +features = [3.6251, 0.0615, 0.0615, -7.3517, -2.7641, 2.8462, 5.0924, ...] +N_feat = len(features) +N_feat_axis = int(N_feat/n_sensors) +``` + +Edge Impulse computes the [Discrete Wavelet Transform (DWT)](https://pywavelets.readthedocs.io/en/latest/ref/dwt-discrete-wavelet-transform.html) for each one of the Wavelet Decomposition levels selected. After that, the features will be extracted. + +In the case of **Wavelets**, the extracted features are *basic statistical values*, *crossing values*, and *entropy.* There are, in total, 14 features per layer as below: + +- \[11\] Statiscal Features: **n5, n25, n75, n95, mean, median,** standard deviation **(std)**, variance **(var)** root mean square **(rms), kurtosis**, and skewness **(skew)**. +- \[2\] Crossing Features: Zero crossing rate **(zcross)** and mean crossing rate **(mcross)** are the times that the signal passes through the baseline (y = 0) and the average level (y = u) per unit of time, respectively +- \[1\] Complexity Feature: **Entropy** is a characteristic measure of the complexity of the signal + +All the above 14 values are calculated for each Layer (including L0, the original signal) + +- The total number of features varies depending on how you set the filter and the number of layers. For example, with \[None\] filtering and Level\[1\], the number of features per axis will be 14 x 2 (L0 and L1) = 28. For the three axes, we will have a total of 84 features. + +### Wavelet Analysis + +Wavelet analysis decomposes the signal (**accX, accY**, **and accZ**) into different frequency components using a set of filters, which separate these components into low-frequency (slowly varying parts of the signal containing long-term patterns), such as **accX_l1, accY_l1, accZ_l1** and, high-frequency (rapidly varying parts of the signal containing short-term patterns) components, such as **accX_d1, accY_d1, accZ_d1**, permitting the extraction of features for further analysis or classification. + +Only the low-frequency components (approximation coefficients, or cA) will be used. In this example, we assume only one level (Single-level Discrete Wavelet Transform), where the function will return a tuple. With a multilevel decomposition, the "Multilevel 1D Discrete Wavelet Transform", the result will be a list (for detail, please see: [Discrete Wavelet Transform (DWT)](https://pywavelets.readthedocs.io/en/latest/ref/dwt-discrete-wavelet-transform.html) ) + +``` python +(accX_l1, accX_d1) = pywt.dwt(accX, wavelet_name) +(accY_l1, accY_d1) = pywt.dwt(accY, wavelet_name) +(accZ_l1, accZ_d1) = pywt.dwt(accZ, wavelet_name) +sensors_l1 = [accX_l1, accY_l1, accZ_l1] + +# Plot power spectrum versus frequency +plt.plot(accX_l1, label='accX') +plt.plot(accY_l1, label='accY') +plt.plot(accZ_l1, label='accZ') +plt.legend(loc='lower right') +plt.xlabel('Time') +plt.ylabel('Value') +plt.title('Wavelet Approximation') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/png/wavelet_input.png){fig-align="center" width="6.5in"} + +### Feature Extraction + +Let's start with the basic statistical features. Note that we apply the function for both the original signals and the resultant cAs from the DWT: + +``` python +def calculate_statistics(signal): + n5 = np.percentile(signal, 5) + n25 = np.percentile(signal, 25) + n75 = np.percentile(signal, 75) + n95 = np.percentile(signal, 95) + median = np.percentile(signal, 50) + mean = np.mean(signal) + std = np.std(signal) + var = np.var(signal) + rms = np.sqrt(np.mean(np.square(signal))) + return [n5, n25, n75, n95, median, mean, std, var, rms] + +stat_feat_l0 = [calculate_statistics(x) for x in sensors] +stat_feat_l1 = [calculate_statistics(x) for x in sensors_l1] +``` + +The Skelness and Kurtosis: + +``` python +skew_l0 = [skew(x, bias=False) for x in sensors] +skew_l1 = [skew(x, bias=False) for x in sensors_l1] +kurtosis_l0 = [kurtosis(x, bias=False) for x in sensors] +kurtosis_l1 = [kurtosis(x, bias=False) for x in sensors_l1] +``` + +**Zero crossing (zcross)** is the number of times the wavelet coefficient crosses the zero axis. It can be used to measure the signal's frequency content since high-frequency signals tend to have more zero crossings than low-frequency signals. + +**Mean crossing (mcross)**, on the other hand, is the number of times the wavelet coefficient crosses the mean of the signal. It can be used to measure the amplitude since high-amplitude signals tend to have more mean crossings than low-amplitude signals. + +``` python +def getZeroCrossingRate(arr): + my_array = np.array(arr) + zcross = float("{0:.2f}".format((((my_array[:-1] * my_array[1:]) < 0).su m())/len(arr))) + return zcross + +def getMeanCrossingRate(arr): + mcross = getZeroCrossingRate(np.array(arr) - np.mean(arr)) + return mcross + +def calculate_crossings(list): + zcross=[] + mcross=[] + for i in range(len(list)): + zcross_i = getZeroCrossingRate(list[i]) + zcross.append(zcross_i) + mcross_i = getMeanCrossingRate(list[i]) + mcross.append(mcross_i) + return zcross, mcross + +cross_l0 = calculate_crossings(sensors) +cross_l1 = calculate_crossings(sensors_l1) +``` + +In wavelet analysis, **entropy** refers to the degree of disorder or randomness in the distribution of wavelet coefficients. Here, we used Shannon entropy, which measures a signal's uncertainty or randomness. It is calculated as the negative sum of the probabilities of the different possible outcomes of the signal multiplied by their base 2 logarithm. In the context of wavelet analysis, Shannon entropy can be used to measure the complexity of the signal, with higher values indicating greater complexity. + +``` python +def calculate_entropy(signal, base=None): + value, counts = np.unique(signal, return_counts=True) + return entropy(counts, base=base) + +entropy_l0 = [calculate_entropy(x) for x in sensors] +entropy_l1 = [calculate_entropy(x) for x in sensors_l1] +``` + +Let's now list all the wavelet features and create a list by layers. + +``` python +L1_features_names = ["L1-n5", "L1-n25", "L1-n75", "L1-n95", "L1-median", "L1-mean", "L1-std", "L1-var", "L1-rms", "L1-skew", "L1-Kurtosis", "L1-zcross", "L1-mcross", "L1-entropy"] + +L0_features_names = ["L0-n5", "L0-n25", "L0-n75", "L0-n95", "L0-median", "L0-mean", "L0-std", "L0-var", "L0-rms", "L0-skew", "L0-Kurtosis", "L0-zcross", "L0-mcross", "L0-entropy"] + +all_feat_l0 = [] +for i in range(len(axis)): + feat_l0 = stat_feat_l0[i]+[skew_l0[i]]+[kurtosis_l0[i]]+[cross_l0[0][i]]+[cross_l0[1][i]]+[entropy_l0[i]] + [print(axis[i]+' '+x+'= ', round(y, 4)) for x,y in zip(L0_features_names, feat_l0)][0] + all_feat_l0.append(feat_l0) +all_feat_l0 = [item for sublist in all_feat_l0 for item in sublist] +print(f"\nAll L0 Features = {len(all_feat_l0)}") + +all_feat_l1 = [] +for i in range(len(axis)): +feat_l1 = stat_feat_l1[i]+[skew_l1[i]]+[kurtosis_l1[i]]+[cross_l1[0][i]]+[cross_l1[1][i]]+[entropy_l1[i]] +[print(axis[i]+' '+x+'= ', round(y, 4)) for x,y in zip(L1_features_names, feat_l1)][0] +all_feat_l1.append(feat_l1) +all_feat_l1 = [item for sublist in all_feat_l1 for item in sublist] +print(f"\nAll L1 Features = {len(all_feat_l1)}") +``` + +![](images/png/wav_result.png){fig-align="center" width="3.58in"} + +## Conclusion + +Edge Impulse Studio is a powerful online platform that can handle the pre-processing task for us. Still, given our engineering perspective, we want to understand what is happening under the hood. This knowledge will help us find the best options and hyper-parameters for tuning our projects. + +Daniel Situnayake wrote in his [blog:](https://situnayake.com/) "Raw sensor data is highly dimensional and noisy. Digital signal processing algorithms help us sift the signal from the noise. DSP is an essential part of embedded engineering, and many edge processors have on-board acceleration for DSP. As an ML engineer, learning basic DSP gives you superpowers for handling high-frequency time series data in your models." 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+++ b/contents/labs/shared/kws_feature_eng/kws_feature_eng.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/kws_feature_eng/kws_feature_eng.qmd b/contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd similarity index 99% rename from contents/kws_feature_eng/kws_feature_eng.qmd rename to contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd index e27bd15b..ebc38f7e 100644 --- a/contents/kws_feature_eng/kws_feature_eng.qmd +++ b/contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd @@ -2,7 +2,7 @@ bibliography: kws_feature_eng.bib --- -# Audio Feature Engineering {.unnumbered} +# KWS Feature Engineering {.unnumbered} ![*DALL·E 3 Prompt: 1950s style cartoon scene set in an audio research room. Two scientists, one holding a magnifying glass and the other taking notes, examine large charts pinned to the wall. These charts depict FFT graphs and time curves related to audio data analysis. The room has a retro ambiance, with wooden tables, vintage lamps, and classic audio analysis tools.*](images/jpg/kws_under_the_hood_ini.jpg){fig-align="center" width="6.5in"} diff --git a/contents/labs/shared/shared.qmd b/contents/labs/shared/shared.qmd new file mode 100644 index 00000000..d948011a --- /dev/null +++ b/contents/labs/shared/shared.qmd @@ -0,0 +1,11 @@ +## Shared Labs {.unnumbered} + +The labs in this section cover topics and techniques that are applicable across different hardware platforms. These labs are designed to be independent of specific boards, allowing you to focus on the fundamental concepts and algorithms used in (tiny) ML applications. + +By exploring these shared labs, you'll gain a deeper understanding of the common challenges and solutions in embedded machine learning. The knowledge and skills acquired here will be valuable regardless of the specific hardware you work with in the future. + +| Exercise | Nicla Vision | XIAO ESP32S3 | +|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------| +| KWS Feature Engineering | ✔ [Link](./shared/kws_feature_eng/kws_feature_eng.qmd) | ✔ [Link](./shared/kws_feature_eng/kws_feature_eng.qmd) | +| DSP Spectral Features Block | ✔ [Link](./shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd) | ✔ [Link](./shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd) | +