diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..48ba709 --- /dev/null +++ b/.gitignore @@ -0,0 +1,140 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*/__pycache__/ +*/*/__pycache__/ +.DS_Store +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +*/.ipynb_checkpoints +*/*/.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# model +Dataset +Dataset/ +.*.ipynb +.vscode +*.csv diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..5567e19 --- /dev/null +++ b/README.md @@ -0,0 +1,34 @@ +# SlamPy + +this project is a wrapper for an aribtrary SLAM alogirthm in python. + +for the slam algorithm is current provided : + +- ORB_SLAM2 +- ORB_SLAM3 + +## ORB_SLAM2 / ORB_SLAM3 requirement + +for this algorithm you need to install the python wrapper for this two algorithm, this wrapper can be found at [ORB_SLAM2-PythonBindings](https://github.com/GiordanoLaminetti/ORB_SLAM2-PythonBindings) for ORB_SLAM2 and [ORB_SLAM3-PythonBindings](https://github.com/GiordanoLaminetti/ORB_SLAM2-PythonBindings/tree/ORBSLAM3) for ORB_SLAM3 + +## Example of usage with ORB_SLAM2 + +the jupyter notebooks **example_usage** show and example of usage with ORB_SLAM2 with a KITTI dataset, the dataset can be found at this [link](http://www.cvlibs.net/datasets/kitti/raw_data.php) + +## Change the settings + +to change the algorithm settings you can modify the **setitng.yaml** file in the line + +> SLAM.alg:'insert here your .py file that contains the wrapper class' + +you can add at the file the other params that the method needs. +to change from ORB_SLAM2 to ORB_SLAM3 you need to change only the **SLAM.alg** entry. + +### ORB_SLAM2/3 params + +the params needed for this 2 algorithms are: + +- a vocabulary file +- a setting file with the intrisec parameters of the cameras and the other configuration params + +in this repo is provided the vocabulary file and the configuration file for ORB_SLAM2/3 for the KITTI_02 camera and the TUM freiburg3 camera in the **slam_metohd/Settings** folder, but you can add your owns using as model the files currently provided. diff --git a/read_depth.py b/read_depth.py new file mode 100644 index 0000000..6bf02bf --- /dev/null +++ b/read_depth.py @@ -0,0 +1,29 @@ +#!/usr/bin/python + +from PIL import Image +import numpy as np + + +def read_depth_KITTI(filename): + """ loads depth map D from png file and returns it as a numpy array, + + """ + depth_png = np.array(Image.open(filename), dtype=int) + # make sure we have a proper 16bit depth map here.. not 8bit! + assert(np.max(depth_png) > 255) + + depth = depth_png.astype(np.float) / 256. + depth[depth_png == 0] = -1. + return depth + + +def read_depth_TUM(filename): + """ loads depth map D from png file and returns it as a numpy array, + + """ + depth_png = np.array(Image.open(filename), dtype=int) + # make sure we have a proper 16bit depth map here.. not 8bit! + assert(np.max(depth_png) > 255) + + depth = (depth_png.astype(np.float) / 256) / 5000. + return depth diff --git a/settings.yaml b/settings.yaml new file mode 100644 index 0000000..d3d9b71 --- /dev/null +++ b/settings.yaml @@ -0,0 +1,10 @@ +SLAM.alg: "OrbSlam3" +#-------------------------------------------------------------------------------------------- +# SLAM Method: change with the name of the .py file in whitch there are the class SLAM +#-------------------------------------------------------------------------------------------- + +#-------------------------------------------------------------------------------------------- +# SLAM Params: add your own to run the algorithm +#-------------------------------------------------------------------------------------------- +SLAM.vocab_path: "slam_method/Settings/ORBvoc.bin" +SLAM.settings_path: "slam_method/Settings/OrbSlam3_KITTI_02.yaml" diff --git a/slam_method/OrbSlam2.py b/slam_method/OrbSlam2.py new file mode 100644 index 0000000..4c531a6 --- /dev/null +++ b/slam_method/OrbSlam2.py @@ -0,0 +1,91 @@ +import orbslam2 +import sys +import os.path +sys.path.append("..") +from pyslam import State +from pyslam import Sensor + +class SLAM(): + def __init__(self, params, sensor_type): + config_file = params['SLAM.settings_path'] + vocab_file = params['SLAM.vocab_path'] + + #check the existence of the configuration file + + if not os.path.exists(config_file): + raise FileNotFoundError(config_file+" not found") + + if not os.path.exists(vocab_file): + raise FileNotFoundError(vocab_file+" not found") + + # ORB_Slam2 don't have the imu interfaces so the STEREO_IMU and + # MONOCULAR_IMU are mapped in the STEREO and MONOCULAR sensor + if sensor_type == Sensor.MONOCULAR or sensor_type == Sensor.MONOCULAR_IMU: + print('the input sensor select is MONOCULAR') + self.slam = orbslam2.System( + vocab_file, config_file, orbslam2.Sensor.MONOCULAR) + self.sensor_type = Sensor.MONOCULAR + if sensor_type == Sensor.STEREO or sensor_type == Sensor.STEREO_IMU: + print('the input sensor select is STEREO') + self.slam = orbslam2.System( + vocab_file, config_file, orbslam2.Sensor.STEREO) + self.sensor_type = Sensor.STEREO + if sensor_type == Sensor.RGBD: + print('the input sensor select is RGBD') + self.slam = orbslam2.System( + vocab_file, config_file, orbslam2.Sensor.RGBD) + self.sensor_type = sensor_type + self.slam.set_use_viewer(False) + self.slam.initialize() + + def process_image_mono(self, image, tframe): + if self.sensor_type == Sensor.MONOCULAR: + self.slam.process_image_mono(image, tframe) + else: + raise Exception("The sensor type is not MONOCULAR") + + def process_image_stereo(self, image_left, image_right, tframe): + if self.sensor_type == Sensor.STEREO: + self.slam.process_image_stereo(image_left, image_right, tframe) + else: + raise Exception("The sensor type is not STEREO") + + def process_image_imu_mono(self, image, tframe, imu): + # map to the MONOCULAR sensor + self.slam.process_image_mono(image, tframe) + + def process_image_imu_stereo(self, image_left, image_right, tframe, imu): + # map to the STEREO sensor + self.slam.process_image_stereo(image_left, image_right, tframe) + + def process_image_rgbd(self, image, tframe): + if self.sensor_type == Sensor.RGBD: + self.slam.process_image_rgbd(image, tframe) + else: + raise Exception("The sensor type is not RGBD") + + def get_pose(self): + if self.slam.get_tracking_state() == orbslam2.TrackingState.OK: + return self.slam.get_frame_pose() + + def get_abs_cloud(self): + if self.slam.get_tracking_state() == orbslam2.TrackingState.OK: + return self.get_tracked_mappoints() + + def get_camera_matrix(self): + return self.slam.get_camera_matrix() + + def get_state(self): + if self.slam.get_tracking_state() == orbslam2.TrackingState.OK: + return State.OK + elif self.slam.get_tracking_state() == orbslam2.TrackingState.LOST: + return State.LOST + elif self.slam.get_tracking_state() == orbslam2.TrackingState.NOT_INITIALIZED: + return State.NOT_INITIALIZED + + + def reset(self): + self.slam.reset() + + def shutdown(self): + self.slam.shutdown() diff --git a/slam_method/OrbSlam3.py b/slam_method/OrbSlam3.py new file mode 100644 index 0000000..cbf57c9 --- /dev/null +++ b/slam_method/OrbSlam3.py @@ -0,0 +1,107 @@ +import orbslam3 +import sys +import os.path +sys.path.append("..") +from pyslam import State +from pyslam import Sensor + +class Slam(): + + def __init__(self, params, sensor_type): + config_file = params['SLAM.settings_path'] + vocab_file = params['SLAM.vocab_path'] + + #check the existence of the configuration file + + if not os.path.exists(config_file): + raise FileNotFoundError(config_file+" not found") + + if not os.path.exists(vocab_file): + raise FileNotFoundError(vocab_file+" not found") + + if sensor_type == Sensor.MONOCULAR: + print('the input sensor select is MONOCULAR') + self.slam = orbslam3.System( + vocab_file, config_file, orbslam3.Sensor.MONOCULAR) + self.sensor_type = sensor_type + + if sensor_type == Sensor.MONOCULAR_IMU: + print('the input sensor select is MONOCULAR_IMU') + self.slam = orbslam3.System( + vocab_file, config_file, orbslam3.Sensor.IMU_MONOCULAR) + self.sensor_type = sensor_type + + if sensor_type == Sensor.STEREO: + print('the input sensor select is STEREO') + self.slam = orbslam3.System( + vocab_file, config_file, orbslam3.Sensor.STEREO) + self.sensor_type = sensor_type + + if sensor_type == Sensor.STEREO_IMU: + print('the input sensor select is STREO_IMU') + self.slam = orbslam3.System( + vocab_file, config_file, orbslam3.Sensor.IMU_STEREO) + self.sensor_type = sensor_type + + if sensor_type == Sensor.RGBD: + print('the input sensor select is RGBD') + self.slam = orbslam3.System( + vocab_file, config_file, orbslam3.Sensor.RGBD) + self.sensor_type = sensor_type + self.slam.set_use_viewer(False) + self.slam.initialize() + + def process_image_mono(self, image, tframe): + if self.sensor_type == Sensor.MONOCULAR: + self.slam.process_image_mono(image, tframe,'0') + else: + raise Exception("The sensor type is not MONOCULAR") + + def process_image_stereo(self, image_left, image_right, tframe): + if self.sensor_type == Sensor.STEREO: + self.slam.process_image_stereo(image_left, image_right, tframe,'0') + else: + raise Exception("The sensor type is not STEREO") + + def process_image_imu_mono(self, image, tframe, imu): + if self.sensor_type == Sensor.MONOCULAR_IMU: + self.slam.process_image_imu_mono(image, tframe, imu,'0') + else: + raise Exception("The sensor type is not MONOCULAR_IMU") + + def process_image_imu_stereo(self,image_left,image_right,tframe,imu): + if self.sensor_type == Sensor.STEREO_IMU: + self.slam.process_image_imu_stereo(image,tframe,imu,'0') + else: + raise Exception("The sensor type is not STREO_IMU") + + def process_image_rgbd(self,image,tframe): + if self.sensor_type == Sensor.RGBD: + self.slam.process_image_rgbd(image,tframe,'0') + else: + raise Exception("The sensor type is not RGBD") + + def get_pose(self): + if self.slam.get_tracking_state() == orbslam3.TrackingState.OK: + return self.slam.get_frame_pose() + + def get_abs_cloud(self): + if self.slam.get_tracking_state() == orbslam3.TrackingState.OK: + return self.slam.get_tracked_mappoints() + + def get_camera_matrix(self): + return self.slam.get_camera_matrix() + + def get_state(self): + if self.slam.get_tracking_state() == orbslam3.TrackingState.OK: + return State.OK + elif self.slam.get_tracking_state() == orbslam3.TrackingState.LOST: + return State.LOST + elif self.slam.get_tracking_state() == orbslam3.TrackingState.NOT_INITIALIZED: + return State.NOT_INITIALIZED + + def reset(self): + self.slam.reset() + + def shutdown(self): + self.slam.shutdown() diff --git a/slam_method/Settings/ORBvoc.bin b/slam_method/Settings/ORBvoc.bin new file mode 100644 index 0000000..727b628 Binary files /dev/null and b/slam_method/Settings/ORBvoc.bin differ diff --git a/slam_method/Settings/OrbSlam2_KITTI_02.yaml b/slam_method/Settings/OrbSlam2_KITTI_02.yaml new file mode 100644 index 0000000..2312ec5 --- /dev/null +++ b/slam_method/Settings/OrbSlam2_KITTI_02.yaml @@ -0,0 +1,26 @@ +%YAML:1.0 +Camera.RGB: 1 +Camera.cx: 696.0217 +Camera.cy: 224.1806 +Camera.fps: 10.0 +Camera.fx: 959.791 +Camera.fy: 956.9251 +Camera.k1: -0.3691481 +Camera.k2: 0.1968681 +Camera.p1: 0.001353473 +Camera.p2: 0.0005677587 +ORBextractor.iniThFAST: 20 +ORBextractor.minThFAST: 7 +ORBextractor.nFeatures: 2000 +ORBextractor.nLevels: 8 +ORBextractor.scaleFactor: 1.2 +Viewer.CameraLineWidth: 2 +Viewer.CameraSize: 0.15 +Viewer.GraphLineWidth: 1 +Viewer.KeyFrameLineWidth: 1 +Viewer.KeyFrameSize: 0.1 +Viewer.PointSize: 2 +Viewer.ViewpointF: 2000 +Viewer.ViewpointX: 0 +Viewer.ViewpointY: -10 +Viewer.ViewpointZ: -0.1 diff --git a/slam_method/Settings/OrbSlam2_TUM_freiburg3_.yaml b/slam_method/Settings/OrbSlam2_TUM_freiburg3_.yaml new file mode 100644 index 0000000..ac42bb6 --- /dev/null +++ b/slam_method/Settings/OrbSlam2_TUM_freiburg3_.yaml @@ -0,0 +1,57 @@ +%YAML:1.0 + +#-------------------------------------------------------------------------------------------- +# Camera Parameters. Adjust them! +#-------------------------------------------------------------------------------------------- + +# Camera calibration and distortion parameters (OpenCV) +Camera.fx: 520.908620 +Camera.fy: 521.007327 +Camera.cx: 325.141442 +Camera.cy: 249.701764 + +Camera.k1: 0.231222 +Camera.k2: -0.784899 +Camera.p1: -0.003257 +Camera.p2: -0.000105 +Camera.k3: 0.917205 + +# Camera frames per second +Camera.fps: 30.0 + +# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale) +Camera.RGB: 1 + +#-------------------------------------------------------------------------------------------- +# ORB Parameters +#-------------------------------------------------------------------------------------------- + +# ORB Extractor: Number of features per image +ORBextractor.nFeatures: 2000 + +# ORB Extractor: Scale factor between levels in the scale pyramid +ORBextractor.scaleFactor: 1.2 + +# ORB Extractor: Number of levels in the scale pyramid +ORBextractor.nLevels: 8 + +# ORB Extractor: Fast threshold +# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response. +# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST +# You can lower these values if your images have low contrast +ORBextractor.iniThFAST: 20 +ORBextractor.minThFAST: 7 + +#-------------------------------------------------------------------------------------------- +# Viewer Parameters +#-------------------------------------------------------------------------------------------- +Viewer.KeyFrameSize: 0.05 +Viewer.KeyFrameLineWidth: 1 +Viewer.GraphLineWidth: 0.9 +Viewer.PointSize: 2 +Viewer.CameraSize: 0.08 +Viewer.CameraLineWidth: 3 +Viewer.ViewpointX: 0 +Viewer.ViewpointY: -0.7 +Viewer.ViewpointZ: -1.8 +Viewer.ViewpointF: 500 \ No newline at end of file diff --git a/slam_method/Settings/OrbSlam3_KITTI_02.yaml b/slam_method/Settings/OrbSlam3_KITTI_02.yaml new file mode 100644 index 0000000..3b19796 --- /dev/null +++ b/slam_method/Settings/OrbSlam3_KITTI_02.yaml @@ -0,0 +1,47 @@ +%YAML:1.0 +Camera.type: "PinHole" +Camera.RGB: 1 +Camera.cx: 696.0217 +Camera.cy: 224.1806 +Camera.fps: 10.0 +Camera.fx: 959.791 +Camera.fy: 956.9251 +Camera.k1: -0.3691481 +Camera.k2: 0.1968681 +Camera.p1: 0.001353473 +Camera.p2: 0.0005677587 +Camera.width: 1241 +Camera.height: 376 +#-------------------------------------------------------------------------------------------- +# ORB Parameters +#-------------------------------------------------------------------------------------------- + +# ORB Extractor: Number of features per image +ORBextractor.nFeatures: 2000 + +# ORB Extractor: Scale factor between levels in the scale pyramid +ORBextractor.scaleFactor: 1.2 + +# ORB Extractor: Number of levels in the scale pyramid +ORBextractor.nLevels: 8 + +# ORB Extractor: Fast threshold +# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response. +# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST +# You can lower these values if your images have low contrast +ORBextractor.iniThFAST: 20 +ORBextractor.minThFAST: 7 + +#-------------------------------------------------------------------------------------------- +# Viewer Parameters +#-------------------------------------------------------------------------------------------- +Viewer.KeyFrameSize: 0.1 +Viewer.KeyFrameLineWidth: 1 +Viewer.GraphLineWidth: 1 +Viewer.PointSize:2 +Viewer.CameraSize: 0.15 +Viewer.CameraLineWidth: 2 +Viewer.ViewpointX: 0 +Viewer.ViewpointY: -10 +Viewer.ViewpointZ: -0.1 +Viewer.ViewpointF: 2000 \ No newline at end of file diff --git a/slam_method/Settings/OrbSlam3_TUM_freiburg3.yaml b/slam_method/Settings/OrbSlam3_TUM_freiburg3.yaml new file mode 100644 index 0000000..24afa5e --- /dev/null +++ b/slam_method/Settings/OrbSlam3_TUM_freiburg3.yaml @@ -0,0 +1,62 @@ +%YAML:1.0 + +#-------------------------------------------------------------------------------------------- +# Camera Parameters. Adjust them! +#-------------------------------------------------------------------------------------------- +Camera.type: "PinHole" + +# Camera calibration and distortion parameters (OpenCV) +Camera.fx: 520.908620 +Camera.fy: 521.007327 +Camera.cx: 325.141442 +Camera.cy: 249.701764 + +Camera.k1: 0.231222 +Camera.k2: -0.784899 +Camera.p1: -0.003257 +Camera.p2: -0.000105 +Camera.k3: 0.917205 + +# Camera frames per second +Camera.fps: 30.0 + +# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale) +Camera.RGB: 1 + +# Camera resolution +Camera.width: 640 +Camera.height: 480 + +#-------------------------------------------------------------------------------------------- +# ORB Parameters +#-------------------------------------------------------------------------------------------- + +# ORB Extractor: Number of features per image +ORBextractor.nFeatures: 2000 + +# ORB Extractor: Scale factor between levels in the scale pyramid +ORBextractor.scaleFactor: 1.2 + +# ORB Extractor: Number of levels in the scale pyramid +ORBextractor.nLevels: 8 + +# ORB Extractor: Fast threshold +# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response. +# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST +# You can lower these values if your images have low contrast +ORBextractor.iniThFAST: 20 +ORBextractor.minThFAST: 7 + +#-------------------------------------------------------------------------------------------- +# Viewer Parameters +#-------------------------------------------------------------------------------------------- +Viewer.KeyFrameSize: 0.05 +Viewer.KeyFrameLineWidth: 1 +Viewer.GraphLineWidth: 0.9 +Viewer.PointSize:2 +Viewer.CameraSize: 0.08 +Viewer.CameraLineWidth: 3 +Viewer.ViewpointX: 0 +Viewer.ViewpointY: -0.7 +Viewer.ViewpointZ: -1.8 +Viewer.ViewpointF: 500 diff --git a/slampy.py b/slampy.py new file mode 100644 index 0000000..d69dd62 --- /dev/null +++ b/slampy.py @@ -0,0 +1,265 @@ +from enum import Enum +import numpy as np +import importlib +import yaml + + +class Sensor(Enum): + """ + This class is used to set the type of the sensor + Values: + MONOCULAR + STEREO + MONOCULAR_IMU + STEREO_IMU + RGBD + """ + MONOCULAR = 1 + STEREO = 2 + MONOCULAR_IMU = 3 + STEREO_IMU = 4 + RGBD = 5 + + +class State(Enum): + """ + This class is used to get the actual state of the tracking + Values: + OK + LOST + NOT_INITIALIZED + """ + OK = 1 + LOST = 2 + NOT_INITIALIZED = 3 + + +class System(): + + """ + This class is a wrapper for the SLAM method in the slam_method folder, + + + Attributes: + params_file (str): the Path to the .yaml file. + sensor_type (Enum): the sensort type of the SLAM + + """ + def __init__(self, params_file, sensor_type): + + # read and process the config file + with open(params_file) as fs: + self.params = yaml.load(fs) + + module = importlib.import_module('slam_method.'+self.params['SLAM.alg']) + self.slam = module.Slam(self.params, sensor_type) + self.pose_array = [] # is an array that contains the pose stored by the SLAM + + def process_image_mono(self, image, tframe): + """ Process an image mono. + + Note: it work only if the sensor type is MONOCULAR + + Args: + image : ndarray of the image + tframe (float): the timestamp when the image was capture + + Returns: + the state of the traking in this frame + + Raises: + Exception: if the sensor type is different from MONOCULAR + + """ + self.image_shape = image.shape + self.slam.process_image_mono(image, tframe) + if self.get_state()==State.OK: + self.pose_array.append(self.get_pose()) + return self.get_state() + + def process_image_stereo(self, image_left, image_right, tframe): + """ Process an image stereo. + + Note: it work only if the sensor type is STEREO + + Args: + image_left : ndarray of the left image + image_right : ndarray of the right image + tframe (float): the timestamp when the image was capture + + Returns: + the state of the traking in this frame + + Raises: + Exception: if the sensor type is different from STEREO + + """ + self.image_shape = image.shape + self.slam.process_image_stereo(image_left, image_right, tframe) + if self.get_state()==State.OK: + self.pose_array.append(self.get_pose()) + return self.get_state() + + def process_image_imu_mono(self, image, tframe, imu): + """ Process an image mono with the imu data. + + Note: it work only if the sensor type is MONOCULAR_IMU + + Args: + image : ndarray of the image + tframe (float): the timestamp when the image was capture + imu : the imu data stored in an float array in the form of [ AccX ,AccY ,AccZ, GyroX, vGyroY, vGyroZ, Timestamp] + + Returns: + the state of the traking in this frame + + Raises: + Exception: if the sensor type is different from MONOCULAR_IMU + + """ + self.image_shape = image.shape + self.slam.process_image_imu_mono(image, tframe, imu) + if self.get_state()==State.OK: + self.pose_array.append(self.get_pose()) + return self.get_state() + + def process_image_imu_stereo(self, image_left, image_right, tframe, imu): + """ Process an image stereo with the imu data. + + Note: it work only if the sensor type is STEREO_IMU + + Args: + image_left : ndarray of the left image + image_right : ndarray of the right image + tframe (float): the timestamp when the image was capture + imu : the imu data stored in an float array in the form of [ AccX ,AccY ,AccZ, GyroX, vGyroY, vGyroZ, Timestamp] + + Returns: + the state of the traking in this frame + + Raises: + Exception: if the sensor type is different from STEREO_IMU + + """ + self.image_shape = image.shape + self.slam.process_image_imu_stereo(image, tframe, imu) + if self.get_state()==State.OK: + self.pose_array.append(self.get_pose()) + return self.get_state() + + def process_image_rgbd(self, image, tframe): + """ Process an rgbd image. + + Note: it work only if the sensor type is RGBD + + Args: + image : ndarray of the RGBD image + tframe (float): the timestamp when the image was capture + + Returns: + the state of the traking in this frame + + Raises: + Exception: if the sensor type is different from RGBD + + """ + self.image_shape = image.shape + self.slam.process_image_rgbd(image, tframe) + if self.get_state()==State.OK: + self.pose_array.append(self.get_pose()) + return self.get_state() + + def get_pose(self, precedent_frame=-1): + """ Get the frame pose between the current frame and an precedent frame in whitch we found a tracking . + + Args: + precedent_frame : >0 is the number of the precedent frame to use to compute the pose between frame, if it's = -1 the precedent frame is the reference frame + + Returns: + the 4x4 pose matrix corresponding to the transormation between the two frame + + Examples: + slam.get_pose() # return the pose between the reference frame and the current ones + slam.get_pose(precedent_frame=1) # return the pose between the current frame and the precedent onse + slam.get_pose(precedent_frame=2) # return the pose between the current frame and the frame states two position before + + """ + if self.get_state()==State.OK: + if precedent_frame<=0: + return self.slam.get_pose() + else: + return np.dot(self.slam.get_pose(), np.linalg.inv(self.pose_array[-precedent_frame])) + + def get_abs_cloud(self): + """ Get the point cloud at the current frame stored in it's aboslute coordinate . + + Return: + an array with the 3D coordinate of the point, None if the traking is failed + + """ + if self.get_state()==State.OK: + return self.slam.get_abs_cloud() + + + def get_point_cloud(self): + """ Get the point cloud at the current frame form the wiev of the current position . + + Return: + an array with the 3D coordinate of the point, None if the traking is failed + + """ + if self.get_state()==State.OK: + return [cp for (cp, _) in self._get_2d_point()] + + def get_depth(self): + """ Get the depth computed in the current image. + + Return: + an array of the image shape with depth when it is aviable otherwise -1 , None if the traking is failed + + """ + if self.get_state()==State.OK: + depth = np.ones(self.image_shape[0:2]) * -1 + for (cp, point) in self._get_2d_point(): + depth[point[1], point[0]] = cp[2] + return depth + + def _get_2d_point(self): + """ This private method is used to compute the transormation between the absolute point to the image point + + Return: + an array of pair (camera view, image point) , None if the traking is failed + + """ + points2D = [] + points = self.get_abs_cloud() + camera_matrix = self.slam.get_camera_matrix() + pose = self.get_pose() + for point in points: + point=np.append(point,[1]).reshape(4,1) + camera_points = np.dot(pose, point) + if camera_points[2] >= 0: + u = camera_matrix[0, 0]*(camera_points[0]/camera_points[2])+camera_matrix[0, 2] + v = camera_matrix[1, 1]*(camera_points[1]/camera_points[2])+camera_matrix[1, 2] + if int(v) in range(0, self.image_shape[0]): + if int(u) in range(0, self.image_shape[1]): + points2D.append([camera_points, (int(u), int(v))]) + return points2D + + def get_state(self): + """ Return the state of the SLAM tracking in the last frame + + Returns: + an State enums corresponding to the state + """ + return self.slam.get_state() + + def shutdown(self): + """ Shutdown the SLAM algorithm + """ + self.slam.shutdown() + + def reset(self): + """ Reset SLAM algorithm + """ + self.slam.reset() diff --git a/usage_example.ipynb b/usage_example.ipynb new file mode 100644 index 0000000..acc6147 --- /dev/null +++ b/usage_example.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## import the library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import slampy\n", + "import cv2\n", + "import numpy as np\n", + "from utils import *\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## params\n", + "the configuration attribute of the algorithm can be found in the setting.yaml file,\n", + "you can change this file in order to execute different algorithm with different options\n", + "\n", + "**image_folder** is the root folder of images " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_folder = 'Dataset/KITTI/2011_09_26/2011_09_26_drive_0001_sync/image_02'\n", + "setting_file ='settings.yaml'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "initialize the system with the **setting_file** and the type of the sensor in this case monocular" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app = pyslam.System(setting_file,pyslam.Sensor.MONOCULAR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## load the images\n", + "in this example we use the KITTI dataset, the images is not provided you can download it , in utils.py is also provide the method for read the TUM images named **load_images_TUM**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_filenames, timestamps = load_images_KITTI(image_folder)\n", + "num_images = len(image_filenames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "run the algorithm with the load dataset and plot the depth map found in each frame if the tracking is OK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for idx in range(num_images):\n", + " \n", + " # load and convert to RGB image \n", + " image = cv2.imread(image_filenames[idx])\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " \n", + " if image is None:\n", + " print(\"failed to load image at {0}\".format(self.idx))\n", + " exit\n", + " t1 = time.time()\n", + " state = app.process_image_mono(image,timestamps[idx])\n", + " t2 = time.time()\n", + " \n", + " if state == pyslam.State.OK:\n", + " \n", + " #get the depth\n", + " depth = app.get_depth()\n", + " \n", + " #plot the figure\n", + " plt.figure(figsize=(10, 10))\n", + " plt.imshow(depth,cmap='gray')\n", + " plt.show()\n", + " \n", + " #sleep the execution if the time is less than the image acquisition\n", + " ttrack = t2 - t1\n", + " t = 0\n", + " if idx < num_images - 1:\n", + " t = timestamps[idx + 1] - timestamps[idx]\n", + " elif idx > 0:\n", + " t = timestamps[idx] - timestamps[idx - 1]\n", + "\n", + " if ttrack < t:\n", + " time.sleep(t - ttrack)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..dce99be --- /dev/null +++ b/utils.py @@ -0,0 +1,157 @@ +from datetime import datetime +import time +import numpy as np +import os +import read_depth + + +def load_images_KITTI(path_to_sequence): + """ Return the sequence of the images found in the path and the corrispondent timestamp + + Args: + path_to_sequence : the sequence in witch we can found the image sequences + + Returns : + two array : one contains the sequence of the image filename and the second the timestamp in whitch they are acquired + + """ + timestamps = [] + t0 = None + with open(os.path.join(path_to_sequence, 'timestamps.txt')) as times_file: + for line in times_file: + if len(line) > 0: + line = line[:-4] + if t0 is None: + t0 = datetime.strptime(line, '%Y-%m-%d %H:%M:%S.%f') + t1 = datetime.strptime(line, '%Y-%m-%d %H:%M:%S.%f') + difference = t1-t0 + timestamps.append(difference.seconds + + difference.microseconds/1000000) + + return [ + os.path.join(path_to_sequence, 'data', str(idx).zfill(10)+".png") + for idx in range(len(timestamps)) + ], timestamps + + +def load_images_TUM(path_to_sequence, file_name): + """ Return the sequence of the images found in the path and the corrispondent timestamp + + Args: + path_to_sequence : the sequence in witch we can found the image sequences + + Returns: + two array : one contains the sequence of the image filename and the second the timestamp in whitch they are acquired + + """ + timestamps = [] + rgb_filenames = [] + t0 = None + with open(os.path.join(path_to_sequence, file_name)) as times_file: + for line in times_file: + if len(line) > 0 and not line.startswith('#'): + t, rgb = line.rstrip().split(' ')[0:2] + rgb_filenames.append(rgb) + timestamps.append(float(t)) + + return [ + os.path.join(path_to_sequence, name) + for name in rgb_filenames + ], timestamps + + +def compute_errors(gt, pred): + """ Computation of error metrics (abs rel,sq rel, rmse, rmse log) between predicted and ground truth depths + + Args: + gt : an array with the ground truth values + pred : an array with the predicted values + + Returns: + abs_rel,sq_rel,rmse,rmse_log : the error + + """ + thresh = np.maximum((gt / pred), (pred / gt)) + a1 = (thresh < 1.25).mean() + a2 = (thresh < 1.25 ** 2).mean() + a3 = (thresh < 1.25 ** 3).mean() + + rmse = (gt - pred) ** 2 + rmse = np.sqrt(rmse.mean()) + + rmse_log = (np.log(gt) - np.log(pred)) ** 2 + rmse_log = np.sqrt(rmse_log.mean()) + + abs_rel = np.mean(np.abs(gt - pred) / gt) + + sq_rel = np.mean(((gt - pred) ** 2) / gt) + + return abs_rel, sq_rel, rmse, rmse_log + + +def convert_scale(points, gt_depth): + """convert the scale of the predictions to the gt + + Args: + points: the predictions depth + gt_filename: the gt references filename + + Returns: the ratio between the predictions and the gt + """ + depth_SLAM = np.array( + [gt_depth[int(img_point[1]), int(img_point[0])] for (_, img_point) in points]) + depth_SLAM_mask = depth_SLAM > 0 + depth_SLAM = depth_SLAM[depth_SLAM_mask] + cp = np.array([cp[2] for (cp, _) in points]) + cp = cp[depth_SLAM_mask] + return np.median(depth_SLAM)/np.median(cp) + + +def get_error_TUM(points, gt_filename): + """Get the realtive gt from it's filename and convert the scale of the predictions in order to compute the error + + Args: + points: the predictions depth + gt_filename: the gt references filename + + Returns: + the error computed on this examples + """ + gt_depth = read_depth.read_depth_TUM(gt_filename) + if points is not None: + ratio = convert_scale(points, gt_depth) + depth = [] + gt = [] + for (cp, img_point) in points: + depth_converted = cp[2]*ratio + if gt_depth[int(img_point[1]), int(img_point[0])] > 0: + depth.append(depth_converted) + gt.append(gt_depth[int(img_point[1]), int(img_point[0])]) + depth = np.array(depth) + gt = np.array(gt) + return compute_errors(gt, depth) + + +def get_error_KITTI(points, gt_filename): + """Get the realtive gt from it's filename and convert the scale of the predictions in order to compute the error + + Args: + points: the predictions depth + gt_filename: the gt references filename + + Returns: + the error computed on this examples + """ + gt_depth = read_depth.read_depth_KITTI(gt_filename) + if points is not None: + ratio = convert_scale(points, gt_depth) + depth = [] + gt = [] + for (cp, img_point) in points: + depth_converted = cp[2]*ratio + if gt_depth[int(img_point[1]), int(img_point[0])] > 0: + depth.append(depth_converted) + gt.append(gt_depth[int(img_point[1]), int(img_point[0])]) + depth = np.array(depth) + gt = np.array(gt) + return compute_errors(gt, depth)