From 11f21b86bc44a6ea0281b443e6b09c3757aab810 Mon Sep 17 00:00:00 2001 From: Tadeas Pala Date: Thu, 7 Nov 2024 12:41:54 +0100 Subject: [PATCH] refactored depth-estimation experiment --- .../depth-estimation/host_depth_estimation.py | 40 ----- .../depth-estimation/host_node | 1 + .../depth-estimation/main.py | 152 ++++++++++++------ ...adepth_192x256_openvino_2021.4_6shave.blob | Bin 10990272 -> 0 bytes .../depth-estimation/requirements.txt | 5 +- 5 files changed, 110 insertions(+), 88 deletions(-) delete mode 100644 gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_depth_estimation.py create mode 120000 gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_node delete mode 100644 gen3/neural-networks/monocular-depth-estimation/depth-estimation/model/megadepth_192x256_openvino_2021.4_6shave.blob diff --git a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_depth_estimation.py b/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_depth_estimation.py deleted file mode 100644 index 6f2939fb..00000000 --- a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_depth_estimation.py +++ /dev/null @@ -1,40 +0,0 @@ -import cv2 -import depthai as dai -import numpy as np - - -class DepthEstimation(dai.node.HostNode): - def __init__(self) -> None: - super().__init__() - - def build(self, preview: dai.Node.Output, nn: dai.Node.Output, nn_shape: tuple[int, int], mbnv2: bool) -> "DepthEstimation": - self.link_args(preview, nn) - self.sendProcessingToPipeline(True) - - self.mbnv2 = mbnv2 - self.nn_shape = (nn_shape[1]//2, nn_shape[0]//2) if mbnv2 else nn_shape - return self - - def process(self, preview: dai.ImgFrame, nn: dai.NNData) -> None: - frame = preview.getCvFrame() - data = nn.getFirstTensor().flatten().reshape((self.nn_shape[1], self.nn_shape[0])) - - # Scale depth to get relative depth - d_min = np.min(data) - d_max = np.max(data) - depth_relative = (data - d_min) / (d_max - d_min) - - depth_relative = np.array(depth_relative) * 255 - depth_relative = depth_relative.astype(np.uint8) - depth_relative = 255 - depth_relative - depth_relative = cv2.applyColorMap(depth_relative, cv2.COLORMAP_INFERNO) - - if self.mbnv2: - frame = cv2.resize(frame, self.nn_shape) - - cv2.imshow("Depth", np.concatenate([frame, depth_relative], axis=1)) - - if cv2.waitKey(1) == ord('q'): - print("Pipeline exited.") - self.stopPipeline() - diff --git a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_node b/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_node new file mode 120000 index 00000000..f4d0b13a --- /dev/null +++ b/gen3/neural-networks/monocular-depth-estimation/depth-estimation/host_node @@ -0,0 +1 @@ +../../../host_node/ \ No newline at end of file diff --git a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/main.py b/gen3/neural-networks/monocular-depth-estimation/depth-estimation/main.py index 54035e39..bc471973 100644 --- a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/main.py +++ b/gen3/neural-networks/monocular-depth-estimation/depth-estimation/main.py @@ -1,61 +1,121 @@ -import depthai as dai import argparse -from pathlib import Path -from host_depth_estimation import DepthEstimation +import cv2 +import depthai as dai +from depthai_nodes import ParsingNeuralNetwork +from host_node.host_depth_color_transform import DepthColorTransform +from host_node.overlay_frames import OverlayFrames +from host_node.visualize_detections_v2 import VisualizeDetectionsV2 parser = argparse.ArgumentParser() -parser.add_argument('-nn', '--neural-network', type=str - , choices=['fast_small', 'fast_large', 'mbnv_small', 'mbnv_large', 'mega'], default='fast_small' - , help="Choose the neural network model used for depth estimation (fast_small is default)") +parser.add_argument( + "-nn", + "--neural-network", + type=str, + choices=[ + "midas_small", + "midas_medium", + "midas_large", + "midas_xlarge", + "midas_xxlarge", + ], + default="midas_small", + help="Choose the neural network model used for depth estimation (midas_small is default)", +) +parser.add_argument( + "-fps", + "--frames-per-second", + type=int, + default=10, + help="Choose the number of frames per second (10 is default)", +) args = parser.parse_args() -nn_path = None -nn_shape = None - -if args.neural_network == "fast_small": - model_description = dai.NNModelDescription(modelSlug="fast-depth", platform="RVC2", modelVersionSlug="320x256") - nn_shape = (320, 256) -elif args.neural_network == "fast_large": - model_description = dai.NNModelDescription(modelSlug="fast-depth", platform="RVC2", modelVersionSlug="640x480") - nn_shape = (640, 480) -elif args.neural_network == "mbnv_small": - model_description = dai.NNModelDescription(modelSlug="depth-estimation", platform="RVC2", modelVersionSlug="320x240") - nn_shape = (320, 240) -elif args.neural_network == "mbnv_large": - model_description = dai.NNModelDescription(modelSlug="depth-estimation", platform="RVC2", modelVersionSlug="640x480") - nn_shape = (640, 480) -elif args.neural_network == "mega": - raise NotImplementedError("Mega model is not supported yet") - nn_path = Path("../depth-estimation/model/megadepth_192x256_openvino_2021.4_6shave.blob").absolute().resolve() - nn_shape = (256, 192) +nn_configs = { + "midas_small": { + "model_slug": "midas-v2-1", + "model_version_slug": "small-192x256", + "size": (256, 192), + }, + "midas_medium": { + "model_slug": "midas-v2-1", + "model_version_slug": "small-256x384", + "size": (384, 256), + }, + "midas_large": { + "model_slug": "midas-v2-1", + "model_version_slug": "small-288x512", + "size": (512, 288), + }, + "midas_xlarge": { + "model_slug": "midas-v2-1", + "model_version_slug": "small-384x512", + "size": (512, 384), + }, + "midas_xxlarge": { + "model_slug": "midas-v2-1", + "model_version_slug": "small-768x1024", + "size": (1024, 768), + }, +} + +selected_nn_config = nn_configs[args.neural_network] + +device = dai.Device() +model_description = dai.NNModelDescription( + modelSlug=selected_nn_config["model_slug"], + platform=device.getPlatform().name, + modelVersionSlug=selected_nn_config["model_version_slug"], +) archive_path = dai.getModelFromZoo(model_description) nn_archive = dai.NNArchive(archive_path) -with dai.Pipeline() as pipeline: +VIDEO_SIZE = (1280, 720) +NN_SIZE = selected_nn_config["size"] +FPS = args.frames_per_second +visualizer = dai.RemoteConnection() + + +with dai.Pipeline(device) as pipeline: print("Creating pipeline...") - cam = pipeline.create(dai.node.Camera).build(boardSocket=dai.CameraBoardSocket.CAM_A) - rgb_preview = cam.requestOutput(size=nn_shape, type=dai.ImgFrame.Type.BGR888p) - - nn = pipeline.create(dai.node.NeuralNetwork) - nn.setNNArchive(nn_archive) - nn.setNumPoolFrames(4) - nn.input.setBlocking(False) - nn.setNumInferenceThreads(2) - rgb_preview.link(nn.input) - - depth = pipeline.create(DepthEstimation).build( - preview=rgb_preview, - nn=nn.out, - nn_shape=nn_shape, - mbnv2=(args.neural_network == "mbnv_small" or args.neural_network == "mbnv_large") + cam = pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) + color_out = cam.requestOutput( + size=VIDEO_SIZE, type=dai.ImgFrame.Type.BGR888p, fps=FPS ) - depth.inputs["preview"].setBlocking(False) - depth.inputs["preview"].setMaxSize(4) - depth.inputs["nn"].setBlocking(False) - depth.inputs["nn"].setMaxSize(4) + nn_resize = pipeline.create(dai.node.ImageManipV2) + nn_resize.initialConfig.addResize(*NN_SIZE) + color_out.link(nn_resize.inputImage) + + nn = pipeline.create(ParsingNeuralNetwork).build( + input=nn_resize.out, nn_source=nn_archive + ) + + visualize_detections = pipeline.create(VisualizeDetectionsV2).build(nn.out) + color_transform = pipeline.create(DepthColorTransform).build( + visualize_detections.output_mask + ) + color_transform.setColormap(cv2.COLORMAP_INFERNO) + + overlay_resize = pipeline.create(dai.node.ImageManipV2) + overlay_resize.initialConfig.addResize(*VIDEO_SIZE) + overlay_resize.setMaxOutputFrameSize(VIDEO_SIZE[0] * VIDEO_SIZE[1] * 3) + color_transform.output.link(overlay_resize.inputImage) + + overlay_frames = pipeline.create(OverlayFrames).build(color_out, overlay_resize.out) + + visualizer.addTopic("Color", color_out) + visualizer.addTopic("Depth", overlay_resize.out) + visualizer.addTopic("Depth overlay", overlay_frames.output) print("Pipeline created.") - pipeline.run() + pipeline.start() + visualizer.registerPipeline(pipeline) + while pipeline.isRunning(): + pipeline.processTasks() + key = visualizer.waitKey(1) + if key == ord("q"): + break diff --git a/gen3/neural-networks/monocular-depth-estimation/depth-estimation/model/megadepth_192x256_openvino_2021.4_6shave.blob 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