Spatial detections on rotated OAK¶
This example is very similar to RGB & MobilenetSSD with spatial data - it only assumes we have OAK rotated by 180° (upside down)
ColorCamera frames are rotated on the sensor itself, by setting camRgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
.
This means all outputs from the node (still/isp/video/preview) will already be rotated.
We rotate depth
frames after the StereoDepth creates them. One might try rotating mono frames before sending them
to the StereoDepth node, but this wouldn’t work as stereo calibration would need to reflect such changes. So we use
the ImageManip node to rotate depth
(code below) and then send it to the MobileNetSpatialDetectionNetwork.
manip = pipeline.createImageManip()
# Vertical + Horizontal flip == rotate frame for 180°
manip.initialConfig.setVerticalFlip(True)
manip.initialConfig.setHorizontalFlip(True)
manip.setFrameType(dai.ImgFrame.Type.RAW16)
stereo.depth.link(manip.inputImage)
MobileNetSpatialDetectionNetwork node then receives correctly rotated color and depth frame, which results in correct spatial detection output.
Similar samples:
Setup¶
Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script
git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py
For additional information, please follow installation guide
This example script requires external file(s) to run. If you are using:
depthai-python, run
python3 examples/install_requirements.py
to download required file(s)dephtai-core, required file(s) will get downloaded automatically when building the example
Source code¶
Also available on GitHub
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #!/usr/bin/env python3 from pathlib import Path import sys import cv2 import depthai as dai import numpy as np ''' Spatial object detections demo for 180° rotated OAK camera. ''' # Get argument first nnBlobPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute()) if len(sys.argv) > 1: nnBlobPath = sys.argv[1] if not Path(nnBlobPath).exists(): import sys raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"') # MobilenetSSD label texts labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] syncNN = True # Create pipeline pipeline = dai.Pipeline() # Define sources and outputs camRgb = pipeline.createColorCamera() spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork) monoLeft = pipeline.create(dai.node.MonoCamera) monoRight = pipeline.create(dai.node.MonoCamera) stereo = pipeline.create(dai.node.StereoDepth) xoutRgb = pipeline.create(dai.node.XLinkOut) xoutNN = pipeline.create(dai.node.XLinkOut) xoutDepth = pipeline.create(dai.node.XLinkOut) xoutRgb.setStreamName("rgb") xoutNN.setStreamName("detections") xoutDepth.setStreamName("depth") # Properties camRgb.setPreviewSize(300, 300) camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) camRgb.setInterleaved(False) camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) camRgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG) monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoLeft.setCamera("left") monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoRight.setCamera("right") # Setting node configs stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) # Align depth map to the perspective of RGB camera, on which inference is done stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A) stereo.setSubpixel(True) stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight()) rotate_stereo_manip = pipeline.createImageManip() rotate_stereo_manip.initialConfig.setVerticalFlip(True) rotate_stereo_manip.initialConfig.setHorizontalFlip(True) rotate_stereo_manip.setFrameType(dai.ImgFrame.Type.RAW16) stereo.depth.link(rotate_stereo_manip.inputImage) spatialDetectionNetwork.setBlobPath(nnBlobPath) spatialDetectionNetwork.setConfidenceThreshold(0.5) spatialDetectionNetwork.input.setBlocking(False) spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5) spatialDetectionNetwork.setDepthLowerThreshold(100) spatialDetectionNetwork.setDepthUpperThreshold(5000) # Linking monoLeft.out.link(stereo.left) monoRight.out.link(stereo.right) camRgb.preview.link(spatialDetectionNetwork.input) if syncNN: spatialDetectionNetwork.passthrough.link(xoutRgb.input) else: camRgb.preview.link(xoutRgb.input) spatialDetectionNetwork.out.link(xoutNN.input) rotate_stereo_manip.out.link(spatialDetectionNetwork.inputDepth) spatialDetectionNetwork.passthroughDepth.link(xoutDepth.input) color = (255, 0, 0) # Connect to device and start pipeline with dai.Device(pipeline) as device: # Output queues will be used to get the rgb frames and nn data from the outputs defined above previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False) detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False) depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False) while True: inPreview = previewQueue.get() inDet = detectionNNQueue.get() depth = depthQueue.get() frame = inPreview.getCvFrame() depthFrame = depth.getFrame() # depthFrame values are in millimeters depth_downscaled = depthFrame[::4] min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1) max_depth = np.percentile(depth_downscaled, 99) depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8) depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT) detections = inDet.detections # If the frame is available, draw bounding boxes on it and show the frame height = frame.shape[0] width = frame.shape[1] for detection in detections: roiData = detection.boundingBoxMapping roi = roiData.roi roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0]) topLeft = roi.topLeft() bottomRight = roi.bottomRight() xmin = int(topLeft.x) ymin = int(topLeft.y) xmax = int(bottomRight.x) ymax = int(bottomRight.y) cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX) # Denormalize bounding box x1 = int(detection.xmin * width) x2 = int(detection.xmax * width) y1 = int(detection.ymin * height) y2 = int(detection.ymax * height) try: label = labelMap[detection.label] except: label = detection.label cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, "{:.2f}".format(detection.confidence*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX) cv2.imshow("depth", depthFrameColor) cv2.imshow("preview", frame) if cv2.waitKey(1) == ord('q'): break |
(Work in progress)