Spatial detections on rotated OAK

1manip = pipeline.createImageManip()
2    # Vertical + Horizontal flip == rotate frame for 180°
3    manip.initialConfig.setVerticalFlip(True)
4    manip.initialConfig.setHorizontalFlip(True)
5    manip.setFrameType(dai.ImgFrame.Type.RAW16)
6    stereo.depth.link(manip.inputImage)

1git clone https://github.com/luxonis/depthai-python.git
2cd depthai-python/examples
3python3 install_requirements.py

1#!/usr/bin/env python3
2
3from pathlib import Path
4import sys
5import cv2
6import depthai as dai
7import numpy as np
8'''
9Spatial object detections demo for 180° rotated OAK camera.
10'''
11
12# Get argument first
13nnBlobPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
14if len(sys.argv) > 1:
15    nnBlobPath = sys.argv[1]
16
17if not Path(nnBlobPath).exists():
18    import sys
19    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
20
21# MobilenetSSD label texts
22labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
23            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
24
25syncNN = True
26
27# Create pipeline
28pipeline = dai.Pipeline()
29
30# Define sources and outputs
31camRgb = pipeline.createColorCamera()
32spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
33monoLeft = pipeline.create(dai.node.MonoCamera)
34monoRight = pipeline.create(dai.node.MonoCamera)
35stereo = pipeline.create(dai.node.StereoDepth)
36
37xoutRgb = pipeline.create(dai.node.XLinkOut)
38xoutNN = pipeline.create(dai.node.XLinkOut)
39xoutDepth = pipeline.create(dai.node.XLinkOut)
40
41xoutRgb.setStreamName("rgb")
42xoutNN.setStreamName("detections")
43xoutDepth.setStreamName("depth")
44
45# Properties
46camRgb.setPreviewSize(300, 300)
47camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
48camRgb.setInterleaved(False)
49camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
50camRgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG)
51
52monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
53monoLeft.setCamera("left")
54monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
55monoRight.setCamera("right")
56
57# Setting node configs
58stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
59# Align depth map to the perspective of RGB camera, on which inference is done
60stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A)
61stereo.setSubpixel(True)
62stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
63
64rotate_stereo_manip = pipeline.createImageManip()
65rotate_stereo_manip.initialConfig.setVerticalFlip(True)
66rotate_stereo_manip.initialConfig.setHorizontalFlip(True)
67rotate_stereo_manip.setFrameType(dai.ImgFrame.Type.RAW16)
68stereo.depth.link(rotate_stereo_manip.inputImage)
69
70spatialDetectionNetwork.setBlobPath(nnBlobPath)
71spatialDetectionNetwork.setConfidenceThreshold(0.5)
72spatialDetectionNetwork.input.setBlocking(False)
73spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
74spatialDetectionNetwork.setDepthLowerThreshold(100)
75spatialDetectionNetwork.setDepthUpperThreshold(5000)
76
77# Linking
78monoLeft.out.link(stereo.left)
79monoRight.out.link(stereo.right)
80
81camRgb.preview.link(spatialDetectionNetwork.input)
82if syncNN:
83    spatialDetectionNetwork.passthrough.link(xoutRgb.input)
84else:
85    camRgb.preview.link(xoutRgb.input)
86
87spatialDetectionNetwork.out.link(xoutNN.input)
88
89rotate_stereo_manip.out.link(spatialDetectionNetwork.inputDepth)
90spatialDetectionNetwork.passthroughDepth.link(xoutDepth.input)
91color = (255, 0, 0)
92# Connect to device and start pipeline
93with dai.Device(pipeline) as device:
94
95    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
96    previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
97    detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)
98    depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
99
100    while True:
101        inPreview = previewQueue.get()
102        inDet = detectionNNQueue.get()
103        depth = depthQueue.get()
104
105        frame = inPreview.getCvFrame()
106        depthFrame = depth.getFrame() # depthFrame values are in millimeters
107
108        depth_downscaled = depthFrame[::4]
109        if np.all(depth_downscaled == 0):
110            min_depth = 0  # Set a default minimum depth value when all elements are zero
111        else:
112            min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
113        max_depth = np.percentile(depth_downscaled, 99)
114        depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
115        depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
116
117        detections = inDet.detections
118
119        # If the frame is available, draw bounding boxes on it and show the frame
120        height = frame.shape[0]
121        width  = frame.shape[1]
122        for detection in detections:
123            roiData = detection.boundingBoxMapping
124            roi = roiData.roi
125            roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
126            topLeft = roi.topLeft()
127            bottomRight = roi.bottomRight()
128            xmin = int(topLeft.x)
129            ymin = int(topLeft.y)
130            xmax = int(bottomRight.x)
131            ymax = int(bottomRight.y)
132            cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
133
134            # Denormalize bounding box
135            x1 = int(detection.xmin * width)
136            x2 = int(detection.xmax * width)
137            y1 = int(detection.ymin * height)
138            y2 = int(detection.ymax * height)
139            try:
140                label = labelMap[detection.label]
141            except:
142                label = detection.label
143            cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
144            cv2.putText(frame, "{:.2f}".format(detection.confidence*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
145            cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
146            cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
147            cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
148
149            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)
150
151        cv2.imshow("depth", depthFrameColor)
152        cv2.imshow("preview", frame)
153
154        if cv2.waitKey(1) == ord('q'):
155            break