RGB & MobilenetSSD with spatial data

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
8import time
9
10'''
11Spatial detection network demo.
12    Performs inference on RGB camera and retrieves spatial location coordinates: x,y,z relative to the center of depth map.
13'''
14
15# Get argument first
16nnBlobPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
17if len(sys.argv) > 1:
18    nnBlobPath = sys.argv[1]
19
20if not Path(nnBlobPath).exists():
21    import sys
22    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
23
24# MobilenetSSD label texts
25labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
26            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
27
28syncNN = True
29
30# Create pipeline
31pipeline = dai.Pipeline()
32
33# Define sources and outputs
34camRgb = pipeline.create(dai.node.ColorCamera)
35spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
36monoLeft = pipeline.create(dai.node.MonoCamera)
37monoRight = pipeline.create(dai.node.MonoCamera)
38stereo = pipeline.create(dai.node.StereoDepth)
39
40xoutRgb = pipeline.create(dai.node.XLinkOut)
41xoutNN = pipeline.create(dai.node.XLinkOut)
42xoutDepth = pipeline.create(dai.node.XLinkOut)
43
44xoutRgb.setStreamName("rgb")
45xoutNN.setStreamName("detections")
46xoutDepth.setStreamName("depth")
47
48# Properties
49camRgb.setPreviewSize(300, 300)
50camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
51camRgb.setInterleaved(False)
52camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
53
54monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
55monoLeft.setCamera("left")
56monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
57monoRight.setCamera("right")
58
59# Setting node configs
60stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
61# Align depth map to the perspective of RGB camera, on which inference is done
62stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A)
63stereo.setSubpixel(True)
64stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
65
66spatialDetectionNetwork.setBlobPath(nnBlobPath)
67spatialDetectionNetwork.setConfidenceThreshold(0.5)
68spatialDetectionNetwork.input.setBlocking(False)
69spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
70spatialDetectionNetwork.setDepthLowerThreshold(100)
71spatialDetectionNetwork.setDepthUpperThreshold(5000)
72
73# Linking
74monoLeft.out.link(stereo.left)
75monoRight.out.link(stereo.right)
76
77camRgb.preview.link(spatialDetectionNetwork.input)
78if syncNN:
79    spatialDetectionNetwork.passthrough.link(xoutRgb.input)
80else:
81    camRgb.preview.link(xoutRgb.input)
82
83spatialDetectionNetwork.out.link(xoutNN.input)
84
85stereo.depth.link(spatialDetectionNetwork.inputDepth)
86spatialDetectionNetwork.passthroughDepth.link(xoutDepth.input)
87
88# Connect to device and start pipeline
89with dai.Device(pipeline) as device:
90
91    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
92    previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
93    detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)
94    depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
95
96    startTime = time.monotonic()
97    counter = 0
98    fps = 0
99    color = (255, 255, 255)
100
101    while True:
102        inPreview = previewQueue.get()
103        inDet = detectionNNQueue.get()
104        depth = depthQueue.get()
105
106        counter+=1
107        current_time = time.monotonic()
108        if (current_time - startTime) > 1 :
109            fps = counter / (current_time - startTime)
110            counter = 0
111            startTime = current_time
112
113        frame = inPreview.getCvFrame()
114
115        depthFrame = depth.getFrame() # depthFrame values are in millimeters
116
117        depth_downscaled = depthFrame[::4]
118        if np.all(depth_downscaled == 0):
119            min_depth = 0  # Set a default minimum depth value when all elements are zero
120        else:
121            min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
122        max_depth = np.percentile(depth_downscaled, 99)
123        depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
124        depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
125
126        detections = inDet.detections
127
128        # If the frame is available, draw bounding boxes on it and show the frame
129        height = frame.shape[0]
130        width  = frame.shape[1]
131        for detection in detections:
132            roiData = detection.boundingBoxMapping
133            roi = roiData.roi
134            roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
135            topLeft = roi.topLeft()
136            bottomRight = roi.bottomRight()
137            xmin = int(topLeft.x)
138            ymin = int(topLeft.y)
139            xmax = int(bottomRight.x)
140            ymax = int(bottomRight.y)
141            cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)
142
143            # Denormalize bounding box
144            x1 = int(detection.xmin * width)
145            x2 = int(detection.xmax * width)
146            y1 = int(detection.ymin * height)
147            y2 = int(detection.ymax * height)
148            try:
149                label = labelMap[detection.label]
150            except:
151                label = detection.label
152            cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
153            cv2.putText(frame, "{:.2f}".format(detection.confidence*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
154            cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
155            cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
156            cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
157
158            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)
159
160        cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, (255,255,255))
161        cv2.imshow("depth", depthFrameColor)
162        cv2.imshow("preview", frame)
163
164        if cv2.waitKey(1) == ord('q'):
165            break