RGB Encoding & Mono with MobilenetSSD & Depth

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
9# Get argument first
10nnPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
11if len(sys.argv) > 1:
12    nnPath = sys.argv[1]
13
14if not Path(nnPath).exists():
15    import sys
16    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
17
18# MobilenetSSD label texts
19labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
20            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
21
22# Create pipeline
23pipeline = dai.Pipeline()
24
25# Define sources and outputs
26camRgb = pipeline.create(dai.node.ColorCamera)
27videoEncoder = pipeline.create(dai.node.VideoEncoder)
28monoRight = pipeline.create(dai.node.MonoCamera)
29monoLeft = pipeline.create(dai.node.MonoCamera)
30depth = pipeline.create(dai.node.StereoDepth)
31manip = pipeline.create(dai.node.ImageManip)
32nn = pipeline.create(dai.node.MobileNetDetectionNetwork)
33
34videoOut = pipeline.create(dai.node.XLinkOut)
35xoutRight = pipeline.create(dai.node.XLinkOut)
36disparityOut = pipeline.create(dai.node.XLinkOut)
37manipOut = pipeline.create(dai.node.XLinkOut)
38nnOut = pipeline.create(dai.node.XLinkOut)
39
40videoOut.setStreamName('h265')
41xoutRight.setStreamName('right')
42disparityOut.setStreamName('disparity')
43manipOut.setStreamName('manip')
44nnOut.setStreamName('nn')
45
46# Properties
47camRgb.setBoardSocket(dai.CameraBoardSocket.CAM_A)
48camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
49monoRight.setCamera("right")
50monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
51monoLeft.setCamera("left")
52monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
53videoEncoder.setDefaultProfilePreset(30, dai.VideoEncoderProperties.Profile.H265_MAIN)
54
55depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
56depth.setRectifyEdgeFillColor(0) # Black, to better see the cutout
57
58nn.setConfidenceThreshold(0.5)
59nn.setBlobPath(nnPath)
60nn.setNumInferenceThreads(2)
61nn.input.setBlocking(False)
62
63# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
64manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
65manip.initialConfig.setResize(300, 300)
66
67# Linking
68camRgb.video.link(videoEncoder.input)
69videoEncoder.bitstream.link(videoOut.input)
70monoRight.out.link(xoutRight.input)
71monoRight.out.link(depth.right)
72monoLeft.out.link(depth.left)
73depth.disparity.link(disparityOut.input)
74depth.rectifiedRight.link(manip.inputImage)
75manip.out.link(nn.input)
76manip.out.link(manipOut.input)
77nn.out.link(nnOut.input)
78
79# Disparity range is used for normalization
80disparityMultiplier = 255 / depth.initialConfig.getMaxDisparity()
81
82# Connect to device and start pipeline
83with dai.Device(pipeline) as device:
84
85    queueSize = 8
86    qRight = device.getOutputQueue("right", queueSize)
87    qDisparity = device.getOutputQueue("disparity", queueSize)
88    qManip = device.getOutputQueue("manip", queueSize)
89    qDet = device.getOutputQueue("nn", queueSize)
90    qRgbEnc = device.getOutputQueue('h265', maxSize=30, blocking=True)
91
92    frame = None
93    frameManip = None
94    frameDisparity = None
95    detections = []
96    offsetX = (monoRight.getResolutionWidth() - monoRight.getResolutionHeight()) // 2
97    color = (255, 0, 0)
98    croppedFrame = np.zeros((monoRight.getResolutionHeight(), monoRight.getResolutionHeight()))
99
100    def frameNorm(frame, bbox):
101        normVals = np.full(len(bbox), frame.shape[0])
102        normVals[::2] = frame.shape[1]
103        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
104
105    videoFile = open('video.h265', 'wb')
106    cv2.namedWindow("right", cv2.WINDOW_NORMAL)
107    cv2.namedWindow("manip", cv2.WINDOW_NORMAL)
108
109    while True:
110        inRight = qRight.tryGet()
111        inManip = qManip.tryGet()
112        inDet = qDet.tryGet()
113        inDisparity = qDisparity.tryGet()
114
115        while qRgbEnc.has():
116            qRgbEnc.get().getData().tofile(videoFile)
117
118        if inRight is not None:
119            frame = inRight.getCvFrame()
120
121        if inManip is not None:
122            frameManip = inManip.getCvFrame()
123
124        if inDisparity is not None:
125            # Apply color map for better visualization
126            frameDisparity = inDisparity.getCvFrame()
127            frameDisparity = (frameDisparity*disparityMultiplier).astype(np.uint8)
128            frameDisparity = cv2.applyColorMap(frameDisparity, cv2.COLORMAP_JET)
129
130        if inDet is not None:
131            detections = inDet.detections
132
133        if frame is not None:
134            for detection in detections:
135                bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
136                bbox[::2] += offsetX
137                cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
138                cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
139                cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
140            # Show the right cam frame
141            cv2.imshow("right", frame)
142
143        if frameDisparity is not None:
144            for detection in detections:
145                bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
146                bbox[::2] += offsetX
147                cv2.rectangle(frameDisparity, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
148                cv2.putText(frameDisparity, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
149                cv2.putText(frameDisparity, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
150            # Show the disparity frame
151            cv2.imshow("disparity", frameDisparity)
152
153        if frameManip is not None:
154            for detection in detections:
155                bbox = frameNorm(frameManip, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
156                cv2.rectangle(frameManip, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
157                cv2.putText(frameManip, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
158                cv2.putText(frameManip, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
159            # Show the manip frame
160            cv2.imshow("manip", frameManip)
161
162        if cv2.waitKey(1) == ord('q'):
163            break
164
165    print("To view the encoded data, convert the stream file (.h265) into a video file (.mp4) using a command below:")
166    print("ffmpeg -framerate 30 -i video.h265 -c copy video.mp4")