RGB & MobilenetSSD

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 cv2
5import depthai as dai
6import numpy as np
7import time
8import argparse
9
10nnPathDefault = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
11parser = argparse.ArgumentParser()
12parser.add_argument('nnPath', nargs='?', help="Path to mobilenet detection network blob", default=nnPathDefault)
13parser.add_argument('-s', '--sync', action="store_true", help="Sync RGB output with NN output", default=False)
14args = parser.parse_args()
15
16if not Path(nnPathDefault).exists():
17    import sys
18    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
19
20# MobilenetSSD label texts
21labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
22            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
23
24# Create pipeline
25pipeline = dai.Pipeline()
26
27# Define sources and outputs
28camRgb = pipeline.create(dai.node.ColorCamera)
29nn = pipeline.create(dai.node.MobileNetDetectionNetwork)
30xoutRgb = pipeline.create(dai.node.XLinkOut)
31nnOut = pipeline.create(dai.node.XLinkOut)
32nnNetworkOut = pipeline.create(dai.node.XLinkOut)
33
34xoutRgb.setStreamName("rgb")
35nnOut.setStreamName("nn")
36nnNetworkOut.setStreamName("nnNetwork");
37
38# Properties
39camRgb.setPreviewSize(300, 300)
40camRgb.setInterleaved(False)
41camRgb.setFps(40)
42# Define a neural network that will make predictions based on the source frames
43nn.setConfidenceThreshold(0.5)
44nn.setBlobPath(args.nnPath)
45nn.setNumInferenceThreads(2)
46nn.input.setBlocking(False)
47
48# Linking
49if args.sync:
50    nn.passthrough.link(xoutRgb.input)
51else:
52    camRgb.preview.link(xoutRgb.input)
53
54camRgb.preview.link(nn.input)
55nn.out.link(nnOut.input)
56nn.outNetwork.link(nnNetworkOut.input);
57
58# Connect to device and start pipeline
59with dai.Device(pipeline) as device:
60
61    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
62    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
63    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
64    qNN = device.getOutputQueue(name="nnNetwork", maxSize=4, blocking=False);
65
66    frame = None
67    detections = []
68    startTime = time.monotonic()
69    counter = 0
70    color2 = (255, 255, 255)
71
72    # nn data (bounding box locations) are in <0..1> range - they need to be normalized with frame width/height
73    def frameNorm(frame, bbox):
74        normVals = np.full(len(bbox), frame.shape[0])
75        normVals[::2] = frame.shape[1]
76        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
77
78    def displayFrame(name, frame):
79        color = (255, 0, 0)
80        for detection in detections:
81            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
82            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
83            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
84            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
85        # Show the frame
86        cv2.imshow(name, frame)
87
88    printOutputLayersOnce = True
89
90    while True:
91        if args.sync:
92            # Use blocking get() call to catch frame and inference result synced
93            inRgb = qRgb.get()
94            inDet = qDet.get()
95            inNN = qNN.get()
96        else:
97            # Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise
98            inRgb = qRgb.tryGet()
99            inDet = qDet.tryGet()
100            inNN = qNN.tryGet()
101
102        if inRgb is not None:
103            frame = inRgb.getCvFrame()
104            cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
105                        (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)
106
107        if inDet is not None:
108            detections = inDet.detections
109            counter += 1
110
111        if printOutputLayersOnce and inNN is not None:
112            toPrint = 'Output layer names:'
113            for ten in inNN.getAllLayerNames():
114                toPrint = f'{toPrint} {ten},'
115            print(toPrint)
116            printOutputLayersOnce = False;
117
118        # If the frame is available, draw bounding boxes on it and show the frame
119        if frame is not None:
120            displayFrame("rgb", frame)
121
122        if cv2.waitKey(1) == ord('q'):
123            break