RGB & YOLOv8 Nano

1#!/usr/bin/env python3
2
3"""
4The code is the same as for Tiny Yolo V3 and V4, the only difference is the blob file
5- Tiny YOLOv3: https://github.com/david8862/keras-YOLOv3-model-set
6- Tiny YOLOv4: https://github.com/TNTWEN/OpenVINO-YOLOV4
7"""
8
9from pathlib import Path
10import sys
11import cv2
12import depthai as dai
13import numpy as np
14import time
15
16# Get yolo v8n model blob file path
17nnPath = str((Path(__file__).parent / Path('../models/yolov8n_coco_640x352.blob')).resolve().absolute())
18if not Path(nnPath).exists():
19    import sys
20    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
21
22# yolo v8 abel texts
23labelMap = [
24    "person",         "bicycle",    "car",           "motorbike",     "aeroplane",   "bus",           "train",
25    "truck",          "boat",       "traffic light", "fire hydrant",  "stop sign",   "parking meter", "bench",
26    "bird",           "cat",        "dog",           "horse",         "sheep",       "cow",           "elephant",
27    "bear",           "zebra",      "giraffe",       "backpack",      "umbrella",    "handbag",       "tie",
28    "suitcase",       "frisbee",    "skis",          "snowboard",     "sports ball", "kite",          "baseball bat",
29    "baseball glove", "skateboard", "surfboard",     "tennis racket", "bottle",      "wine glass",    "cup",
30    "fork",           "knife",      "spoon",         "bowl",          "banana",      "apple",         "sandwich",
31    "orange",         "broccoli",   "carrot",        "hot dog",       "pizza",       "donut",         "cake",
32    "chair",          "sofa",       "pottedplant",   "bed",           "diningtable", "toilet",        "tvmonitor",
33    "laptop",         "mouse",      "remote",        "keyboard",      "cell phone",  "microwave",     "oven",
34    "toaster",        "sink",       "refrigerator",  "book",          "clock",       "vase",          "scissors",
35    "teddy bear",     "hair drier", "toothbrush"
36]
37
38syncNN = True
39
40# Create pipeline
41pipeline = dai.Pipeline()
42
43# Define sources and outputs
44camRgb = pipeline.create(dai.node.ColorCamera)
45detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
46xoutRgb = pipeline.create(dai.node.XLinkOut)
47nnOut = pipeline.create(dai.node.XLinkOut)
48
49xoutRgb.setStreamName("rgb")
50nnOut.setStreamName("nn")
51
52# Properties
53camRgb.setPreviewSize(640, 352)
54camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
55camRgb.setInterleaved(False)
56camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
57camRgb.setFps(40)
58
59# Network specific settings
60detectionNetwork.setConfidenceThreshold(0.5)
61detectionNetwork.setNumClasses(80)
62detectionNetwork.setCoordinateSize(4)
63detectionNetwork.setIouThreshold(0.5)
64detectionNetwork.setBlobPath(nnPath)
65detectionNetwork.setNumInferenceThreads(2)
66detectionNetwork.input.setBlocking(False)
67
68# Linking
69camRgb.preview.link(detectionNetwork.input)
70if syncNN:
71    detectionNetwork.passthrough.link(xoutRgb.input)
72else:
73    camRgb.preview.link(xoutRgb.input)
74
75detectionNetwork.out.link(nnOut.input)
76
77# Connect to device and start pipeline
78with dai.Device(pipeline) as device:
79
80    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
81    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
82    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
83
84    frame = None
85    detections = []
86    startTime = time.monotonic()
87    counter = 0
88    color2 = (255, 255, 255)
89
90    # nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
91    def frameNorm(frame, bbox):
92        normVals = np.full(len(bbox), frame.shape[0])
93        normVals[::2] = frame.shape[1]
94        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
95
96    def displayFrame(name, frame):
97        color = (255, 0, 0)
98        for detection in detections:
99            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
100            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
101            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
102            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
103        # Show the frame
104        cv2.imshow(name, frame)
105
106    while True:
107        if syncNN:
108            inRgb = qRgb.get()
109            inDet = qDet.get()
110        else:
111            inRgb = qRgb.tryGet()
112            inDet = qDet.tryGet()
113
114        if inRgb is not None:
115            frame = inRgb.getCvFrame()
116            cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
117                        (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)
118
119        if inDet is not None:
120            detections = inDet.detections
121            counter += 1
122
123        if frame is not None:
124            displayFrame("rgb", frame)
125
126        if cv2.waitKey(1) == ord('q'):
127            break