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- #!/usr/bin/env python3
- from pathlib import Path
- import cv2
- import depthai as dai
- import numpy as np
- import time
- import argparse
- labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
- "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
- nnPathDefault = str((Path(__file__).parent / Path('models/mobilenet-ssd_openvino_2021.4_5shave.blob')).resolve().absolute())
- # Create pipeline
- pipeline = dai.Pipeline()
- # Define sources and outputs
- camRgb = pipeline.create(dai.node.ImageManip)
- spatialDetectionNetwork = pipeline.create(dai.node.MobileNetDetectionNetwork)
- objectTracker = pipeline.create(dai.node.ObjectTracker)
- xoutFrame = pipeline.create(dai.node.XLinkOut)
- xinFrame = pipeline.create(dai.node.XLinkIn)
- trackerOut = pipeline.create(dai.node.XLinkOut)
- xinFrame.setStreamName("inFrame")
- xoutFrame.setStreamName("preview")
- trackerOut.setStreamName("tracklets")
- # Properties
- camRgb.initialConfig.setResize(300,300)
- camRgb.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
- camRgb.setKeepAspectRatio(True)
- camRgb.initialConfig.setResizeThumbnail(300,300)
- # setting node configs
- spatialDetectionNetwork.setBlobPath(nnPathDefault)
- spatialDetectionNetwork.setConfidenceThreshold(0.5)
- spatialDetectionNetwork.input.setBlocking(False)
- objectTracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM)
- objectTracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.SMALLEST_ID)
- # Linking
- xinFrame.out.link(camRgb.inputImage)
- camRgb.out.link(spatialDetectionNetwork.input)
- objectTracker.passthroughTrackerFrame.link(xoutFrame.input) #this function is used to show the tracking frame
- objectTracker.out.link(trackerOut.input)
- #link rgb camera's output to xoutRgb
- spatialDetectionNetwork.passthrough.link(objectTracker.inputTrackerFrame)
- spatialDetectionNetwork.passthrough.link(objectTracker.inputDetectionFrame)
- spatialDetectionNetwork.out.link(objectTracker.inputDetections)
- def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
- return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()
- # Connect to device and start pipeline
- with dai.Device(pipeline) as device:
- cap = cv2.VideoCapture("walking.mp4")
- qIn = device.getInputQueue(name="inFrame")
- preview = device.getOutputQueue("preview", 4, False)
- tracklets = device.getOutputQueue("tracklets", 4, False)
- startTime = time.monotonic()
- counter = 0
- fps = 0
- color = (255, 255, 255)
- from threading import Thread
- def send_frames(queue, cap):
- while True:
- ret, rgb = cap.read()
- if not ret:
- print("Can't receive frame (stream end?). Exiting ...")
- break
- rgbImg = dai.ImgFrame()
- rgbImg.setData(to_planar(rgb, (300, 300)))
- rgbImg.setType(dai.ImgFrame.Type.BGR888p)
- rgbImg.setTimestamp(time.monotonic())
- rgbImg.setWidth(300)
- rgbImg.setHeight(300)
- qIn.send(rgbImg)
- send_thread = Thread(target=send_frames, args=(qIn, cap,))
- send_thread.start()
- while send_thread.is_alive():
- imgFrame = preview.get()
- print("RGB Image Sent")
- print("imgFrame received")
- track = tracklets.get()
- print("tracklets received")
- frame = imgFrame.getCvFrame()
- trackletsData = track.tracklets
- print("trackletsData", trackletsData)
- cv2.imshow("tracker", frame)
- if cv2.waitKey(1) == ord('q'):
- break
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