#!/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_6shave.blob')).resolve().absolute()) parser = argparse.ArgumentParser() parser.add_argument('nnPath', nargs='?', help="Path to mobilenet detection network blob", default=nnPathDefault) parser.add_argument('-ff', '--full_frame', action="store_true", help="Perform tracking on full RGB frame", default=False) args = parser.parse_args() fullFrameTracking = args.full_frame # Create pipeline pipeline = dai.Pipeline() # Define sources and outputs camRgb = pipeline.create(dai.node.ColorCamera) detectionNetwork = pipeline.create(dai.node.MobileNetDetectionNetwork) objectTracker = pipeline.create(dai.node.ObjectTracker) xlinkOut = pipeline.create(dai.node.XLinkOut) trackerOut = pipeline.create(dai.node.XLinkOut) xlinkOut.setStreamName("preview") trackerOut.setStreamName("tracklets") # Properties camRgb.setPreviewSize(300, 300) camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) camRgb.setInterleaved(False) camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) camRgb.setFps(40) # testing MobileNet DetectionNetwork detectionNetwork.setBlobPath(args.nnPath) detectionNetwork.setConfidenceThreshold(0.5) detectionNetwork.input.setBlocking(False) objectTracker.setDetectionLabelsToTrack([15]) # track only person # possible tracking types: ZERO_TERM_COLOR_HISTOGRAM, ZERO_TERM_IMAGELESS, SHORT_TERM_IMAGELESS, SHORT_TERM_KCF objectTracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM) # take the smallest ID when new object is tracked, possible options: SMALLEST_ID, UNIQUE_ID objectTracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.SMALLEST_ID) # Linking camRgb.preview.link(detectionNetwork.input) objectTracker.passthroughTrackerFrame.link(xlinkOut.input) if fullFrameTracking: camRgb.video.link(objectTracker.inputTrackerFrame) else: detectionNetwork.passthrough.link(objectTracker.inputTrackerFrame) detectionNetwork.passthrough.link(objectTracker.inputDetectionFrame) detectionNetwork.out.link(objectTracker.inputDetections) objectTracker.out.link(trackerOut.input) # Connect to device and start pipeline with dai.Device(pipeline) as device: preview = device.getOutputQueue("preview", 4, False) tracklets = device.getOutputQueue("tracklets", 4, False) startTime = time.monotonic() counter = 0 fps = 0 frame = None while(True): imgFrame = preview.get() track = tracklets.get() counter+=1 current_time = time.monotonic() if (current_time - startTime) > 1 : fps = counter / (current_time - startTime) counter = 0 startTime = current_time color = (255, 0, 0) frame = imgFrame.getCvFrame() trackletsData = track.tracklets for t in trackletsData: roi = t.roi.denormalize(frame.shape[1], frame.shape[0]) x1 = int(roi.topLeft().x) y1 = int(roi.topLeft().y) x2 = int(roi.bottomRight().x) y2 = int(roi.bottomRight().y) try: label = labelMap[t.label] except: label = t.label cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"ID: {[t.id]}", (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, t.status.name, (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.rectangle(frame, (x1, y1), (x2, y2), color, cv2.FONT_HERSHEY_SIMPLEX) cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color) cv2.imshow("tracker", frame) if cv2.waitKey(1) == ord('q'): break