#!/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