Hi jakaskerl
Here's the code
from pathlib import Path
import cv2
import depthai as dai
import time
from environs import Env
env = Env()env.read_env()
MxID = env('MxID')# Set your custom ROI coordinates (x, y, width, height)custom_roi = (350, 250, 640, 640) # Example coordinates, adjust as needed
# tiny yolo v4 label textslabelMap = [ "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
nnPath = str((Path(__file__).parent / Path('model/yolov6n_coco_640x640_openvino_2022.1_6shave.blob')).resolve().absolute())
# Create pipelinepipeline = dai.Pipeline()
# Define sources and outputscamRgb = pipeline.create(dai.node.ColorCamera)detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)objectTracker = pipeline.create(dai.node.ObjectTracker)
xlinkOut = pipeline.create(dai.node.XLinkOut)trackerOut = pipeline.create(dai.node.XLinkOut)
xlinkOut.setStreamName("preview")trackerOut.setStreamName("tracklets")
# Creating Manip nodemanip = pipeline.create(dai.node.ImageManip)# Setting CropRect for the Region of Interestmanip.initialConfig.setCropRect(*custom_roi)# Setting Resize for the neural network input sizemanip.initialConfig.setResize(640, 640)# Setting maximum output frame size based on the desired output dimensionsmax_output_width = 640max_output_height = 640max_output_frame_size = 3 * max_output_width * max_output_height # Assuming 3 channels for BGR imagemanip.setMaxOutputFrameSize(max_output_frame_size)
# Propertiesif MxID == "14442C10C1AD3FD700": camRgb.setImageOrientation(dai.CameraImageOrientation.HORIZONTAL_MIRROR)camRgb.setPreviewSize(640, 640)camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)camRgb.setInterleaved(False)camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)camRgb.setFps(40)
# Network specific settingsdetectionNetwork.setConfidenceThreshold(0.5)detectionNetwork.setNumClasses(80)detectionNetwork.setCoordinateSize(4)# detectionNetwork.setAnchors([10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319]) #YOLOv4 uchun# detectionNetwork.setAnchorMasks({"side26": [1, 2, 3], "side13": [3, 4, 5]})detectionNetwork.setAnchors([10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326]) #YOLOv5 uchundetectionNetwork.setAnchorMasks({"side52": [0,1,2], "side26": [3,4,5], "side13": [6,7,8]})detectionNetwork.setIouThreshold(0.5)detectionNetwork.setBlobPath(nnPath)detectionNetwork.setNumInferenceThreads(2)detectionNetwork.input.setBlocking(False)
objectTracker.setDetectionLabelsToTrack([0]) # track only person# possible tracking types: ZERO_TERM_COLOR_HISTOGRAM, ZERO_TERM_IMAGELESS, SHORT_TERM_IMAGELESS, SHORT_TERM_KCFobjectTracker.setTrackerType(dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM)# take the smallest ID when new object is tracked, possible options: SMALLEST_ID, UNIQUE_IDobjectTracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.SMALLEST_ID)
#Linking# Connecting Manip node to ColorCameracamRgb.preview.link(manip.inputImage)# Connecting Manip node to YoloDetectionNetworkmanip.out.link(detectionNetwork.input)# camRgb.preview.link(detectionNetwork.input)objectTracker.passthroughTrackerFrame.link(xlinkOut.input)
detectionNetwork.passthrough.link(objectTracker.inputTrackerFrame)
detectionNetwork.passthrough.link(objectTracker.inputDetectionFrame)detectionNetwork.out.link(objectTracker.inputDetections)objectTracker.out.link(trackerOut.input)
device = dai.DeviceInfo(MxID)
# Connect to device and start pipelinewith dai.Device(pipeline, device) 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) text_color = (0, 0, 255) rectangle = (111, 147, 26)
frame = imgFrame.getCvFrame() trackletsData = track.tracklets for t in trackletsData: if t.status.name == "TRACKED": 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 # if t.status.name == 'TRACKED': cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, text_color) cv2.putText(frame, f"ID: {[t.id]}", (x1 + 10, y1 + 45), cv2.FONT_HERSHEY_TRIPLEX, 0.5, text_color) cv2.putText(frame, t.status.name, (x1 + 10, y1 + 70), cv2.FONT_HERSHEY_TRIPLEX, 0.5, text_color) cv2.rectangle(frame, (x1, y1), (x2, y2), rectangle, cv2.FONT_HERSHEY_SIMPLEX)
cv2.putText(frame, "FPS: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.6, text_color)
cv2.imshow("tracker", frame)
if cv2.waitKey(1) == ord('q'): break
