Gen2-Yolo Device Decoding, Object Tracking

Hi jakaskerl
Great work bro appriciate it so much

Is it possible to put ROI before detection and tracking?

Hi jakaskerl
Thank you very much, I will continue working on this project if I have any questions I'll notify you

Thanks,
Fakhrullo

Hi jakaskerl

How are you doing bro? I have a question regarding that project, I adjusted the project as I want and it's working as expected, now I have to test detection and tracking with crowd, currently I can't put camera to street or any public places, so I wanted to test it with videos. However, I couldn't manage to integrate the video reading in the project, I tried using this method https://docs.luxonis.com/projects/api/en/latest/samples/ObjectTracker/object_tracker_video/#source-code
In the beginnign I tried to add this to my code but I couldn't get it right, so I added Yolo to the code in the documentation I mentioned above. But it's not running on device, how can I test my project with video? can you help me with that please.

Thanks,
Fakhrullo

Hi jakaskerl

I added manip mode as you said, I added like this:
# Creating Manip node
manip = pipeline.create(dai.node.ImageManip)
# Setting CropRect for the Region of Interest
manip.initialConfig.setCropRect(*custom_roi)
# Setting Resize for the neural network input size
manip.initialConfig.setResize(640, 640)
# Setting maximum output frame size based on the desired output dimensions
max_output_width = 640
max_output_height = 640
max_output_frame_size = 3 * max_output_width * max_output_height # Assuming 3 channels for BGR image
manip.setMaxOutputFrameSize(max_output_frame_size)
# Connecting Manip node to ColorCamera
camRgb.preview.link(manip.inputImage)
# Connecting Manip node to YoloDetectionNetwork
manip.out.link(detectionNetwork.input)

But I'm getting black screen, Where am I making mistake? is there any tutorial or documentation how to do that?

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

Hi jakaskerl
Yeah, basically I want to retrieve both full and cropped frames, to achieve that what should I do?

jakaskerl Thanks bro