Detection problem with Mobile Net on video from host

erik
i'm trying live inferencing and video inferencing on the yolov8 nano
pipeline

def init_pipeline():

pipeline = depthai.Pipeline()

cam_rgb = pipeline.createColorCamera()

detection_nn = pipeline.createYoloDetectionNetwork()

cam_rgb.setResolution(

    depthai.ColorCameraProperties.SensorResolution.THE_4_K)

cam_rgb.setPreviewSize(640, 640)

cam_rgb.setInterleaved(True)

xout_rgb = pipeline.createXLinkOut()

xout_rgb.setStreamName("rgb")

cam_rgb.preview.link(xout_rgb.input)

cam_rgb.setPreviewKeepAspectRatio(False)

manip1 = pipeline.createImageManip()

manip1.setMaxOutputFrameSize(1244160)

manip1.initialConfig.setResize(sizeX, sizeY)

cam_rgb.preview.link(manip1.inputImage)

manip1.initialConfig.setFrameType(depthai.ImgFrame.Type.BGR888p)

manip1.inputImage.setBlocking(True)

if args.videoPath is not None:

    xinFrame = pipeline.create(depthai.node.XLinkIn)

    xinFrame.setStreamName("inFrame")

    xinFrame.out.link(manip1.inputImage)

    xinFrame.setMaxDataSize(1920\*1080\*3)

    nnPass = pipeline.create(depthai.node.XLinkOut)

    nnPass.setStreamName("pass")

    detection_nn.passthrough.link(xout_rgb.input)

else:

    xinFrame = None

# Extract the values from the JSON

num_classes = config['nn_config']['NN_specific_metadata']['classes']

coordinates = config['nn_config']['NN_specific_metadata']['coordinates']

anchors = config['nn_config']['NN_specific_metadata']['anchors']

anchor_masks = config['nn_config']['NN_specific_metadata']['anchor_masks']

iou_threshold = config['nn_config']['NN_specific_metadata']['iou_threshold']

# Set the values

detection_nn.setNumClasses(num_classes)

detection_nn.setCoordinateSize(coordinates)

detection_nn.setAnchors(anchors)

detection_nn.setAnchorMasks(anchor_masks)

detection_nn.setIouThreshold(iou_threshold)

detection_nn.setConfidenceThreshold(0.5)

# detection_nn.setNumInferenceThreads(2)

detection_nn.input.setBlocking(True)





# Blob is the Neural Network file, compiled for MyriadX. It contains both the definition and weights of the model

# We're using a blobconverter tool to retreive the MobileNetSSD blob automatically from OpenVINO Model Zoo

# detection_nn.setBlobPath(blobconverter.from_zoo(name='mobilenet-ssd', shaves=6))

# Next, we filter out the detections that are below a confidence threshold. Confidence can be anywhere between <0..1>

# Next, we link the camera 'preview' output to the neural network detection input, so that it can produce detections

manip1.out.link(detection_nn.input)

if customModel is True:

    nnPath = str(

        (parentDir / Path('../../data/' + model)).resolve().absolute())

    # print(nnPath)

    detection_nn.setBlobPath(nnPath)

    print("Custom Model" + nnPath + "Size: " +

          str(sizeX) + "x" + str(sizeY))

else:

    detection_nn.setBlobPath(blobconverter.from_zoo(

        name='person-detection-0106', shaves=6))

    print("Model from OpenVINO Zoo" + "Size: " +

          str(sizeX) + "x" + str(sizeY))

xout_nn = pipeline.createXLinkOut()

xout_nn.setStreamName("nn")

detection_nn.out.link(xout_nn.input)

return pipeline

def detect_and_count():

global outputFrame, lock, zones_current_count, listeners, loop

pipeline = init_pipeline()

inputFrameShape = (sizeX, sizeY)

with depthai.Device(pipeline) as device:

    q_rgb = device.getOutputQueue("rgb")

    q_nn = device.getOutputQueue("nn")

    qPass = device.getOutputQueue("pass")

    # q_manip = device.getInputQueue("")

    baseTs = time.monotonic()

    simulatedFps = 30

    frame = None

    detections = []

    timestamp = datetime.utcnow()

    zone_data = []

    def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:

        return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()

    if args.videoPath is not None:

        videoPath = str(

            (parentDir / Path('../../data/' + video_source)).resolve().absolute())

        cap = cv2.VideoCapture(videoPath, cv2.CAP_FFMPEG)

    # loop over frames from the video stream

    while True:

        if args.videoPath is not None:

            read_correctly, frame = cap.read()

            

            if not read_correctly:

                break

            if args.videoPath is not None:

                q_vid = device.getInputQueue(name="inFrame")

                img = depthai.ImgFrame()

                img.setType(depthai.RawImgFrame.Type.BGR888p)

                img.setData(to_planar(frame, inputFrameShape))

                img.setTimestamp(baseTs)

                baseTs += 1/simulatedFps

                img.setWidth(inputFrameShape[0])

                img.setHeight(inputFrameShape[1])

                q_vid.send(img)

                # in_vid = q_vid.tryGet()

                print("hello", timestamp)

                if args.videoPath is not None:

                    print("video")

                    frame = qPass.get().getCvFrame()

                

        in_rgb = q_rgb.tryGet()

        in_nn = q_nn.tryGet()

        

        if in_rgb is not None and args.videoPath is None:

            print("live")

            frame = in_rgb.getCvFrame()

        

        if in_nn is not None:

            print("detect")

            detections = in_nn.detections

            zone_data += check_overlap(frame, detections)

            print("done",timestamp)

            now = datetime.utcnow()

            if now.second != timestamp.second:

                t = threading.Thread(

                    target=insert_data, args=(zone_data, ))

                t.daemon = True

                t.start()

                zone_data = []

            timestamp = now

            with lock:

                outputFrame = frame.copy()

                print("finish")

        if args.videoPath is not None:

            ret, frame = cap.read()

            if not ret:

                print("video over", timestamp)

                cap.release()

                break

                # at any time, you can press "q" and exit the main loop, therefore exiting the program itself

        if cv2.waitKey(1) == ord('q'):

            break

parser = argparse.ArgumentParser()

parser.add_argument('-v', '--videoPath',

                    help="Path to video frame", default=None)

args = parser.parse_args()

video_source = args.videoPath

what's happening is both the live and video inferening is happening at a time and it stops after 30 seconds
any take on what i'm doing wrong?

krishnashravan Please provide minimal repro example.

erik
Hi erik i have attached the files here
the test 4 file gives a error where the video keeps on changing the size of the preview

the test 5 file gives a error where the video shifts to live inferencing in between the frames
the common error that i found was that both these stop working after 30 seconds
only live inferencing works fine but the video inferencing stops after 30 seconds
let me know if u need anything else

This isn't reproducible.

 python .\test4.py
Traceback (most recent call last):
  File "D:\Downloads\yolov8-testing-pt-files-New%20folder\test4.py", line 175, in <module>
    parser = argparse.ArgumentParser()
NameError: name 'argparse' is not defined

erik
i have made the changes and use the yolov8n.json file for the metadata
here

Again, this is not reproducible. Did you even try running the test4.py/test5.py?

erik

sorry erik
but i fixed it

@erik

Hi, can you please provide the code to run the inference on a video file using yolov5?

Thankyou @erik

There are modules for object Tracking using mobilenet ssd but it did not work well for yolov5.
can you please give module for object tracker using yolov5.

@erik

Whether we can run dlib tracker on-device together with yolov5 object detection?

@Unknown it's exactly the same, you can link YoloDetectionNetwork the same way you link MobileNetDetectionNetwork to the ObjectTracker node.

@erik okay sure, Thank you.
Can you please let me know about the query given below.
Whether we can run dlib tracker on-device together with yolov5 object detection?

Unknown
Depthai has proprietary tracking algorithms that can run on RVC. Addding custom tracking (on-device) is not possible on RVC2.
You would have to port the tracking to host side. The rest of the pipeline (yolo and others) should still be run on device.

Thanks,
Jaka

Thankyou @jakaskerl