Detection problem with Mobile Net on video from host

Hi pierreia ,
The code looks good to me (normalization of bounding boxes), I think the problem is with the model and/or deocding (so anchors/masks). I would also check for incorrect color order and/or incorrect channel layout (both mentioned here).
Thanks, Erik

Hi pierreia , could you share the code with the mobilened from blobconverter?
THnaks, Erik

Hi pierreia .
The only problem in the code above is that it's not synced. You could either sync frames+detections with host-side syncing, or just use passthrough frame like I did below:

#!/usr/bin/env python3

from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
from time import monotonic

import blobconverter

# Get argument first
nnPath = 'mobilenet-ssd_openvino_2021.4_8shave.blob'
videoPath = 'traffic_5mn.mp4'
if len(sys.argv) > 2:
    nnPath = sys.argv[1]
    videoPath = sys.argv[2]

if not Path(nnPath).exists() or not Path(videoPath).exists():
    import sys
    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')

# MobilenetSSD label texts
labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
            "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

xinFrame = pipeline.create(dai.node.XLinkIn)
xinFrame.setStreamName("inFrame")
xinFrame.out.link(nn.input)
# Properties
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(True)

# Linking
nnOut = pipeline.create(dai.node.XLinkOut)
nnOut.setStreamName("nn")
nn.out.link(nnOut.input)

nnPass = pipeline.create(dai.node.XLinkOut)
nnPass.setStreamName("pass")
nn.passthrough.link(nnPass.input)


# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    # Input queue will be used to send video frames to the device.
    qIn = device.getInputQueue(name="inFrame")
    # Output queue will be used to get nn data from the video frames.
    qDet = device.getOutputQueue(name="nn", maxSize=6, blocking=True)
    qPass = device.getOutputQueue("pass")

    frame = None
    detections = []

    # nn data, being the bounding box locations, are in <0..1> range - they need to be normalized with frame width/height
    def frameNorm(frame, bbox):
        normVals = np.full(len(bbox), frame.shape[0])
        normVals[::2] = frame.shape[1]
        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)

    def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
        return cv2.resize(arr, shape).transpose(2, 0, 1).flatten()

    def displayFrame(name, frame):
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
        # Show the frame
        cv2.imshow(name, frame)

    cap = cv2.VideoCapture(videoPath)
    while cap.isOpened():
        read_correctly, frame = cap.read()
        if not read_correctly:
            break

        img = dai.ImgFrame()
        resized = to_planar(frame, (300, 300))
        img.setTimestamp(monotonic())
        img.setType(dai.RawImgFrame.Type.BGR888p)
        img.setSize(300, 300)
        img.setData(resized)
        qIn.send(img)

        inDet = qDet.tryGet()

        if inDet is not None:
            detections = inDet.detections
            frame = qPass.get().getCvFrame()
            displayFrame("passthrough", frame)

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

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.

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?

@Unknown perhaps look at
https://docs-old.luxonis.com/projects/sdk/en/latest/features/replaying/
SDK also has good support for YOLO models.

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.