Oak-d Pro Object tracking

Hi @li_you_chen
I tested it on images of cats and dogs, with the swapped model inside this example: https://docs.luxonis.com/projects/api/en/latest/samples/Yolo/tiny_yolo/

Could you post the output of detections:

inDet = qDet.tryGet()

if inDet is not None:
            detections = inDet.detections
            print(detections)

You can't interchange Yolo blobs and Mbnet, they have completely different decoding functions.

Thanks,
Jaka

Hi @li_you_chen
No, it's yolo. I'm just saying you can not use a yolo blob inside MobileNetDetectionNetwork.

#!/usr/bin/env python3

"""
The code is the same as for Tiny Yolo V3 and V4, the only difference is the blob file
- Tiny YOLOv3: https://github.com/david8862/keras-YOLOv3-model-set
- Tiny YOLOv4: https://github.com/TNTWEN/OpenVINO-YOLOV4
"""

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

# tiny yolo v4 label texts
labelMap = [
    "cat", "dog"
]

syncNN = True

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
xoutRgb = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")
nnOut.setStreamName("nn")

# Properties
camRgb.setPreviewSize(320, 320)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
camRgb.setFps(40)

# Network specific settings
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.setNumClasses(2)
detectionNetwork.setIouThreshold(.5)
detectionNetwork.setBlobPath("catdog320.blob")
detectionNetwork.setNumInferenceThreads(2)
detectionNetwork.input.setBlocking(False)

# Linking
camRgb.preview.link(detectionNetwork.input)
if syncNN:
    detectionNetwork.passthrough.link(xoutRgb.input)
else:
    camRgb.preview.link(xoutRgb.input)

detectionNetwork.out.link(nnOut.input)

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

    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

    frame = None
    detections = []
    startTime = time.monotonic()
    counter = 0
    color2 = (255, 255, 255)

    # 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 displayFrame(name, frame):
        color = (255, 0, 0)
        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]), color, 2)
        # Show the frame
        cv2.imshow(name, frame)

    while True:
        if syncNN:
            inRgb = qRgb.get()
            inDet = qDet.get()
        else:
            inRgb = qRgb.tryGet()
            inDet = qDet.tryGet()

        if inRgb is not None:
            frame = inRgb.getCvFrame()
            cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
                        (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)

        if inDet is not None:
            detections = inDet.detections
            counter += 1

        if frame is not None:
            displayFrame("rgb", frame)

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

Does this work for you? Cause it doesn't for me and I think the model is not created correctly.

Thanks ,
Jaka

Hi @li_you_chen
This is the exact same script as tiny_yolo in the examples. I only changed the model to catdog and the label names respectively. Can you recheck please?

Thanks
Jaka

Hi @li_you_chen

li_you_chen full of wrong boundingbox , even I raise threshold

This suggest a problem with the model. Did you test it off depthai?

Thanks,
Jaka

Hi @li_you_chen
Yes, of course you can. The model may be different but the on-device decoding is done in a way that ensures the output structure is the same, so they can both be linked to an object tracker.

If you train your own mobilenet model, the example you sent should be working without any changes (well, except for the model path).

Thanks,
Jaka

Hi @li_you_chen
We have some here but we ditched MBNet a while ago and mostly use YOLO now. If you wish you can train YOLO as well.

Thanks,
Jaka