22.2 - RGB & TinyYoloV4 decoding on device

This example shows how to run TinyYoloV4 on the RGB input frame, and how to display both the RGB preview and the metadata results from the TinyYoloV4 on the preview. Decoding is done on Myriad instead on the host.

Configurable, network dependent parameters are required for correct decoding: setNumClasses - number of YOLO classes setCoordinateSize - size of coordinate setAnchors - yolo anchors setAnchorMasks - anchorMasks26, anchorMasks13 (anchorMasks52 - additionally for full YOLOv4) setIouThreshold - intersection over union threshold setConfidenceThreshold - confidence threshold above which objects are detected

Demo

Setup

Please run the following command to install the required dependencies

 python3 -m pip install -U pip
 python3 -m pip install opencv-python
 python3 -m pip install -U --force-reinstall depthai

For additional information, please follow installation guide

This example also requires YOLOv4-tiny blob (tiny-yolo-v4_openvino_2021.2_6shave.blob file) to work - you can download it from here

Source code

Also available on GitHub

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#!/usr/bin/env python3

"""
Tiny-yolo-v4 device side decoding demo
The code is the same as for Tiny-yolo-V3, the only difference is the blob file.
The blob was compiled following this tutorial: 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 = [
    "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"
]


syncNN = True

# Get argument first
nnPath = str((Path(__file__).parent / Path('models/tiny-yolo-v4_openvino_2021.2_6shave.blob')).resolve().absolute())
if len(sys.argv) > 1:
    nnPath = sys.argv[1]

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

# Start defining a pipeline
pipeline = dai.Pipeline()

# Define a source - color camera
camRgb = pipeline.createColorCamera()
camRgb.setPreviewSize(416, 416)
camRgb.setInterleaved(False)
camRgb.setFps(40)

# Network specific settings
detectionNetwork = pipeline.createYoloDetectionNetwork()
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.setNumClasses(80)
detectionNetwork.setCoordinateSize(4)
detectionNetwork.setAnchors(np.array([10,14, 23,27, 37,58, 81,82, 135,169, 344,319]))
detectionNetwork.setAnchorMasks({"side26": np.array([1, 2, 3]), "side13": np.array([3, 4, 5])})
detectionNetwork.setIouThreshold(0.5)

detectionNetwork.setBlobPath(nnPath)
detectionNetwork.setNumInferenceThreads(2)
detectionNetwork.input.setBlocking(False)

camRgb.preview.link(detectionNetwork.input)

# Create outputs
xoutRgb = pipeline.createXLinkOut()
xoutRgb.setStreamName("rgb")
if syncNN:
    detectionNetwork.passthrough.link(xoutRgb.input)
else:
    camRgb.preview.link(xoutRgb.input)

nnOut = pipeline.createXLinkOut()
nnOut.setStreamName("detections")
detectionNetwork.out.link(nnOut.input)

# Connect and start the 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="detections", maxSize=4, blocking=False)

    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 displayFrame(name, frame):
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
            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.imshow(name, frame)

    startTime = time.monotonic()
    counter = 0

    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, color=(255, 255, 255))

        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

Got questions?

We’re always happy to help with code or other questions you might have.