DepthAI Mask/No mask YoloV3 AI training example

mask-nomask-yolov3.py

#!/usr/bin/env python3

"""
Tiny-yolo-v3 device side decoding demo
  YOLO v3 Tiny is a real-time object detection model implemented with Keras* from
  this repository <https://github.com/david8862/keras-YOLOv3-model-set> and converted
  to TensorFlow* framework. This model was pretrained on COCO* dataset with 80 classes.
"""

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

# Get argument first
nnPath = str((Path(__file__).parent / Path('models/tiny-yolo-v3_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"')

# Tiny yolo v3 label texts
labelMap = [
    "mask", "no mask"
]

syncNN = True

# Create pipeline
pipeline = dai.Pipeline()
pipeline.setOpenVINOVersion(dai.OpenVINO.Version.VERSION_2021_3)

# Define sources and outputs
camRgb = pipeline.createColorCamera()
detectionNetwork = pipeline.createYoloDetectionNetwork()
xoutRgb = pipeline.createXLinkOut()
nnOut = pipeline.createXLinkOut()

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

# Properties
camRgb.setPreviewSize(416, 416)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
camRgb.setFps(40)

# Network specific settings
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.setNumClasses(2)
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)

# 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