video_mobilenet.py

#!/usr/bin/env python3

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

# Get argument first
parentDir = Path(__file__).parent
nnPath = str(
    (parentDir / Path("models/mobilenet-ssd_openvino_2021.2_8shave.blob"))
    .resolve()
    .absolute()
)
videoPath = str((parentDir / Path("models/construction_vest.mp4")).resolve().absolute())
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()
pipeline.setOpenVINOVersion(dai.OpenVINO.Version.VERSION_2021_2)

# Define sources and outputs
nn = pipeline.createMobileNetDetectionNetwork()

xinFrame = pipeline.createXLinkIn()
nnOut = pipeline.createXLinkOut()

xinFrame.setStreamName("inFrame")
nnOut.setStreamName("nn")

# Properties
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Linking
xinFrame.out.link(nn.input)
nn.out.link(nnOut.input)

fps = 0.0

# 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=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 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)
            cv2.putText(
                frame,
                "FPS: " + str(fps),
                (20, 20),
                cv2.FONT_HERSHEY_TRIPLEX,
                1,
                255,
            )
        # Show the frame
        cv2.imshow(name, frame)

    cap = cv2.VideoCapture(videoPath)
    while cap.isOpened():
        t0 = perf_counter()
        read_correctly, frame = cap.read()
        if not read_correctly:
            break
        frame = cv2.resize(frame, (800, 450))

        img = dai.ImgFrame()
        img.setData(to_planar(frame, (300, 300)))
        img.setTimestamp(monotonic())
        img.setWidth(300)
        img.setHeight(300)
        qIn.send(img)

        t1 = perf_counter()
        inDet = qDet.get()

        if inDet is not None:
            detections = inDet.detections
        print("Inference time:", perf_counter() - t1)

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

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

        fps = int(1.0 / (perf_counter() - t0))