REALSENCE D435のカメラ映像でPyTorchとYOLOv5を使ってリアルタイムに物体検出する方法

#Yoloを使った物体検出
def predict(img):
    #推論を実行
    result = model(img)
    result.render()

    #戻り値
    return result.ims[0]

import pyrealsense2 as rs
import numpy as np
import cv2
import cv2
import torch

# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

#YOLOのモデルをロード
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') 

#PyTorchを使った物体検出
def predict(img):
    #推論を実行
    result = model(img)
    result.render()

    #戻り値
    return result.ims[0]

#メイン処理
def main():
    # Start streaming
    pipeline.start(config)

    try:
        while True:

            # Wait for a coherent pair of frames: depth and color
            frames = pipeline.wait_for_frames()
            depth_frame = frames.get_depth_frame()
            color_frame = frames.get_color_frame()
            if not depth_frame or not color_frame:
                continue

            # Convert images to numpy arrays
            depth_image = np.asanyarray(depth_frame.get_data())
            color_image = np.asanyarray(color_frame.get_data())

            #推論実行
            color_image = predict(color_image)

            # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

            # Stack both images horizontally
            images = np.hstack((color_image, depth_colormap))

            # Show images
            cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('RealSense', images)
            cv2.waitKey(1)

    finally:

        # Stop streaming
        pipeline.stop()


if __name__ == '__main__':

    main()