道路分割

from openvino.inference_engine import IECore
import numpy as np
import time
import cv2 as cv


def ssd_video_demo():
    ie = IECore()
    for device in ie.available_devices:
        print(device)

    model_xml = "/home/bhc/BHC/model/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml"
    model_bin = "/home/bhc/BHC/model/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.bin"

    net = ie.read_network(model=model_xml, weights=model_bin)
    input_blob = next(iter(net.input_info))
    out_blob = next(iter(net.outputs))

    n, c, h, w = net.input_info[input_blob].input_data.shape
    print(n, c, h, w)

    cap = cv.VideoCapture("2.mp4")
    exec_net = ie.load_network(network=net, device_name="CPU")

    while True:
        ret, frame = cap.read()
        if ret is not True:
            break
        image = cv.resize(frame, (w, h))
        image = image.transpose(2, 0, 1)
        inf_start = time.time()
        res = exec_net.infer(inputs={input_blob:[image]})
        inf_end = time.time() - inf_start
        res = res[out_blob]                                             #( 1, 4, 512, 896 )=B, C, H, W
        res = np.squeeze(res, 0)                                        #4=(BG, road, curb, mark)
        res = res.transpose(1, 2, 0)
        res = np.argmax(res, 2)                                         #4状态中最大概率的情况
        print(res)
        hh, ww = res.shape
        mask = np.zeros((hh, ww, 3), dtype=np.uint8)
        mask[np.where(res == 0)] = (0, 255, 255)                        #背景颜色
        mask[np.where(res == 1)] = (0, 255, 255)                        #路颜色
        mask[np.where(res == 2)] = (255, 0, 255)                        #路边基石
        mask[np.where(res == 3)] = (255,0, 255)                         #路上基线颜色
        mask = cv.resize(mask, (frame.shape[1], frame.shape[0]))
        result = cv.addWeighted(frame, 0.5, mask, 0.5, 0)
        cv.putText(result, "infer time(ms): %.3f, FPS: %.2f"%(inf_end*1000, 1/(inf_end+0.0001)), (10, 50),
                   cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 255), 2, 8)
        cv.imshow("Pedestrian Detection", result)
        c = cv.waitKey(1)
        if c == 27:
            break
    cv.waitKey(0)
    cv.destroyAllWindows()


if __name__ == "__main__":
    ssd_video_demo()