#代码实现从视频流中每隔5s抓取一帧进行图像处理识别钢筋交叉点
#需要在func函数中添加sigmoid激活函数，该函数能有效提高识别准确度，并有效删除无用框，但是会更耗时
import cv2
import time
# from rknnpool import rknnPoolExecutor
# 图像处理函数，实际应用过程中需要自行修改
from func import myFunc
from rknnlite.api import RKNNLite
cap = cv2.VideoCapture('./out19.mp4')
# cap = cv2.VideoCapture(0)
modelPath = "./rknnModel/best.rknn"



if __name__ == '__main__':
    rknn = RKNNLite(verbose=True)
    rknn_lite = RKNNLite()
    print('--> Load RKNN model')
    ret = rknn_lite.load_rknn(modelPath)
    if ret != 0:
        print('Load RKNN model failed')
        exit(ret)
    print('done')
    ret = rknn_lite.init_runtime()
    while(cap.isOpened()):
        ret,frame = cap.read()
        if not ret:
            cap.release()
            exit(-1)
        points = myFunc(rknn_lite,frame)
        print("points",points)
        time.sleep(0.1)