基于aidlux的ai模型边缘设备模型部署实战（3.图像分类）

import cv2
import numpy as np
import time
from tqdm import tqdm

def generate_video(input_path='videos/fruit.mp4'):
    filehead = input_path.split('/')[-1]
    output_path = "out-" + filehead
    
    print('视频开始处理',input_path)
    
    # 获取视频总帧数
    cap = cv2.VideoCapture(input_path)
    frame_count = 0
    while(cap.isOpened()):
        success, frame = cap.read()
        frame_count += 1
        if not success:
            break
    cap.release()
    print('视频总帧数为',frame_count)
    
    # cv2.namedWindow('Crack Detection and Measurement Video Processing')
    cap = cv2.VideoCapture(input_path)
    frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))
    # fourcc = cv2.VideoWriter_fourcc(*'XVID')
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    fps = cap.get(cv2.CAP_PROP_FPS)

    out = cv2.VideoWriter(output_path, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))
    
    # 进度条绑定视频总帧数
    with tqdm(total=frame_count-1) as pbar:
        try:
            while(cap.isOpened()):
                success, frame = cap.read()
                if not success:
                    break

                # 处理帧
                # frame_path = './temp_frame.png'
                # cv2.imwrite(frame_path, frame)
                try:
                    frame = process_frame(frame)
                except:
                    print('报错！', error)
                    pass
                
                if success == True:
                    # cv2.imshow('Video Processing', frame)
                    out.write(frame)

                    # 进度条更新一帧
                    pbar.update(1)

                # if cv2.waitKey(1) & 0xFF == ord('q'):
                    # break
        except:
            print('中途中断')
            pass

    cv2.destroyAllWindows()
    out.release()
    cap.release()
    print('视频已保存', output_path)