Python OpenCV 保存mp4视频

2024-07-16 09:14| 来源: 网络整理| 查看: 265

文章目录问题描述代码封装参考文献

问题描述

本文代码、图片及模型下载地址

代码

0为摄像头，换为路径则读取视频文件

import cv2 cap = cv2.VideoCapture(0) # 打开摄像头 fourcc = cv2.VideoWriter_fourcc(*'MP4V') # 视频编解码器 fps = cap.get(cv2.CAP_PROP_FPS) # 帧数 width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 宽高 out = cv2.VideoWriter('result.mp4', fourcc, fps, (width, height)) # 写入视频 while cap.isOpened(): ret, frame = cap.read() if ret == True: out.write(frame) # 写入帧 cv2.imshow('frame', frame) if cv2.waitKey(1) & 0xFF == ord('q'): # q退出 break else: break cap.release() out.release() cv2.destroyAllWindows()

推荐阅读

视频编解码器FOURCC

封装

加载处理图像处理函数直接保存视频

process_video.py

import cv2 from timeit import default_timer as timer def process_video(filename=0, func=None, output='result.mp4', verbose=0): """处理视频 :param filename: 视频源，默认为摄像头 :param func: 处理每一帧的函数名 :param output: 保存的文件名 :param verbose: 可视化，0不可视化，1显示处理后的结果，2显示对比结果 """ cap = cv2.VideoCapture(filename) # 打开摄像头 fourcc = cv2.VideoWriter_fourcc(*'MP4V') # 视频编解码器 fps = cap.get(cv2.CAP_PROP_FPS) # 帧数 width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 宽高 out = cv2.VideoWriter(output, fourcc, fps, (width, height)) # 写入视频 if verbose > 0 or filename == 0: print('英文下输入q停止') count = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 accum_time = 0 # 累计时间 curr_fps = 0 # 当前帧数 prev_time = timer() # 上一段的时间 while cap.isOpened(): if count > 0: current = cap.get(cv2.CAP_PROP_POS_FRAMES) # 当前第几帧 curr_time = timer() # 当前时间 exec_time = curr_time - prev_time # 处理时间 prev_time = curr_time # 上一段的时间设为当前时间 accum_time = accum_time + exec_time # 累计时间 curr_fps = curr_fps + 1 if accum_time >= 1: accum_time = accum_time - 1 print('进度:{:.2f}%\tFPS:{}'.format(current / count * 100, curr_fps)) curr_fps = 0 # 重置帧数 ret, frame = cap.read() if ret == True: result = func(frame) if func else frame out.write(result) # 写入帧 if verbose > 0 or filename == 0: cv2.imshow('after', result) if verbose == 2: cv2.imshow('before', frame) if cv2.waitKey(1) & 0xFF == ord('q'): # q退出 break else: break cap.release() out.release() cv2.destroyAllWindows() if __name__ == '__main__': process_video()

main.py

import cv2 import numpy as np from process_video import process_video def flip(frame): """图像翻转""" return cv2.flip(frame, 0) net = cv2.dnn.readNetFromTensorflow( 'model/opencv_face_detector_uint8.pb', 'model/opencv_face_detector.pbtxt' ) # TensorFlow模型 def face_detect(frame): """人脸检测""" frame = frame.copy() height, width, channel = frame.shape # 高、宽、通道数 blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) # 调整大小并降低光照的影响 net.setInput(blob) # 设置输入 detections = net.forward() # 检测结果 faces = detections[0, 0] # 人脸结果 for face in faces: confidence = face[2] # 置信度 if confidence > 0.5: # 置信度阈值设为0.5 box = face[3:7] * np.array([width, height, width, height]) # 人脸矩形框坐标 pt1 = int(box[0]), int(box[1]) # 左上角坐标 pt2 = int(box[2]), int(box[3]) # 右下角坐标 cv2.rectangle(frame, pt1, pt2, (0, 255, 0), thickness=2) # 画出人脸矩形框 text = '{:.2f}%'.format(confidence * 100) # 置信度文本 startX, startY = pt1 y = startY - 10 if startY - 10 > 10 else startY + 10 org = (startX, y) # 文本的左下角坐标 cv2.putText(frame, text, org, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2) # 画出置信度 return frame if __name__ == '__main__': process_video('test.mp4', func=face_detect, verbose=2)

效果

verbose=1 在这里插入图片描述 verbose=2

参考文献 OpenCV-Python 官方文档视频编解码器FOURCC

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