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Python3.6.6 OpenCV 将视频中人物标记或者打马赛克或加图片并保存为不同格式

news 2025/7/11 0:45:56

1、轻松识别视频人物并做出标记

需安装face_recongnition与dlib，过程有点困难，还请网上查找方法

import face_recognition
import cv2
#镜像源 -i https://pypi.mirrors.ustc.edu.cn/simple 
# 加载视频
video_file = 'E:\\videos\\1.mp4'
video_capture = cv2.VideoCapture(video_file)width = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(video_capture.get(cv2.CAP_PROP_FPS))
frame_count = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))# 设置视频格式
fourcc = cv2.VideoWriter_fourcc(*'XVID')
# 调用VideoWrite（）函数
size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
video_writer = cv2.VideoWriter('output1.avi', fourcc, fps, size)count = 0 
# 通过循环读取视频的每一帧
while True and count < 200:ret, frame = video_capture.read()# 如果正确读取帧，ret为Trueif not ret:break# 将帧转换为灰度图像，因为人脸识别对颜色不敏感gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 使用face_recognition库的API进行人脸定位face_locations = face_recognition.face_locations(gray_frame)# 遍历所有找到的人脸for top, right, bottom, left in face_locations:# 画出人脸框cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)# 显示帧#cv2.imshow('Video', frame)if not video_writer is False:video_writer.write(frame)count = count + 1# 按'q'退出循环if cv2.waitKey(1) & 0xFF == ord('q'):break# 释放视频捕获对象
video_capture.release()
# 关闭所有OpenCV窗口
cv2.destroyAllWindows()

实现效果

2、实现视频人物加图，代码如下

import cv2
import numpy as np
import face_recognition
from PIL import Image # 加载视频
cap = cv2.VideoCapture('E:\\videos\\1.mp4')# 图片加密马赛克
def apply_mosaic(frame, mosaic_image, x, y, w, h):#print(mosaic_image.shape)mosaic_image = cv2.resize(mosaic_image, (w, h))#print(mosaic_image.shape)#cv2.imwrite('1.png',mosaic_image)#roi = frame[y:y+h, x:x+w]image_np = np.array(mosaic_image)#print(mosaic_image.shape)#print(frame.shape)#frame[y:y+h, x:x+w] = image_np#cv2.addWeighted(mosaic_image, 0, roi, 1, 0)for i in range(h):for j in range(w):#if(y+i<frame_height and x+j<frame_width):frame[x+i, y+j] = image_np[i, j]
# 加载图片
mosaic_image = cv2.imread('masaike.png')# 读取视频的宽度和高度
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))# 设置马赛克的位置和大小
x, y, w, h = 50, 50, 100, 100# 写入视频
#out = cv2.VideoWriter('output_video.avi', cv2.VideoWriter_fourcc(*'XVID'), 20.0, (frame_width, frame_height))width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))# 设置视频格式
fourcc = cv2.VideoWriter_fourcc(*'XVID')
# 调用VideoWrite（）函数
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
video_writer = cv2.VideoWriter('output2.avi', fourcc, fps, size)count = 0 while cap.isOpened() and count < 250:ret, frame = cap.read()if ret and count > 50:# 将帧转换为灰度图像，因为人脸识别对颜色不敏感gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 使用face_recognition库的API进行人脸定位face_locations = face_recognition.face_locations(gray_frame)# 遍历所有找到的人脸for top, right, bottom, left in face_locations:# 画出人脸框#cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)# 应用马赛克apply_mosaic(frame, mosaic_image, top, left, abs(top-bottom), abs(right-left))# 输出帧video_writer.write(frame)count = count + 1# 显示帧#cv2.imshow('Video', frame)# 按 'q' 退出循环if cv2.waitKey(1) & 0xFF == ord('q'):breakelif count >= 250:breakcount = count + 1# 释放资源
cap.release()
video_writer.release()
cv2.destroyAllWindows()

实现效果如下