OpenCv之视频人脸识别

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一、人脸检测

案例代码如下:

import cv2
import numpy as np

video =  cv2.VideoCapture('1.mp4')
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
while True:
    retval,image = video.read() #     retval boolean类型表名是否获得了图片
    if not retval:
        print('视频读取完成，没有图片')
        break
    faces = face_detector.detectMultiScale(image)
    for x,y,w,h in faces:
        cv2.rectangle(image,pt1=(x,y),pt2 = (x+w,y+h),color=[0,0,255],thickness=2)
    cv2.imshow('video',image)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break

cv2.waitKey(1)
video.release()

二、马赛克处理

视频帧率、宽度、高度、图片个数计算:

import cv2
import numpy as np

video =  cv2.VideoCapture('1.mp4')
# 获得视频中的属性
fps = video.get(propId=cv2.CAP_PROP_FPS) #得到帧率
width = video.get(propId=cv2.CAP_PROP_FRAME_WIDTH) #宽度
height = video.get(propId=cv2.CAP_PROP_XI_HEIGHT) #高度
count = video.get(propId=cv2.CAP_PROP_FRAME_COUNT) #多少图片
print('--视频帧率',fps)
print(width,height,count)
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
while True:
    retval,image = video.read() #     retval boolean类型表名是否获得了图片
    if not retval:
        print('视频读取完成，没有图片')
        break
    faces = face_detector.detectMultiScale(image)
    for x,y,w,h in faces:
        # cv2.rectangle(image,pt1=(x,y),pt2 = (x+w,y+h),color=[0,0,255],thickness=2)
        face = image[y:y+h,x:x+w]
        face = face[::10,::10]
        face = np.repeat(face,10,axis=0)
        face = np.repeat(face,10,axis =1)
        image[y:y+h,x:x+w] = face[:h,:w]

    cv2.imshow('video',image)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break

cv2.waitKey(1)
video.release()

三、写视频

案例代码如下:

import cv2
import numpy as np

video =  cv2.VideoCapture('1.mp4')
wr = cv2.VideoWriter(filename = 'gray.mp4',
                fourcc = cv2.VideoWriter.fourcc(*'MP4'),
                fps = 24, #视频帧率
                framesize = (640,360)) #图片尺寸
# 获得视频中的属性
fps = video.get(propId=cv2.CAP_PROP_FPS) #得到帧率
width = video.get(propId=cv2.CAP_PROP_FRAME_WIDTH) #宽度
height = video.get(propId=cv2.CAP_PROP_XI_HEIGHT) #高度
count = video.get(propId=cv2.CAP_PROP_FRAME_COUNT) #多少图片
print('--视频帧率',fps)
print(width,height,count)
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
while True:
    retval,image = video.read() #     retval boolean类型表名是否获得了图片
    if not retval:
        print('视频读取完成，没有图片')
        break
    image = cv2.resize(image,(640,360))
    gray = cv2.cvtColor(image,code = cv2.COLOR_BGR2GRAY)
    np.repeat(gray.reshape(360,640,1),3,axis=2) #蓝绿红变为三维，但值是一样的
    # wr.write(gray)
    faces = face_detector.detectMultiScale(image)
    for x,y,w,h in faces:
        # cv2.rectangle(image,pt1=(x,y),pt2 = (x+w,y+h),color=[0,0,255],thickness=2)
        face = image[y:y+h,x:x+w]
        face = face[::10,::10]
        face = np.repeat(face,10,axis=0)
        face = np.repeat(face,10,axis =1)
        image[y:y+h,x:x+w] = face[:h,:w]
    wr.write(image) #彩色图片是三维的,而黑白图片是二维的
    cv2.imshow('video',image)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break

cv2.waitKey(1)
video.release()
wr.release()

四、摄像头识别人脸

案例代码如下:

import cv2
import numpy as np

# 打开本机摄像头
cap = cv2.VideoCapture(0)
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')

while True:
    flag,frame = cap.read() #flag是否读取了图片
    if not flag:
        break
    # 转为灰度图片
    gray = cv2.cvtColor(frame,code = cv2.COLOR_BGR2GRAY)
    faces = face_detector.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=10)
    for x,y,w,h in faces:
        cv2.rectangle(frame,
                      pt1=(x,y),
                      pt2=(x+w,y+h),
                      color=[0,0,255],
                      thickness=2)
    cv2.imshow('face',frame)
    key = cv2.waitKey(1000//24)
    if key == ord('q'):
        break

cv2.destroyAllWindows()
cap.release()