【Python+OpenCV】实现检测场景内是否有物体移动，并进行人脸检测抓拍

import cv2
import time

save_path = './face/'
face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('./cascades/haarcascade_eye.xml')

camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头

# 判断视频是否打开
if (camera.isOpened()):
print('Open')
else:
print('摄像头未打开')

# 测试用,查看视频size
size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT)))
print('size:'+repr(size))

fps = 5  # 帧率
pre_frame = None  # 总是取视频流前一帧做为背景相对下一帧进行比较
i = 0
while True:
start = time.time()
grabbed, frame_lwpCV = camera.read() # 读取视频流
gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY) # 转灰度图

if not grabbed:
break
end = time.time()

# 人脸检测部分
faces = face_cascade.detectMultiScale(gray_lwpCV, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(frame_lwpCV, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray_lwpCV = gray_lwpCV[y:y + h / 2, x:x + w] # 检出人脸区域后，取上半部分，因为眼睛在上边啊，这样精度会高一些
roi_frame_lwpCV = frame_lwpCV[y:y + h / 2, x:x + w]
cv2.imwrite(save_path + str(i) + '.jpg', frame_lwpCV[y:y + h, x:x + w]) # 将检测到的人脸写入文件
i += 1
eyes = eye_cascade.detectMultiScale(roi_gray_lwpCV, 1.03, 5) # 在人脸区域继续检测眼睛
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_frame_lwpCV, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
cv2.imshow('lwpCVWindow', frame_lwpCV)

# 运动检测部分
seconds = end - start
if seconds < 1.0 / fps:
time.sleep(1.0 / fps - seconds)
gray_lwpCV = cv2.resize(gray_lwpCV, (500, 500))
# 用高斯滤波进行模糊处理，进行处理的原因：每个输入的视频都会因自然震动、光照变化或者摄像头本身等原因而产生噪声。对噪声进行平滑是为了避免在运动和跟踪时将其检测出来。
gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)

# 在完成对帧的灰度转换和平滑后，就可计算与背景帧的差异，并得到一个差分图（different map）。还需要应用阈值来得到一幅黑白图像，并通过下面代码来膨胀（dilate）图像，从而对孔（hole）和缺陷（imperfection）进行归一化处理
if pre_frame is None:
pre_frame = gray_lwpCV
else:
img_delta = cv2.absdiff(pre_frame, gray_lwpCV)
thresh = cv2.threshold(img_delta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
image, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
if cv2.contourArea(c) < 1000: # 设置敏感度
continue
else:
print("咦,有什么东西在动0.0")
break
pre_frame = gray_lwpCV
key = cv2.waitKey(1) & 0xFF
# 按'q'健退出循环
if key == ord('q'):
break
# When everything done, release the capture
camera.release()
cv2.destroyAllWindows()