import cv2
import timecamera = cv2.VideoCapture(0)
if camera is None:print('请先连接摄像头')exit()fps = 5 # 帧率
pre_frame = None  # 总是取前一帧做为背景（不用考虑环境影响）

play_music = Falsewhile True:start = time.time()res, cur_frame = camera.read()if res != True:breakend = time.time()seconds = end - startif seconds < 1.0/fps:time.sleep(1.0/fps - seconds)cv2.imshow('img', cur_frame)key = cv2.waitKey(30) & 0xffif key == 27:breakgray_img = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)gray_img = cv2.resize(gray_img, (500, 500))gray_img = cv2.GaussianBlur(gray_img, (21, 21), 0)if pre_frame is None:pre_frame = gray_imgelse:img_delta = cv2.absdiff(pre_frame, gray_img)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continueelse:#print(cv2.contourArea(c))print("前一帧和当前帧不一样了, 有什么东西在动!")play_music = Truebreakpre_frame = gray_imgcamera.release()
cv2.destroyAllWindows()

import cv2
import timesave_path = './face/'
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('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:breakend = 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 += 1eyes = 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 - startif 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_lwpCVelse: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continueelse:print("咦,有什么东西在动")breakpre_frame = gray_lwpCVkey = 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()