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'''
测试下检测场景内是否有物体移动,若有,打印信息
'''
import cv2
import time
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
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)
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()