Tidy files
Former-commit-id: 3ad18d4bae036b9c55576f4c8b726a32a4e30c76
This commit is contained in:
@ -1,97 +1,95 @@
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import cv2
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import numpy as np
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from winsound import PlaySound,SND_ALIAS,SND_ASYNC
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from time import time
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# Beep(500,500)
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# camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头
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# camera = cv2.VideoCapture("opt.flv")
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camera = cv2.VideoCapture("http://192.168.124.17:8081/live.flv")
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# 判断视频是否打开
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if (camera.isOpened()):
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print('Open')
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else:
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print('摄像头未打开')
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# 测试用,查看视频size
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size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)), int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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print('size:'+repr(size))
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es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))
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kernel = np.ones((5, 5), np.uint8)
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background = None
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cnt = 0
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tot = 0
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last = time()
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mask = np.array([(0, 0),(282, 0),(282, 26),(0, 26)])
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# cv2.waitKey(5)
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while True:
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# 读取视频流
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try:
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grabbed, frame_lwpCV = camera.read()
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cv2.waitKey(5)
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cv2.fillConvexPoly(frame_lwpCV, mask,(0,255,0))
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cv2.imshow("src",frame_lwpCV)
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except cv2.error:
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print("ignore this frame")
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continue
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# 对帧进行预处理，先转灰度图，再进行高斯滤波。
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# 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。
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gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY)
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gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)
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# 将第一帧设置为整个输入的背景
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if background is None:
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background = gray_lwpCV
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continue
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# 对于每个从背景之后读取的帧都会计算其与北京之间的差异，并得到一个差分图（different map）。
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# 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
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diff = cv2.absdiff(background, gray_lwpCV)
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# cv2.imshow('abs', diff)
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diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] # 二值化阈值处理
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diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀
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# 显示矩形框
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contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 该函数计算一幅图像中目标的轮廓
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contours = [c for c in contours if cv2.contourArea(c) >= 1500]
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# 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值
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total=0
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for c in contours:
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# if cv2.contourArea(c) < 1500:
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# continue
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total += cv2.contourArea(c)
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(x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框
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cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2)
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cv2.imshow('contours', frame_lwpCV)
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cv2.imshow('dis', diff)
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if len(contours)>0 and total>10000:
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cnt+=1
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if cnt > 5:
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if time()-last<5:
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print(time(),last)
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tot+=1
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else:
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print("else: ",time(),last)
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tot = 1
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last = time()
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cnt = 0
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print("################################")
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PlaySound("SystemAsterisk",SND_ALIAS|SND_ASYNC)
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background = gray_lwpCV
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else:
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cnt=cnt-1 if cnt>0 else 0
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if tot>4:
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PlaySound("SystemHand",SND_ALIAS)
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exit()
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key = cv2.waitKey(1) & 0xFF
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# 按'L'健退出循环
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if key == ord('L'):
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break
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camera.release()
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cv2.destroyAllWindows()
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import cv2
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import numpy as np
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from winsound import PlaySound,SND_ALIAS,SND_ASYNC
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from time import time
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# Beep(500,500)
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camera = cv2.VideoCapture("http://192.168.124.17:8081/live.flv")
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# 判断视频是否打开
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if (camera.isOpened()):
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print('Open')
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else:
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print('摄像头未打开')
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# 测试用,查看视频size
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size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)), int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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print('size:'+repr(size))
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es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))
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kernel = np.ones((5, 5), np.uint8)
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background = None
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cnt = 0
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tot = 0
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last = time()
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mask = np.array([(0, 0),(282, 0),(282, 26),(0, 26)])
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# cv2.waitKey(5)
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while True:
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# 读取视频流
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try:
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grabbed, frame_lwpCV = camera.read()
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cv2.waitKey(5)
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cv2.fillConvexPoly(frame_lwpCV, mask,(0,255,0))
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cv2.imshow("src",frame_lwpCV)
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except cv2.error:
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print("ignore this frame")
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continue
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# 对帧进行预处理，先转灰度图，再进行高斯滤波。
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# 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。
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gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY)
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gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)
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# 将第一帧设置为整个输入的背景
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if background is None:
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background = gray_lwpCV
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continue
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# 对于每个从背景之后读取的帧都会计算其与北京之间的差异，并得到一个差分图（different map）。
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# 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
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diff = cv2.absdiff(background, gray_lwpCV)
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# cv2.imshow('abs', diff)
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diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] # 二值化阈值处理
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diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀
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# 显示矩形框
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contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 该函数计算一幅图像中目标的轮廓
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contours = [c for c in contours if cv2.contourArea(c) >= 1500]
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# 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值
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total=0
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for c in contours:
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# if cv2.contourArea(c) < 1500:
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# continue
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total += cv2.contourArea(c)
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(x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框
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cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2)
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cv2.imshow('contours', frame_lwpCV)
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cv2.imshow('dis', diff)
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if len(contours)>0 and total>10000:
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cnt+=1
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if cnt > 5:
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if time()-last<5:
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print(time(),last)
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tot+=1
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else:
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print("else: ",time(),last)
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tot = 1
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last = time()
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cnt = 0
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print("################################")
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PlaySound("SystemAsterisk",SND_ALIAS|SND_ASYNC)
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background = gray_lwpCV
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else:
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cnt=cnt-1 if cnt>0 else 0
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if tot>4:
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PlaySound("SystemHand",SND_ALIAS)
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exit()
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key = cv2.waitKey(1) & 0xFF
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# 按'L'健退出循环
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if key == ord('L'):
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break
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camera.release()
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cv2.destroyAllWindows()
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