flt6
3356e36e73
Former-commit-id: 3beec1f80e66a86223b0f0f5c98a1e1405a90a29 Former-commit-id: 39d11dd75ed5b66095bb2603c54ca790236af6af
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
import cv2
|
||
import numpy as np
|
||
from winsound import PlaySound,SND_ALIAS,SND_ASYNC
|
||
from time import time
|
||
# Beep(500,500)
|
||
|
||
# camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头
|
||
# camera = cv2.VideoCapture("opt.flv")
|
||
camera = cv2.VideoCapture("http://192.168.124.17:8081/live.flv")
|
||
# 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))
|
||
|
||
es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))
|
||
kernel = np.ones((5, 5), np.uint8)
|
||
background = None
|
||
cnt = 0
|
||
tot = 0
|
||
last = time()
|
||
mask = np.array([(0, 0),(282, 0),(282, 26),(0, 26)])
|
||
|
||
# cv2.waitKey(5)
|
||
|
||
while True:
|
||
# 读取视频流
|
||
try:
|
||
grabbed, frame_lwpCV = camera.read()
|
||
cv2.waitKey(5)
|
||
cv2.fillConvexPoly(frame_lwpCV, mask,(0,255,0))
|
||
cv2.imshow("src",frame_lwpCV)
|
||
except cv2.error:
|
||
print("ignore this frame")
|
||
continue
|
||
# 对帧进行预处理，先转灰度图，再进行高斯滤波。
|
||
# 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.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY)
|
||
gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)
|
||
# 将第一帧设置为整个输入的背景
|
||
if background is None:
|
||
background = gray_lwpCV
|
||
continue
|
||
# 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）。
|
||
# 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
|
||
diff = cv2.absdiff(background, gray_lwpCV)
|
||
# cv2.imshow('abs', diff)
|
||
diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] # 二值化阈值处理
|
||
diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀
|
||
# 显示矩形框
|
||
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
|
||
contours = [c for c in contours if cv2.contourArea(c) >= 1500]
|
||
# 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
|
||
total=0
|
||
for c in contours:
|
||
# if cv2.contourArea(c) < 1500:
|
||
# continue
|
||
total += cv2.contourArea(c)
|
||
(x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框
|
||
cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2)
|
||
|
||
cv2.imshow('contours', frame_lwpCV)
|
||
cv2.imshow('dis', diff)
|
||
|
||
if len(contours)>0 and total>10000:
|
||
cnt+=1
|
||
if cnt > 5:
|
||
if time()-last<5:
|
||
print(time(),last)
|
||
tot+=1
|
||
else:
|
||
print("else: ",time(),last)
|
||
tot = 1
|
||
last = time()
|
||
cnt = 0
|
||
print("################################")
|
||
PlaySound("SystemAsterisk",SND_ALIAS|SND_ASYNC)
|
||
background = gray_lwpCV
|
||
else:
|
||
cnt=cnt-1 if cnt>0 else 0
|
||
|
||
if tot>4:
|
||
PlaySound("SystemHand",SND_ALIAS)
|
||
exit()
|
||
|
||
key = cv2.waitKey(1) & 0xFF
|
||
# 按'L'健退出循环
|
||
if key == ord('L'):
|
||
break
|
||
|
||
|
||
camera.release()
|
||
cv2.destroyAllWindows()
|