import numpy as np

from math import *

import sys

import os

import glob

import argparse

import cv2 as cv

drag_start = None#全局变量取方块鼠标拖拽时使用

sel = (0,0,0,0)#全局变量 长方形左上颌右下定点坐标存储

def onmouse(event, x, y, flags, param):#鼠标事件响应函数

global drag_start, sel

if event == cv.EVENT_LBUTTONDOWN:#左键按下时记录当前初始坐标，并初始化矩形sel

drag_start = x, y

sel = 0,0,0,0

elif event == cv.EVENT_LBUTTONUP:#鼠标左键叹弹起时响应

if sel[2] > sel[0] and sel[3] > sel[1]:#判断右下角坐标是否大于左上角

patch = gray[sel[1]:sel[3],sel[0]:sel[2]]#取矩形区域内像素作为patch图像

result = cv.matchTemplate(gray,patch,cv.TM_CCOEFF_NORMED)

result = np.abs(result)**3

val, result = cv.threshold(result, 0.01, 0, cv.THRESH_TOZERO)#将低于0。01的值赋值为0

result8 = cv.normalize(result,None,0,255,cv.NORM_MINMAX,cv.CV_8U)#将result转化到0-255区间

cv.imshow("result", result8)

drag_start = None

elif drag_start:

#print flags

if flags & cv.EVENT_FLAG_LBUTTON:#取当前坐标与初始坐标较小的为矩形坐标左上，较大的为右下

minpos = min(drag_start[0], x), min(drag_start[1], y)

maxpos = max(drag_start[0], x), max(drag_start[1], y)

sel = minpos[0], minpos[1], maxpos[0], maxpos[1]

img = cv.cvtColor(gray, cv.COLOR_GRAY2BGR)

cv.rectangle(img, (sel[0], sel[1]), (sel[2], sel[3]), (0,255,255), 1)

cv.imshow("gray", img)

else:

print "selection is complete"

drag_start = None

if __name__ == '__main__':

parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images')

parser.add_argument("-i","--input", default='../', help="Input directory.")

args = parser.parse_args()

path = args.input#获取图像路径参数

cv.namedWindow("gray",1)

cv.setMouseCallback("gray", onmouse)

'''''Loop through all the images in the directory'''

for infile in glob.glob( os.path.join(path, '*.*') ):#遍历文件夹下的图片文件

ext = os.path.splitext(infile)[1][1:] #get the filename extenstion

if ext == "png" or ext == "jpg" or ext == "bmp" or ext == "tiff" or ext == "pbm":

print infile

img=cv.imread(infile,1)

if img == None:

continue

sel = (0,0,0,0)

drag_start = None

gray=cv.cvtColor(img, cv.COLOR_BGR2GRAY)

cv.imshow("gray",gray)

if (cv.waitKey() & 255) == 27:

break

cv.destroyAllWindows()

#decoding:utf-8

#!/usr/bin/env python

import numpy as np

from math import *

import sys

import os

import glob

import argparse

import cv2 as cv

drag_start = None#全局变量取方块鼠标拖拽时使用

sel = (0,0,0,0)#全局变量 长方形左上颌右下定点坐标存储

def onmouse(event, x, y, flags, param):#鼠标事件响应函数

global drag_start, sel

if event == cv.EVENT_LBUTTONDOWN:#左键按下时记录当前初始坐标，并初始化矩形sel

drag_start = x, y

sel = 0,0,0,0

elif event == cv.EVENT_LBUTTONUP:#鼠标左键叹弹起时响应

if sel[2] > sel[0] and sel[3] > sel[1]:#判断右下角坐标是否大于左上角

patch = gray[sel[1]:sel[3],sel[0]:sel[2]]#取矩形区域内像素作为patch图像

result = cv.matchTemplate(gray,patch,cv.TM_CCOEFF_NORMED)

result = np.abs(result)**3

val, result = cv.threshold(result, 0.01, 0, cv.THRESH_TOZERO)#将低于0。01的值赋值为0

result8 = cv.normalize(result,None,0,255,cv.NORM_MINMAX,cv.CV_8U)#将result转化到0-255区间

cv.imshow("result", result8)

drag_start = None

elif drag_start:

#print flags

if flags & cv.EVENT_FLAG_LBUTTON:#取当前坐标与初始坐标较小的为矩形坐标左上，较大的为右下

minpos = min(drag_start[0], x), min(drag_start[1], y)

maxpos = max(drag_start[0], x), max(drag_start[1], y)

sel = minpos[0], minpos[1], maxpos[0], maxpos[1]

img = cv.cvtColor(gray, cv.COLOR_GRAY2BGR)

cv.rectangle(img, (sel[0], sel[1]), (sel[2], sel[3]), (0,255,255), 1)

cv.imshow("gray", img)

else:

print "selection is complete"

drag_start = None

if __name__ == '__main__':

parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images')

parser.add_argument("-i","--input", default='../', help="Input directory.")

args = parser.parse_args()

path = args.input#获取图像路径参数

cv.namedWindow("gray",1)

cv.setMouseCallback("gray", onmouse)

'''''Loop through all the images in the directory'''

for infile in glob.glob( os.path.join(path, '*.*') ):#遍历文件夹下的图片文件

ext = os.path.splitext(infile)[1][1:] #get the filename extenstion

if ext == "png" or ext == "jpg" or ext == "bmp" or ext == "tiff" or ext == "pbm":

print infile

img=cv.imread(infile,1)

if img == None:

continue

sel = (0,0,0,0)

drag_start = None

gray=cv.cvtColor(img, cv.COLOR_BGR2GRAY)

cv.imshow("gray",gray)

if (cv.waitKey() & 255) == 27:

break

cv.destroyAllWindows()

import argparse

import datetime

import imutils

import time

import cv2

ap = argparse.ArgumentParser()

ap.add_argument("-v", "--video", help="path to the video file")

ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")

args = vars(ap.parse_args())

if args.get("video", None) is None:

camera = cv2.VideoCapture(0)

time.sleep(0.25)

else:

camera = cv2.VideoCapture(args["video"])

firstFrame = None

while True:

# 获取当前帧并初始化occupied/unoccupied文本

(grabbed, frame) = camera.read()

text = "Unoccupied"

# 如果不能抓取到一帧，说明我们到了视频的结尾

if not grabbed:

break

# 调整该帧的大小，转换为灰阶图像并且对其进行高斯模糊

frame = imutils.resize(frame, width=500)

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

gray = cv2.GaussianBlur(gray, (21, 21), 0)

# 如果第一帧是None，对其进行初始化

if firstFrame is None:

firstFrame = gray

continue

frameDelta = cv2.absdiff(firstFrame, gray)

thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

# 扩展阀值图像填充孔洞，然后找到阀值图像上的轮廓

thresh = cv2.dilate(thresh, None, iterations=2)

(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,

cv2.CHAIN_APPROX_SIMPLE)

# 遍历轮廓

for c in cnts:

# if the contour is too small, ignore it

if cv2.contourArea(c) < args["min_area"]:

continue

# compute the bounding box for the contour, draw it on the frame,

# and update the text

# 计算轮廓的边界框，在当前帧中画出该框

(x, y, w, h) = cv2.boundingRect(c)

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

text = "Occupied"

# 在当前帧上写文字以及时间戳

cv2.putText(frame, "Room Status: {}".format(text), (10, 20),

cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),

(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

cv2.imshow("Security Feed", frame)

cv2.imshow("Thresh", thresh)

cv2.imshow("Frame Delta", frameDelta)

key = cv2.waitKey(1) &amp; 0xFF

# 如果q键被按下，跳出循环

if key == ord("q"):

break

camera.release()

cv2.destroyAllWindows()

#coding:utf-8

# 导入必要的软件包

import argparse

import datetime

import imutils

import time

import cv2

# 创建参数解析器并解析参数

ap = argparse.ArgumentParser()

ap.add_argument("-v", "--video", help="path to the video file")

ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")

args = vars(ap.parse_args())

# 如果video参数为None，那么我们从摄像头读取数据

if args.get("video", None) is None:

camera = cv2.VideoCapture(0)

time.sleep(0.25)

# 否则我们读取一个视频文件

else:

camera = cv2.VideoCapture(args["video"])

# 初始化视频流的第一帧

firstFrame = None

# 遍历视频的每一帧

while True:

# 获取当前帧并初始化occupied/unoccupied文本

(grabbed, frame) = camera.read()

text = "Unoccupied"

# 如果不能抓取到一帧，说明我们到了视频的结尾

if not grabbed:

break

# 调整该帧的大小，转换为灰阶图像并且对其进行高斯模糊

frame = imutils.resize(frame, width=500)

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

gray = cv2.GaussianBlur(gray, (21, 21), 0)

# 如果第一帧是None，对其进行初始化

if firstFrame is None:

firstFrame = gray

continue

a = 1

while a < 100:

print a

a += 1

#中文

a = 1

while a < 100:

print a

a += 1