import cv2

 cameraCapture = cv2.VideoCapture(0)

 fps = 30

 size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),

         int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))

 videoWriter = cv2.VideoWriter(

     'MyOutputVid.avi', cv2.VideoWriter_fourcc('I', '', '', ''),

     fps, size)

 success, frame = cameraCapture.read()

 numFrameRemaining = 10*fps - 1

 while success and numFrameRemaining > 0:

     videoWriter.write(frame)

     success, frame = cameraCapture.read()

     numFrameRemaining -= 1

 cameraCapture.release()

 import cv2

 import numpy

 img = cv2.imread("data/aero1.jpg")

 cv2.imshow('Test Image', img)

 cv2.waitKey()

 cv2.destroyAllWindows()

 import cv2

 import numpy

 clicked = False

 def onMouse(event, x, y, flags, param):

     global clicked

     if event == cv2.EVENT_FLAG_LBUTTON:

         clicked = True

 cameraCapture = cv2.VideoCapture(0)

 cv2.namedWindow('MyWindow')

 cv2.setMouseCallback('MyWindow', onMouse)

 print 'Show camera feed. Click window or press any key to stop'

 success, frame = cameraCapture.read()

 while success and cv2.waitKey(1) == -1 and not clicked:

     cv2.imshow('MyWindow', frame)

     success, frame = cameraCapture.read()

 cv2.destroyWindow('MyWindow')

 cameraCapture.release()

import cv2

import numpy as np

from scipy import ndimage

# kernel defined by ourself

kernel_3x3 = np.array([[-1, -1, -1],

                       [-1, 8, -1],

                       [-1, -1, -1]])

kernel_5x5 = np.array([[-1, -1, -1, -1, -1],

                       [-1, 1, 2, 1, -1],

                       [-1, 2, 4, 2, -1],

                       [-1, 1, 2, 1, -1],

                       [-1, -1, -1, -1, -1]])

# http://docs.opencv.org/3.1.0/d4/da8/group__imgcodecs.html

# in func "imread", 0 means trans to gray image

img = cv2.imread("data/lena.jpg", 0)

k3 = ndimage.convolve(img, kernel_3x3)

k5 = ndimage.convolve(img, kernel_5x5)

"""Gaussian kernel: The function convolves the source image

 with the specified Gaussian kernel. 

 GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) -> dst

 @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be

    .   positive and odd. Or, they can be zero's and then they are computed from sigma.

 @param sigmaX Gaussian kernel standard deviation in X direction."""

# here is a per-defined Gaussian Blur and the

# kernel is set to 11x11, start in X axis direction

# Attention: GaussianBlur is a low pass filter but the above two are high pass filters

# after minus with the origin image, finally equivalent to a high pass filter

blurred = cv2.GaussianBlur(img, (11, 11), 0)

g_hpf = img - blurred

cv2.imshow("3x3", k3)

cv2.imshow("5x5", k5)

cv2.imshow("g_hpf", g_hpf)

cv2.waitKey()

cv2.destroyAllWindows()

import cv2

import numpy as np

import filters

from scipy import ndimage

img = cv2.imread("data/lena.jpg", 0)

cv2.imwrite("lena_edge.jpg", cv2.Canny(img, 200, 300))

cv2.imshow("lena", cv2.imread("lena_edge.jpg"))

cv2.waitKey()

cv2.destroyAllWindows()

# -*- coding: utf-8 -*

import cv2

import numpy as np

import filters

from scipy import ndimage

# dark, blank image

img = np.zeros((200, 200), dtype=np.uint8)

# assign the middle square white

img[50:150, 50:150] = 255

# 二值化

ret, thresh = cv2.threshold(img, 127, 255, 0)

image, contours, hierarchy = cv2.findContours(thresh,

                                              cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)

img = cv2.drawContours(color, contours, -1, (0, 255, 0), 2)

cv2.imshow("contours", color)

cv2.waitKey()

cv2.destroyAllWindows()

# -*- coding: utf-8 -*

import cv2

import numpy as np

img = cv2.pyrDown(cv2.imread("pycv-master/chapter3/hammer.jpg", cv2.IMREAD_UNCHANGED))

ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) , 127, 255, cv2.THRESH_BINARY)

image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:

  # find bounding box coordinates

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

  # 画一个正方形

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

  # 画最贴近的正方形，有一定的旋转

  # find minimum area

  rect = cv2.minAreaRect(c)

  # calculate coordinates of the minimum area rectangle

  box = cv2.boxPoints(rect)

  # normalize coordinates to integers

  box = np.int0(box)

  # draw contours

  cv2.drawContours(img, [box], 0, (0,0, 255), 3)

  # 画一个圆正好包裹住

  # calculate center and radius of minimum enclosing circle

  (x,y),radius = cv2.minEnclosingCircle(c)

  # cast to integers

  center = (int(x),int(y))

  radius = int(radius)

  # draw the circle

  img = cv2.circle(img,center,radius,(0,255,0),2)

cv2.drawContours(img, contours, -1, (255, 0, 0), 1)

cv2.imshow("contours", img)

cv2.waitKey()

cv2.destroyAllWindows()

cv2.imwrite("hammer_contours.jpg", img)