python opencv cv2 基础操作1

#coding=utf-8

import cv2  #cv2.__version__==3.2.0
import numpy as np


#读取，显示,写
# img=cv2.imread('test1.jpg',cv2.IMREAD_ANYCOLOR)
# # cv2.imshow('show', img)
# print(np.shape(img))
# # cv2.waitKey()
# img_crop=img[10:100,10:200]
# cv2.imwrite('img_write.tif',img_crop)
# exit()

#颜色转换
# print([x for x in dir(cv2) if x.startswith('COLOR_')])
# img=cv2.imread('test1.jpg',cv2.IMREAD_ANYCOLOR)
# img_gray=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
# # cv2.imshow('show', img_gray)
# cv2.imshow('all',img)
# cv2.imshow('R',img[:,:,0])
# cv2.imshow('G',img[:,:,1])
# cv2.imshow('B',img[:,:,2])
# r_img=img[:,:,0]
# print(np.shape(r_img))
# cv2.waitKey()

#Image translation
# img=cv2.imread('test1.jpg',cv2.IMREAD_ANYCOLOR)
# num_rows,num_cols=img.shape[:2]
# # T=[[1,0,tx][0,1,ty]]
# translation_matrix = np.float32([ [1,0,-70], [0,1,110] ])
# img_translation = cv2.warpAffine(img, translation_matrix, (num_cols,num_rows))
# cv2.imshow('Translation', img_translation)
# cv2.waitKey()

#Image rotation
# img=cv2.imread('test1.jpg',cv2.IMREAD_ANYCOLOR)
# num_rows, num_cols = img.shape[:2]
# # R=[[cos@ -sin@],[sin@ cos@]]
# rotation_matrix = cv2.getRotationMatrix2D(center=(num_cols/2, num_rows/2), angle=1.2, scale=1)
# img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
# cv2.imshow('Rotation', img_rotation)
# cv2.waitKey()

#Image scaling
# img=cv2.imread('test1.jpg',cv2.IMREAD_ANYCOLOR)
# img_scaled = cv2.resize(img,None,fx=1.2, fy=1.2, interpolation =cv2.INTER_LINEAR)
# cv2.imshow('Scaling - Linear Interpolation', img_scaled)
# img_scaled =cv2.resize(img,None,fx=1.2, fy=1.2, interpolation = cv2.INTER_CUBIC)
# cv2.imshow('Scaling - Cubic Interpolation', img_scaled)
# img_scaled =cv2.resize(img,(450, 400), interpolation = cv2.INTER_AREA)
# cv2.imshow('Scaling - Skewed Size', img_scaled)
# cv2.waitKey()

#Afine transform 仿射
# img = cv2.imread('test1.jpg')
# rows, cols = img.shape[:2]
# # src_points = np.float32([[0,0], [cols-1,0], [0,rows-1]])
# # dst_points = np.float32([[0,0], [int(0.6*(cols-1)),0], [int(0.4*(cols-1)),rows-1]])
# # affine_matrix = cv2.getAffineTransform(src_points, dst_points)
# # img_output = cv2.warpAffine(img, affine_matrix, (cols,rows))
# src_points = np.float32([[0,0], [cols-1,0], [0,rows-1], [cols-1,rows-1]])
# dst_points = np.float32([[0,0], [cols-1,0], [int(0.33*cols),rows-1],[int(0.66*cols),rows-1]])
# projective_matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# img_output = cv2.warpPerspective(img, projective_matrix, (cols,rows))
# cv2.imshow('Input', img)
# cv2.imshow('Output', img_output)
# cv2.waitKey()

#2D convolution 卷积处理
# img = cv2.imread('test1.jpg')
# rows, cols = img.shape[:2]
# kernel_identity = np.array([[0,0,0], [0,1,0], [0,0,0]])
# kernel_3x3 = np.ones((3,3), np.float32) / 9.0
# kernel_5x5 = np.ones((5,5), np.float32) / 25.0
# cv2.imshow('Original', img)
# output = cv2.filter2D(img, -1, kernel_identity)
# cv2.imshow('Identity filter', output)
# output = cv2.filter2D(img, -1, kernel_3x3)
# cv2.imshow('3x3 filter', output)
# output = cv2.filter2D(img, -1, kernel_5x5)
# cv2.imshow('5x5 filter', output)
# cv2.waitKey(0)

#边缘检测
# img = cv2.imread('test1.jpg', cv2.IMREAD_GRAYSCALE)
# rows, cols = img.shape
# sobel_horizontal = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
# sobel_vertical = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
# kernel_1 = np.array([[-1,0,1], [-2,0,2], [-1,0,1]])
# kernel_2 = np.array([[-1,-2,-1], [0,0,0], [1,2,1]])
# output=cv2.filter2D(img, -1,kernel=kernel_1)
# cv2.imshow('ker1',output)
# output2=cv2.filter2D(img, -1,kernel=kernel_2)
# cv2.imshow('ker2',output2)
# # cv2.imshow('Original', img)
# # cv2.imshow('Sobel horizontal', sobel_horizontal)
# # cv2.imshow('Sobel vertical', sobel_vertical)
# cv2.waitKey(0)

#erode dilate 腐蚀膨胀
# img = cv2.imread('test1.jpg', cv2.IMREAD_ANYCOLOR)
# kernel = np.ones((5,5), np.uint8)
# img_erosion = cv2.erode(img, kernel, iterations=1)
# img_dilation = cv2.dilate(img, kernel, iterations=1)
# cv2.imshow('Input', img)
# cv2.imshow('Erosion', img_erosion)
# cv2.imshow('Dilation', img_dilation)
# cv2.waitKey(0)

#装饰
# img = cv2.imread('test1.jpg')
# rows, cols = img.shape[:2]
# # generating vignette mask using Gaussian kernels
# kernel_x = cv2.getGaussianKernel(cols,200)
# kernel_y = cv2.getGaussianKernel(rows,200)
# kernel = kernel_y * kernel_x.T
# mask = 255 * kernel / np.linalg.norm(kernel)
# output = np.copy(img)
# # applying the mask to each channel in the input image
# for i in range(3):
#     output[:,:,i] = output[:,:,i] * mask
#     cv2.imshow('Original', img)
#     cv2.imshow('Vignette', output)
#     cv2.waitKey(0)

#灰度图像：直方图均衡化
# img = cv2.imread('test1.jpg', cv2.IMREAD_GRAYSCALE)
# # equalize the histogram of the input image
# histeq = cv2.equalizeHist(img)
# cv2.imshow('Input', img)
# cv2.imshow('Histogram equalized', histeq)
# cv2.waitKey(0)

#连通域
# img = cv2.imread('test1.jpg', cv2.IMREAD_ANYCOLOR)
# print(img.shape)
# img_graycolor=cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# ret,img_gray=cv2.threshold(img_graycolor, 127, 255, cv2.THRESH_BINARY)#cv2.THRESH_OTSU
# img_contours,contours, _ = cv2.findContours(img_gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

参数

第一个参数是寻找轮廓的图像；

第二个参数表示轮廓的检索模式，有四种（本文介绍的都是新的cv2接口）：     cv2.RETR_EXTERNAL表示只检测外轮廓     cv2.RETR_LIST检测的轮廓不建立等级关系     cv2.RETR_CCOMP建立两个等级的轮廓，上面的一层为外边界，里面的一层为内孔的边界信息。如果内孔内还有一个连通物体，这个物体的边界也在顶层。     cv2.RETR_TREE建立一个等级树结构的轮廓。

第三个参数method为轮廓的近似办法     cv2.CHAIN_APPROX_NONE存储所有的轮廓点，相邻的两个点的像素位置差不超过1，即max（abs（x1-x2），abs（y2-y1））==1     cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息     cv2.CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS使用teh-Chinl chain 近似算法

# print(len(contours))
# # print(contours)
# # cv2.drawContours(img, np.array(contours), -1, (0,0,255), 1)
# # cv2.imshow('drawContours', img)
# # cv2.waitKey()
# for contour in contours:
#     area = cv2.contourArea(contour)
#     x,y,w,h=cv2.boundingRect(contour)
#     print(x,y,w,h,area)
#     cv2.rectangle(img, (x,y), (x+w,y+h), (0,0,255))
# cv2.imshow('rectangle', img)
# cv2.waitKey()


#Image warping
import math
img = cv2.imread('test1.jpg', cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape
#####################
# Vertical wave
# img_output = np.zeros(img.shape, dtype=img.dtype)
# for i in range(rows):
#     for j in range(cols):
#         offset_x = int(25.0 * math.sin(2 * 3.14 * i / 180))
#         offset_y = 0
#         if j+offset_x < rows:
#             img_output[i,j] = img[i,(j+offset_x)%cols]
#         else:
#             img_output[i,j] = 0
# cv2.imshow('Input', img)
# cv2.imshow('Vertical wave', img_output)
#####################
#####################
# Horizontal wave
# img_output = np.zeros(img.shape, dtype=img.dtype)
# for i in range(rows):
#     for j in range(cols):
#         offset_x = 0
#         offset_y = int(16.0 * math.sin(2 * 3.14 * j / 150))
#         if i+offset_y < rows:
#             img_output[i,j] = img[(i+offset_y)%rows,j]
#         else:
#             img_output[i,j] = 0
# cv2.imshow('Horizontal wave', img_output)
#####################
#####################
# Concave effect
img_output = np.zeros(img.shape, dtype=img.dtype)
for i in range(rows):
    for j in range(cols):
        offset_x = int(30.0 * math.sin(2 * 3.14 * i / (2*cols)))
        offset_y = 0
        if j+offset_x < cols:
            img_output[i,j] = img[i,(j+offset_x)%cols]
        else:
            img_output[i,j] = 0
cv2.imshow('Concave', img_output)
cv2.waitKey()