[雪峰磁针石博客]python opencv3实例(对象识别和增强现实)1-图像几何转换-低调大师

[雪峰磁针石博客]python opencv3实例(对象识别和增强现实)1-图像几何转换

2018-10-14 612

原书代码地址

在本章中，我们将学习如何应用酷的几何效果到图像。知识点

如何安装OpenCV-Python
如何读取，显示和保存图像
如何转换到多个颜色空间
如何应用几何变换，例如平移，旋转，和缩放
如何使用仿射和投影变换来应用几何效果

本书英文版本：OpenCV 3.x with Python By Example 2nd 2018.pdf

更多opencv相关书籍：最佳人工智能图像处理工具OpenCV书籍下载

安装OpenCV-Python

安装

本教程基于python3，ubuntu 16，在windows环境可能可以运行。

Linux

#!python
pip3 install opencv-python

Windows

#!python
pip install opencv-python

文档：

[官网](http://docs.opencv.org/) [opencv-python官方文档](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html)

读取显示和保存图片

01_reading_image.py 读取显示图片

import cv2

img = cv2.imread('./images/input.jpg')
cv2.imshow('Input image', img)

cv2.waitKey()

02_saving_image.py 保存为灰度图

import cv2

gray_img = cv2.imread('images/input.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('Grayscale', gray_img)
cv2.imwrite('images/output.jpg', gray_img)

cv2.waitKey()

转换格式

03_convert_image_format.py

In [1]: import cv2

In [2]: print([x for x in dir(cv2) if x.startswith('COLOR_')])
    ['COLOR_BAYER_BG2BGR', 'COLOR_BAYER_BG2BGRA', 'COLOR_BAYER_BG2BGR_EA', 'COLOR_BAYER_BG2BGR_VNG', 'COLOR_BAYER_BG2GRAY', 'COLOR_BAYER_BG2RGB', 'COLOR_BAYER_BG2RGBA', 'COLOR_BAYER_BG2RGB_EA', 'COLOR_BAYER_BG2RGB_VNG', 'COLOR_BAYER_GB2BGR', 'COLOR_BAYER_GB2BGRA', 'COLOR_BAYER_GB2BGR_EA', 'COLOR_BAYER_GB2BGR_VNG', 'COLOR_BAYER_GB2GRAY', 'COLOR_BAYER_GB2RGB', 'COLOR_BAYER_GB2RGBA', 'COLOR_BAYER_GB2RGB_EA', 'COLOR_BAYER_GB2RGB_VNG', 'COLOR_BAYER_GR2BGR', 'COLOR_BAYER_GR2BGRA', 'COLOR_BAYER_GR2BGR_EA', 'COLOR_BAYER_GR2BGR_VNG', 'COLOR_BAYER_GR2GRAY', 'COLOR_BAYER_GR2RGB', 'COLOR_BAYER_GR2RGBA', 'COLOR_BAYER_GR2RGB_EA', 'COLOR_BAYER_GR2RGB_VNG', 'COLOR_BAYER_RG2BGR', 'COLOR_BAYER_RG2BGRA', 'COLOR_BAYER_RG2BGR_EA', 'COLOR_BAYER_RG2BGR_VNG', 'COLOR_BAYER_RG2GRAY', 'COLOR_BAYER_RG2RGB', 'COLOR_BAYER_RG2RGBA', 'COLOR_BAYER_RG2RGB_EA', 'COLOR_BAYER_RG2RGB_VNG', 'COLOR_BGR2BGR555', 'COLOR_BGR2BGR565', 'COLOR_BGR2BGRA', 'COLOR_BGR2GRAY', 'COLOR_BGR2HLS', 'COLOR_BGR2HLS_FULL', 'COLOR_BGR2HSV', 'COLOR_BGR2HSV_FULL', 'COLOR_BGR2LAB', 'COLOR_BGR2LUV', 'COLOR_BGR2Lab', 'COLOR_BGR2Luv', 'COLOR_BGR2RGB', 'COLOR_BGR2RGBA', 'COLOR_BGR2XYZ', 'COLOR_BGR2YCR_CB', 'COLOR_BGR2YCrCb', 'COLOR_BGR2YUV', 'COLOR_BGR2YUV_I420', 'COLOR_BGR2YUV_IYUV', 'COLOR_BGR2YUV_YV12', 'COLOR_BGR5552BGR', 'COLOR_BGR5552BGRA', 'COLOR_BGR5552GRAY', 'COLOR_BGR5552RGB', 'COLOR_BGR5552RGBA', 'COLOR_BGR5652BGR', 'COLOR_BGR5652BGRA', 'COLOR_BGR5652GRAY', 'COLOR_BGR5652RGB', 'COLOR_BGR5652RGBA', 'COLOR_BGRA2BGR', 'COLOR_BGRA2BGR555', 'COLOR_BGRA2BGR565', 'COLOR_BGRA2GRAY', 'COLOR_BGRA2RGB', 'COLOR_BGRA2RGBA', 'COLOR_BGRA2YUV_I420', 'COLOR_BGRA2YUV_IYUV', 'COLOR_BGRA2YUV_YV12', 'COLOR_BayerBG2BGR', 'COLOR_BayerBG2BGRA', 'COLOR_BayerBG2BGR_EA', 'COLOR_BayerBG2BGR_VNG', 'COLOR_BayerBG2GRAY', 'COLOR_BayerBG2RGB', 'COLOR_BayerBG2RGBA', 'COLOR_BayerBG2RGB_EA', 'COLOR_BayerBG2RGB_VNG', 'COLOR_BayerGB2BGR', 'COLOR_BayerGB2BGRA', 'COLOR_BayerGB2BGR_EA', 'COLOR_BayerGB2BGR_VNG', 'COLOR_BayerGB2GRAY', 'COLOR_BayerGB2RGB', 'COLOR_BayerGB2RGBA', 'COLOR_BayerGB2RGB_EA', 'COLOR_BayerGB2RGB_VNG', 'COLOR_BayerGR2BGR', 'COLOR_BayerGR2BGRA', 'COLOR_BayerGR2BGR_EA', 'COLOR_BayerGR2BGR_VNG', 'COLOR_BayerGR2GRAY', 'COLOR_BayerGR2RGB', 'COLOR_BayerGR2RGBA', 'COLOR_BayerGR2RGB_EA', 'COLOR_BayerGR2RGB_VNG', 'COLOR_BayerRG2BGR', 'COLOR_BayerRG2BGRA', 'COLOR_BayerRG2BGR_EA', 'COLOR_BayerRG2BGR_VNG', 'COLOR_BayerRG2GRAY', 'COLOR_BayerRG2RGB', 'COLOR_BayerRG2RGBA', 'COLOR_BayerRG2RGB_EA', 'COLOR_BayerRG2RGB_VNG', 'COLOR_COLORCVT_MAX', 'COLOR_GRAY2BGR', 'COLOR_GRAY2BGR555', 'COLOR_GRAY2BGR565', 'COLOR_GRAY2BGRA', 'COLOR_GRAY2RGB', 'COLOR_GRAY2RGBA', 'COLOR_HLS2BGR', 'COLOR_HLS2BGR_FULL', 'COLOR_HLS2RGB', 'COLOR_HLS2RGB_FULL', 'COLOR_HSV2BGR', 'COLOR_HSV2BGR_FULL', 'COLOR_HSV2RGB', 'COLOR_HSV2RGB_FULL', 'COLOR_LAB2BGR', 'COLOR_LAB2LBGR', 'COLOR_LAB2LRGB', 'COLOR_LAB2RGB', 'COLOR_LBGR2LAB', 'COLOR_LBGR2LUV', 'COLOR_LBGR2Lab', 'COLOR_LBGR2Luv', 'COLOR_LRGB2LAB', 'COLOR_LRGB2LUV', 'COLOR_LRGB2Lab', 'COLOR_LRGB2Luv', 'COLOR_LUV2BGR', 'COLOR_LUV2LBGR', 'COLOR_LUV2LRGB', 'COLOR_LUV2RGB', 'COLOR_Lab2BGR', 'COLOR_Lab2LBGR', 'COLOR_Lab2LRGB', 'COLOR_Lab2RGB', 'COLOR_Luv2BGR', 'COLOR_Luv2LBGR', 'COLOR_Luv2LRGB', 'COLOR_Luv2RGB', 'COLOR_M_RGBA2RGBA', 'COLOR_RGB2BGR', 'COLOR_RGB2BGR555', 'COLOR_RGB2BGR565', 'COLOR_RGB2BGRA', 'COLOR_RGB2GRAY', 'COLOR_RGB2HLS', 'COLOR_RGB2HLS_FULL', 'COLOR_RGB2HSV', 'COLOR_RGB2HSV_FULL', 'COLOR_RGB2LAB', 'COLOR_RGB2LUV', 'COLOR_RGB2Lab', 'COLOR_RGB2Luv', 'COLOR_RGB2RGBA', 'COLOR_RGB2XYZ', 'COLOR_RGB2YCR_CB', 'COLOR_RGB2YCrCb', 'COLOR_RGB2YUV', 'COLOR_RGB2YUV_I420', 'COLOR_RGB2YUV_IYUV', 'COLOR_RGB2YUV_YV12', 'COLOR_RGBA2BGR', 'COLOR_RGBA2BGR555', 'COLOR_RGBA2BGR565', 'COLOR_RGBA2BGRA', 'COLOR_RGBA2GRAY', 'COLOR_RGBA2M_RGBA', 'COLOR_RGBA2RGB', 'COLOR_RGBA2YUV_I420', 'COLOR_RGBA2YUV_IYUV', 'COLOR_RGBA2YUV_YV12', 'COLOR_RGBA2mRGBA', 'COLOR_XYZ2BGR', 'COLOR_XYZ2RGB', 'COLOR_YCR_CB2BGR', 'COLOR_YCR_CB2RGB', 'COLOR_YCrCb2BGR', 'COLOR_YCrCb2RGB', 'COLOR_YUV2BGR', 'COLOR_YUV2BGRA_I420', 'COLOR_YUV2BGRA_IYUV', 'COLOR_YUV2BGRA_NV12', 'COLOR_YUV2BGRA_NV21', 'COLOR_YUV2BGRA_UYNV', 'COLOR_YUV2BGRA_UYVY', 'COLOR_YUV2BGRA_Y422', 'COLOR_YUV2BGRA_YUNV', 'COLOR_YUV2BGRA_YUY2', 'COLOR_YUV2BGRA_YUYV', 'COLOR_YUV2BGRA_YV12', 'COLOR_YUV2BGRA_YVYU', 'COLOR_YUV2BGR_I420', 'COLOR_YUV2BGR_IYUV', 'COLOR_YUV2BGR_NV12', 'COLOR_YUV2BGR_NV21', 'COLOR_YUV2BGR_UYNV', 'COLOR_YUV2BGR_UYVY', 'COLOR_YUV2BGR_Y422', 'COLOR_YUV2BGR_YUNV', 'COLOR_YUV2BGR_YUY2', 'COLOR_YUV2BGR_YUYV', 'COLOR_YUV2BGR_YV12', 'COLOR_YUV2BGR_YVYU', 'COLOR_YUV2GRAY_420', 'COLOR_YUV2GRAY_I420', 'COLOR_YUV2GRAY_IYUV', 'COLOR_YUV2GRAY_NV12', 'COLOR_YUV2GRAY_NV21', 'COLOR_YUV2GRAY_UYNV', 'COLOR_YUV2GRAY_UYVY', 'COLOR_YUV2GRAY_Y422', 'COLOR_YUV2GRAY_YUNV', 'COLOR_YUV2GRAY_YUY2', 'COLOR_YUV2GRAY_YUYV', 'COLOR_YUV2GRAY_YV12', 'COLOR_YUV2GRAY_YVYU', 'COLOR_YUV2RGB', 'COLOR_YUV2RGBA_I420', 'COLOR_YUV2RGBA_IYUV', 'COLOR_YUV2RGBA_NV12', 'COLOR_YUV2RGBA_NV21', 'COLOR_YUV2RGBA_UYNV', 'COLOR_YUV2RGBA_UYVY', 'COLOR_YUV2RGBA_Y422', 'COLOR_YUV2RGBA_YUNV', 'COLOR_YUV2RGBA_YUY2', 'COLOR_YUV2RGBA_YUYV', 'COLOR_YUV2RGBA_YV12', 'COLOR_YUV2RGBA_YVYU', 'COLOR_YUV2RGB_I420', 'COLOR_YUV2RGB_IYUV', 'COLOR_YUV2RGB_NV12', 'COLOR_YUV2RGB_NV21', 'COLOR_YUV2RGB_UYNV', 'COLOR_YUV2RGB_UYVY', 'COLOR_YUV2RGB_Y422', 'COLOR_YUV2RGB_YUNV', 'COLOR_YUV2RGB_YUY2', 'COLOR_YUV2RGB_YUYV', 'COLOR_YUV2RGB_YV12', 'COLOR_YUV2RGB_YVYU', 'COLOR_YUV420P2BGR', 'COLOR_YUV420P2BGRA', 'COLOR_YUV420P2GRAY', 'COLOR_YUV420P2RGB', 'COLOR_YUV420P2RGBA', 'COLOR_YUV420SP2BGR', 'COLOR_YUV420SP2BGRA', 'COLOR_YUV420SP2GRAY', 'COLOR_YUV420SP2RGB', 'COLOR_YUV420SP2RGBA', 'COLOR_YUV420p2BGR', 'COLOR_YUV420p2BGRA', 'COLOR_YUV420p2GRAY', 'COLOR_YUV420p2RGB', 'COLOR_YUV420p2RGBA', 'COLOR_YUV420sp2BGR', 'COLOR_YUV420sp2BGRA', 'COLOR_YUV420sp2GRAY', 'COLOR_YUV420sp2RGB', 'COLOR_YUV420sp2RGBA', 'COLOR_mRGBA2RGBA']

04_image_color_split.py

import cv2

img = cv2.imread('./images/input.jpg', cv2.IMREAD_COLOR)
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

yuv_img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
y,u,v = cv2.split(yuv_img)

cv2.imshow('Grayscale image', gray_img)
cv2.imshow('Y channel', y)
cv2.imshow('U channel', u)
cv2.imshow('V channel', v)

cv2.waitKey()

合并图像色带

05_image_color_merge.py

import cv2

img = cv2.imread('./images/input.jpg', cv2.IMREAD_COLOR)

g,b,r = cv2.split(img)
gbr_img = cv2.merge((g,b,r))
rbr_img = cv2.merge((r,b,r))

cv2.imshow('Original', img)
cv2.imshow('GRB', gbr_img)
cv2.imshow('RBR', rbr_img)

cv2.waitKey()

移动translation

06_image_translation.py

import cv2
import numpy as np

img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]

translation_matrix = np.float32([ [1,0,70], [0,1,110] ])
img_translation = cv2.warpAffine(img, translation_matrix, (num_cols + 70, num_rows + 110))
translation_matrix = np.float32([ [1,0,-30], [0,1,-50] ])
img_translation = cv2.warpAffine(img_translation, translation_matrix, (num_cols + 70 + 30, num_rows + 110 + 50))

cv2.imshow('Translation', img_translation)
cv2.waitKey()

旋转

07_image_rotation.py

import cv2
import numpy as np

img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]

translation_matrix = np.float32([ [1,0,70], [0,1,110] ])
img_translation = cv2.warpAffine(img, translation_matrix, (num_cols + 70, num_rows + 110))
translation_matrix = np.float32([ [1,0,-30], [0,1,-50] ])
img_translation = cv2.warpAffine(img_translation, translation_matrix, (num_cols + 70 + 30, num_rows + 110 + 50))

cv2.imshow('Translation', img_translation)
cv2.waitKey()

缩放

08_image_scaling.py

import cv2

img = cv2.imread('images/input.jpg')

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()

仿射变换

09_affine_transformation.py

import cv2
import numpy as np

img = cv2.imread('images/input.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))

cv2.imshow('Input', img)
cv2.imshow('Output', img_output)
cv2.waitKey()

射影变换

10_projective_transformation.py

import cv2
import numpy as np

img = cv2.imread('images/input.jpg')
rows, cols = img.shape[:2]

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()

图像变形

11_image_warping.py

import numpy as np 
import math

img = cv2.imread('images/input.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)

##################### 
# Both horizontal and vertical

img_output = np.zeros(img.shape, dtype=img.dtype)

for i in range(rows): 
    for j in range(cols): 
        offset_x = int(20.0 * math.sin(2 * 3.14 * i / 150)) 
        offset_y = int(20.0 * math.cos(2 * 3.14 * j / 150)) 
        if i+offset_y < rows and j+offset_x < cols: 
            img_output[i,j] = img[(i+offset_y)%rows,(j+offset_x)%cols] 
        else: 
            img_output[i,j] = 0

cv2.imshow('Multidirectional 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(128.0 * 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()

参考资料

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python 字符串(1)

字符串操练一下字符串吧。 >>> print "good good study, day day up" good good study, day day up 在python中，通常用一对双引号、或者单引号来包裹一个字符串。或者说，要定义一个字符串，就用双引号或者单引号。转义字符例子: >>> print "小明说：\"你好啊\"" 小明说"我没有少圆明园" 这个例子中，为了打印出那句含有双引号的字符串，也就是双引号是字符串的一部分了，使用了一个符号：\，在python中，将这个符号叫做转义符。本来双引号表示包括字符串，它不是字符串一部分，但是如果前面有转义符，那么它就失去了原来的含义，转化为字符串的一部分，相当于一个特殊字符了。转义符在打印第二句话： >>> print 'what\'s your name?' what's your name? 另外，双引号和单引号还可以嵌套，比如下面的句子中，单引号在双引号里面，虽然没有在单引号前面加转义符，但是它被认为是字符串一部分，而不是包裹字符串的符号 >>> ...

2018-10-15

488

字符串复制字符串复制的方法： >>> a = "I like python and can teach you to learn it." >>> print a I like python and can teach you to learn it. >>> b = a >>> print b I like python and can teach you to learn it. >>> print a I like python and can teach you to learn it. 复制非常简单，类似与赋值一样。可以理解为那个字符串本来跟a连接着，通过b=a，a从自己手里分处一股绳子给了b，这样两者都可以指向那个字符串了。字符串长度方法: >>> a="hello" >>> len(a) 5 使用的是一个函数len(object)。得到的结果就是该字符串长度。 >>> m = len(a) #把结果返回后赋值给一个变量 &g...

2018-10-15

654

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