图像基本操作

访问并修改像素值

>>> import cv2
>>> import numpy as np
>>> img = cv2.imread('messi5.jpg')
>>> px = img[100,100]
>>> print px
[157 166 200]

# accessing only blue pixel，opencv图像存储为大端格式：BGR
>>> blue = img[100,100,0]
>>> print blue
157
>>> green = img[100,100,1]
>>> print green
166
>>> red = img[100,100,2]
>>> print red
200
# modify the pixel values
>>> img[100,100] = [255,255,255]
>>> print img[100,100]
[255 255 255]

Numpy 是经过优化的快速矩阵计算库，单独读写某一个像素点速度很慢，以上几个像素操作方法，其实更适合操作一个图像区域。如果要操作单个像素点，推荐使用array.item() and array.itemset()

# accessing RED value
>>> img.item(10,10,2)
59
# modifying RED value
>>> img.itemset((10,10,2),100)
>>> img.item(10,10,2)
100

访问图像的属性

图像的属性主要包括图像的行、列、像素的通道数、图像的类型、像素的个数等。以下几个函数主要访问图像的属性。

# img.shape属性返回图像的行、列、颜色通道数（如果是彩色图像）
# 如果是灰度图像，此属性只返回图像的行、列大小
>>> print img.shape
(342, 548, 3)

# 图像的总像素个数
>>> print img.size
562248

#图像每一个像素数据类型
>>> print img.dtype
uint8
#img.dtype is very important while debugging because a large number of errors in OpenCV-Python code is caused by invalid datatype.

设置图像区域

典型操作，例如人眼检测，最好先进行人脸检测，然后在检测到的人脸范围内进行人眼检测，眼睛总是在脸上，因此先进行脸部检测，可以大大缩小眼睛检测的范围。从而提高人眼检测速度。
图像的区域操作同样使用numpy

# 将图像的一个区域复制到另一个区域
>>> roi = img[280:340, 330:390]
>>> img[273:333, 100:160] = roi

图像分割与合并

>>> b,g,r = cv2.split(img)
>>> img = cv2.merge((b,g,r))
#切片操作
>>> b = img[:,:,0]
>>> img[:,:,2] = 0

cv2.split()函数是一个耗时操作，谨慎使用。

画图像边框

cv2.copyMakeBorder()函数用于为图像画边框 ，函数的参数说明如下：

• src - input image
• top, bottom, left, right - border width in number of pixels in corresponding directions
• borderType - Flag defining what kind of border to be added. It can be following types:
  • cv2.BORDER_CONSTANT - Adds a constant colored border. The value should be given as next argument.
  • cv2.BORDER_REFLECT - Border will be mirror reflection of the border elements, like this : fedcba|abcdefgh|hgfedcb
  • cv2.BORDER_REFLECT_101 or cv2.BORDER_DEFAULT - Same as above, but with a slight change, like this : gfedcb|abcdefgh|gfedcba
  • cv2.BORDER_REPLICATE - Last element is replicated throughout, like this: aaaaaa|abcdefgh|hhhhhhh
  • cv2.BORDER_WRAP - Can’t explain, it will look like this : cdefgh|abcdefgh|abcdefg
• value - Color of border if border type is cv2.BORDER_CONSTANT

import cv2
import numpy as np
from matplotlib import pyplot as plt

BLUE = [255,0,0]

img1 = cv2.imread('opencv_logo.png')

replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP)
constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE)

plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101')
plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP')
plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT')

plt.show()

以上操作后画出的边框示例如下：

图像的数学操作

主要学习 cv2.add(), cv2.addWeighted()两个函数

图像叠加

numpy相加为取模计算
opecv的add函数为饱和计算

>>> x = np.uint8([250])
>>> y = np.uint8([10])

>>> print cv2.add(x,y) # 250+10 = 260 => 255
[[255]]

>>> print x+y          # 250+10 = 260 % 256 = 4
[4]

图像融合

图像的融合公式：g(x) = (1-a)f₀(x) + af₁(x)；a的取值范围是0—1；
cv2.addWeighted()函数的图像融合：g(x) = (1-a)f₀(x) + af₁(x) + b

img1 = cv2.imread('ml.png')
img2 = cv2.imread('opencv_logo.jpg')

dst = cv2.addWeighted(img1,0.7,img2,0.3,0)

cv2.imshow('dst',dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

融合图像示例：

图像位操作

图像位操作主要包括：AND、OR、 NOT、 XOR

# Load two images
img1 = cv2.imread('messi5.jpg')
img2 = cv2.imread('opencv_logo.png')

# I want to put logo on top-left corner, So I create a ROI
rows,cols,channels = img2.shape
roi = img1[0:rows, 0:cols ]

# Now create a mask of logo and create its inverse mask also
img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)

# Now black-out the area of logo in ROI
img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)

# Take only region of logo from logo image.
img2_fg = cv2.bitwise_and(img2,img2,mask = mask)

# Put logo in ROI and modify the main image
dst = cv2.add(img1_bg,img2_fg)
img1[0:rows, 0:cols ] = dst

cv2.imshow('res',img1)
cv2.waitKey(0)
cv2.destroyAllWindows()

位操作后图像示例：

Python OpenCV代码检测与速度优化

• cv2.getTickCount：获得当前的时钟tick数
• cv2.getTickFrequency：获得时钟频率，即每秒的tick数

img1 = cv2.imread('messi5.jpg')
e1 = cv2.getTickCount()
for i in xrange(5,49,2):
    img1 = cv2.medianBlur(img1,i)
e2 = cv2.getTickCount()
t = (e2 - e1)/cv2.getTickFrequency()
print t
# Result I got is 0.521107655 seconds

• cv2.useOptimized()：检测是否开启优化
• cv2.setUseOptimized()：设置是否优化

# check if optimization is enabled
In [5]: cv2.useOptimized()
Out[5]: True

In [6]: %timeit res = cv2.medianBlur(img,49)
10 loops, best of 3: 34.9 ms per loop

# Disable it
In [7]: cv2.setUseOptimized(False)

In [8]: cv2.useOptimized()
Out[8]: False

In [9]: %timeit res = cv2.medianBlur(img,49)
10 loops, best of 3: 64.1 ms per loop

本篇比较麻烦的就是位操作了，分析好久，还没完全弄明白；有待更新。