Chainer 入门教程（9) 基于OpenCV的基本的图像处理教程

Chainer 入门教程（9）基于OpenCV的基本的图像处理教程

Posted by 徐志平 on December 26, 2017

基于OpenCV的基本的图像处理教程

如果您对图像处理不熟悉，可以在阅读卷积神经网络相关内容之前阅读这篇文章。

OpenCV是图像处理库，支持以numpy.ndarray格式加载图像，保存图像以及转换图像颜色格式（RGB，YUV，灰度等）调整大小和其他有用的图像处理功能。

要在Linux或者MacOS上安装opencv，执行如下代码：

git clone https://github.com/opencv/opencv

git clone https://github.com/opencv/opencv_contrib


cd ~/opencv

mkdir build

cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE     -D PYTHON3_LIBRARY=/Users/你自己的用户名/anaconda3/lib/libpython3.6m.dylib     -D PYTHON3_INCLUDE_DIR=/Users/你自己的用户名/anaconda3/include/python3.6m     -D PYTHON_DEFAULT_EXECUTABLE=/Users/你自己的用户名/anaconda3/bin/python3     -D PYTHON_PACKAGES_PATH=/Users/你自己的用户名/anaconda3/lib/python3.6/site-packages     -D INSTALL_C_EXAMPLES=OFF     -D INSTALL_PYTHON_EXAMPLES=ON     -D BUILD_EXAMPLES=ON     -D BUILD_opencv_python3=ON     -D BUILD_opencv_python2=OFF     -D CUDA_GENERATION=Pascal     -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules ..

在CUDA_GENERATION部分，我假设你装的GPU是Nvidia 的Pascal级别的显卡（譬如，我的就是GTX 1080），其他的配置请自行百度。

make
sudo make install

在build目录下/lib/python3文件夹里面应该有cv2.cpython-36m-darwin.so或者类似的文件，将其改名为cv2.so，并且拷贝到anaconda3/lib/python3.6/site-packages目录中，然后执行python并且输入import cv2，如果一切正常的话，将不会报错，这样你就可以在Python中使用OpenCV扩展了。

import os
 
import matplotlib.pyplot as plt
import cv2
 
%matplotlib inline

def readRGBImage(imagepath):
    image = cv2.imread(imagepath)  # Height, Width, Channel
    (major, minor, _) = cv2.__version__.split(".")
    if major == '3':
        # version 3 is used, need to convert
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    else:
        # Version 2 is used, not necessary to convert
        pass
    return image

加载并保存图像

  • cv2.imread 用于加载图像。
  • cv2.imwrite 用于保存图像。
  • plt.imshow 绘图，plt.savefig 保存绘图图像。

OpenCV的图像格式通常是3维（如果图像是灰度，则为2维）。第一维是高度，第二维是宽度，第三维是通道（RGB，YUV等）。

要转换颜色格式可以使用cv2.cvtColor。细节写在下一节。

# Read image from file, save image with matplotlib using `imshow` function
basedir = './data/images'
imagepath = os.path.join(basedir, 'sample.jpeg')
#image = cv2.imread(imagepath, cv2.IMREAD_GRAYSCALE)
image = readRGBImage(imagepath)
# Width and Height shows pixel size of this image 
# Channel=3 indicates the RGB channel 
print('image.shape (Height, Width, Channel) = ', image.shape)

image.shape (Height, Width, Channel) =  (360, 640, 3)

cv2.imwrite('out.jpg', image) 

True

plt.imshow(image)
plt.savefig('out_plt.png')

png

更改颜色格式

cv2.cvtColor用于转换颜色格式。请注意，openCV版本3以B，G，R的顺序读取图像颜色。但是，matplotlib处理在R，G，B中的图像颜色。因此，您需要转换颜色顺序，请参阅前面定义的readRGBImage函数。如果图像是灰度图像，则图像是二维数组,第一维是高度，第二维是宽度。

gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
 
# Gray scale image is 2 dimension, No channel dimension. 
print('gray_image.shape (Height, Width) = ', gray_image.shape)

gray_image.shape (Height, Width) =  (360, 640)

plt.imshow(gray_image,cmap="gray")
plt.savefig('out_gray_plt.png')

png

cv2.imwrite('cv_gray.png',gray_image)

True

保存在磁盘上的文件内容如下：

调整图像大小

cv2.imread 调整图像大小。请注意，大小应该按照宽度，高度的顺序来指定。

print('image.shape (Height, Width, Channel) = ', image.shape)

image.shape (Height, Width, Channel) =  (360, 640, 3)

# Resize image to half size
height, width = image.shape[:2]
half_image = cv2.resize(image, (width//2, height//2))  # size must be int
print('half_image.shape (Height, Width, Channel) = ', half_image.shape)
plt.imshow(half_image)

half_image.shape (Height, Width, Channel) =  (180, 320, 3)





<matplotlib.image.AxesImage at 0x13a89d8d0>

png

plt.savefig('out_half.jpeg')

<matplotlib.figure.Figure at 0x13a85d828>

# Resize image by specifying longer side size
def resize_longedge(image, pixel):
    """Resize the input image
    
    Longer edge size will be `pixel`, and aspect ratio doesn't change
    """
    height, width = image.shape[:2]
    longer_side = max(height, width)
    ratio = float(pixel) / longer_side
    return cv2.resize(image, None, fx=ratio, fy=ratio)  # size must be int

resized128_image = resize_longedge(image, 128)
print('resized128_image.shape (Height, Width, Channel) = ', resized128_image.shape)
plt.imshow(resized128_image)

resized128_image.shape (Height, Width, Channel) =  (72, 128, 3)





<matplotlib.image.AxesImage at 0x13a85d080>

png

裁剪

numpy切片可以用于裁剪图像。

height, width = half_image.shape[:2]
 
crop_length = min(height, width)
 
height_start = (height - crop_length) // 2
width_start = (width - crop_length) // 2
 
cropped_image = half_image[
                height_start:height_start+crop_length, 
                width_start:width_start+crop_length,
                :] 
print('cropped_image.shape (Height, Width, Channel) = ', cropped_image.shape)
plt.imshow(cropped_image)

cropped_image.shape (Height, Width, Channel) =  (180, 180, 3)





<matplotlib.image.AxesImage at 0x13a369d30>

png

基于通道的图像处理

RGB通道操作。理解“通道”的含义在深度学习中很重要。下面的代码提供了一些样例每个通道代表什么。

# Show RGB channel separately in gray scale
 
fig, axes = plt.subplots(1, 3)
 
# image[:, :, 0] is R channel.
axes[0].set_title('R channel')
axes[0].imshow(image[:, :, 0], cmap='gray')
# image[:, :, 1] is G channel.
axes[1].set_title('G channel')
axes[1].imshow(image[:, :, 1], cmap='gray')
# image[:, :, 2] is B channel.
axes[2].set_title('B channel')
axes[2].imshow(image[:, :, 2], cmap='gray')

<matplotlib.image.AxesImage at 0x13a2e3b38>

png

plt.savefig(os.path.join(basedir, 'RGB_gray.jpg'))

<matplotlib.figure.Figure at 0x13a9a17b8>

# Show RGB channel separately in color
fig, axes = plt.subplots(1, 3)
 
# image[:, :, 0] is R channel, replace the rest by 0.
imageR = image.copy()
imageR[:, :, 1:3] = 0
axes[0].set_title('R channel')
axes[0].imshow(imageR)
 
# image[:, :, 1] is G channel, replace the rest by 0.
imageG = image.copy()
imageG[:, :, [0, 2]] = 0
axes[1].set_title('G channel')
axes[1].imshow(imageG)
 
# image[:, :, 2] is B channel, replace the rest by 0.
imageB = image.copy()
imageB[:, :, 0:2] = 0
axes[2].set_title('B channel')
axes[2].imshow(imageB)

<matplotlib.image.AxesImage at 0x13aa6e390>

png