使用python进行图片处理，现在需要读出图片的任意一块区域，并将其转化为一维数组，方便后续卷积操作的使用。
下面使用两种方法进行处理：

convert 函数

from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltdef ImageToMatrix(filename): im = Image.open(filename) # 读取图片 im.show() # 显示图片 width,height = im.size print("width is :" + str(width)) print("height is :" + str(height)) im = im.convert("L") # pic --> mat 转换，可以选择不同的模式，下面有函数源码具体说明 data = im.getdata() data = np.matrix(data,dtype='float')/255.0 new_data = np.reshape(data * 255.0,(height,width)) new_im = Image.fromarray(new_data) # 显示从矩阵数据得到的图片 new_im.show() return new_datadef MatrixToImage(data): data = data*255 new_im = Image.fromarray(data.astype(np.uint8)) return new_im''' convert(self, mode=None, matrix=None, dither=None, palette=0, colors=256) | Returns a converted copy of this image. For the "P" mode, this | method translates pixels through the palette. If mode is | omitted, a mode is chosen so that all information in the image | and the palette can be represented without a palette. | | The current version supports all possible conversions between | "L", "RGB" and "CMYK." The **matrix** argument only supports "L" | and "RGB". | | When translating a color image to black and white (mode "L"), | the library uses the ITU-R 601-2 luma transform:: | | L = R * 299/1000 + G * 587/1000 + B * 114/1000 | | The default method of converting a greyscale ("L") or "RGB" | image into a bilevel (mode "1") image uses Floyd-Steinberg | dither to approximate the original image luminosity levels. If | dither is NONE, all non-zero values are set to 255 (white). To | use other thresholds, use the :py:meth:`~PIL.Image.Image.point` | method. | | :param mode: The requested mode. See: :ref:`concept-modes`. | :param matrix: An optional conversion matrix. If given, this | should be 4- or 12-tuple containing floating point values. | :param dither: Dithering method, used when converting from | mode "RGB" to "P" or from "RGB" or "L" to "1". | Available methods are NONE or FLOYDSTEINBERG (default). | :param palette: Palette to use when converting from mode "RGB" | to "P". Available palettes are WEB or ADAPTIVE. | :param colors: Number of colors to use for the ADAPTIVE palette. | Defaults to 256. | :rtype: :py:class:`~PIL.Image.Image` | :returns: An :py:class:`~PIL.Image.Image` object.'''

原图：

filepath = "./imgs/"imgdata = ImageToMatrix("./imgs/0001.jpg")print(type(imgdata))print(imgdata.shape)plt.imshow(imgdata) # 显示图片plt.axis('off') # 不显示坐标轴plt.show()

运行结果：

mpimg 函数

import matplotlib.pyplot as plt # plt 用于显示图片import matplotlib.image as mpimg # mpimg 用于读取图片import numpy as npdef readPic(picname, filename): img = mpimg.imread(picname) # 此时 img 就已经是一个 np.array 了，可以对它进行任意处理 weight,height,n = img.shape #(512, 512, 3) print("the original pic: \n" + str(img)) plt.imshow(img) # 显示图片 plt.axis('off') # 不显示坐标轴 plt.show() # 取reshape后的矩阵的第一维度数据，即所需要的数据列表 img_reshape = img.reshape(1,weight*height*n)[0] print("the 1-d image data :\n "+str(img_reshape)) # 截取（300，300）区域的一小块（12*12*3），将该区域的图像数据转换为一维数组 img_cov = np.random.randint(1,2,(12,12,3)) # 这里使用np.ones()初始化数组，会出现数组元素为float类型，使用np.random.randint确保其为int型 for j in range(12): for i in range(12): img_cov[i][j] = img[300+i][300+j] img_reshape = img_cov.reshape(1,12*12*3)[0] print((img_cov)) print(img_reshape) # 打印该12*12*3区域的图像 plt.imshow(img_cov) plt.axis('off') plt.show() # 写文件 # open:以append方式打开文件，如果没找到对应的文件，则创建该名称的文件 with open(filename, 'a') as f: f.write(str(img_reshape)) return img_reshapeif __name__ == '__main__': picname = './imgs/0001.jpg' readPic(picname, "data.py")

读出的数据（12*12*3），每个像素点以R、G、B的顺序排列，以及该区域显示为图片的效果：

参考：python 读取并显示图片的两种方法

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