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 Image

import numpy as np

import matplotlib.pyplot as plt

def 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_data

def 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 np

def 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_reshape

if __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：

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