使用python進行圖片處理，現在需要讀出圖片的任意一塊區域，并將其轉化為一維數組，方便后續卷積操作的使用。

下面使用兩種方法進行處理：

convert 函數

from PIL import Image

import numpy as np

import matplotlib.pyplot as plt

def ImageToMatrix(filename):

im = Image.open(filename) # 讀取圖片

im.show() # 顯示圖片

width,height = im.size

print("width is :" + str(width))

print("height is :" + str(height))

im = im.convert("L") # pic --> mat 轉換，可以選擇不同的模式，下面有函數源碼具體說明

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)

# 顯示從矩陣數據得到的圖片

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.

'''

原圖：

filepath = "./imgs/"

imgdata = ImageToMatrix("./imgs/0001.jpg")

print(type(imgdata))

print(imgdata.shape)

plt.imshow(imgdata) # 顯示圖片

plt.axis('off') # 不顯示坐標軸

plt.show()

運行結果：

mpimg 函數

import matplotlib.pyplot as plt # plt 用于顯示圖片

import matplotlib.image as mpimg # mpimg 用于讀取圖片

import numpy as np

def readPic(picname, filename):

img = mpimg.imread(picname)

# 此時 img 就已經是一個 np.array 了，可以對它進行任意處理

weight,height,n = img.shape #(512, 512, 3)

print("the original pic: \n" + str(img))

plt.imshow(img) # 顯示圖片

plt.axis('off') # 不顯示坐標軸

plt.show()

# 取reshape后的矩陣的第一維度數據，即所需要的數據列表

img_reshape = img.reshape(1,weight*height*n)[0]

print("the 1-d image data :\n "+str(img_reshape))

# 截取(300，300)區域的一小塊(12*12*3)，將該區域的圖像數據轉換為一維數組

img_cov = np.random.randint(1,2,(12,12,3)) # 這里使用np.ones()初始化數組，會出現數組元素為float類型，使用np.random.randint確保其為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)

# 打印該12*12*3區域的圖像

plt.imshow(img_cov)

plt.axis('off')

plt.show()

# 寫文件

# open:以append方式打開文件，如果沒找到對應的文件，則創建該名稱的文件

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")

讀出的數據(12*12*3)，每個像素點以R、G、B的順序排列，以及該區域顯示為圖片的效果：

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