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
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
from pil import imageimport numpy as npimport matplotlib.pyplot as pltdef imagetomatrix(filename): 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：
|
1
2
3
4
5
6
7
8
9
|
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
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
|
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：
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/sinat_34022298/article/details/79533934
