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
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from pil import image
import numpy as np
import matplotlib.pyplot as plt
def 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_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".
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| when translating a color image to black and white (mode "l"),
| the library uses the itu-r 601-2 luma transform::
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| l = r * 299/1000 + g * 587/1000 + b * 114/1000
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| 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.
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| :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.
'''
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原图：
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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()
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运行结果：
mpimg 函数
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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")
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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