在 python 中将图像划分为 5x5 块并计算每个块的直方图

2022-01-09 00:00:00 python computer-vision python-2.7 python-idle image-processing

问题描述

使用 Python，我必须:

Using Python, I have to:

  • 将 Test_Image 和 Reference_image 分成 5x5 块，
  • 计算每个块的直方图，并将其与另一个图像中的相同块进行比较.
    例如:image1(1,1) 和 image2(1,1).
  • 比较两张图片的相似度(应该是变换不变的).
  • Divide a Test_Image and Reference_image into 5x5 blocks,
  • Compute a histogram for each block, and compare it with the same block in the other image.
    For Example: image1(1,1) with image2(1,1).
  • Compare the similarity between two images (should be transform invariant).

到目前为止，我已经使用 hist=numpy.histogram(image,bins=256)

So far, I have calculated the histogram of the whole image using hist=numpy.histogram(image,bins=256)

我想分割图像，然后计算所有这些块的直方图.我还想使用 Bhattacharya 的系数来衡量相似度.

I want to divide an image, and later compute the histogram for all those blocks . I also want to use Bhattacharya's coefficient to measure the similarity.

谁能指导我如何通过这个?在此先感谢:)

Can anyone guide me with how to go through this one? Thanks in advance :)

解决方案

不知道是不是你要找的这样的东西，这是蛮力版本.它可能很慢.但它完成了工作但是，您必须决定如何处理边界.这将不包括边界，除非窗口完全适合

Not sure if it is something like this you are looking for, This is the brute-force version.and it's probably quite slow.but it does the job You have to decide what to do with the boundaries though. This will not include the boundary unless the window fits exactly

import numpy as numpy

grey_levels = 256
# Generate a test image
test_image = numpy.random.randint(0,grey_levels, size=(11,11))

# Define the window size
windowsize_r = 5
windowsize_c = 5

# Crop out the window and calculate the histogram
for r in range(0,test_image.shape[0] - windowsize_r, windowsize_r):
    for c in range(0,test_image.shape[1] - windowsize_c, windowsize_c):
        window = test_image[r:r+windowsize_r,c:c+windowsize_c]
        hist = numpy.histogram(window,bins=grey_levels)

下面是结果，完整的图像在最后.r,c 代表窗口的左上角

Below is the result and the full image is at the end. r,c represents the topleft corner of the window

r=0,c=0
[[ 63 173 131 205 239]
 [106  37 156  48  81]
 [ 85  85 119  60 228]
 [236  79 247   1 206]
 [ 97  50 117  96 206]]

r=0,c=5
[[108 241 155 214 183]
 [202   2 236 183 225]
 [214 141   1 185 115]
 [  4 234 249  95  67]
 [232 217 116 211  24]]

r=5,c=0
[[179 155  41  47 190]
 [159  69 211  41  92]
 [ 64 184 187 104 245]
 [190 199  71 228 166]
 [117  56  92   5 186]]

r=5,c=5
[[ 68   6  69  63 242]
 [213 133 139  59  44]
 [236  69 148 196 215]
 [ 41 228 198 115 107]
 [109 236 191  48  53]]

[[ 63 173 131 205 239 108 241 155 214 183  42]
 [106  37 156  48  81 202   2 236 183 225   4]
 [ 85  85 119  60 228 214 141   1 185 115  80]
 [236  79 247   1 206   4 234 249  95  67 203]
 [ 97  50 117  96 206 232 217 116 211  24 242]
 [179 155  41  47 190  68   6  69  63 242 162]
 [159  69 211  41  92 213 133 139  59  44 196]
 [ 64 184 187 104 245 236  69 148 196 215  91]
 [190 199  71 228 166  41 228 198 115 107  82]
 [117  56  92   5 186 109 236 191  48  53  65]
 [177 170 114 163 101  54  80  25 112  35  85]]

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