Python对图像进行分块及复原
2024-05-10 01:33:10
本文系转载，原文地址：https://blog.csdn.net/wanzhen4330/article/details/84317071
目录
- 1.读取原始图像
- 2.网格划分，将图像划分为m*n块
- 2.1分块后图像的存储问题
- 2.2图像的裁剪
- 2.3图像长宽的整除问题
- 方法一：四舍五入法
- 方法二：图像缩放法
- 3.显示分块后的图像
- 4.分块图像的还原
- 5.参考文献
基本思路：根据图像尺寸创建一个 ( m + 1 ) × ( n + 1 ) ( m + 1 ) × ( n + 1 ) ( m + 1 ) × ( n + 1 ) (m+1)×(n+1)(m+1)×(n+1) (m+1) \times(n+1) (m+1)×(n+1)(m+1)×(n+1)(m+1)×(n+1) (m+1)×(n+1)个均匀的网格顶点坐标，对于图像块来说每个图像块的左上角和右下角可以唯一确定一个图像块，这样就可以利用网格顶点坐标对原始图像进行裁剪。
注意：完整的Python源程序可以在我的CSDN上下载（ 点击进入下载界面），没有积分的小伙伴把下面的各个程序整理起来即可。
1.读取原始图像
import numpy as np
import matplotlib.pyplot as plt
import cv2
img = cv2.imread(‘cat.jpg’)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
h, w = img.shape[0], img.shape[1]
fig1 = plt.figure(‘原始图像’)
plt.imshow(img)
plt.axis(‘off’)
plt.title(‘Original image’)
print( ‘\t\t\t 原始图像形状:\n’, ‘\t\t\t’,img.shape )
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2.网格划分，将图像划分为m*n块
这里有三个要点：图像的存储问题，图像的裁剪方法，长宽不能整除的问题。下面逐一介绍
2.1分块后图像的存储问题
由于事先不知道m和n的具体值，因此也就不知道图像的分块数目具体是多少个。必须想个办法来存储这 m × n m × n m × n m×nm×n m\times n m×nm×nm×n3×1两个图像块，这会导致图像分块的不均匀，也会导致前面的五维张量不合理。一般来说，我们要分块的图像都是很大的，而划分的块数一般不会特别多，所以舍入误差带来的几个像素的差异完全可以忽略，针对这种情况，一个比较好的思路就是在五维张量的填充中只对已知位置的像素进行填充，而把其他位置的像素值仍然设置为0。
完整程序为：
def divide_method1(img,m,n):#分割成m行n列h, w = img.shape[0],img.shape[1]gx=np.round(gx).astype(np.int)gy=np.round(gy).astype(np.int)
divide_image <span class="token operator">=</span> np<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span><span class="token punctuation">[</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token builtin">int</span><span class="token punctuation">(</span>h<span class="token operator">*</span><span class="token number">1.0</span><span class="token operator">/</span><span class="token punctuation">(</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token operator">+</span><span class="token number">0.5</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token builtin">int</span><span class="token punctuation">(</span>w<span class="token operator">*</span><span class="token number">1.0</span><span class="token operator">/</span><span class="token punctuation">(</span>n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token operator">+</span><span class="token number">0.5</span><span class="token punctuation">)</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> np<span class="token punctuation">.</span>uint8<span class="token punctuation">)</span><span class="token comment">#这是一个五维的张量，前面两维表示分块后图像的位置（第m行，第n列），后面三维表示每个分块后的图像信息</span>
<span class="token keyword">for</span> i <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token keyword">for</span> j <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span> divide_image<span class="token punctuation">[</span>i<span class="token punctuation">,</span>j<span class="token punctuation">,</span><span class="token number">0</span><span class="token punctuation">:</span>gy<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token operator">-</span>gy<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">:</span>gx<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token operator">-</span>gx<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token operator">=</span> img<span class="token punctuation">[</span>gy<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">:</span>gy<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> gx<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">:</span>gx<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">]</span><span class="token comment">#这样写比a[i,j,...]=要麻烦，但是可以避免网格分块的时候，有些图像块的比其他图像块大一点或者小一点的情况引起程序出错</span>
<span class="token keyword">return</span> divide_image
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方法二：图像缩放法
这个方法很简单，将图像缩放一下，让其满足整除关系即可。
def divide_method2(img,m,n):#分割成m行n列h, w = img.shape[0],img.shape[1]grid_h=int(h*1.0/(m-1)+0.5)#每个网格的高grid_w=int(w*1.0/(n-1)+0.5)#每个网格的宽
<span class="token comment">#满足整除关系时的高、宽</span>
h<span class="token operator">=</span>grid_h<span class="token operator">*</span><span class="token punctuation">(</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span>
w<span class="token operator">=</span>grid_w<span class="token operator">*</span><span class="token punctuation">(</span>n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token comment">#图像缩放</span>
img_re<span class="token operator">=</span>cv2<span class="token punctuation">.</span>resize<span class="token punctuation">(</span>img<span class="token punctuation">,</span><span class="token punctuation">(</span>w<span class="token punctuation">,</span>h<span class="token punctuation">)</span><span class="token punctuation">,</span>cv2<span class="token punctuation">.</span>INTER_LINEAR<span class="token punctuation">)</span><span class="token comment"># 也可以用img_re=skimage.transform.resize(img, (h,w)).astype(np.uint8)</span>
<span class="token comment">#plt.imshow(img_re)</span>
gx<span class="token punctuation">,</span> gy <span class="token operator">=</span> np<span class="token punctuation">.</span>meshgrid<span class="token punctuation">(</span>np<span class="token punctuation">.</span>linspace<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span> w<span class="token punctuation">,</span> n<span class="token punctuation">)</span><span class="token punctuation">,</span>np<span class="token punctuation">.</span>linspace<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span> h<span class="token punctuation">,</span> m<span class="token punctuation">)</span><span class="token punctuation">)</span>
gx<span class="token operator">=</span>gx<span class="token punctuation">.</span>astype<span class="token punctuation">(</span>np<span class="token punctuation">.</span><span class="token builtin">int</span><span class="token punctuation">)</span>
gy<span class="token operator">=</span>gy<span class="token punctuation">.</span>astype<span class="token punctuation">(</span>np<span class="token punctuation">.</span><span class="token builtin">int</span><span class="token punctuation">)</span>divide_image <span class="token operator">=</span> np<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span><span class="token punctuation">[</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> grid_h<span class="token punctuation">,</span> grid_w<span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> np<span class="token punctuation">.</span>uint8<span class="token punctuation">)</span><span class="token comment">#这是一个五维的张量，前面两维表示分块后图像的位置（第m行，第n列），后面三维表示每个分块后的图像信息</span><span class="token keyword">for</span> i <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>m<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token keyword">for</span> j <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>n<span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span> divide_image<span class="token punctuation">[</span>i<span class="token punctuation">,</span>j<span class="token punctuation">,</span><span class="token punctuation">.</span><span class="token punctuation">.</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token operator">=</span>img_re<span class="token punctuation">[</span>gy<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">:</span>gy<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> gx<span class="token punctuation">[</span>i<span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token punctuation">]</span><span class="token punctuation">:</span>gx<span class="token punctuation">[</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">[</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">]</span>
<span class="token keyword">return</span> divide_image
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3.显示分块后的图像
def display_blocks(divide_image):# m,n=divide_image.shape[0],divide_image.shape[1]for i in range(m):for j in range(n):plt.subplot(m,n,i*n+j+1)plt.imshow(divide_image[i,j,:])plt.axis('off')plt.title('block:'+str(i*n+j+1))
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方法一：四舍五入法
m=3
n=4
divide_image1=divide_method1(img,m+1,n+1)#该函数中m+1和n+1表示网格点个数，m和n分别表示分块的块数
fig2 = plt.figure('分块后的子图像:四舍五入法')
display_blocks(divide_image1)
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方法二：图像缩放法
divide_image2=divide_method2(img,m+1,n+1)#该函数中m+1和n+1表示网格点个数，m和n分别表示分块的块数
fig3 = plt.figure('分块后的子图像:图像缩放法')
display_blocks(divide_image2)
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4.分块图像的还原
主要目的是将分块后的图像拼接起来，还原成一幅完整的大图像。有两个方法：
1.使用opencv。其实opencv自带的有图像拼接的函数，hconcat函数：用于两个Mat矩阵或者图像的水平拼接；vconcat函数：用于两个Mat矩阵或者图像的垂直拼接。
2.自己动手写。图像拼接是图像分块的逆过程，首先创建一个空的还原后的图像，然后将对于位置填充上对应的像素即可。
由于事先不知道具体分成多少块，使用opencv拼接图像是很麻烦的。为了简单，我们还是选择第２种方法，自己动手丰衣足食！
def image_concat(divide_image):m,n,grid_h, grid_w=[divide_image.shape[0],divide_image.shape[1],#每行，每列的图像块数divide_image.shape[2],divide_image.shape[3]]#每个图像块的尺寸
restore_image <span class="token operator">=</span> np<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span><span class="token punctuation">[</span>m<span class="token operator">*</span>grid_h<span class="token punctuation">,</span> n<span class="token operator">*</span>grid_w<span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> np<span class="token punctuation">.</span>uint8<span class="token punctuation">)</span>
restore_image<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>grid_h<span class="token punctuation">,</span><span class="token number">0</span><span class="token punctuation">:</span><span class="token punctuation">]</span>
<span class="token keyword">for</span> i <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>m<span class="token punctuation">)</span><span class="token punctuation">:</span><span class="token keyword">for</span> j <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>n<span class="token punctuation">)</span><span class="token punctuation">:</span>restore_image<span class="token punctuation">[</span>i<span class="token operator">*</span>grid_h<span class="token punctuation">:</span><span class="token punctuation">(</span>i<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token operator">*</span>grid_h<span class="token punctuation">,</span>j<span class="token operator">*</span>grid_w<span class="token punctuation">:</span><span class="token punctuation">(</span>j<span class="token operator">+</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token operator">*</span>grid_w<span class="token punctuation">]</span><span class="token operator">=</span>divide_image<span class="token punctuation">[</span>i<span class="token punctuation">,</span>j<span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">]</span>
<span class="token keyword">return</span> restore_image
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下面分别显示‘四舍五入法’和‘图像缩放法’得到的分块图像的还原结果。
fig4 = plt.figure('分块图像的还原')
restore_image1=image_concat(divide_image1)#四舍五入法分块还原
restore_image2=image_concat(divide_image2)#图像缩放法分块还原
plt.subplot(1,2,1)
plt.imshow(restore_image1)
plt.axis('off')
plt.title('Rounding')
plt.subplot(1,2,2)
plt.imshow(restore_image2)
plt.axis('off')
plt.title('Scaling')
print('\t\t\t还原后的图像尺寸')
print('\t‘四舍五入法’：', restore_image1.shape,'\t''‘图像缩放法’：', restore_image2.shape)
plt.show()
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5.参考文献
[1]图像的水平方向或者垂直方向拼接https://blog.csdn.net/mangobar/article/details/79656417
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