Python OpenCV 图片拆分与拼接

男娘i 2022-12-19 08:47 327阅读 0赞

本文主要实现图片的简单拼接与拆分

一、拆分

示例图片：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70][]

拆分代码如下：

主要思路：读取图片后拆分为两个矩阵，再显示即可

import numpy as np
    import cv2
    
    path = './1.jpg'
    
    img = cv2.imread(path)
    
    # the image height
    sum_rows = img.shape[0]
    # the image length
    sum_cols = img.shape[1]
    part1 = img[0:sum_rows, 0:sum_cols // 2]
    part2 = img[0:sum_rows, sum_cols // 2:sum_cols]
    
    cv2.imshow('part1', part1)
    cv2.imshow('part2', part2)
    
    cv2.waitKey()
    cv2.imwrite('1_1.jpg', part1)
    cv2.imwrite('1_2.jpg', part2)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 1][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 2][]

二、图片拼接

主要思路：分别读取图片，新建一个目标像素大小的0矩阵，最后将读取的图片替换新建矩阵中目标位置上的元素即可。

拼接代码如下：

import numpy as np
    import cv2
    sum_rows = 1080
    sum_cols = 1920
    # new image
    final_matrix = np.zeros((sum_rows, sum_cols, 3), np.uint8)
    
    path1 = './1_1.jpg'
    path2 = './1_2.jpg'
    
    img1 = cv2.imread(path1)
    img2 = cv2.imread(path2)
    
    
    # change
    final_matrix[0:sum_rows, 0:sum_cols // 2] = img1
    final_matrix[0:sum_rows, sum_cols // 2:sum_cols] = img2
    
    cv2.imshow('image', final_matrix)
    cv2.waitKey()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 3][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70]: /images/20221120/85af4547b7af4e85b47903bc76ddf1bc.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20201106105420580.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 2]: /images/20221120/6b8898ddd78c4035af84ae4228f11ebc.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/2020101720083741.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FpbmdmZW5neGQx,size_16,color_FFFFFF,t_70