patch.py

import numpy as np
import matplotlib.pyplot as plt
import cv2


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

    # 满足整除关系时的高、宽
    h = grid_h * (m - 1)
    w = grid_w * (n - 1)

    # 图像缩放
    img_re = cv2.resize(img, (w, h),
                        cv2.INTER_LINEAR)  # 也可以用img_re=skimage.transform.resize(img, (h,w)).astype(np.uint8)
    # plt.imshow(img_re)
    gx, gy = np.meshgrid(np.linspace(0, w, n), np.linspace(0, h, m))
    gx = gx.astype(int)
    gy = gy.astype(int)

    divide_image = np.zeros([m - 1, n - 1, grid_h, grid_w, 3],
                            np.uint8)  # 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

    for i in range(m - 1):
        for j in range(n - 1):
            divide_image[i, j, ...] = img_re[
                                      gy[i][j]:gy[i + 1][j + 1], gx[i][j]:gx[i + 1][j + 1], :]
    return divide_image


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))

if __name__ == '__main__':
    img = cv2.imread('D:\wp\circumference/two_tumors\segment35/10.png')
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = divide_method2(img, 3, 3)
    display_blocks(img)
    #h, w = img.shape[0], img.shape[1]
    #cv2.imshow('original figture', img)
    #cv2.waitKey()
    print('原始图像形状:\n',img.shape)