根据站内的matlab代码修改成python版本。

import numpy as np
import matplotlib.pyplot as plt
import cv2
from pylab import mpl# 设置中文显示字体
mpl.rcParams["font.sans-serif"] = ["SimHei"]def LCM_computation(patch_LCM_in):row, col = patch_LCM_in.shape  # 对patch而言，行=列patch_LCM_in = np.array(patch_LCM_in, dtype=np.double)  # 改变数据类型# 分为3x3个cells，无论patch的尺寸，都是3*3个cellcell_size = row // 3# 计算中心cell的最大值 是一个标量L_n = np.max(patch_LCM_in[cell_size + 1:cell_size * 2, cell_size + 1:cell_size * 2])  # 选中patch中心区域，求最大值L_n_2 = L_n ** 2# 计算周边cell的均值,周边共3^2-1个cell,编号如下：# 1 2 3# 4 0 5# 6 7 8m_1 = np.mean(patch_LCM_in[0:cell_size, 0:cell_size])m_2 = np.mean(patch_LCM_in[0:cell_size, cell_size:cell_size * 2])m_3 = np.mean(patch_LCM_in[0:cell_size, cell_size * 2:cell_size * 3])m_4 = np.mean(patch_LCM_in[cell_size:cell_size * 2, 0:cell_size])m_5 = np.mean(patch_LCM_in[cell_size:cell_size * 2, cell_size * 2:cell_size * 3])m_6 = np.mean(patch_LCM_in[cell_size * 2:cell_size * 3, 0:cell_size])m_7 = np.mean(patch_LCM_in[cell_size * 2:cell_size * 3, cell_size:cell_size * 2])m_8 = np.mean(patch_LCM_in[cell_size * 2:cell_size * 3, cell_size * 2:cell_size * 3])# 计算C_nm_cell = np.array([L_n_2 / m_1, L_n_2 / m_2, L_n_2 / m_3, L_n_2 / m_4, L_n_2 / m_5, L_n_2 / m_6, L_n_2 / m_7, L_n_2 / m_8])C_n = np.min(m_cell)# Replace the value of the central pixel with the Cn# patch_LCM_in[cell_size + 1:cell_size * 2, cell_size + 1:cell_size * 2] = C_nreturn C_ndef MLCM_computation(I_MLCM_in):I_MLCM_in = np.array(I_MLCM_in, dtype=np.double)row, col = I_MLCM_in.shapescales = np.array([9, 15, 21, 27])  # patch的尺寸有9x9,15x15,21x21,27x27l_max = scales.shape[0]  # 对应论文lmax=[1,4],l_max是4# Compute Cl according to Algorithm 1C_map_scales = np.zeros((row, col, l_max))for i in range(l_max):  # 对应不同尺度for j in range(0, row - scales[i] + 1):  # 单一尺度下以patch为单位做遍历，j是行for k in range(0, col - scales[i] + 1):  # k是列temp_patch = I_MLCM_in[j:j + scales[i], k:k + scales[i]]C_n = LCM_computation(temp_patch)  # 对patch执行Algorithm 1C_map_scales[j + scales[i] // 2, k + scales[i] // 2, i] = C_n# 这部分计算，生成4张对比度图，其中尺度最大的对比度图有效像元数最小，每个方向减去(scales(4)-1)/2=13max_margin = (scales[-1] - 1) // 2# 对4种尺度对比图的共同部分取最大值，作为输出C_hat = np.zeros((row - scales[-1] + 1, col - scales[-1] + 1))for i in range(row - scales[-1] + 1):for j in range(col - scales[-1] + 1):temp = np.array([C_map_scales[i + max_margin, j + max_margin, 0],C_map_scales[i + max_margin, j + max_margin, 1],C_map_scales[i + max_margin, j + max_margin, 2],C_map_scales[i + max_margin, j + max_margin, 3]])C_hat[i, j] = np.max(temp)# fig, axs = plt.subplots(2, 2, figsize=(10, 10))# fig.suptitle('Contrast Maps at Different Scales')## X, Y = np.meshgrid(np.arange(1, row + 1), np.arange(1, col + 1))## for i, ax in enumerate(axs.flatten()):#     mesh = ax.pcolormesh(X, Y, C_map_scales[:, :, i], shading='auto', cmap='gray')#     ax.set_title(f'v={scales[i]}x{scales[i]} Contrast Map')#     ax.set_xlabel('row')#     ax.set_ylabel('col')#     fig.colorbar(mesh, ax=ax)## plt.show()return C_hat, max_margindef target_detection(C_hat, threshold, max_margin, I_in):# 用阈值生成maskrow, col = C_hat.shapemask = np.zeros((row, col), dtype=np.uint8)target_pixel_num = 0  # 统计小目标在mask中占据的像元数for i in range(row):for j in range(col):if C_hat[i, j] > threshold:mask[i, j] = 1target_pixel_num += 1# 再把mask填入原图的中区域，四周各空max_marginrow, col = I_in.shapeI_out = np.zeros((row, col), dtype=np.uint8)I_out[max_margin:row - max_margin, max_margin:col - max_margin] = maskreturn I_out, target_pixel_numdef sqrt_matrix(C_hat, mean_C_hat):C_hat = np.array(C_hat, dtype=np.double)return np.var(C_hat, ddof=1)  # 使用ddof=1来得到样本方差if __name__ == "__main__":# 读取测试图像I_read = cv2.imread("./data/40.bmp", cv2.IMREAD_GRAYSCALE)# 图像大小转换为 256*256I_read = cv2.resize(I_read, (256, 256), interpolation=cv2.INTER_NEAREST)# 检测RGB，转为灰度图if len(I_read.shape) == 3:  # 如果图像是3通道的，即RGB图像I_in = cv2.cvtColor(I_read, cv2.COLOR_BGR2GRAY)else:  # 如果图像已经是单通道的，即灰度图像I_in = I_read# 转为double型I_in = np.double(I_in)# 计算最终的Multiscale LCM[C_hat, max_margin] = MLCM_computation(I_in)# 计算均值、标准差mean_C_hat = np.mean(C_hat)sqrt_C_hat = np.sqrt(sqrt_matrix(C_hat, mean_C_hat))# 计算阈值k_Th = 4threshold = mean_C_hat + k_Th * sqrt_C_hat# 根据阈值判断，输出二值探测结果和统计小目标在mask中占据的像元数[I_out, target_pixel_num] = target_detection(C_hat, threshold, max_margin, I_in)print(target_pixel_num)# 显示输入和输出plt.figure(figsize=(10, 5))plt.subplot(1, 2, 1)plt.imshow(I_in, cmap='gray', vmin=0, vmax=255)plt.title('原图')plt.axis('off')plt.subplot(1, 2, 2)plt.imshow(I_out, cmap='gray', vmin=0, vmax=1)plt.title('二值化输出')plt.axis('off')plt.show()