221 lines
7.3 KiB
Python
221 lines
7.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from egnn_core.data import load_data_v3,load_data_v6
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import os
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import json
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from skimage.feature import graycomatrix
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#根据连接来遍历点的表
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#第一步,遍历edge index.T里面的第一列对应的点的序号[:,0]
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#第二步,根据序号去找对应的label是否为普通,即0,如果等于0进入下一步,不然跳过;这一步是为了排除掉非普通原子的
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#第三步,判断普通的原子的邻居是不是也是普通的:
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#先找edge index.T里面的第一列对应的点的序号[:,1],即邻居有哪些
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#去判断这些邻居有多少个属于线缺陷,即label是2,并计数
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#记住,此时要把该点从表中永远排除,避免重复技术
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# edge_index = np.array([[1, 3], [4, 5], [1, 7], [1, 10]])
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# labels = np.array([0,0,0,2,0,2,0,2,0,0,2])
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# 指定文件夹路径
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folder_path = '/home/gao/mouclear/analyze_center/new+label'
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img_size = 2048
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# target_size = [150,200]
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target_size = [165,185]
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def calculate_entropy(image):
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hist, _ = np.histogram(image, bins=range(256), range=(0, 255))
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probabilities = hist / np.sum(hist)
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entropy = -np.sum(probabilities[probabilities > 0] * np.log2(probabilities[probabilities > 0]))
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return entropy
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def calculate_mean_entropy(image):
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# 确定图像的尺寸
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height, width = image.shape
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# 计算每块的大小
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block_height = height // 2
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block_width = width // 2
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# 初始化熵列表
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entropies = []
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# 计算四块的熵
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for i in range(2):
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for j in range(2):
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# 计算区域的坐标
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start_row, end_row = i * block_height, (i + 1) * block_height
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start_col, end_col = j * block_width, (j + 1) * block_width
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# 提取区域
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region = image[start_row:end_row, start_col:end_col]
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# 计算熵并添加到列表
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entropies.append(calculate_entropy(region))
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# 计算熵的均值和方差
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mean_entropy = np.mean(entropies)
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variance_entropy = np.var(entropies)
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return mean_entropy, variance_entropy
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def calculate_entropy_v2(image, distances=[1], angles=[0], levels=256, return_matrix=False):
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"""
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计算基于灰度共生矩阵的熵。
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参数:
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- image: 二维NumPy数组，表示灰度图像。
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- distances: 考虑的像素距离。
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- angles: 考虑的角度，可以是0, 90, 135, 45度。
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- levels: 灰度级别数量，默认为256。
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- return_matrix: 是否返回灰度共生矩阵，默认为False。
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返回:
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- entropy: 熵值。
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- glcm (如果return_matrix为True): 灰度共生矩阵。
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"""
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# 确保image是uint8类型
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image = image.astype(np.uint8)
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# 计算灰度共生矩阵
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glcm = graycomatrix(image, distances, angles, levels, symmetric=True, normed=True)
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# 创建一个与glcm形状相同的掩码，用于过滤零概率值
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mask = glcm > 0
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# 计算每个GLCM的熵，只对非零概率值进行计算
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glcm_entropy = -np.sum(glcm * np.log2(glcm) * mask, axis=1) / np.sum(mask, axis=1)
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# 取平均熵作为图像的熵
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entropy = np.mean(glcm_entropy[glcm_entropy > 0]) # 排除NaN值
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#
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# # 计算每个GLCM的熵
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# glcm_prob = glcm / np.sum(glcm)
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# glcm_entropy = -np.sum(glcm_prob * np.log2(glcm_prob), axis=1)
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# 取平均熵作为图像的熵
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# entropy = np.mean(glcm_entropy)
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if return_matrix:
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return entropy, glcm
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else:
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return entropy
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def process_json_file(json_file):
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json_filename = os.path.basename(json_file)
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json_filename_without_ext = os.path.splitext(json_filename)[0]
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image_filename = f"{json_filename_without_ext}.png"
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points, edge_index, labels, lights = load_data_v6(json_file) # 假设这是你的数据加载函数
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graph_index = np.where(labels == 3)
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center_index = np.where(labels == 4)
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vacancy = np.where(labels == 5)
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combined_index = np.array(center_index[0].tolist() + graph_index[0].tolist() + vacancy[0].tolist())
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# print(len(combined_index))
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selected_point = []
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selected_light = []
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selected_label = []
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for i in combined_index:
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selected_point.append(points[i])
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selected_light.append(lights[i])
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selected_label.append(labels[i])
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selected_point = np.array(selected_point)
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selected_light = np.array(selected_light)
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selected_label = np.array(selected_label)
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bg = np.zeros((img_size, img_size))
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plt.figure(figsize=(9, 9))
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for label, point in zip(selected_label, selected_point):
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img_h, img_w = bg.shape # 背景图像的高度和宽度
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h, w = point
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ps = 1 # 因为 light 是 3x3 的区域，所以 ps 应该是 1
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hs = np.clip(h - ps, 0, img_h - ps - 1)
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ws = np.clip(w - ps, 0, img_w - ps - 1)
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he = hs + 2 * ps + 1 # 3x3 区域的结束行索引
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we = ws + 2 * ps + 1 # 3x3 区域的结束列索引
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if label == 3:
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array_3x3 = np.full((3, 3), 200)
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if label == 4:
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if json_filename_without_ext !='2_2':
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array_3x3 = np.full((3,3), 50)
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else:
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array_3x3 = np.full((3, 3), 200)
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if label == 5:
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array_3x3 = np.full((3, 3), 50)
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# 绘制到背景图像上
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bg[hs:he, ws:we] = array_3x3
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# 绘制完所有 light 后，找到被修改的区域
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modified_rows, modified_cols = np.where(bg != 0)
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# 计算最小和最大索引，以确定被修改区域的范围
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min_row, max_row = min(modified_rows), max(modified_rows)
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min_col, max_col = min(modified_cols), max(modified_cols)
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# 计算需要裁剪的边界，以保持统一尺寸
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pad_height = max_row - min_row
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pad_width = max_col - min_col
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pad_top = (target_size[0] - pad_height) // 2
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pad_left = (target_size[1] - pad_width) // 2
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# 确保裁剪尺寸不超出原始图片尺寸
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pad_top = min(pad_top, img_size - max_row)
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pad_left = min(pad_left, img_size - max_col)
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# 裁剪背景图像，只保留被修改的区域，并添加边界以保持统一尺寸
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cropped_bg = bg[
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min_row - pad_top:min_row - pad_top + target_size[0],
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min_col - pad_left:min_col - pad_left + target_size[1]
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]
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# 裁剪背景图像，只保留被修改的区域
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# cropped_bg = bg[min_row-1:max_row + 1, min_col-1:max_col + 1]
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# 绘制裁剪后的背景图像
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plt.figure(figsize=(9, 9))
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plt.imshow(cropped_bg, cmap='gray') # 使用 'nearest' 插值以保持像素的完整性
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plt.axis('off')
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plt.tight_layout()
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plt.savefig(image_filename, bbox_inches='tight', pad_inches=0)
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plt.close() # 关闭绘图以释放资源
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# 计算 bg 图像的熵
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# entropy = calculate_entropy(cropped_bg)
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# print(f"The entropy of the image is: {entropy}")
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# entropy = calculate_entropy_v2(cropped_bg)
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# print("灰度共生矩阵的熵:", entropy)
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mean_entropy, variance_entropy = calculate_mean_entropy(cropped_bg)
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print(f"熵的均值: {mean_entropy}")
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print(f"熵的方差: {variance_entropy}")
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# 可以选择再次显示图像和熵值
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plt.imshow(cropped_bg, cmap='gray')
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# plt.title(f"Image Entropy: {entropy}")
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plt.axis('off')
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plt.show()
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# 遍历文件夹中的所有文件
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for filename in os.listdir(folder_path):
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if filename.endswith('.json'):
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print(filename)
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json_file = os.path.join(folder_path, filename)
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process_json_file(json_file)
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