173 lines
5.7 KiB
Python
173 lines
5.7 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_v4
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import os
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import json
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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/2'
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img_size = 2048
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def draw_light(img, point, light, ps=1):
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img_h, img_w = img.shape
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h, w = point
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# 计算窗口的边界
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hs = np.clip(h - ps, 0, img_h - 1)
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ws = np.clip(w - ps, 0, img_w - 1)
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he = np.clip(h + ps, 0, img_h - 1)
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we = np.clip(w + ps, 0, img_w - 1)
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# 从light中提取窗口区域的像素值
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window = light.reshape((he - hs, we - ws))
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# 绘制窗口到背景图像上
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img[hs:he, ws:we] = window
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return img
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def find_adj(edge_index,adj_indexs):
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matching_indices = []
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for matching_index in adj_indexs:
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matching_indices.append([index for index, (first, _) in enumerate(edge_index) if (first == matching_index)])
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matching_indices = [item for sublist in matching_indices if sublist for item in sublist]
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adj_indexs = []
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for matching_index in matching_indices:
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adj_indexs.append(edge_index[matching_index][1])
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return adj_indexs, matching_indices
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def process_json_file(json_file,img_file):
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points, edge_index, labels, lights = load_data_v3(json_file) # 假设这是你的数据加载函数
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edge_index = edge_index.T
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# 找到数组中等于1的元素的索引
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center_index = np.where(labels == 3)
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center_point = points[center_index]
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# center_s = edge_index[center_index][:, 0]
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adj_1_indexs = []
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matching_indices_1 = [index for index, (first, _) in enumerate(edge_index) if (first == center_index )]
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for matching_index in matching_indices_1:
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adj_1_indexs.append(edge_index[matching_index][1])
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adj_2_indexs,matching_indices_2 = find_adj(edge_index,adj_1_indexs)
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adj_3_indexs,matching_indices_3 = find_adj(edge_index,adj_2_indexs)
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adj_4_indexs,matching_indices_4 = find_adj(edge_index,adj_3_indexs)
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combined_index = list(set(adj_1_indexs + adj_2_indexs + adj_3_indexs + adj_4_indexs))
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matching_indices = list(set(matching_indices_1 + matching_indices_2 + matching_indices_3 + matching_indices_4))
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print(len(combined_index))
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print(len(matching_indices))
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selected_point = []
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selected_light = []
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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_point = np.array(selected_point)
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selected_light = np.array(selected_light)
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bg_size = (img_size, img_size)
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bg = np.zeros((img_size, img_size))
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# plt.figure(figsize=(9, 9))
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# 计算所有 light 区域的边界
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all_xs = selected_point[:, 1]
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all_ys = selected_point[:, 0]
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min_x = min(np.min(all_xs), 0)
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max_x = max(np.max(all_xs), 2048 - 1)
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min_y = min(np.min(all_ys), 0)
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max_y = max(np.max(all_ys), 2048 - 1)
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for light, point in zip(selected_light, selected_point):
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h, w = point
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# 计算 light 区域在背景上的平移坐标
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translation_x = max(max_x - light.max() - 1, 0)
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translation_y = max(max_y - light.min() - 1, 0)
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# 确保平移后的区域在背景范围内
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hs = np.clip(h - translation_y, 0, bg_size[0])
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ws = np.clip(w - translation_x, 0, bg_size[1])
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# 绘制 light 区域
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bg[hs:hs + light.shape[0], ws:ws + light.shape[1]] = light
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# 裁剪图像到最小显示范围
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plt.xlim(min_x, max_x)
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plt.ylim(min_y, max_y)
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plt.imshow(bg, cmap='gray')
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#
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# min_x = np.min(selected_point[:, 1])
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# max_x = np.max(selected_point[:, 1])
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# min_y = np.min(selected_point[:, 0])
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# max_y = np.max(selected_point[:, 0])
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#
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# # 计算边界框的宽度和高度
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# width = max_x - min_x
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# height = max_y - min_y
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#
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# # 为边界框添加一些边界（例如，原始宽度和高度的10%）
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# padding = 0.1
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# border_x = int(width * padding)
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# border_y = int(height * padding)
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#
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# # 调整背景图大小
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# new_width = int(width + 2 * border_x)
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# new_height = int(height + 2 * border_y)
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# bg = np.zeros((new_height, new_width))
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#
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# # 调整点的坐标
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# selected_point[:, 1] -= min_x - border_x
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# selected_point[:, 0] -= min_y - border_y
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#
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# plt.figure(figsize=(new_width / 100, new_height / 100)) # 单位为英寸，100是默认的dpi
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# plt.imshow(bg, cmap='gray')
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#
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# for point in selected_point:
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# # print(points[i])
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# # if labels[i] == 0:
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# plt.scatter(point[1], point[0], s=18, c='white', zorder=2)
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# # elif labels[i] == 1 or 2:
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# # plt.scatter(points[i][1], points[i][0], s=18, c='yellow', zorder=2)
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plt.axis('off')
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plt.tight_layout()
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# plt.tight_layout()
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plt.savefig('2_.png')
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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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if filename.endswith('.jpg'):
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print(filename)
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img_file = os.path.join(folder_path, filename)
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process_json_file(json_file,img_file)
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