98 lines
2.5 KiB
Python
98 lines
2.5 KiB
Python
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import cv2
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import copy
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import glob
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import json
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import torch
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import numpy as np
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from PIL import Image
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from torch_geometric.data import Data
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from torch_geometric.data import InMemoryDataset, download_url, Dataset
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def get_y_3(y, edge_index):
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conn_matrix = np.zeros((len(y), len(y)))
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for s, e in edge_index.transpose():
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conn_matrix[s, e] = 1
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conn_matrix[e, s] = 1
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y_sv_norm = np.array(y != 0, np.uint8)
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y_pred = np.array(y != 0, np.uint8)
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for i in range(len(y_pred)):
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if y_pred[i] != 0:
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try:
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n1 = np.where(conn_matrix[i] != 0)[0]
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n2 = np.concatenate([np.where(conn_matrix[item] != 0)[0] for item in n1])
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ner = np.concatenate([n1, n2])
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ner = set(ner)
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ner.remove(i)
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ner = list(ner)
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if np.max(y_sv_norm[ner]) == 0:
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y_pred[i] = 2
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except:
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None
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else:
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continue
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return y_pred
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def get_light(img, point):
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h, w = point
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return img[h, w]
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def get_edge_index(points, lights, max_ner=3, dis_thres=35, light_thres=120):
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s = []
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t = []
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connected_id = []
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for i, p in enumerate(points):
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p = p.reshape(1, -1)
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xysub = np.abs(p - points)
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diss = np.sqrt(xysub[:, 0] ** 2 + xysub[:, 1] ** 2)
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ner_idxs = np.argsort(diss)[1: 1+max_ner].tolist()
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for j, item in enumerate(ner_idxs):
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if item in connected_id:
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ner_idxs.pop(j)
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ner_idxs = np.array(ner_idxs)
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ner_diss = diss[ner_idxs]
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ner_final_idxs = ner_idxs[ner_diss < dis_thres].tolist()
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if lights[i] > light_thres:
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s += [i] * len(ner_final_idxs)
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t += ner_final_idxs
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connected_id += [i]
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edge_index = np.array([s, t])
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return edge_index
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def load_data(json_path):
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data_dict = {
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'Norm': 0,
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'LineSV': 1,
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'SV': 2
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}
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img_path = json_path.replace('.json', '.jpg')
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img = np.array(Image.open(img_path))
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with open(json_path) as f:
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json_data = json.load(f)
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points = np.array([item['points'][0][::-1] for item in json_data['shapes']], np.int32)
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labels = np.array([data_dict[item['label']] for item in json_data['shapes']], np.uint8)
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lights = np.array([get_light(img, point) for point in points])
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edge_index = get_edge_index(points, lights)
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return points, edge_index, labels, lights
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