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import os |
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import json |
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from joblib import Parallel, delayed, parallel_backend |
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from glob import glob |
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from tqdm import tqdm |
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import numpy as np |
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import argparse |
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def load_semantic_anno(semantic_txt): |
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semantic_color = [] |
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obj_name_list = [] |
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color_2_name = {} |
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color_2_id = {} |
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with open(semantic_txt) as f: |
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lines = f.readlines()[1:] |
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for line in lines: |
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obj_id = int(line.split(',')[0]) |
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color_str = line.split(',')[1] |
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if len(color_str) != 6: |
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color_str = '0' * (6 - len(color_str)) + color_str |
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r = int(color_str[0:2], 16) |
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g = int(color_str[2:4], 16) |
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b = int(color_str[4:6], 16) |
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obj_name = line.split(',')[2][1:-1] |
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obj_name_list.append(obj_name) |
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rgb_value = np.array([r, g, b], dtype=np.uint8).reshape(1, 3) |
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semantic_color.append(rgb_value) |
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color_2_name[(r, g, b)] = obj_name |
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color_2_id[(r, g, b)] = obj_id |
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return np.concatenate(semantic_color, axis=0), obj_name_list, color_2_name, color_2_id |
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def scene_proc(scene_input): |
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scene_name = scene_input.split('/')[-1] |
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scene_uid = scene_name.split('-')[1] |
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sem_dir = scene_input + '/' + scene_uid + '.semantic' |
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print(scene_name) |
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semantic_anno_color, obj_name_list, color_2_name, color_2_id = load_semantic_anno(sem_dir+'.txt') |
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tgt_id2obj_id = {} |
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semantic_anno_set = set(list(zip(*(semantic_anno_color.T)))) |
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for _i, sem in enumerate(tqdm(semantic_anno_set)): |
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obj_name = color_2_name[(sem[0], sem[1], sem[2])] |
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obj_id = color_2_id[(sem[0], sem[1], sem[2])] |
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tgt_id2obj_id[_i+1] = (obj_id, obj_name) |
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json.dump(tgt_id2obj_id, open(os.path.join(scene_input, 'tgt_id2obj_id.json'), 'w'), indent=4) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--data_root', type=str, default='./hm3d-train-annots', help='data root for hm-semantics data') |
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args = parser.parse_args() |
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scene_list = glob(args.data_root + '/*') |
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with parallel_backend('multiprocessing', n_jobs=1): |
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Parallel()(delayed(scene_proc)(scene) for scene in scene_list) |
