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""" |
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@date: 2021/6/25 |
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@description: |
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""" |
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import os |
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import json |
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from dataset.communal.read import read_image, read_label |
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from dataset.communal.base_dataset import BaseDataset |
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from utils.logger import get_logger |
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class MP3DDataset(BaseDataset): |
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def __init__(self, root_dir, mode, shape=None, max_wall_num=0, aug=None, camera_height=1.6, logger=None, |
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split_list=None, patch_num=256, keys=None, for_test_index=None): |
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super().__init__(mode, shape, max_wall_num, aug, camera_height, patch_num, keys) |
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if logger is None: |
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logger = get_logger() |
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self.root_dir = root_dir |
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split_dir = os.path.join(root_dir, 'split') |
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label_dir = os.path.join(root_dir, 'label') |
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img_dir = os.path.join(root_dir, 'image') |
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if split_list is None: |
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with open(os.path.join(split_dir, f"{mode}.txt"), 'r') as f: |
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split_list = [x.rstrip().split() for x in f] |
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split_list.sort() |
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if for_test_index is not None: |
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split_list = split_list[:for_test_index] |
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self.data = [] |
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invalid_num = 0 |
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for name in split_list: |
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name = "_".join(name) |
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img_path = os.path.join(img_dir, f"{name}.png") |
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label_path = os.path.join(label_dir, f"{name}.json") |
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if not os.path.exists(img_path): |
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logger.warning(f"{img_path} not exists") |
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invalid_num += 1 |
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continue |
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if not os.path.exists(label_path): |
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logger.warning(f"{label_path} not exists") |
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invalid_num += 1 |
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continue |
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with open(label_path, 'r') as f: |
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label = json.load(f) |
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if self.max_wall_num >= 10: |
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if label['layoutWalls']['num'] < self.max_wall_num: |
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invalid_num += 1 |
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continue |
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elif self.max_wall_num != 0 and label['layoutWalls']['num'] != self.max_wall_num: |
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invalid_num += 1 |
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continue |
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self.data.append([img_path, label_path]) |
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logger.info( |
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f"Build dataset mode: {self.mode} max_wall_num: {self.max_wall_num} valid: {len(self.data)} invalid: {invalid_num}") |
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def __getitem__(self, idx): |
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rgb_path, label_path = self.data[idx] |
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label = read_label(label_path, data_type='MP3D') |
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image = read_image(rgb_path, self.shape) |
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output = self.process_data(label, image, self.patch_num) |
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return output |
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if __name__ == "__main__": |
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import numpy as np |
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from PIL import Image |
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from tqdm import tqdm |
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from visualization.boundary import draw_boundaries |
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from visualization.floorplan import draw_floorplan |
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from utils.boundary import depth2boundaries |
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from utils.conversion import uv2xyz |
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modes = ['test', 'val'] |
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for i in range(1): |
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for mode in modes: |
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print(mode) |
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mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode=mode, aug={ |
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'STRETCH': True, |
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'ROTATE': True, |
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'FLIP': True, |
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'GAMMA': True |
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}) |
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save_dir = f'../src/dataset/mp3d/visualization/{mode}' |
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if not os.path.isdir(save_dir): |
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os.makedirs(save_dir) |
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bar = tqdm(mp3d_dataset, ncols=100) |
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for data in bar: |
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bar.set_description(f"Processing {data['id']}") |
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boundary_list = depth2boundaries(data['ratio'], data['depth'], step=None) |
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pano_img = draw_boundaries(data['image'].transpose(1, 2, 0), boundary_list=boundary_list, show=True) |
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Image.fromarray((pano_img * 255).astype(np.uint8)).save( |
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os.path.join(save_dir, f"{data['id']}_boundary.png")) |
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floorplan = draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=True, |
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marker_color=None, center_color=0.8, show_radius=None) |
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Image.fromarray((floorplan.squeeze() * 255).astype(np.uint8)).save( |
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os.path.join(save_dir, f"{data['id']}_floorplan.png")) |
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