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