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dataset.py
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dataset.py
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import numpy as np
import torch.utils.data as data
import torchvision.transforms as transforms
import os
import random
from other_tools.dataloader import read_images, read_points, resample_pcd
#from utils import *
class ShapeNet(data.Dataset):
def __init__(self,
train=True,
npoints=['2048', '4096'],
dataset_name='shapenet'):
self.dataset = dataset_name
if train:
if self.dataset == 'suncg':
self.list_path = './list_pcd/train_suncg.list'
elif self.dataset == 'fusion':
self.list_path = './list_pcd/train_fusion.list'
elif self.dataset == '3rscan':
self.list_path = './list_pcd/train_3rscan.list'
elif self.dataset == 'eye':
self.list_path = './list_pcd/train_eye.list'
elif self.dataset == 'shapenet':
self.list_path = './list_pcd/train_shapenet.list'
else:
if self.dataset == 'suncg':
self.list_path = './list_pcd/valid_suncg.list'
elif self.dataset == 'fusion':
self.list_path = './list_pcd/test_fusion.list'
elif self.dataset == '3rscan':
self.list_path = './list_pcd/valid_3rscan.list'
elif self.dataset == 'eye':
self.list_path = './list_pcd/test_eye.list'
elif self.dataset == 'shapenet':
self.list_path = './list_pcd/valid_shapenet.list'
self.npoints = npoints
self.train = train
with open(os.path.join(self.list_path)) as file:
self.model_list = [line.strip().replace('/', '/') for line in file]
random.shuffle(self.model_list)
self.len = len(self.model_list)
def __getitem__(self, index):
model_id = self.model_list[index]
scan_id = index
if self.train:
if self.dataset == 'suncg':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/SUNCG_Yida/train/pcd_partial/",
'%s.pcd' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/SUNCG_Yida/train/pcd_complete/",
'%s.pcd' % model_id), self.dataset)
elif self.dataset == 'fusion':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/050_200/train/pcd_partial/",
'%s.pcd' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/050_200/train/pcd_complete/",
'%s.pcd' % model_id), self.dataset)
elif self.dataset == '3rscan':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/3RSCAN/train/partial/",
'%s.ply' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/3RSCAN/train/complete/",
'%s.ply' % model_id), self.dataset)
elif self.dataset == 'eye':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/eye_fb/train/partial/",
'%s.ply' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/eye_fb/train/complete/",
'%s.ply' % model_id), self.dataset)
images = []
elif self.dataset == 'shapenet':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/shapenet/train/partial/",
'%s.h5' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/shapenet/train/gt/",
'%s.h5' % model_id), self.dataset)
images = read_images(model_id)[:, :, :, :3]
else:
if self.dataset == 'suncg':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/SUNCG_Yida/test/pcd_partial/",
'%s.pcd' % model_id))
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/SUNCG_Yida/test/pcd_complete/",
'%s.pcd' % model_id), self.dataset)
elif self.dataset == 'fusion':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/050_200/test/pcd_partial/",
'%s.pcd' % model_id))
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/050_200/test/pcd_complete/",
'%s.pcd' % model_id), self.dataset)
elif self.dataset == '3rscan':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/3RSCAN/test/partial/",
'%s.ply' % model_id))
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/3RSCAN/test/complete/",
'%s.ply' % model_id), self.dataset)
elif self.dataset == 'eye':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/eye_fb/test/partial/",
'%s.ply' % model_id))
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/eye_fb/test/complete/",
'%s.ply' % model_id), self.dataset)
images = []
elif self.dataset == 'shapenet':
part, part_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/shapenet/val/partial/",
'%s.h5' % model_id), self.dataset)
comp, comp_color = read_points(
os.path.join(
"/media/wangyida/HDD/database/shapenet/val/gt/",
'%s.h5' % model_id), self.dataset)
images = read_images(model_id)[:, :, :, :3]
part_sampled, idx_sampled = resample_pcd(part, int(self.npoints[0]))
part_seg = np.round(part_color[idx_sampled] * 11)
comp_sampled, idx_sampled = resample_pcd(comp, int(self.npoints[1]))
comp_seg = np.round(comp_color[idx_sampled] * 11)
"""
comp_seg = []
for i in range (1, 12):
import ipdb; ipdb.set_trace()
comp_seg.append(resample_pcd(comp_sampled[comp_color == i], 512))
"""
# images = images[:, 52: 52+32, 52: 52+32, :]
return model_id, part_sampled, comp_sampled, part_seg, comp_seg, images
def __len__(self):
return self.len