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dataset.py
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dataset.py
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import numpy as np
import json
import os
import os.path as osp
from PIL import Image
from PIL import ImageDraw
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
class SieveDataset(data.Dataset):
"""
Dataset for SieveNet.
"""
def __init__(self, opt):
super(SieveDataset, self).__init__()
# base setting
self.opt = opt
self.root = opt.dataroot
self.datamode = opt.datamode
self.stage = opt.stage
self.data_list = opt.data_list
self.fine_height = opt.fine_height
self.fine_width = opt.fine_width
self.radius = opt.radius
self.data_path = osp.join(opt.dataroot, opt.datamode)
self.transform = transforms.Compose([ \
transforms.ToTensor(), \
transforms.Normalize((0.5,), (0.5,))])
# load data list
im_names = []
c_names = []
with open(osp.join(opt.dataroot, opt.data_list), 'r') as f:
for line in f.readlines():
im_name, c_name = line.strip().split()
im_names.append(im_name)
c_names.append(c_name)
self.im_names = im_names
self.c_names = c_names
def name(self):
return "SieveDataset"
def __getitem__(self, index):
c_name = self.c_names[index]
im_name = self.im_names[index]
# cloth image & cloth mask
#sievenet is not using cloth mask in any stage
c = Image.open(osp.join(self.data_path, 'cloth', c_name))
c = self.transform(c) # [-1,1]
# person image
im = Image.open(osp.join(self.data_path, 'image', im_name))
im = self.transform(im) # [-1,1]
# load parsing image
parse_name = im_name.replace('.jpg', '.png')
im_parse = Image.open(osp.join(self.data_path, 'image-parse', parse_name))
'''
0: backgrounnd, 1: hat, 2: hair, 3: glove, 4: sunglasses, 5: upper-clothers,
6: dress, 7: coat, 8: socks, 9: pants, 10: jumpsiuts,
11: scarf, 12: skirt, 13: face, 14: left-arm, 15: right-arm,
16: left-leg, 17: right-leg, 18: left shoe, 19 right shoe
'''
parse_array = np.array(im_parse)
#shape of person
parse_shape = (parse_array > 0).astype(np.float32)
#head of person
parse_head = (parse_array == 1).astype(np.float32) + \
(parse_array == 2).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 13).astype(np.float32)
#cloth person is wearing
#here try-on is of upper cloth
parse_cloth = (parse_array == 5).astype(np.float32) + \
(parse_array == 6).astype(np.float32) + \
(parse_array == 7).astype(np.float32)
#background in image of person
parse_background = (parse_array == 0).astype(np.float32)
#texture translation prior required in last stage
parse_ttp = (parse_array == 1).astype(np.float32) + \
(parse_array == 2).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 13).astype(np.float32) + \
(parse_array == 3).astype(np.float32) + \
(parse_array == 8).astype(np.float32) + \
(parse_array == 9).astype(np.float32) + \
(parse_array == 10).astype(np.float32) + \
(parse_array == 11).astype(np.float32) + \
(parse_array == 12).astype(np.float32) + \
(parse_array == 14).astype(np.float32) + \
(parse_array == 3).astype(np.float32) + \
(parse_array >= 15).astype(np.float32)
ptexttp = torch.from_numpy(parse_ttp)
im_ttp = im * ptexttp - (1- ptexttp) # [-1,1], fill 0 for other parts
im_parse = torch.from_numpy(parse_array) #[0,19]
# shape downsample
parse_shape = Image.fromarray((parse_shape*255).astype(np.uint8))
parse_shape = parse_shape.resize((self.fine_width//16, self.fine_height//16), Image.BILINEAR)
parse_shape = parse_shape.resize((self.fine_width, self.fine_height), Image.BILINEAR)
shape = self.transform(parse_shape) # [-1,1]
phead = torch.from_numpy(parse_head) # [0,1]
pcm = torch.from_numpy(parse_cloth) # [0,1]
# upper cloth
im_c = im * pcm + (1 - pcm) # [-1,1], fill 1 for other parts
pcm = pcm.unsqueeze(0)
im_h = im * phead - (1 - phead) # [-1,1], fill 0 for other parts
# load pose points
pose_name = im_name.replace('.jpg', '_keypoints.json')
with open(osp.join(self.data_path, 'pose', pose_name), 'r') as f:
pose_label = json.load(f)
pose_data = pose_label['people'][0]['pose_keypoints']
pose_data = np.array(pose_data)
pose_data = pose_data.reshape((-1,3))
point_num = pose_data.shape[0]
pose_map = torch.zeros(point_num, self.fine_height, self.fine_width)
r = self.radius
im_pose = Image.new('L', (self.fine_width, self.fine_height))
pose_draw = ImageDraw.Draw(im_pose)
for i in range(point_num):
one_map = Image.new('L', (self.fine_width, self.fine_height))
draw = ImageDraw.Draw(one_map)
pointx = pose_data[i,0]
pointy = pose_data[i,1]
if pointx > 1 and pointy > 1:
draw.rectangle((pointx-r, pointy-r, pointx+r, pointy+r), 'white', 'white')
pose_draw.rectangle((pointx-r, pointy-r, pointx+r, pointy+r), 'white', 'white')
one_map = self.transform(one_map)
pose_map[i] = one_map[0]
# just for visualization
im_pose = self.transform(im_pose)
# cloth-agnostic representation
agnostic = torch.cat([shape, im_h, pose_map], 0)
if self.stage == 'GMM':
im_g = Image.open(self.opt.grid_path)
im_g = self.transform(im_g)
else:
im_g = ''
if self.stage == 'GMM':
result = {
'c_name': c_name, # for visualization
'im_name': im_name, # for visualization or ground truth
'cloth': c, # for input
'image': im, # for visualization
'agnostic': agnostic, # for input
'parse_cloth': im_c, # for ground truth
'head': im_h, # for visualization
'grid_image': im_g, # for visualization
}
if self.stage == 'SEG':
result = {
'c_name': c_name, # for visualization
'im_name': im_name, # for visualization or ground truth
'cloth': c, # for input
'image': im, # for visualization
'agnostic': agnostic, # for input
'parse_model':im_parse, # for ground truth
}
if self.stage == 'TOM':
result = {
'c_name': c_name, # for visualization
'im_name': im_name, # for visualization or ground truth
'cloth': c, # for input
'image': im, # for visualization
'agnostic': agnostic, # for input
'parse_cloth_mask':pcm, # for ground truth
'texture_t_prior': im_ttp, # for input
'parse_cloth': im_c, # for ground truth
'head': im_h, # for visualization
}
return result
def __len__(self):
return len(self.im_names)
class SieveDataLoader(object):
def __init__(self, opt, dataset):
super(SieveDataLoader, self).__init__()
self.runmode = opt.runmode
if opt.shuffle :
train_sampler = torch.utils.data.sampler.RandomSampler(dataset)
else:
train_sampler = None
self.data_loader = torch.utils.data.DataLoader(
dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None),
num_workers=opt.workers, pin_memory=True, sampler=train_sampler)
self.dataset = dataset
self.data_iter = self.data_loader.__iter__()
def next_batch(self):
try:
batch = self.data_iter.__next__()
except StopIteration:
if self.runmode == 'train':
self.data_iter = self.data_loader.__iter__()
batch = self.data_iter.__next__()
if self.runmode == 'test' :
batch = None
return batch