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cp_dataset.py
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cp_dataset.py
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# coding=utf-8
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
from PIL import ImageDraw
import os.path as osp
import numpy as np
import json
class CPDataset(data.Dataset):
"""Dataset for CP-VTON+.
"""
def __init__(self, opt):
super(CPDataset, self).__init__()
# base setting
self.opt = opt
self.root = opt.dataroot
self.datamode = opt.datamode # train or test or self-defined
self.stage = opt.stage # GMM or TOM
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, 0.5), (0.5, 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 "CPDataset"
def __getitem__(self, index):
c_name = self.c_names[index]
im_name = self.im_names[index]
if self.stage == 'GMM':
c = Image.open(osp.join(self.data_path, 'cloth', c_name))
cm = Image.open(osp.join(self.data_path, 'cloth-mask', c_name)).convert('L')
else:
c = Image.open(osp.join(self.data_path, 'warp-cloth', im_name)) # c_name, if that is used when saved
cm = Image.open(osp.join(self.data_path, 'warp-mask', im_name)).convert('L') # c_name, if that is used when saved
c = self.transform(c) # [-1,1]
cm_array = np.array(cm)
cm_array = (cm_array >= 128).astype(np.float32)
cm = torch.from_numpy(cm_array) # [0,1]
cm.unsqueeze_(0)
# person image
im = Image.open(osp.join(self.data_path, 'image', im_name))
im = self.transform(im) # [-1,1]
"""
LIP labels
[(0, 0, 0), # 0=Background
(128, 0, 0), # 1=Hat
(255, 0, 0), # 2=Hair
(0, 85, 0), # 3=Glove
(170, 0, 51), # 4=SunGlasses
(255, 85, 0), # 5=UpperClothes
(0, 0, 85), # 6=Dress
(0, 119, 221), # 7=Coat
(85, 85, 0), # 8=Socks
(0, 85, 85), # 9=Pants
(85, 51, 0), # 10=Jumpsuits
(52, 86, 128), # 11=Scarf
(0, 128, 0), # 12=Skirt
(0, 0, 255), # 13=Face
(51, 170, 221), # 14=LeftArm
(0, 255, 255), # 15=RightArm
(85, 255, 170), # 16=LeftLeg
(170, 255, 85), # 17=RightLeg
(255, 255, 0), # 18=LeftShoe
(255, 170, 0) # 19=RightShoe
(170, 170, 50) # 20=Skin/Neck/Chest (Newly added after running dataset_neck_skin_correction.py)
]
"""
# load parsing image
parse_name = im_name.replace('.jpg', '.png')
im_parse = Image.open(
# osp.join(self.data_path, 'image-parse', parse_name)).convert('L')
osp.join(self.data_path, 'image-parse-new', parse_name)).convert('L') # updated new segmentation
parse_array = np.array(im_parse)
im_mask = Image.open(
osp.join(self.data_path, 'image-mask', parse_name)).convert('L')
mask_array = np.array(im_mask)
# parse_shape = (parse_array > 0).astype(np.float32) # CP-VTON body shape
# Get shape from body mask (CP-VTON+)
parse_shape = (mask_array > 0).astype(np.float32)
if self.stage == 'GMM':
parse_head = (parse_array == 1).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 13).astype(
np.float32) # CP-VTON+ GMM input (reserved regions)
else:
parse_head = (parse_array == 1).astype(np.float32) + \
(parse_array == 2).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 9).astype(np.float32) + \
(parse_array == 12).astype(np.float32) + \
(parse_array == 13).astype(np.float32) + \
(parse_array == 16).astype(np.float32) + \
(parse_array == 17).astype(
np.float32) # CP-VTON+ TOM input (reserved regions)
parse_cloth = (parse_array == 5).astype(np.float32) + \
(parse_array == 6).astype(np.float32) + \
(parse_array == 7).astype(np.float32) # upper-clothes labels
# shape downsample
parse_shape_ori = Image.fromarray((parse_shape*255).astype(np.uint8))
parse_shape = parse_shape_ori.resize(
(self.fine_width//16, self.fine_height//16), Image.BILINEAR)
parse_shape = parse_shape.resize(
(self.fine_width, self.fine_height), Image.BILINEAR)
parse_shape_ori = parse_shape_ori.resize(
(self.fine_width, self.fine_height), Image.BILINEAR)
shape_ori = self.transform(parse_shape_ori) # [-1,1]
shape = self.transform(parse_shape) # [-1,1]
phead = torch.from_numpy(parse_head) # [0,1]
# phand = torch.from_numpy(parse_hand) # [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
im_h = im * phead - (1 - phead) # [-1,1], fill -1 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('grid.png')
im_g = self.transform(im_g)
else:
im_g = ''
pcm.unsqueeze_(0) # CP-VTON+
result = {
'c_name': c_name, # for visualization
'im_name': im_name, # for visualization or ground truth
'cloth': c, # for input
'cloth_mask': cm, # for input
'image': im, # for visualization
'agnostic': agnostic, # for input
'parse_cloth': im_c, # for ground truth
'shape': shape, # for visualization
'head': im_h, # for visualization
'pose_image': im_pose, # for visualization
'grid_image': im_g, # for visualization
'parse_cloth_mask': pcm, # for CP-VTON+, TOM input
'shape_ori': shape_ori, # original body shape without resize
}
return result
def __len__(self):
return len(self.im_names)
class CPDataLoader(object):
def __init__(self, opt, dataset):
super(CPDataLoader, self).__init__()
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:
self.data_iter = self.data_loader.__iter__()
batch = self.data_iter.__next__()
return batch
if __name__ == "__main__":
print("Check the dataset for geometric matching module!")
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--dataroot", default="data")
parser.add_argument("--datamode", default="train")
parser.add_argument("--stage", default="GMM")
parser.add_argument("--data_list", default="train_pairs.txt")
parser.add_argument("--fine_width", type=int, default=192)
parser.add_argument("--fine_height", type=int, default=256)
parser.add_argument("--radius", type=int, default=3)
parser.add_argument("--shuffle", action='store_true',
help='shuffle input data')
parser.add_argument('-b', '--batch-size', type=int, default=4)
parser.add_argument('-j', '--workers', type=int, default=1)
opt = parser.parse_args()
dataset = CPDataset(opt)
data_loader = CPDataLoader(opt, dataset)
print('Size of the dataset: %05d, dataloader: %04d'
% (len(dataset), len(data_loader.data_loader)))
first_item = dataset.__getitem__(0)
first_batch = data_loader.next_batch()
from IPython import embed
embed()