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datasets.py
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datasets.py
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import json
from os import path as osp
import numpy as np
from PIL import Image, ImageDraw
import torch
from torch.utils import data
from torchvision import transforms
class VITONDataset(data.Dataset):
def __init__(self, opt):
super(VITONDataset, self).__init__()
self.load_height = opt.load_height
self.load_width = opt.load_width
self.semantic_nc = opt.semantic_nc
self.data_path = osp.join(opt.dataset_dir, opt.dataset_mode)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# load data list
img_names = []
c_names = []
with open(osp.join(opt.dataset_dir, opt.dataset_list), 'r') as f:
for line in f.readlines():
img_name, c_name = line.strip().split()
img_names.append(img_name)
c_names.append(c_name)
self.img_names = img_names
self.c_names = dict()
self.c_names['unpaired'] = c_names
def get_parse_agnostic(self, parse, pose_data):
parse_array = np.array(parse)
parse_upper = ((parse_array == 5).astype(np.float32) +
(parse_array == 6).astype(np.float32) +
(parse_array == 7).astype(np.float32))
parse_neck = (parse_array == 10).astype(np.float32)
r = 10
agnostic = parse.copy()
# mask arms
for parse_id, pose_ids in [(14, [2, 5, 6, 7]), (15, [5, 2, 3, 4])]:
mask_arm = Image.new('L', (self.load_width, self.load_height), 'black')
mask_arm_draw = ImageDraw.Draw(mask_arm)
i_prev = pose_ids[0]
for i in pose_ids[1:]:
if (pose_data[i_prev, 0] == 0.0 and pose_data[i_prev, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0):
continue
mask_arm_draw.line([tuple(pose_data[j]) for j in [i_prev, i]], 'white', width=r*10)
pointx, pointy = pose_data[i]
radius = r*4 if i == pose_ids[-1] else r*15
mask_arm_draw.ellipse((pointx-radius, pointy-radius, pointx+radius, pointy+radius), 'white', 'white')
i_prev = i
parse_arm = (np.array(mask_arm) / 255) * (parse_array == parse_id).astype(np.float32)
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_arm * 255), 'L'))
# mask torso & neck
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_upper * 255), 'L'))
agnostic.paste(0, None, Image.fromarray(np.uint8(parse_neck * 255), 'L'))
return agnostic
def get_img_agnostic(self, img, parse, pose_data):
parse_array = np.array(parse)
parse_head = ((parse_array == 4).astype(np.float32) +
(parse_array == 13).astype(np.float32))
parse_lower = ((parse_array == 9).astype(np.float32) +
(parse_array == 12).astype(np.float32) +
(parse_array == 16).astype(np.float32) +
(parse_array == 17).astype(np.float32) +
(parse_array == 18).astype(np.float32) +
(parse_array == 19).astype(np.float32))
r = 20
agnostic = img.copy()
agnostic_draw = ImageDraw.Draw(agnostic)
length_a = np.linalg.norm(pose_data[5] - pose_data[2])
length_b = np.linalg.norm(pose_data[12] - pose_data[9])
point = (pose_data[9] + pose_data[12]) / 2
pose_data[9] = point + (pose_data[9] - point) / length_b * length_a
pose_data[12] = point + (pose_data[12] - point) / length_b * length_a
# mask arms
agnostic_draw.line([tuple(pose_data[i]) for i in [2, 5]], 'gray', width=r*10)
for i in [2, 5]:
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'gray', 'gray')
for i in [3, 4, 6, 7]:
if (pose_data[i - 1, 0] == 0.0 and pose_data[i - 1, 1] == 0.0) or (pose_data[i, 0] == 0.0 and pose_data[i, 1] == 0.0):
continue
agnostic_draw.line([tuple(pose_data[j]) for j in [i - 1, i]], 'gray', width=r*10)
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'gray', 'gray')
# mask torso
for i in [9, 12]:
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*3, pointy-r*6, pointx+r*3, pointy+r*6), 'gray', 'gray')
agnostic_draw.line([tuple(pose_data[i]) for i in [2, 9]], 'gray', width=r*6)
agnostic_draw.line([tuple(pose_data[i]) for i in [5, 12]], 'gray', width=r*6)
agnostic_draw.line([tuple(pose_data[i]) for i in [9, 12]], 'gray', width=r*12)
agnostic_draw.polygon([tuple(pose_data[i]) for i in [2, 5, 12, 9]], 'gray', 'gray')
# mask neck
pointx, pointy = pose_data[1]
agnostic_draw.rectangle((pointx-r*7, pointy-r*7, pointx+r*7, pointy+r*7), 'gray', 'gray')
agnostic.paste(img, None, Image.fromarray(np.uint8(parse_head * 255), 'L'))
agnostic.paste(img, None, Image.fromarray(np.uint8(parse_lower * 255), 'L'))
return agnostic
def __getitem__(self, index):
img_name = self.img_names[index]
c_name = {}
c = {}
cm = {}
for key in self.c_names:
c_name[key] = self.c_names[key][index]
c[key] = Image.open(osp.join(self.data_path, 'cloth', c_name[key])).convert('RGB')
c[key] = transforms.Resize(self.load_width, interpolation=2)(c[key])
cm[key] = Image.open(osp.join(self.data_path, 'cloth-mask', c_name[key]))
cm[key] = transforms.Resize(self.load_width, interpolation=0)(cm[key])
c[key] = self.transform(c[key]) # [-1,1]
cm_array = np.array(cm[key])
cm_array = (cm_array >= 128).astype(np.float32)
cm[key] = torch.from_numpy(cm_array) # [0,1]
cm[key].unsqueeze_(0)
# load pose image
pose_name = img_name.replace('.jpg', '_rendered.png')
pose_rgb = Image.open(osp.join(self.data_path, 'openpose-img', pose_name))
pose_rgb = transforms.Resize(self.load_width, interpolation=2)(pose_rgb)
pose_rgb = self.transform(pose_rgb) # [-1,1]
pose_name = img_name.replace('.jpg', '_keypoints.json')
with open(osp.join(self.data_path, 'openpose-json', pose_name), 'r') as f:
pose_label = json.load(f)
pose_data = pose_label['people'][0]['pose_keypoints_2d']
pose_data = np.array(pose_data)
pose_data = pose_data.reshape((-1, 3))[:, :2]
# load parsing image
parse_name = img_name.replace('.jpg', '.png')
parse = Image.open(osp.join(self.data_path, 'image-parse', parse_name))
parse = transforms.Resize(self.load_width, interpolation=0)(parse)
parse_agnostic = self.get_parse_agnostic(parse, pose_data)
parse_agnostic = torch.from_numpy(np.array(parse_agnostic)[None]).long()
labels = {
0: ['background', [0, 10]],
1: ['hair', [1, 2]],
2: ['face', [4, 13]],
3: ['upper', [5, 6, 7]],
4: ['bottom', [9, 12]],
5: ['left_arm', [14]],
6: ['right_arm', [15]],
7: ['left_leg', [16]],
8: ['right_leg', [17]],
9: ['left_shoe', [18]],
10: ['right_shoe', [19]],
11: ['socks', [8]],
12: ['noise', [3, 11]]
}
parse_agnostic_map = torch.zeros(20, self.load_height, self.load_width, dtype=torch.float)
parse_agnostic_map.scatter_(0, parse_agnostic, 1.0)
new_parse_agnostic_map = torch.zeros(self.semantic_nc, self.load_height, self.load_width, dtype=torch.float)
for i in range(len(labels)):
for label in labels[i][1]:
new_parse_agnostic_map[i] += parse_agnostic_map[label]
# load person image
img = Image.open(osp.join(self.data_path, 'image', img_name))
img = transforms.Resize(self.load_width, interpolation=2)(img)
img_agnostic = self.get_img_agnostic(img, parse, pose_data)
img = self.transform(img)
img_agnostic = self.transform(img_agnostic) # [-1,1]
result = {
'img_name': img_name,
'c_name': c_name,
'img': img,
'img_agnostic': img_agnostic,
'parse_agnostic': new_parse_agnostic_map,
'pose': pose_rgb,
'cloth': c,
'cloth_mask': cm,
}
return result
def __len__(self):
return len(self.img_names)
class VITONDataLoader:
def __init__(self, opt, dataset):
super(VITONDataLoader, self).__init__()
if opt.shuffle:
train_sampler = data.sampler.RandomSampler(dataset)
else:
train_sampler = None
self.data_loader = data.DataLoader(
dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None),
num_workers=opt.workers, pin_memory=True, drop_last=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