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import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image, ImageDraw
import os.path as osp
import numpy as np
import json
class CPDatasetTest(data.Dataset):
"""
Test Dataset for CP-VTON.
"""
def __init__(self, opt):
super(CPDatasetTest, self).__init__()
# base setting
self.opt = opt
self.root = opt.dataroot
self.datamode = opt.datamode # train or test or self-defined
self.data_list = opt.data_list
self.fine_height = opt.fine_height
self.fine_width = opt.fine_width
self.semantic_nc = opt.semantic_nc
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 = dict()
self.c_names['paired'] = im_names
self.c_names['unpaired'] = c_names
def name(self):
return "CPDataset"
def get_agnostic(self, im, im_parse, pose_data):
parse_array = np.array(im_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))
agnostic = im.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
r = int(length_a / 16) + 1
# 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*5, pointy-r*9, pointx+r*5, pointy), 'gray', 'gray')
# mask arms
agnostic_draw.line([tuple(pose_data[i]) for i in [2, 5]], 'gray', width=r*12)
for i in [2, 5]:
pointx, pointy = pose_data[i]
agnostic_draw.ellipse((pointx-r*5, pointy-r*6, pointx+r*5, pointy+r*6), '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')
for parse_id, pose_ids in [(14, [5, 6, 7]), (15, [2, 3, 4])]:
mask_arm = Image.new('L', (768, 1024), 'white')
mask_arm_draw = ImageDraw.Draw(mask_arm)
pointx, pointy = pose_data[pose_ids[0]]
mask_arm_draw.ellipse((pointx-r*5, pointy-r*6, pointx+r*5, pointy+r*6), 'black', 'black')
for i in pose_ids[1:]:
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
mask_arm_draw.line([tuple(pose_data[j]) for j in [i - 1, i]], 'black', width=r*10)
pointx, pointy = pose_data[i]
if i != pose_ids[-1]:
mask_arm_draw.ellipse((pointx-r*5, pointy-r*5, pointx+r*5, pointy+r*5), 'black', 'black')
mask_arm_draw.ellipse((pointx-r*4, pointy-r*4, pointx+r*4, pointy+r*4), 'black', 'black')
parse_arm = (np.array(mask_arm) / 255) * (parse_array == parse_id).astype(np.float32)
agnostic.paste(im, None, Image.fromarray(np.uint8(parse_arm * 255), 'L'))
agnostic.paste(im, None, Image.fromarray(np.uint8(parse_head * 255), 'L'))
agnostic.paste(im, None, Image.fromarray(np.uint8(parse_lower * 255), 'L'))
return agnostic
def __getitem__(self, index):
im_name = self.im_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.fine_width, interpolation=2)(c[key])
cm[key] = Image.open(osp.join(self.data_path, 'cloth-mask', c_name[key]))
cm[key] = transforms.Resize(self.fine_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)
# person image
im_pil_big = Image.open(osp.join(self.data_path, 'image', im_name))
im_pil = transforms.Resize(self.fine_width, interpolation=2)(im_pil_big)
im = self.transform(im_pil)
# load parsing image
parse_name = im_name.replace('.jpg', '.png')
im_parse_pil_big = Image.open(osp.join(self.data_path, 'image-parse-v3', parse_name))
im_parse_pil = transforms.Resize(self.fine_width, interpolation=0)(im_parse_pil_big)
parse = torch.from_numpy(np.array(im_parse_pil)[None]).long()
im_parse = self.transform(im_parse_pil.convert('RGB'))
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_map = torch.FloatTensor(20, self.fine_height, self.fine_width).zero_()
parse_map = parse_map.scatter_(0, parse, 1.0)
new_parse_map = torch.FloatTensor(self.semantic_nc, self.fine_height, self.fine_width).zero_()
for i in range(len(labels)):
for label in labels[i][1]:
new_parse_map[i] += parse_map[label]
parse_onehot = torch.FloatTensor(1, self.fine_height, self.fine_width).zero_()
for i in range(len(labels)):
for label in labels[i][1]:
parse_onehot[0] += parse_map[label] * i
# load image-parse-agnostic
image_parse_agnostic = Image.open(osp.join(self.data_path, 'image-parse-agnostic-v3.2', parse_name))
image_parse_agnostic = transforms.Resize(self.fine_width, interpolation=0)(image_parse_agnostic)
parse_agnostic = torch.from_numpy(np.array(image_parse_agnostic)[None]).long()
image_parse_agnostic = self.transform(image_parse_agnostic.convert('RGB'))
parse_agnostic_map = torch.FloatTensor(20, self.fine_height, self.fine_width).zero_()
parse_agnostic_map = parse_agnostic_map.scatter_(0, parse_agnostic, 1.0)
new_parse_agnostic_map = torch.FloatTensor(self.semantic_nc, self.fine_height, self.fine_width).zero_()
for i in range(len(labels)):
for label in labels[i][1]:
new_parse_agnostic_map[i] += parse_agnostic_map[label]
# parse cloth & parse cloth mask
pcm = new_parse_map[3:4]
im_c = im * pcm + (1 - pcm)
# load pose points
pose_name = im_name.replace('.jpg', '_rendered.png')
pose_map = Image.open(osp.join(self.data_path, 'openpose_img', pose_name))
pose_map = transforms.Resize(self.fine_width, interpolation=2)(pose_map)
pose_map = self.transform(pose_map) # [-1,1]
pose_name = im_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 densepose
densepose_name = im_name.replace('image', 'image-densepose')
densepose_map = Image.open(osp.join(self.data_path, 'image-densepose', densepose_name))
densepose_map = transforms.Resize(self.fine_width, interpolation=2)(densepose_map)
densepose_map = self.transform(densepose_map) # [-1,1]
agnostic = self.get_agnostic(im_pil_big, im_parse_pil_big, pose_data)
agnostic = transforms.Resize(self.fine_width, interpolation=2)(agnostic)
agnostic = self.transform(agnostic)
result = {
'c_name': c_name, # for visualization
'im_name': im_name, # for visualization or ground truth
# intput 1 (clothfloww)
'cloth': c, # for input
'cloth_mask': cm, # for input
# intput 2 (segnet)
'parse_agnostic': new_parse_agnostic_map,
'densepose': densepose_map,
'pose': pose_map, # for conditioning
# GT
'parse_onehot' : parse_onehot, # Cross Entropy
'parse': new_parse_map, # GAN Loss real
'pcm': pcm, # L1 Loss & vis
'parse_cloth': im_c, # VGG Loss & vis
# visualization
'image': im, # for visualization
'agnostic' : agnostic
}
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, 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 |