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import os.path
from data.base_dataset import BaseDataset, get_params, get_transform, normalize
from data.image_folder import make_dataset, make_dataset_test
from PIL import Image
import torch
import json
import numpy as np
import os.path as osp
from PIL import ImageDraw
class AlignedDataset(BaseDataset):
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.diction = {}
self.fine_height = 256
self.fine_width = 192
self.radius = 5
# load data list from pairs file
human_names = []
cloth_names = []
with open(os.path.join(opt.dataroot, opt.datapairs), 'r') as f:
for line in f.readlines():
h_name, c_name = line.strip().split()
human_names.append(h_name)
cloth_names.append(c_name)
self.human_names = human_names
self.cloth_names = cloth_names
self.dataset_size = len(human_names)
# input A (label maps)
dir_A = '_A' if self.opt.label_nc == 0 else '_label'
self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
self.A_paths = sorted(make_dataset(self.dir_A))
self.fine_height = 256
self.fine_width = 192
self.radius = 5
# input A test (label maps)
dir_A = '_A' if self.opt.label_nc == 0 else '_label'
self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
self.A_paths = sorted(make_dataset_test(self.dir_A))
# input B (real images)
dir_B = '_B' if self.opt.label_nc == 0 else '_img'
self.dir_B = os.path.join(opt.dataroot, opt.phase + dir_B)
self.B_paths = sorted(make_dataset(self.dir_B))
self.dataset_size = len(self.A_paths)
self.build_index(self.B_paths)
dir_E = '_edge'
self.dir_E = os.path.join(opt.dataroot, opt.phase + dir_E)
self.E_paths = sorted(make_dataset(self.dir_E))
self.ER_paths = make_dataset(self.dir_E)
dir_M = '_mask'
self.dir_M = os.path.join(opt.dataroot, opt.phase + dir_M)
self.M_paths = sorted(make_dataset(self.dir_M))
self.MR_paths = make_dataset(self.dir_M)
dir_MC = '_colormask'
self.dir_MC = os.path.join(opt.dataroot, opt.phase + dir_MC)
self.MC_paths = sorted(make_dataset(self.dir_MC))
self.MCR_paths = make_dataset(self.dir_MC)
dir_C = '_color'
self.dir_C = os.path.join(opt.dataroot, opt.phase + dir_C)
self.C_paths = sorted(make_dataset(self.dir_C))
self.CR_paths = make_dataset(self.dir_C)
# self.build_index(self.C_paths)
dir_A = '_A' if self.opt.label_nc == 0 else '_label'
self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
self.A_paths = sorted(make_dataset_test(self.dir_A))
def random_sample(self, item):
name = item.split('/')[-1]
name = name.split('-')[0]
lst = self.diction[name]
new_lst = []
for dir in lst:
if dir != item:
new_lst.append(dir)
return new_lst[np.random.randint(len(new_lst))]
def build_index(self, dirs):
for k, dir in enumerate(dirs):
name = dir.split('/')[-1]
name = name.split('-')[0]
# print(name)
for k, d in enumerate(dirs[max(k-20, 0):k+20]):
if name in d:
if name not in self.diction.keys():
self.diction[name] = []
self.diction[name].append(d)
else:
self.diction[name].append(d)
def __getitem__(self, index):
train_mask = 9600
# input A (label maps)
box = []
# for k,x in enumerate(self.A_paths):
# if '000386' in x :
# index=k
# break
test = np.random.randint(2032)
# for k, s in enumerate(self.B_paths):
# if '006581' in s:
# test = k
# break
# get names from the pairs file
c_name = self.cloth_names[index]
h_name = self.human_names[index]
# A_path = self.A_paths[index]
A_path = osp.join(self.dir_A, h_name.replace(".jpg", ".png"))
A = Image.open(A_path).convert('L')
params = get_params(self.opt, A.size)
if self.opt.label_nc == 0:
transform_A = get_transform(self.opt, params)
A_tensor = transform_A(A.convert('RGB'))
else:
transform_A = get_transform(
self.opt, params, method=Image.NEAREST, normalize=False)
A_tensor = transform_A(A) * 255.0
B_tensor = inst_tensor = feat_tensor = 0
# input B (real images)
# B_path = self.B_paths[index]
B_path = osp.join(self.dir_B, h_name)
name = B_path.split('/')[-1]
B = Image.open(B_path).convert('RGB')
transform_B = get_transform(self.opt, params)
B_tensor = transform_B(B)
# input M (masks)
M_path = B_path # self.M_paths[np.random.randint(1)]
MR_path = B_path # self.MR_paths[np.random.randint(1)]
M = Image.open(M_path).convert('L')
MR = Image.open(MR_path).convert('L')
M_tensor = transform_A(MR)
### input_MC (colorMasks)
MC_path = B_path # self.MC_paths[1]
MCR_path = B_path # self.MCR_paths[1]
MCR = Image.open(MCR_path).convert('L')
MC_tensor = transform_A(MCR)
### input_C (color)
# print(self.C_paths)
# C_path = self.C_paths[test]
C_path = osp.join(self.dir_C, c_name)
C = Image.open(C_path).convert('RGB')
C_tensor = transform_B(C)
# Edge
# E_path = self.E_paths[test]
E_path = osp.join(self.dir_E, c_name)
# print(E_path)
E = Image.open(E_path).convert('L')
E_tensor = transform_A(E)
# Pose
pose_name = B_path.replace('.jpg', '_keypoints.json').replace('.png', '_keypoints.json').replace(
'test_img', 'test_pose')
with open(osp.join(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 = transform_B(one_map.convert('RGB'))
pose_map[i] = one_map[0]
P_tensor = pose_map
input_dict = {'label': A_tensor, 'image': B_tensor,
'path': A_path, 'name': A_path.split("/")[-1],
'edge': E_tensor, 'color': C_tensor, 'mask': M_tensor, 'colormask': MC_tensor, 'pose': P_tensor
}
return input_dict
def __len__(self):
return len(self.A_paths) // self.opt.batchSize * self.opt.batchSize
def name(self):
return 'AlignedDataset'
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