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import os.path
import random
import torchvision.transforms as transforms
import torch
from data.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import cv2
import csv
def getfeats(featpath):
trans_points = np.empty([5, 2], dtype=np.int64)
with open(featpath, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=' ')
for ind, row in enumerate(reader):
trans_points[ind, :] = row
return trans_points
def tocv2(ts):
img = (ts.numpy() / 2 + 0.5) * 255
img = img.astype('uint8')
img = np.transpose(img, (1, 2, 0))
img = img[:, :, ::-1] # rgb->bgr
return img
def dt(img):
if (img.shape[2] == 3):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# convert to BW
ret1, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret2, thresh2 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
dt1 = cv2.distanceTransform(thresh1, cv2.DIST_L2, 5)
dt2 = cv2.distanceTransform(thresh2, cv2.DIST_L2, 5)
dt1 = dt1 / dt1.max() # ->[0,1]
dt2 = dt2 / dt2.max()
return dt1, dt2
def getSoft(size, xb, yb, boundwidth=5.0):
xarray = np.tile(np.arange(0, size[1]), (size[0], 1))
yarray = np.tile(np.arange(0, size[0]), (size[1], 1)).transpose()
cxdists = []
cydists = []
for i in range(len(xb)):
xba = np.tile(xb[i], (size[1], 1)).transpose()
yba = np.tile(yb[i], (size[0], 1))
cxdists.append(np.abs(xarray - xba))
cydists.append(np.abs(yarray - yba))
xdist = np.minimum.reduce(cxdists)
ydist = np.minimum.reduce(cydists)
manhdist = np.minimum.reduce([xdist, ydist])
im = (manhdist + 1) / (boundwidth + 1) * 1.0
im[im >= 1.0] = 1.0
return im
class AlignedDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
imglist = 'datasets/apdrawing_list/%s/%s.txt' % (opt.phase, opt.dataroot)
if os.path.exists(imglist):
lines = open(imglist, 'r').read().splitlines()
lines = sorted(lines)
self.AB_paths = [line.split()[0] for line in lines]
if len(lines[0].split()) == 2:
self.B_paths = [line.split()[1] for line in lines]
else:
self.dir_AB = os.path.join(opt.dataroot, opt.phase)
self.AB_paths = sorted(make_dataset(self.dir_AB))
assert (opt.resize_or_crop == 'resize_and_crop')
def __getitem__(self, index):
AB_path = self.AB_paths[index]
AB = Image.open(AB_path).convert('RGB')
w, h = AB.size
if w / h == 2:
w2 = int(w / 2)
A = AB.crop((0, 0, w2, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
B = AB.crop((w2, 0, w, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
else: # if w/h != 2, need B_paths
A = AB.resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
B = Image.open(self.B_paths[index]).convert('RGB')
B = B.resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
A = transforms.ToTensor()(A)
B = transforms.ToTensor()(B)
w_offset = random.randint(0, max(0, self.opt.loadSize - self.opt.fineSize - 1))
h_offset = random.randint(0, max(0, self.opt.loadSize - self.opt.fineSize - 1))
A = A[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize] # C,H,W
B = B[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
A = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A)
B = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(B)
if self.opt.which_direction == 'BtoA':
input_nc = self.opt.output_nc
output_nc = self.opt.input_nc
else:
input_nc = self.opt.input_nc
output_nc = self.opt.output_nc
flipped = False
if (not self.opt.no_flip) and random.random() < 0.5:
flipped = True
idx = [i for i in range(A.size(2) - 1, -1, -1)]
idx = torch.LongTensor(idx)
A = A.index_select(2, idx)
B = B.index_select(2, idx)
if input_nc == 1: # RGB to gray
tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
A = tmp.unsqueeze(0)
if output_nc == 1: # RGB to gray
tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114
B = tmp.unsqueeze(0)
item = {'A': A, 'B': B,
'A_paths': AB_path, 'B_paths': AB_path}
if self.opt.use_local:
regions = ['eyel', 'eyer', 'nose', 'mouth']
basen = os.path.basename(AB_path)[:-4] + '.txt'
if self.opt.region_enm in [0, 1]:
featdir = self.opt.lm_dir
featpath = os.path.join(featdir, basen)
feats = getfeats(featpath)
if flipped:
for i in range(5):
feats[i, 0] = self.opt.fineSize - feats[i, 0] - 1
tmp = [feats[0, 0], feats[0, 1]]
feats[0, :] = [feats[1, 0], feats[1, 1]]
feats[1, :] = tmp
mouth_x = int((feats[3, 0] + feats[4, 0]) / 2.0)
mouth_y = int((feats[3, 1] + feats[4, 1]) / 2.0)
ratio = self.opt.fineSize / 256
EYE_H = self.opt.EYE_H * ratio
EYE_W = self.opt.EYE_W * ratio
NOSE_H = self.opt.NOSE_H * ratio
NOSE_W = self.opt.NOSE_W * ratio
MOUTH_H = self.opt.MOUTH_H * ratio
MOUTH_W = self.opt.MOUTH_W * ratio
center = torch.LongTensor(
[[feats[0, 0], feats[0, 1] - 4 * ratio], [feats[1, 0], feats[1, 1] - 4 * ratio],
[feats[2, 0], feats[2, 1] - NOSE_H / 2 + 16 * ratio], [mouth_x, mouth_y]])
item['center'] = center
rhs = [int(EYE_H), int(EYE_H), int(NOSE_H), int(MOUTH_H)]
rws = [int(EYE_W), int(EYE_W), int(NOSE_W), int(MOUTH_W)]
if self.opt.soft_border:
soft_border_mask4 = []
for i in range(4):
xb = [np.zeros(rhs[i]), np.ones(rhs[i]) * (rws[i] - 1)]
yb = [np.zeros(rws[i]), np.ones(rws[i]) * (rhs[i] - 1)]
soft_border_mask = getSoft([rhs[i], rws[i]], xb, yb)
soft_border_mask4.append(torch.Tensor(soft_border_mask).unsqueeze(0))
item['soft_' + regions[i] + '_mask'] = soft_border_mask4[i]
for i in range(4):
item[regions[i] + '_A'] = A[:, int(center[i, 1] - rhs[i] / 2):int(center[i, 1] + rhs[i] / 2),
int(center[i, 0] - rws[i] / 2):int(center[i, 0] + rws[i] / 2)]
item[regions[i] + '_B'] = B[:, int(center[i, 1] - rhs[i] / 2):int(center[i, 1] + rhs[i] / 2),
int(center[i, 0] - rws[i] / 2):int(center[i, 0] + rws[i] / 2)]
if self.opt.soft_border:
item[regions[i] + '_A'] = item[regions[i] + '_A'] * soft_border_mask4[i].repeat(
int(input_nc / output_nc), 1, 1)
item[regions[i] + '_B'] = item[regions[i] + '_B'] * soft_border_mask4[i]
if self.opt.compactmask:
cmasks0 = []
cmasks = []
for i in range(4):
if flipped and i in [0, 1]:
cmaskpath = os.path.join(self.opt.cmask_dir, regions[1 - i], basen[:-4] + '.png')
else:
cmaskpath = os.path.join(self.opt.cmask_dir, regions[i], basen[:-4] + '.png')
im_cmask = Image.open(cmaskpath)
cmask0 = transforms.ToTensor()(im_cmask)
if flipped:
cmask0 = cmask0.index_select(2, idx)
if output_nc == 1 and cmask0.shape[0] == 3:
tmp = cmask0[0, ...] * 0.299 + cmask0[1, ...] * 0.587 + cmask0[2, ...] * 0.114
cmask0 = tmp.unsqueeze(0)
cmask0 = (cmask0 >= 0.5).float()
cmasks0.append(cmask0)
cmask = cmask0.clone()
if self.opt.region_enm in [0, 1]:
cmask = cmask[:, int(center[i, 1] - rhs[i] / 2):int(center[i, 1] + rhs[i] / 2),
int(center[i, 0] - rws[i] / 2):int(center[i, 0] + rws[i] / 2)]
elif self.opt.region_enm in [2]: # need to multiply cmask
item[regions[i] + '_A'] = (A / 2 + 0.5) * cmask * 2 - 1
item[regions[i] + '_B'] = (B / 2 + 0.5) * cmask * 2 - 1
cmasks.append(cmask)
item['cmaskel'] = cmasks[0]
item['cmasker'] = cmasks[1]
item['cmask'] = cmasks[2]
item['cmaskmo'] = cmasks[3]
if self.opt.hair_local:
mask = torch.ones(B.shape)
if self.opt.region_enm == 0:
for i in range(4):
mask[:, int(center[i, 1] - rhs[i] / 2):int(center[i, 1] + rhs[i] / 2),
int(center[i, 0] - rws[i] / 2):int(center[i, 0] + rws[i] / 2)] = 0
if self.opt.soft_border:
imgsize = self.opt.fineSize
maskn = mask[0].numpy()
masks = [np.ones([imgsize, imgsize]), np.ones([imgsize, imgsize]), np.ones([imgsize, imgsize]),
np.ones([imgsize, imgsize])]
masks[0][1:] = maskn[:-1]
masks[1][:-1] = maskn[1:]
masks[2][:, 1:] = maskn[:, :-1]
masks[3][:, :-1] = maskn[:, 1:]
masks2 = [maskn - e for e in masks]
bound = np.minimum.reduce(masks2)
bound = -bound
xb = []
yb = []
for i in range(4):
xbi = [int(center[i, 0] - rws[i] / 2), int(center[i, 0] + rws[i] / 2 - 1)]
ybi = [int(center[i, 1] - rhs[i] / 2), int(center[i, 1] + rhs[i] / 2 - 1)]
for j in range(2):
maskx = bound[:, xbi[j]]
masky = bound[ybi[j], :]
tmp_a = torch.from_numpy(maskx) * xbi[j]
tmp_b = torch.from_numpy(1 - maskx)
xb += [tmp_b * 10000 + tmp_a]
tmp_a = torch.from_numpy(masky) * ybi[j]
tmp_b = torch.from_numpy(1 - masky)
yb += [tmp_b * 10000 + tmp_a]
soft = 1 - getSoft([imgsize, imgsize], xb, yb)
soft = torch.Tensor(soft).unsqueeze(0)
mask = (torch.ones(mask.shape) - mask) * soft + mask
elif self.opt.region_enm == 1:
for i in range(4):
cmask0 = cmasks0[i]
rec = torch.zeros(B.shape)
rec[:, int(center[i, 1] - rhs[i] / 2):int(center[i, 1] + rhs[i] / 2),
int(center[i, 0] - rws[i] / 2):int(center[i, 0] + rws[i] / 2)] = 1
mask = mask * (torch.ones(B.shape) - cmask0 * rec)
elif self.opt.region_enm == 2:
for i in range(4):
cmask0 = cmasks0[i]
mask = mask * (torch.ones(B.shape) - cmask0)
hair_A = (A / 2 + 0.5) * mask.repeat(int(input_nc / output_nc), 1, 1) * 2 - 1
hair_B = (B / 2 + 0.5) * mask * 2 - 1
item['hair_A'] = hair_A
item['hair_B'] = hair_B
item['mask'] = mask # mask out eyes, nose, mouth
if self.opt.bg_local:
bgdir = self.opt.bg_dir
bgpath = os.path.join(bgdir, basen[:-4] + '.png')
im_bg = Image.open(bgpath)
mask2 = transforms.ToTensor()(im_bg) # mask out background
if flipped:
mask2 = mask2.index_select(2, idx)
mask2 = (mask2 >= 0.5).float()
hair_A = (A / 2 + 0.5) * mask.repeat(int(input_nc / output_nc), 1, 1) * mask2.repeat(
int(input_nc / output_nc), 1, 1) * 2 - 1
hair_B = (B / 2 + 0.5) * mask * mask2 * 2 - 1
bg_A = (A / 2 + 0.5) * (torch.ones(mask2.shape) - mask2).repeat(int(input_nc / output_nc), 1,
1) * 2 - 1
bg_B = (B / 2 + 0.5) * (torch.ones(mask2.shape) - mask2) * 2 - 1
item['hair_A'] = hair_A
item['hair_B'] = hair_B
item['bg_A'] = bg_A
item['bg_B'] = bg_B
item['mask'] = mask
item['mask2'] = mask2
if (self.opt.isTrain and self.opt.chamfer_loss):
if self.opt.which_direction == 'AtoB':
img = tocv2(B)
else:
img = tocv2(A)
dt1, dt2 = dt(img)
dt1 = torch.from_numpy(dt1)
dt2 = torch.from_numpy(dt2)
dt1 = dt1.unsqueeze(0)
dt2 = dt2.unsqueeze(0)
item['dt1gt'] = dt1
item['dt2gt'] = dt2
if self.opt.isTrain and self.opt.emphasis_conti_face:
face_mask_path = os.path.join(self.opt.facemask_dir, basen[:-4] + '.png')
face_mask = Image.open(face_mask_path)
face_mask = transforms.ToTensor()(face_mask) # [0,1]
if flipped:
face_mask = face_mask.index_select(2, idx)
item['face_mask'] = face_mask
return item
def __len__(self):
return len(self.AB_paths)
def name(self):
return 'AlignedDataset'
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