def get_TImage(self, tparsing_path, size, params, BigSizeFlag):
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
TParsing = Image.open(tparsing_path)
TParsing_size = TParsing.size
if TParsing_size != (1920, 1080) and BigSizeFlag:
TParsing = TParsing.resize((1920, 1080), Image.NEAREST)
elif not BigSizeFlag:
TParsing = TParsing.resize((192, 256), Image.NEAREST)
TParsing_np = np.array(TParsing)
TParsing_new_np = np.zeros_like(TParsing_np)
TParsing_new_np[(TParsing_np == 1) | (TParsing_np == 2)] = 1
TParsing_new_np[(TParsing_np == 3) | (TParsing_np == 5) |
(TParsing_np == 6) | (TParsing_np == 7) |
(TParsing_np == 11)] = 2
TParsing_new_np[(TParsing_np == 8) | (TParsing_np == 9) |
(TParsing_np == 12)] = 3
TParsing_new_np[(TParsing_np == 4) | (TParsing_np == 13)] = 4
TParsing_new_np[(TParsing_np == 14)] = 5
TParsing_new_np[(TParsing_np == 15)] = 6
TParsing_new_np[(TParsing_np == 16)] = 7
TParsing_new_np[(TParsing_np == 17)] = 8
TParsing_new_np[(TParsing_np == 10)] = 9
TParsing_new_np[(TParsing_np == 18)] = 10
TParsing_new_np[(TParsing_np == 19)] = 11
TParsing_new = Image.fromarray(TParsing_new_np)
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=False,
method=Image.NEAREST)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=False,
method=Image.NEAREST)
TParsing_scaled = transform_scale(TParsing_new) * 255.0
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.NEAREST)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.NEAREST)
return TParsing_scaled
def get_SImage(self, sparsing_path, simg_path, size, params, BigSizeFlag):
SI = Image.open(simg_path).convert('RGB')
if SI.size != (1920,1080) and BigSizeFlag:
SI = SI.resize((1920,1080), Image.BICUBIC)
elif not BigSizeFlag:
SI = SI.resize((192,256), Image.BICUBIC)
SFG_np = np.array(SI)
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
SParsing = Image.open(sparsing_path)
SParsing_size = SParsing.size
if SParsing_size != (1920,1080) and SParsing_size != (192,256) and BigSizeFlag:
SParsing = SParsing.resize((1920,1080), Image.NEAREST)
elif not BigSizeFlag and SParsing_size != (192,256):
SParsing = SParsing.resize((192,256), Image.NEAREST)
SParsing_np = np.array(SParsing)
if SParsing_size == (192,256):
SParsing_new_np = SParsing_np
SParsing_new_np[(SParsing_np != 1) & (SParsing_np != 2)] = 0
else:
SParsing_new_np = np.zeros_like(SParsing_np)
SParsing_new_np[(SParsing_np == 3) | (SParsing_np == 5) | (SParsing_np == 6) | (SParsing_np == 7) | (SParsing_np == 11)] = 1
SParsing_new_np[(SParsing_np == 8) | (SParsing_np == 9) | (SParsing_np == 12)] = 2
if BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=True, method=Image.NEAREST, color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=True, method=Image.NEAREST, color_aug=False)
SParsing_new = Image.fromarray(SParsing_new_np)
if SParsing_size != (192,256) and BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=False, method=Image.NEAREST, color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=False, method=Image.NEAREST, color_aug=False)
SParsing_scaled = transform_scale(SParsing_new)*255.0
if BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=True, method=Image.BICUBIC, color_aug=self.opt.color_aug)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=True, method=Image.BICUBIC, color_aug=self.opt.color_aug)
SFG_full_scaled = transform_scale(Image.fromarray(SFG_np))
SFG_np[:,:,0][(SParsing_new_np == 0)] = 0
SFG_np[:,:,1][(SParsing_new_np == 0)] = 0
SFG_np[:,:,2][(SParsing_new_np == 0)] = 0
SFG_scaled = transform_scale(Image.fromarray(SFG_np))
return SParsing_scaled, SFG_scaled, SFG_full_scaled
def get_TImage(self, tparsing_path, timg_path, size, params, BigSizeFlag):
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
TI = Image.open(timg_path).convert('RGB')
if TI.size != (1920,1080) and BigSizeFlag:
TI = TI.resize((1920,1080), Image.BICUBIC)
elif not BigSizeFlag:
TI = TI.resize((192,256), Image.BICUBIC)
TFG_np = np.array(TI)
TParsing = Image.open(tparsing_path)
TParsing_size = TParsing.size
if TParsing_size != (1920,1080) and TParsing_size != (192,256) and BigSizeFlag:
TParsing = TParsing.resize((1920,1080), Image.NEAREST)
elif not BigSizeFlag and TParsing_size != (192,256):
TParsing = TParsing.resize((192,256), Image.NEAREST)
TParsing_np = np.array(TParsing)
TParsing_new_np = np.zeros_like(TParsing_np)
TParsing_new_np[(TParsing_np == 3) | (TParsing_np == 5) | (TParsing_np == 6) | (TParsing_np == 7) | (TParsing_np == 11)] = 1
TParsing_new_np[(TParsing_np == 8) | (TParsing_np == 9) | (TParsing_np == 12)] = 2
TParsing_new = Image.fromarray(TParsing_new_np)
if TParsing_size != (192,256) and BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=False, method=Image.NEAREST, color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=False, method=Image.NEAREST, color_aug=False)
TParsing_scaled = transform_scale(TParsing_new)*255.0
if BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=True, method=Image.NEAREST, color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=True, method=Image.NEAREST, color_aug=False)
if BigSizeFlag:
transform_scale = get_transform(self.opt, params, normalize=True, method=Image.BICUBIC, color_aug=self.opt.color_aug)
else:
transform_scale = get_transform_fixed(self.opt, params, normalize=True, method=Image.BICUBIC, color_aug=self.opt.color_aug)
TFG_np[:,:,0][(TParsing_new_np == 0)] = 0
TFG_np[:,:,1][(TParsing_new_np == 0)] = 0
TFG_np[:,:,2][(TParsing_new_np == 0)] = 0
TFG_scaled = transform_scale(Image.fromarray(TFG_np))
return TParsing_scaled, TFG_scaled
def get_bg_image(self, bg_path, size, params, BigSizeFlag):
if os.path.exists(bg_path):
BG = Image.open(bg_path).convert('RGB')
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.BICUBIC)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.BICUBIC)
BG_scaled = transform_scale(BG)
if BigSizeFlag:
BG_scaled = self.crop(BG_scaled)
BG_flag = True
else:
print('No available background input found in: ' + bg_path)
BG_scaled = -torch.ones(3, 256, 192)
BG_flag = False
return BG_scaled, BG_flag
def get_bg_image(self, bg_path, size, params, BigSizeFlag):
if os.path.exists(bg_path):
BG = Image.open(bg_path).convert('RGB')
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
else:
transform_scale = get_transform_fixed(
self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
BG_scaled = transform_scale(BG)
BG_scaled = self.crop(BG_scaled)
BG_flag = True
else:
BG_scaled = -torch.ones(3, 256, 192)
BG_flag = False
return BG_scaled, BG_flag
def get_SImage(self, spose_path, sparsing_path, sfg_path, simg_path, size,
params, BigSizeFlag):
SI = Image.open(simg_path).convert('RGB')
if SI.size != (1920, 1080) and BigSizeFlag:
SI = SI.resize((1920, 1080), Image.BICUBIC)
elif not BigSizeFlag:
SI = SI.resize((192, 256), Image.BICUBIC)
SFG_np = np.array(SI)
SFG_full_np = np.array(SI)
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
SPose_array, _ = read_keypoints(spose_path, size, random_drop_prob,
self.opt.remove_face_labels,
self.opt.basic_point_only)
SPose = Image.fromarray(SPose_array)
if SPose.size != (1920, 1080) and BigSizeFlag:
SPose = SPose.resize((1920, 1080), Image.NEAREST)
elif not BigSizeFlag:
SPose = SPose.resize((192, 256), Image.NEAREST)
SPose_np = np.array(SPose)
SParsing = Image.open(sparsing_path)
SParsing_size = SParsing.size
if SParsing_size != (1920, 1080) and SParsing_size != (
192, 256) and BigSizeFlag:
SParsing = SParsing.resize((1920, 1080), Image.NEAREST)
elif not BigSizeFlag and SParsing_size != (192, 256):
SParsing = SParsing.resize((192, 256), Image.NEAREST)
SParsing_np = np.array(SParsing)
if SParsing_size == (192, 256):
SParsing_new_np = SParsing_np
else:
SParsing_new_np = np.zeros_like(SParsing_np)
SParsing_new_np[(SParsing_np == 3) | (SParsing_np == 5) |
(SParsing_np == 6) | (SParsing_np == 7) |
(SParsing_np == 11)] = 1
SParsing_new_np[(SParsing_np == 8) | (SParsing_np == 9) |
(SParsing_np == 12)] = 2
SParsing_new_np[(SParsing_np == 1) | (SParsing_np == 2)] = 3
SParsing_new_np[(SParsing_np == 4) | (SParsing_np == 13)] = 4
SParsing_new_np[(SParsing_np == 14)] = 5
SParsing_new_np[(SParsing_np == 15)] = 6
SParsing_new_np[(SParsing_np == 16)] = 7
SParsing_new_np[(SParsing_np == 17)] = 8
SParsing_new_np[(SParsing_np == 10)] = 9
SParsing_new_np[(SParsing_np == 18)] = 10
SParsing_new_np[(SParsing_np == 19)] = 11
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.NEAREST,
color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.NEAREST,
color_aug=False)
SPose_scaled = transform_scale(Image.fromarray(SPose_np))
SParsing_new = Image.fromarray(SParsing_new_np)
if SParsing_size != (192, 256) and BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=False,
method=Image.NEAREST,
color_aug=False)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=False,
method=Image.NEAREST,
color_aug=False)
SParsing_scaled = transform_scale(SParsing_new) * 255.0
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
SI_scaled = transform_scale(SI)
SFG_full_np[(SParsing_new_np == 0)] = 0
SFG_full_scaled = transform_scale(Image.fromarray(SFG_full_np))
if SI.size != (192, 256) and BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.BICUBIC,
color_aug=self.opt.color_aug)
if SI.size != (192, 256):
SFG_np[(SParsing_new_np != 1) & (SParsing_new_np != 2) &
(SParsing_new_np != 3)] = 0
SFG_scaled = transform_scale(Image.fromarray(SFG_np))
return SPose_scaled, SParsing_scaled, SFG_scaled, SFG_full_scaled, SI_scaled
def get_TImage(self, tpose_path, tparsing_path, timg_path, size, params,
BigSizeFlag):
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
TPose_array, translation = read_keypoints(tpose_path, size,
random_drop_prob,
self.opt.remove_face_labels,
self.opt.basic_point_only)
TPose = Image.fromarray(TPose_array)
if TPose.size != (1920, 1080) and BigSizeFlag:
TPose = TPose.resize((1920, 1080), Image.NEAREST)
elif not BigSizeFlag:
TPose = TPose.resize((192, 256), Image.NEAREST)
TPose_uncloth_tmp_np = np.array(TPose)
TPose_cloth_tmp_np = np.array(TPose)
TI = Image.open(timg_path).convert('RGB')
if TI.size != (1920, 1080) and BigSizeFlag:
TI = TI.resize((1920, 1080), Image.BICUBIC)
elif not BigSizeFlag:
TI = TI.resize((192, 256), Image.BICUBIC)
TFG_tmp_np = np.array(TI)
TFG_uncloth_tmp_np = np.array(TI)
TFG_cloth_tmp_np = np.array(TI)
TParsing = Image.open(tparsing_path)
TParsing_size = TParsing.size
if TParsing_size != (1920, 1080) and TParsing_size != (
192, 256) and BigSizeFlag:
TParsing = TParsing.resize((1920, 1080), Image.NEAREST)
elif not BigSizeFlag and TParsing_size != (192, 256):
TParsing = TParsing.resize((192, 256), Image.NEAREST)
TParsing_np = np.array(TParsing)
TParsing_new_np = np.zeros_like(TParsing_np)
TParsing_new_np[(TParsing_np == 1) | (TParsing_np == 2)] = 1
TParsing_new_np[(TParsing_np == 3) | (TParsing_np == 5) |
(TParsing_np == 6) | (TParsing_np == 7) |
(TParsing_np == 11)] = 2
TParsing_new_np[(TParsing_np == 8) | (TParsing_np == 9) |
(TParsing_np == 12)] = 3
TParsing_new_np[(TParsing_np == 4) | (TParsing_np == 13)] = 4
TParsing_new_np[(TParsing_np == 14)] = 5
TParsing_new_np[(TParsing_np == 15)] = 6
TParsing_new_np[(TParsing_np == 16)] = 7
TParsing_new_np[(TParsing_np == 17)] = 8
TParsing_new_np[(TParsing_np == 10)] = 9
TParsing_new_np[(TParsing_np == 18)] = 10
TParsing_new_np[(TParsing_np == 19)] = 11
TParsing_uncloth_np = np.zeros_like(TParsing_np)
TParsing_uncloth_np[(TParsing_np == 1) | (TParsing_np == 2)] = 1
TParsing_uncloth_np[(TParsing_np == 4) | (TParsing_np == 13)] = 2
TParsing_uncloth_np[(TParsing_np == 14)] = 3
TParsing_uncloth_np[(TParsing_np == 15)] = 4
TParsing_uncloth_np[(TParsing_np == 16)] = 5
TParsing_uncloth_np[(TParsing_np == 17)] = 6
TParsing_uncloth_np[(TParsing_np == 10)] = 7
TParsing_uncloth_np[(TParsing_np == 18)] = 8
TParsing_uncloth_np[(TParsing_np == 19)] = 9
TParsing_cloth_np = np.zeros_like(TParsing_new_np)
TParsing_cloth_np[(TParsing_new_np == 2) | (TParsing_new_np == 3)] = \
TParsing_new_np[(TParsing_new_np == 2) | (TParsing_new_np == 3)] - 1
TParsing_new = Image.fromarray(TParsing_new_np)
TParsing_uncloth = Image.fromarray(TParsing_uncloth_np)
TParsing_cloth = Image.fromarray(TParsing_cloth_np)
if TParsing_size != (192, 256) and BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=False,
method=Image.NEAREST)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=False,
method=Image.NEAREST)
TParsing_uncloth_scaled = transform_scale(TParsing_uncloth) * 255.0
TParsing_cloth_scaled = transform_scale(TParsing_cloth) * 255.0
TParsing_scaled = transform_scale(TParsing_new) * 255.0
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.NEAREST)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.NEAREST)
TPose_scaled = transform_scale(TPose)
TPose_uncloth_tmp_np[:, :, 0][(TParsing_uncloth_np == 0)] = 0
TPose_uncloth_tmp_np[:, :, 1][(TParsing_uncloth_np == 0)] = 0
TPose_uncloth_tmp_np[:, :, 2][(TParsing_uncloth_np == 0)] = 0
TPose_uncloth_scaled = transform_scale(
Image.fromarray(TPose_uncloth_tmp_np))
TPose_cloth_tmp_np[:, :, 0][(TParsing_cloth_np == 0)] = 0
TPose_cloth_tmp_np[:, :, 1][(TParsing_cloth_np == 0)] = 0
TPose_cloth_tmp_np[:, :, 2][(TParsing_cloth_np == 0)] = 0
TPose_cloth_scaled = transform_scale(
Image.fromarray(TPose_cloth_tmp_np))
if BigSizeFlag:
transform_scale = get_transform(self.opt,
params,
normalize=True,
method=Image.BICUBIC)
else:
transform_scale = get_transform_fixed(self.opt,
params,
normalize=True,
method=Image.BICUBIC)
TFG_tmp_np[:, :, 0][(TParsing_new_np == 0)] = 0
TFG_tmp_np[:, :, 1][(TParsing_new_np == 0)] = 0
TFG_tmp_np[:, :, 2][(TParsing_new_np == 0)] = 0
TFG_scaled = transform_scale(Image.fromarray(TFG_tmp_np))
TFG_uncloth_tmp_np[:, :, 0][(TParsing_uncloth_np == 0)] = 0
TFG_uncloth_tmp_np[:, :, 1][(TParsing_uncloth_np == 0)] = 0
TFG_uncloth_tmp_np[:, :, 2][(TParsing_uncloth_np == 0)] = 0
TFG_uncloth_scaled = transform_scale(
Image.fromarray(TFG_uncloth_tmp_np))
TFG_cloth_tmp_np[:, :, 0][(TParsing_cloth_np == 0)] = 0
TFG_cloth_tmp_np[:, :, 1][(TParsing_cloth_np == 0)] = 0
TFG_cloth_tmp_np[:, :, 2][(TParsing_cloth_np == 0)] = 0
TFG_cloth_scaled = transform_scale(Image.fromarray(TFG_cloth_tmp_np))
return TPose_scaled, TParsing_scaled, TFG_scaled, TPose_uncloth_scaled, TParsing_uncloth_scaled, TFG_uncloth_scaled, TPose_cloth_scaled, TParsing_cloth_scaled, TFG_cloth_scaled