def get_image(self, A_path, size, params, input_type):
if input_type != 'openpose':
A_img = Image.open(A_path).convert('RGB')
else:
random_drop_prob = self.opt.random_drop_prob if self.opt.isTrain else 0
A_img = Image.fromarray(
read_keypoints(A_path, size, random_drop_prob,
self.opt.remove_face_labels,
self.opt.basic_point_only))
if input_type == 'densepose' and self.opt.isTrain:
# randomly remove labels
A_np = np.array(A_img)
part_labels = A_np[:, :, 2]
for part_id in range(1, 25):
if (np.random.rand() < self.opt.random_drop_prob):
A_np[(part_labels == part_id), :] = 0
if self.opt.remove_face_labels:
A_np[(part_labels == 23) | (part_labels == 24), :] = 0
A_img = Image.fromarray(A_np)
is_img = input_type == 'img'
method = Image.BICUBIC if is_img else Image.NEAREST
transform_scaleA = get_transform(self.opt, params, method=method)
A_scaled = transform_scaleA(A_img)
return A_scaled
def get_image(self,
A_path,
size,
params,
crop_coords,
input_type,
ppl_idx=None,
op=None,
ref_face_pts=None):
if A_path is None: return None, None
opt = self.opt
is_img = input_type == 'img'
method = Image.BICUBIC if is_img else Image.NEAREST
if input_type == 'openpose':
# get image from openpose keypoints
A_img, pose_pts, face_pts = read_keypoints(opt, A_path, size, 0,
False,
opt.basic_point_only,
opt.remove_face_labels,
ppl_idx, ref_face_pts)
# randomly crop the image
A_img, crop_coords = self.crop_person_region(
A_img, crop_coords, pose_pts, size)
else:
A_img = self.read_data(A_path)
A_img, _ = self.crop_person_region(A_img, crop_coords)
if input_type == 'densepose': # remove other ppl in the densepose map
A_img = self.remove_other_ppl(A_img, A_path, crop_coords, op)
transform_scaleA = get_transform(self.opt,
params,
method=method,
color_aug=is_img and opt.isTrain)
A_scaled = transform_scaleA(A_img).float()
if input_type == 'densepose': # renormalize the part labels
A_scaled[2, :, :] = (
(A_scaled[2, :, :] * 0.5 + 0.5) * 255 / 24 - 0.5) / 0.5
if input_type == 'openpose':
return A_scaled, A_img, crop_coords, face_pts
return A_scaled
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
