def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.diction = {}
# 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 initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
print("label_nc: ", self.opt.label_nc)
### input A (label maps)
if opt.isTrain or opt.use_encoded_image:
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.AR_paths = make_dataset(self.dir_A)
### input A inter 1 (label maps)
if opt.isTrain or opt.use_encoded_image:
dir_A_inter_1 = '_label_inter_1'
self.dir_A_inter_1 = os.path.join(opt.dataroot,
opt.phase + dir_A_inter_1)
self.A_paths_inter_1 = sorted(make_dataset(self.dir_A_inter_1))
### input A inter 2 (label maps)
if opt.isTrain or opt.use_encoded_image:
dir_A_inter_2 = '_label_inter_2'
self.dir_A_inter_2 = os.path.join(opt.dataroot,
opt.phase + dir_A_inter_2)
self.A_paths_inter_2 = sorted(make_dataset(self.dir_A_inter_2))
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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))
dir_AR = '_AR' if self.opt.label_nc == 0 else '_labelref'
self.dir_AR = os.path.join(opt.dataroot, opt.phase + dir_AR)
self.AR_paths = sorted(make_dataset_test(self.dir_AR))
### 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.BR_paths = sorted(make_dataset(self.dir_B))
self.dataset_size = len(self.A_paths)
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
### input A (label maps)
if opt.isTrain or opt.use_encoded_image:
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))
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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.BR_paths = sorted(make_dataset(self.dir_B))
self.dataset_size = len(self.A_paths)
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 initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.diction = {}
im_names = []
c_names = []
if opt.data_list != None:
opt.phase = "combined"
with open(osp.join(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)
### input A (label maps)
if opt.isTrain or opt.use_encoded_image:
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.AR_paths = make_dataset(self.dir_A)
if opt.data_list != None:
self.A_paths = [
os.path.join(self.dir_A, n).replace(".jpg", ".png")
for n in im_names
]
self.fine_height = 256
self.fine_width = 192
self.radius = 5
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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))
dir_AR = '_AR' if self.opt.label_nc == 0 else '_labelref'
self.dir_AR = os.path.join(opt.dataroot, opt.phase + dir_AR)
self.AR_paths = sorted(make_dataset_test(self.dir_AR))
### 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.BR_paths = sorted(make_dataset(self.dir_B))
if opt.data_list != None:
self.B_paths = [os.path.join(self.dir_B, n) for n in im_names]
self.dataset_size = len(self.A_paths)
self.build_index(self.B_paths)
### input E (edge_maps)
if opt.isTrain or opt.use_encoded_image:
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)
if opt.data_list != None:
self.E_paths = [os.path.join(self.dir_E, n) for n in c_names]
# ### input M (masks)
# if opt.isTrain or opt.use_encoded_image:
# 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)
#
# ### input MC(color_masks)
# if opt.isTrain or opt.use_encoded_image:
# 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)
### input C(color)
if opt.isTrain or opt.use_encoded_image:
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)
if opt.data_list != None:
self.C_paths = [os.path.join(self.dir_C, n) for n in c_names]
# self.build_index(self.C_paths)
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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 initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.diction = {}
### input A (label maps)
if opt.isTrain or opt.use_encoded_image:
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.AR_paths = make_dataset(self.dir_A)
self.fine_height = 256
self.fine_width = 192
self.radius = 5
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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))
dir_AR = '_AR' if self.opt.label_nc == 0 else '_labelref'
self.dir_AR = os.path.join(opt.dataroot, opt.phase + dir_AR)
self.AR_paths = sorted(make_dataset_test(self.dir_AR))
### 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.BR_paths = sorted(make_dataset(self.dir_B))
self.dataset_size = len(self.A_paths)
self.build_index(self.B_paths)
### input E (edge_maps)
if opt.isTrain or opt.use_encoded_image:
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)
### input M (masks)
if opt.isTrain or opt.use_encoded_image:
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)
### input MC(color_masks)
if opt.isTrain or opt.use_encoded_image:
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)
### input C(color)
if opt.isTrain or opt.use_encoded_image:
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)
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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 initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.diction = {}
### input A (label maps)
if opt.isTrain or opt.use_encoded_image:
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.AR_paths = make_dataset(self.dir_A)
self.fine_height = 256
self.fine_width = 192
self.radius = 5
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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))
dir_AR = '_AR' if self.opt.label_nc == 0 else '_labelref'
self.dir_AR = os.path.join(opt.dataroot, opt.phase + dir_AR)
self.AR_paths = sorted(make_dataset_test(self.dir_AR))
### 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.BR_paths = sorted(make_dataset(self.dir_B))
self.dataset_size = len(self.A_paths)
self.build_index(self.B_paths)
### input E (edge_maps) cloth
if opt.isTrain or opt.use_encoded_image:
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.E_paths = make_dataset_cloth(self.dir_E)
self.ER_paths = make_dataset_cloth(self.dir_E)
### input M (masks)
if opt.isTrain or opt.use_encoded_image:
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)
### input MC(color_masks) cloth
if opt.isTrain or opt.use_encoded_image:
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.MC_paths = make_dataset_cloth(self.dir_MC)
self.MCR_paths = make_dataset_cloth(self.dir_MC)
### input C(color) cloth
if opt.isTrain or opt.use_encoded_image:
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.C_paths = make_dataset_cloth(self.dir_C)
self.CR_paths = make_dataset_cloth(self.dir_C)
# self.build_index(self.C_paths)
### input A test (label maps)
if not (opt.isTrain or opt.use_encoded_image):
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 VS (VTON Segmentation)
if opt.isTrain or opt.use_encoded_image:
dir_VS = '_seg'
self.dir_VS = os.path.join(opt.dataroot, opt.phase + dir_VS)
self.VS_paths = sorted(make_dataset(self.dir_VS))
self.VSR_paths = make_dataset(self.dir_VS)
### input S (Mesh Shape)
if opt.isTrain or opt.use_encoded_image:
dir_S = '_mesh'
self.dir_S = os.path.join(opt.dataroot, opt.phase + dir_S)
self.S_paths = sorted(make_dataset(self.dir_S))
self.SR_paths = make_dataset(self.dir_S)
### input D (Dense Pose)
if opt.isTrain or opt.use_encoded_image:
dir_D = '_dense'
self.dir_D = os.path.join(opt.dataroot, opt.phase + dir_D)
self.D_paths = sorted(make_dataset(self.dir_D))
self.DR_paths = make_dataset(self.dir_D)
### input CLM (Cloth Fashion Landmarks)
if opt.isTrain or opt.use_encoded_image:
dir_CLM = '_landmarks_cloth'
self.dir_CLM = os.path.join(opt.dataroot, opt.phase + dir_CLM)
self.CLM_paths = sorted(make_dataset(self.dir_CLM))
self.CLMR_paths = make_dataset(self.dir_CLM)
