本文整理汇总了Python中data.image_folder.make_dataset方法的典型用法代码示例。如果您正苦于以下问题：Python image_folder.make_dataset方法的具体用法？Python image_folder.make_dataset怎么用？Python image_folder.make_dataset使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类data.image_folder的用法示例。
在下文中一共展示了image_folder.make_dataset方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'
self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'
self.A_size = len(self.A_paths) # get the size of dataset A
self.B_size = len(self.B_paths) # get the size of dataset B
btoA = self.opt.direction == 'BtoA'
input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image
output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image
self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))
self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1)) 开发者ID:Mingtzge，项目名称:2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement，代码行数:21，代码来源:unaligned_dataset.py
示例2: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
# self.transform = get_transform(opt)
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.transform = transforms.Compose(transform_list) 示例3: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.loadSize = opt.loadSize
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
if opt.phase == 'train':
self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C')
self.C_paths = make_dataset(self.dir_C)
self.C_paths = sorted(self.C_paths)
self.C_size = len(self.C_paths) 开发者ID:csqiangwen，项目名称:Single-Image-Reflection-Removal-Beyond-Linearity，代码行数:23，代码来源:synthesis_dataset.py
示例4: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.phase = opt.phase
self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C')
if opt.phase == 'train':
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.dir_W = os.path.join(opt.dataroot, opt.phase + 'W')
self.C_paths = make_dataset(self.dir_C)
if opt.phase == 'train':
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.W_paths = make_dataset(self.dir_W)
self.C_paths = sorted(self.C_paths)
if opt.phase == 'train':
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.W_paths = sorted(self.W_paths)
self.C_size = len(self.C_paths) 开发者ID:csqiangwen，项目名称:Single-Image-Reflection-Removal-Beyond-Linearity，代码行数:25，代码来源:removal_dataset.py
示例5: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
### input A (label maps)
self.label_paths = sorted(make_dataset(opt.label_root))
# self.simage_paths = sorted(make_dataset(opt.input_image_root))
### input B (real images)
if opt.isTrain:
self.rimage_paths = sorted(make_dataset(opt.real_image_root))
### instance maps
if not opt.no_instance:
self.dir_inst = os.path.join(opt.dataroot, opt.phase + '_inst')
self.inst_paths = sorted(make_dataset(self.dir_inst))
### load precomputed instance-wise encoded features
if opt.load_features:
self.dir_feat = os.path.join(opt.dataroot, opt.phase + '_feat')
print('----------- loading features from %s ----------' % self.dir_feat)
self.feat_paths = sorted(make_dataset(self.dir_feat))
x = 'train' if opt.isTrain else 'test'
self.crop_coor = torch.load('../data/%s/%s/face_crop_coor.torch'% (opt.dataset_name, x))
self.dataset_size = len(self.label_paths) 示例6: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, 'synthia', 'RGB')
self.dir_B = os.path.join(opt.dataroot, 'cityscapes', 'leftImg8bit')
self.dir_A_label = os.path.join(opt.dataroot, 'synthia', 'GT', 'parsed_LABELS')
self.dir_B_label = os.path.join(opt.dataroot, 'cityscapes', 'gtFine')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
self.A_labels = make_dataset(self.dir_A_label)
self.B_labels = make_cs_labels(self.dir_B_label)
self.A_labels = sorted(self.A_labels)
self.B_labels = sorted(self.B_labels)
self.transform = get_transform(opt)
self.label_transform = get_label_transform(opt) 示例7: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, 'gta5', 'images')
self.dir_B = os.path.join(opt.dataroot, 'cityscapes', 'leftImg8bit')
self.dir_A_label = os.path.join(opt.dataroot, 'gta5', 'labels')
self.dir_B_label = os.path.join(opt.dataroot, 'cityscapes', 'gtFine')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
self.A_labels = make_dataset(self.dir_A_label)
self.B_labels = make_cs_labels(self.dir_B_label)
self.A_labels = sorted(self.A_labels)
self.B_labels = sorted(self.B_labels)
self.transform = get_transform(opt)
self.label_transform = get_label_transform(opt) 示例8: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, dataroot, load_size=64):
self.root = dataroot
self.load_size = load_size
self.dir_p0 = os.path.join(self.root, 'p0')
self.p0_paths = make_dataset(self.dir_p0)
self.p0_paths = sorted(self.p0_paths)
self.dir_p1 = os.path.join(self.root, 'p1')
self.p1_paths = make_dataset(self.dir_p1)
self.p1_paths = sorted(self.p1_paths)
transform_list = []
transform_list.append(transforms.Scale(load_size))
transform_list += [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),(0.5, 0.5, 0.5))]
self.transform = transforms.Compose(transform_list)
# judgement directory
self.dir_S = os.path.join(self.root, 'same')
self.same_paths = make_dataset(self.dir_S,mode='np')
self.same_paths = sorted(self.same_paths) 示例9: __init__
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory
self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths
assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image
self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc
self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc 开发者ID:Mingtzge，项目名称:2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement，代码行数:14，代码来源:aligned_dataset.py
示例10: __init__
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))
input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc
self.transform = get_transform(opt, grayscale=(input_nc == 1)) 开发者ID:Mingtzge，项目名称:2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement，代码行数:12，代码来源:single_dataset.py
示例11: __init__
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir = os.path.join(opt.dataroot, opt.phase)
self.AB_paths = sorted(make_dataset(self.dir, opt.max_dataset_size))
assert(opt.input_nc == 1 and opt.output_nc == 2 and opt.direction == 'AtoB')
self.transform = get_transform(self.opt, convert=False) 开发者ID:Mingtzge，项目名称:2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement，代码行数:13，代码来源:colorization_dataset.py
示例12: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
self.transform = get_transform(opt) 示例13: __init__
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))
self.transform = get_transform(opt, grayscale=(self.opt.input_nc == 1)) 示例14: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
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')
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.transform = transforms.Compose(transform_list) 示例15: initialize
# 需要导入模块: from data import image_folder [as 别名]
# 或者: from data.image_folder import make_dataset [as 别名]
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_scribbles = os.path.join(opt.dataroot, 'scribbles') #'pix2pix') #'scribbles' ) #'masks')
self.dir_images = os.path.join(opt.dataroot, 'images') #os.path.join(opt.dataroot, 'images')
self.classes = sorted(os.listdir(self.dir_images)) # sorted so that the same order in all cases; check if you've to change this with other models
self.num_classes = len(self.classes)
self.scribble_paths = []
self.images_paths = []
for cl in self.classes:
self.scribble_paths.append(sorted( make_dataset( os.path.join( self.dir_scribbles , cl ) ) ) )
self.images_paths.append( sorted( make_dataset( os.path.join( self.dir_images , cl ) ) ) )
self.cum_sizes = []
self.sizes = []
size =0
for i in range(self.num_classes):
size += len(self.scribble_paths[i])
self.cum_sizes.append(size)
self.sizes.append(size)
self.transform = get_transform(opt)
self.sparse_transform = get_sparse_transform(opt)
self.mask_transform = get_mask_transform(opt)