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Python albumentations.RandomGamma方法代码示例

本文整理汇总了Python中albumentations.RandomGamma方法的典型用法代码示例。如果您正苦于以下问题:Python albumentations.RandomGamma方法的具体用法?Python albumentations.RandomGamma怎么用?Python albumentations.RandomGamma使用的例子?那么, 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albumentations的用法示例。


在下文中一共展示了albumentations.RandomGamma方法的4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: get_transform

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import RandomGamma [as 别名]
def get_transform(train: bool) -> Callable:
    train_initial_size = 2048
    crop_min_max_height = (400, 533)
    crop_width = 512
    crop_height = 384
    if train:
        transforms = [
            A.LongestMaxSize(max_size=train_initial_size),
            A.RandomSizedCrop(
                min_max_height=crop_min_max_height,
                width=crop_width,
                height=crop_height,
                w2h_ratio=crop_width / crop_height,
            ),
            A.HueSaturationValue(
                hue_shift_limit=7,
                sat_shift_limit=10,
                val_shift_limit=10,
            ),
            A.RandomBrightnessContrast(),
            A.RandomGamma(),
        ]
    else:
        test_size = int(train_initial_size *
                        crop_height / np.mean(crop_min_max_height))
        print(f'Test image max size {test_size} px')
        transforms = [
            A.LongestMaxSize(max_size=test_size),
        ]
    transforms.extend([
        ToTensor(),
    ])
    return A.Compose(
        transforms,
        bbox_params={
            'format': 'coco',
            'min_area': 0,
            'min_visibility': 0.5,
            'label_fields': ['labels'],
        },
    ) 
开发者ID:lopuhin,项目名称:kaggle-kuzushiji-2019,代码行数:43,代码来源:dataset.py

示例2: get_augumentation

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import RandomGamma [as 别名]
def get_augumentation(phase, width=512, height=512, min_area=0., min_visibility=0.):
    list_transforms = []
    if phase == 'train':
        list_transforms.extend([
            albu.augmentations.transforms.LongestMaxSize(
                max_size=width, always_apply=True),
            albu.PadIfNeeded(min_height=height, min_width=width,
                             always_apply=True, border_mode=0, value=[0, 0, 0]),
            albu.augmentations.transforms.RandomResizedCrop(
                height=height,
                width=width, p=0.3),
            albu.augmentations.transforms.Flip(),
            albu.augmentations.transforms.Transpose(),
            albu.OneOf([
                albu.RandomBrightnessContrast(brightness_limit=0.5,
                                              contrast_limit=0.4),
                albu.RandomGamma(gamma_limit=(50, 150)),
                albu.NoOp()
            ]),
            albu.OneOf([
                albu.RGBShift(r_shift_limit=20, b_shift_limit=15,
                              g_shift_limit=15),
                albu.HueSaturationValue(hue_shift_limit=5,
                                        sat_shift_limit=5),
                albu.NoOp()
            ]),
            albu.CLAHE(p=0.8),
            albu.HorizontalFlip(p=0.5),
            albu.VerticalFlip(p=0.5),
        ])
    if(phase == 'test' or phase == 'valid'):
        list_transforms.extend([
            albu.Resize(height=height, width=width)
        ])
    list_transforms.extend([
        albu.Normalize(mean=(0.485, 0.456, 0.406),
                       std=(0.229, 0.224, 0.225), p=1),
        ToTensor()
    ])
    if(phase == 'test'):
        return albu.Compose(list_transforms)
    return albu.Compose(list_transforms, bbox_params=albu.BboxParams(format='pascal_voc', min_area=min_area,
                                                                     min_visibility=min_visibility, label_fields=['category_id'])) 
开发者ID:toandaominh1997,项目名称:EfficientDet.Pytorch,代码行数:45,代码来源:augmentation.py

示例3: get_transform

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import RandomGamma [as 别名]
def get_transform(
        *,
        train: bool,
        test_height: int,
        crop_width: int,
        crop_height: int,
        scale_aug: float,
        color_hue_aug: int,
        color_sat_aug: int,
        color_val_aug: int,
        normalize: bool = True,
        ) -> Callable:
    train_initial_size = 3072  # this value should not matter any more?
    crop_ratio = crop_height / test_height
    crop_min_max_height = tuple(
        int(train_initial_size * crop_ratio * (1 + sign * scale_aug))
        for sign in [-1, 1])
    if train:
        transforms = [
            LongestMaxSizeRandomSizedCrop(
                max_size=train_initial_size,
                min_max_height=crop_min_max_height,
                width=crop_width,
                height=crop_height,
                w2h_ratio=crop_width / crop_height,
            ),
            A.HueSaturationValue(
                hue_shift_limit=color_hue_aug,
                sat_shift_limit=color_sat_aug,
                val_shift_limit=color_val_aug,
            ),
            A.RandomBrightnessContrast(),
            A.RandomGamma(),
        ]
    else:
        transforms = [
            A.LongestMaxSize(max_size=test_height),
        ]
    if normalize:
        transforms.append(A.Normalize())
    transforms.extend([
        ToTensor(),
    ])
    return A.Compose(
        transforms,
        bbox_params={
            'format': 'coco',
            'min_area': 0,
            'min_visibility': 0.99,
            'label_fields': ['labels'],
        },
    ) 
开发者ID:lopuhin,项目名称:kaggle-kuzushiji-2019,代码行数:54,代码来源:dataset.py

示例4: __init__

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import RandomGamma [as 别名]
def __init__(self,
                 base_dir='../../data/apolloscape',
                 road_record_list=[{'road':'road02_seg','record':[22, 23, 24, 25, 26]}, {'road':'road03_seg', 'record':[7, 8, 9, 10, 11, 12]}],
                 split='train',
                 ignore_index=255,
                 debug=False):
        self.debug = debug
        self.base_dir = Path(base_dir)
        self.ignore_index = ignore_index
        self.split = split
        self.img_paths = []
        self.lbl_paths = []

        for road_record in road_record_list:
          self.road_dir = self.base_dir / Path(road_record['road'])
          self.record_list = road_record['record']

          for record in self.record_list:
            img_paths_tmp = self.road_dir.glob(f'ColorImage/Record{record:03}/Camera 5/*.jpg')
            lbl_paths_tmp = self.road_dir.glob(f'Label/Record{record:03}/Camera 5/*.png')

            img_paths_basenames = {Path(img_path.name).stem for img_path in img_paths_tmp}
            lbl_paths_basenames = {Path(lbl_path.name).stem.replace('_bin', '') for lbl_path in lbl_paths_tmp}

            intersection_basenames = img_paths_basenames & lbl_paths_basenames

            img_paths_intersection = [self.road_dir / Path(f'ColorImage/Record{record:03}/Camera 5/{intersection_basename}.jpg')
                                      for intersection_basename in intersection_basenames]
            lbl_paths_intersection = [self.road_dir / Path(f'Label/Record{record:03}/Camera 5/{intersection_basename}_bin.png')
                                      for intersection_basename in intersection_basenames]

            self.img_paths += img_paths_intersection
            self.lbl_paths += lbl_paths_intersection

        self.img_paths.sort()
        self.lbl_paths.sort()
        print(len(self.img_paths), len(self.lbl_paths))
        assert len(self.img_paths) == len(self.lbl_paths)

        self.resizer = albu.Resize(height=512, width=1024)
        self.augmenter = albu.Compose([albu.HorizontalFlip(p=0.5),
                                       # albu.RandomRotate90(p=0.5),
                                       albu.Rotate(limit=10, p=0.5),
                                       # albu.CLAHE(p=0.2),
                                       # albu.RandomContrast(p=0.2),
                                       # albu.RandomBrightness(p=0.2),
                                       # albu.RandomGamma(p=0.2),
                                       # albu.GaussNoise(p=0.2),
                                       # albu.Cutout(p=0.2)
                                       ])
        self.img_transformer = transforms.Compose([transforms.ToTensor(),
                                                   transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                                                        std=[0.229, 0.224, 0.225])])
        self.lbl_transformer = torch.LongTensor 
开发者ID:nyoki-mtl,项目名称:pytorch-segmentation,代码行数:56,代码来源:apolloscape.py


注:本文中的albumentations.RandomGamma方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。