Python albumentations.HueSaturationValue方法代码示例

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [as 别名]
def test_transform_pipeline_serialization(seed, image, mask):
    aug = A.Compose(
        [
            A.OneOrOther(
                A.Compose(
                    [
                        A.Resize(1024, 1024),
                        A.RandomSizedCrop(min_max_height=(256, 1024), height=512, width=512, p=1),
                        A.OneOf(
                            [
                                A.RandomSizedCrop(min_max_height=(256, 512), height=384, width=384, p=0.5),
                                A.RandomSizedCrop(min_max_height=(256, 512), height=512, width=512, p=0.5),
                            ]
                        ),
                    ]
                ),
                A.Compose(
                    [
                        A.Resize(1024, 1024),
                        A.RandomSizedCrop(min_max_height=(256, 1025), height=256, width=256, p=1),
                        A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1),
                    ]
                ),
            ),
            A.HorizontalFlip(p=1),
            A.RandomBrightnessContrast(p=0.5),
        ]
    )
    serialized_aug = A.to_dict(aug)
    deserialized_aug = A.from_dict(serialized_aug)
    set_seed(seed)
    aug_data = aug(image=image, mask=mask)
    set_seed(seed)
    deserialized_aug_data = deserialized_aug(image=image, mask=mask)
    assert np.array_equal(aug_data["image"], deserialized_aug_data["image"])
    assert np.array_equal(aug_data["mask"], deserialized_aug_data["mask"]) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [as 别名]
def test_transform_pipeline_serialization_with_bboxes(seed, image, bboxes, bbox_format, labels):
    aug = A.Compose(
        [
            A.OneOrOther(
                A.Compose([A.RandomRotate90(), A.OneOf([A.HorizontalFlip(p=0.5), A.VerticalFlip(p=0.5)])]),
                A.Compose([A.Rotate(p=0.5), A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1)]),
            ),
            A.HorizontalFlip(p=1),
            A.RandomBrightnessContrast(p=0.5),
        ],
        bbox_params={"format": bbox_format, "label_fields": ["labels"]},
    )
    serialized_aug = A.to_dict(aug)
    deserialized_aug = A.from_dict(serialized_aug)
    set_seed(seed)
    aug_data = aug(image=image, bboxes=bboxes, labels=labels)
    set_seed(seed)
    deserialized_aug_data = deserialized_aug(image=image, bboxes=bboxes, labels=labels)
    assert np.array_equal(aug_data["image"], deserialized_aug_data["image"])
    assert np.array_equal(aug_data["bboxes"], deserialized_aug_data["bboxes"]) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [as 别名]
def test_transform_pipeline_serialization_with_keypoints(seed, image, keypoints, keypoint_format, labels):
    aug = A.Compose(
        [
            A.OneOrOther(
                A.Compose([A.RandomRotate90(), A.OneOf([A.HorizontalFlip(p=0.5), A.VerticalFlip(p=0.5)])]),
                A.Compose([A.Rotate(p=0.5), A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1)]),
            ),
            A.HorizontalFlip(p=1),
            A.RandomBrightnessContrast(p=0.5),
        ],
        keypoint_params={"format": keypoint_format, "label_fields": ["labels"]},
    )
    serialized_aug = A.to_dict(aug)
    deserialized_aug = A.from_dict(serialized_aug)
    set_seed(seed)
    aug_data = aug(image=image, keypoints=keypoints, labels=labels)
    set_seed(seed)
    deserialized_aug_data = deserialized_aug(image=image, keypoints=keypoints, labels=labels)
    assert np.array_equal(aug_data["image"], deserialized_aug_data["image"])
    assert np.array_equal(aug_data["keypoints"], deserialized_aug_data["keypoints"]) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [as 别名]
def augment(image, boxes):
    h, w, _ = image.shape
    labels, boxes_coord = boxes[:, 0], boxes[:, 1:]
    labels = labels.tolist()
    boxes_coord = boxes_coord * h     # 得到原图尺寸下的坐标（未归一化的坐标）
    boxes_coord[:, 0] = np.clip(boxes_coord[:, 0]-boxes_coord[:, 2]/2, a_min=0, a_max=None)   # 确保x_min和y_min有效
    boxes_coord[:, 1] = np.clip(boxes_coord[:, 1]-boxes_coord[:, 3]/2, a_min=0, a_max=None)
    boxes_coord = boxes_coord.tolist()      # [x_min, y_min, width, height]

    # 在这里设置数据增强的方法
    aug = A.Compose([
        A.HorizontalFlip(p=0.5),
        # A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10, p=0.5),
        # A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=5, border_mode=0, p=0.5)
    ], bbox_params={'format':'coco', 'label_fields': ['category_id']})

    augmented = aug(image=image, bboxes=boxes_coord, category_id=labels)

    # 经过aug之后，如果把boxes变没了，则返回原来的图片
    if augmented['bboxes']:
        image = augmented['image']

        boxes_coord = np.array(augmented['bboxes']) # x_min, y_min, w, h → x, y, w, h
        boxes_coord[:, 0] = boxes_coord[:, 0] + boxes_coord[:, 2]/2
        boxes_coord[:, 1] = boxes_coord[:, 1] + boxes_coord[:, 3]/2
        boxes_coord = boxes_coord / h
        labels = np.array(augmented['category_id'])[:, None]
        boxes = np.concatenate((labels, boxes_coord), 1)

    return image, boxes 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [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'],
        },
    ) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [as 别名]
def test_force_apply():
    """
    Unit test for https://github.com/albumentations-team/albumentations/issues/189
    """
    aug = A.Compose(
        [
            A.OneOrOther(
                A.Compose(
                    [
                        A.RandomSizedCrop(min_max_height=(256, 1025), height=512, width=512, p=1),
                        A.OneOf(
                            [
                                A.RandomSizedCrop(min_max_height=(256, 512), height=384, width=384, p=0.5),
                                A.RandomSizedCrop(min_max_height=(256, 512), height=512, width=512, p=0.5),
                            ]
                        ),
                    ]
                ),
                A.Compose(
                    [
                        A.RandomSizedCrop(min_max_height=(256, 1025), height=256, width=256, p=1),
                        A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1),
                    ]
                ),
            ),
            A.HorizontalFlip(p=1),
            A.RandomBrightnessContrast(p=0.5),
        ]
    )

    res = aug(image=np.zeros((1248, 1248, 3), dtype=np.uint8))
    assert res["image"].shape[0] in (256, 384, 512)
    assert res["image"].shape[1] in (256, 384, 512) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [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'])) 

# 需要导入模块: import albumentations [as 别名]
# 或者: from albumentations import HueSaturationValue [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'],
        },
    )