Python albumentations.Compose方法代碼示例

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def __init__(
        self,
        data_root: str,
        split: str,
        tokenizer: SentencePieceBPETokenizer,
        image_transform: Callable = T.DEFAULT_IMAGE_TRANSFORM,
        max_caption_length: int = 30,
        use_single_caption: bool = False,
        percentage: float = 100.0,
    ):
        lmdb_path = os.path.join(data_root, f"serialized_{split}.lmdb")
        self.reader = LmdbReader(lmdb_path, percentage=percentage)

        self.image_transform = image_transform
        self.caption_transform = alb.Compose(
            [
                T.NormalizeCaption(),
                T.TokenizeCaption(tokenizer),
                T.TruncateCaptionTokens(max_caption_length),
            ]
        )
        self.use_single_caption = use_single_caption
        self.padding_idx = tokenizer.token_to_id("<unk>") 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def get_training_augmentation(resize_to=(320,640), crop_size=(288,576)):
    print('[get_training_augmentation] crop_size:', crop_size, ', resize_to:', resize_to) 

    train_transform = [
        albu.HorizontalFlip(p=0.5),
        albu.VerticalFlip(p=0.5),
        albu.ShiftScaleRotate(scale_limit=0.20, rotate_limit=10, shift_limit=0.1, p=0.5, border_mode=cv2.BORDER_CONSTANT, value=0),
        albu.GridDistortion(p=0.5),
        albu.Resize(*resize_to),
        albu.RandomCrop(*crop_size),
        albu.ChannelShuffle(),
        albu.InvertImg(),
        albu.ToGray(),
        albu.Normalize(),
    ]

    return albu.Compose(train_transform) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
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"]) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
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"]) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def __init__(self,
                 transforms,
                 bbox_params=None,
                 keymap=None,
                 update_pad_shape=False,
                 skip_img_without_anno=False):
        if Compose is None:
            raise RuntimeError('albumentations is not installed')

        self.transforms = transforms
        self.filter_lost_elements = False
        self.update_pad_shape = update_pad_shape
        self.skip_img_without_anno = skip_img_without_anno

        # A simple workaround to remove masks without boxes
        if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
                and 'filter_lost_elements' in bbox_params):
            self.filter_lost_elements = True
            self.origin_label_fields = bbox_params['label_fields']
            bbox_params['label_fields'] = ['idx_mapper']
            del bbox_params['filter_lost_elements']

        self.bbox_params = (
            self.albu_builder(bbox_params) if bbox_params else None)
        self.aug = Compose([self.albu_builder(t) for t in self.transforms],
                           bbox_params=self.bbox_params)

        if not keymap:
            self.keymap_to_albu = {
                'img': 'image',
                'gt_masks': 'masks',
                'gt_bboxes': 'bboxes'
            }
        else:
            self.keymap_to_albu = keymap
        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()} 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def augment(image, boxes):
    h, w, _ = image.shape
    labels, boxes_coord = boxes[:, 0], boxes[:, 1:]
    labels = labels.tolist()
    boxes_coord = boxes_coord * h     # 得到原圖尺寸下的坐標（未歸一化的坐標）
    boxes_coord[:, 0] = np.clip(boxes_coord[:, 0]-boxes_coord[:, 2]/2, a_min=0, a_max=None)   # 確保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]

    # 在這裏設置數據增強的方法
    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)

    # 經過aug之後，如果把boxes變沒了，則返回原來的圖片
    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 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
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'],
        },
    ) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def main():
    image = cv2.imread("images/image_1.jpg")

    keypoints = cv2.goodFeaturesToTrack(
        cv2.cvtColor(image, cv2.COLOR_RGB2GRAY), maxCorners=100, qualityLevel=0.5, minDistance=5
    ).squeeze(1)

    bboxes = [(kp[0] - 10, kp[1] - 10, kp[0] + 10, kp[1] + 10) for kp in keypoints]

    disp_image = visualize(image, keypoints, bboxes)
    plt.figure(figsize=(10, 10))
    plt.imshow(cv2.cvtColor(disp_image, cv2.COLOR_RGB2BGR))
    plt.tight_layout()
    plt.show()

    aug = A.Compose(
        [A.ShiftScaleRotate(scale_limit=0.1, shift_limit=0.2, rotate_limit=10, always_apply=True)],
        bbox_params=A.BboxParams(format="pascal_voc", label_fields=["bbox_labels"]),
        keypoint_params=A.KeypointParams(format="xy"),
    )

    for _i in range(10):
        data = aug(image=image, keypoints=keypoints, bboxes=bboxes, bbox_labels=np.ones(len(bboxes)))

        aug_image = data["image"]
        aug_image = visualize(aug_image, data["keypoints"], data["bboxes"])

        plt.figure(figsize=(10, 10))
        plt.imshow(cv2.cvtColor(aug_image, cv2.COLOR_RGB2BGR))
        plt.tight_layout()
        plt.show() 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def get_test_augmentation(resize_to=(320,640)):
    """Add paddings to make image shape divisible by 32"""
    test_transform = [
        albu.Resize(*resize_to),
        albu.Normalize(),
    ]
    return albu.Compose(test_transform) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def pad_mask_image(self, mask, image, img_id, crop_shape):
        composed = Compose([PadIfNeeded(crop_shape[0], crop_shape[1], p=1),
                            RandomCrop(crop_shape[0], crop_shape[1], p=1)], p=1)

        if np.sum(mask) != 0:

            s = 0
            tries = 0
            while s == 0:
                # crop = composed(crop_shape[0], crop_shape[1])
                croped = composed(image=image, mask=mask)

                image_padded = croped['image']
                mask_padded = croped['mask']
                # print(mask_padded.shape)
                s = np.sum(mask_padded)
                tries += 1
                if tries > 5:
                    break
        else:

            croped = composed(image=image, mask=mask)
            image_padded = croped['image']
            mask_padded = croped['mask']
            
        return mask_padded, image_padded 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def get_test_transforms():
        return A.Compose([A.Normalize(mean=(0.485, ), std=(0.229, ))]) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def parse_albu(configs: List[dict]):
    res = []
    for config in configs:
        assert 'name' in config, f'name is required in {config}'
        config = config.copy()
        name = config.pop('name')
        if name == 'Compose':
            items = config.pop('items')
            aug = A.Compose(parse_albu(items), **config)
        else:
            aug = getattr(A, name)(**config)
        res.append(aug)
    return res 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def __init__(self, base_dir='../data/cityscapes', split='train',
                 affine_augmenter=None, image_augmenter=None, target_size=(1024, 2048),
                 net_type='unet', ignore_index=255, debug=False):
        self.debug = debug
        self.base_dir = Path(base_dir)
        assert net_type in ['unet', 'deeplab']
        self.net_type = net_type
        self.ignore_index = ignore_index
        self.split = 'val' if split == 'valid' else split

        self.img_paths = sorted(self.base_dir.glob(f'leftImg8bit/{self.split}/*/*leftImg8bit.png'))
        self.lbl_paths = sorted(self.base_dir.glob(f'gtFine/{self.split}/*/*gtFine_labelIds.png'))
        assert len(self.img_paths) == len(self.lbl_paths)

        # Resize
        if isinstance(target_size, str):
            target_size = eval(target_size)
        if self.split == 'train':
            if self.net_type == 'deeplab':
                target_size = (target_size[0] + 1, target_size[1] + 1)
            self.resizer = albu.Compose([albu.RandomScale(scale_limit=(-0.5, 0.5), p=1.0),
                                         PadIfNeededRightBottom(min_height=target_size[0], min_width=target_size[1],
                                                                value=0, ignore_index=self.ignore_index, p=1.0),
                                         albu.RandomCrop(height=target_size[0], width=target_size[1], p=1.0)])
        else:
            self.resizer = None

        # Augment
        if self.split == 'train':
            self.affine_augmenter = affine_augmenter
            self.image_augmenter = image_augmenter
        else:
            self.affine_augmenter = None
            self.image_augmenter = None 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
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) 

# 需要導入模塊: import albumentations [as 別名]
# 或者: from albumentations import Compose [as 別名]
def test_additional_targets_for_image_only(augmentation_cls, params):
    aug = A.Compose([augmentation_cls(always_apply=True, **params)], additional_targets={"image2": "image"})
    for _i in range(10):
        image1 = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
        image2 = image1.copy()
        res = aug(image=image1, image2=image2)
        aug1 = res["image"]
        aug2 = res["image2"]
        assert np.array_equal(aug1, aug2)