Python albumentations.RandomRotate90() Examples

def __init__(
        self,
        input_key: str = "image",
        output_key: str = "rotation_factor",
        targets_key: str = None,
        rotate_probability: float = 1.0,
        hflip_probability: float = 0.5,
        one_hot_classes: int = None,
    ):
        """
        Args:
            input_key (str): input key to use from annotation dict
            output_key (str): output key to use to store the result
        """
        self.input_key = input_key
        self.output_key = output_key
        self.targets_key = targets_key
        self.rotate_probability = rotate_probability
        self.hflip_probability = hflip_probability
        self.rotate = albu.RandomRotate90()
        self.hflip = albu.HorizontalFlip()
        self.one_hot_classes = (
            one_hot_classes * 8 if one_hot_classes is not None else None
        ) 

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"]) 

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"]) 

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