Source code for albumentations.core.composition

from __future__ import division

import random
import warnings

import numpy as np

from albumentations.augmentations.keypoints_utils import convert_keypoints_from_albumentations, filter_keypoints, \
    convert_keypoints_to_albumentations, check_keypoints
from albumentations.core.transforms_interface import DualTransform
from albumentations.imgaug.transforms import DualIAATransform
from albumentations.augmentations.bbox_utils import convert_bboxes_from_albumentations, \
    convert_bboxes_to_albumentations, filter_bboxes, check_bboxes

__all__ = ['Compose', 'OneOf', 'OneOrOther']

def find_dual_start_end(transforms):
    dual_start_end = None
    last_dual = None
    for idx, transform in enumerate(transforms):
        if isinstance(transform, DualTransform):
            last_dual = idx
            if dual_start_end is None:
                dual_start_end = [idx]
        if isinstance(transform, BaseCompose):
            inside = find_dual_start_end(transform)
            if inside is not None:
                last_dual = idx
                if dual_start_end is None:
                    dual_start_end = [idx]
    if dual_start_end is not None:
    return dual_start_end

def find_always_apply_transforms(transforms):
    new_transforms = []
    for transform in transforms:
        if isinstance(transform, BaseCompose):
        elif transform.always_apply:
    return new_transforms

def set_always_apply(transforms):
    for t in transforms:
        t.always_apply = True

class BaseCompose(object):
    def __init__(self, transforms, p):
        self.transforms = transforms
        self.p = p

    def __getitem__(self, item):
        return self.transforms[item]

    def add_targets(self, additional_targets):
        if additional_targets:
            for t in self.transforms:

[docs]class Compose(BaseCompose): """Compose transforms and handle all transformations regrading bounding boxes Args: transforms (list): list of transformations to compose. bbox_params (dict): Parameters for bounding boxes transforms keypoint_params (dict): Parameters for keypoints transforms additional_targets (dict): Dict with keys - new target name, values - old target name. ex: {'image2': 'image'} p (float): probability of applying all list of transforms. Default: 1.0. **bbox_params** dictionary contains the following keys: * **format** (*str*): format of bounding boxes. Should be 'coco', 'pascal_voc' or 'albumentations'. If None - don't use bboxes. The `coco` format of a bounding box looks like `[x_min, y_min, width, height]`, e.g. [97, 12, 150, 200]. The `pascal_voc` format of a bounding box looks like `[x_min, y_min, x_max, y_max]`, e.g. [97, 12, 247, 212]. The `albumentations` format of a bounding box looks like `pascal_voc`, but between [0, 1], in other words: [x_min, y_min, x_max, y_max]`, e.g. [0.2, 0.3, 0.4, 0.5]. * | **label_fields** (*list*): list of fields that are joined with boxes, e.g labels. | Should be same type as boxes. * | **min_area** (*float*): minimum area of a bounding box. All bounding boxes whose | visible area in pixels is less than this value will be removed. Default: 0.0. * | **min_visibility** (*float*): minimum fraction of area for a bounding box | to remain this box in list. Default: 0.0. """ def __init__(self, transforms, preprocessing_transforms=[], postprocessing_transforms=[], to_tensor=None, bbox_params={}, keypoint_params={}, additional_targets={}, p=1.0): if preprocessing_transforms: warnings.warn("preprocessing transforms are deprecated, use always_apply flag for this purpose. " "will be removed in 0.3.0", DeprecationWarning) set_always_apply(preprocessing_transforms) if postprocessing_transforms: warnings.warn("postprocessing transforms are deprecated, use always_apply flag for this purpose" "will be removed in 0.3.0", DeprecationWarning) set_always_apply(postprocessing_transforms) if to_tensor is not None: warnings.warn("to_tensor in Compose is deprecated, use always_apply flag for this purpose" "will be removed in 0.3.0", DeprecationWarning) to_tensor.always_apply = True # todo deprecated _transforms = (preprocessing_transforms + [t for t in transforms if t is not None] + postprocessing_transforms) if to_tensor is not None: _transforms.append(to_tensor) super(Compose, self).__init__(_transforms, p) self.bboxes_name = 'bboxes' self.keypoints_name = 'keypoints' self.params = { self.bboxes_name: bbox_params, self.keypoints_name: keypoint_params } self.bbox_format = bbox_params.get('format', None) self.bbox_label_fields = bbox_params.get('label_fields', []) self.keypoints_format = keypoint_params.get('format', None) self.keypoints_label_fields = keypoint_params.get('label_fields', []) # IAA-based augmentations supports only transformation of xy keypoints. # If your keypoints formats is other than 'xy' we emit warning to let user # be aware that angle and size will not be modified. if self.keypoints_format is not None and self.keypoints_format != 'xy': for transform in self.transforms: if isinstance(transform, DualIAATransform): warnings.warn("{} transformation supports only 'xy' keypoints " "augmentation. You have '{}' keypoints format. Scale " "and angle WILL NOT BE transformed.".format(transform.__class__.__name__, self.keypoints_format)) break self.add_targets(additional_targets) def __call__(self, force_apply=False, **data): need_to_run = force_apply or random.random() < self.p transforms = self.transforms if need_to_run else find_always_apply_transforms(self.transforms) dual_start_end = None if self.params[self.bboxes_name] or self.params[self.keypoints_name]: dual_start_end = find_dual_start_end(transforms) if (self.params[self.bboxes_name] and len(data.get(self.bboxes_name, [])) and len(data[self.bboxes_name][0]) < 5): if 'label_fields' not in self.params[self.bboxes_name]: raise Exception("Please specify 'label_fields' in 'bbox_params' or add labels to the end of bbox " "because bboxes must have labels") if 'label_fields' in self.params[self.bboxes_name]: if not all(l in data.keys() for l in self.params[self.bboxes_name]['label_fields']): raise Exception("Your 'label_fields' are not valid - them must have same names as params in dict") if 'label_fields' in self.params[self.keypoints_name]: if not all(l in data.keys() for l in self.params[self.keypoints_name]['label_fields']): raise Exception("Your 'label_fields' are not valid - them must have same names as params in " "'keypoint_params' dict") for idx, t in enumerate(transforms): if dual_start_end is not None and idx == dual_start_end[0]: if self.params[self.bboxes_name]: data = data_preprocessing(self.bboxes_name, self.params[self.bboxes_name], check_bboxes, convert_bboxes_to_albumentations, data) if self.params[self.keypoints_name]: data = data_preprocessing(self.keypoints_name, self.params[self.keypoints_name], check_keypoints, convert_keypoints_to_albumentations, data) data = t(force_apply=force_apply, **data) if dual_start_end is not None and idx == dual_start_end[1]: if self.params[self.bboxes_name]: data = data_postprocessing(self.bboxes_name, self.params[self.bboxes_name], check_bboxes, filter_bboxes, convert_bboxes_from_albumentations, data) if self.params[self.keypoints_name]: data = data_postprocessing(self.keypoints_name, self.params[self.keypoints_name], check_keypoints, filter_keypoints, convert_keypoints_from_albumentations, data) return data
def data_postprocessing(data_name, params, check_fn, filter_fn, convert_fn, data): rows, cols = data['image'].shape[:2] additional_params = {} if data_name == 'bboxes': additional_params['min_area'] = params.get('min_area', 0.0), additional_params['min_visibility'] = params.get('min_visibility', 0.0) elif data_name == 'keypoints': additional_params['remove_invisible'] = bool(params.get('remove_invisible', True)) else: raise Exception('Not known data_name') data[data_name] = filter_fn(data[data_name], rows, cols, **additional_params) if params['format'] == 'albumentations': check_fn(data[data_name]) else: data[data_name] = convert_fn(data[data_name], params['format'], rows, cols, check_validity=bool(params.get('remove_invisible', True))) data = remove_label_fields_from_data(data_name, params.get('label_fields', []), data) return data def data_preprocessing(data_name, params, check_fn, convert_fn, data): if data_name not in data: raise Exception('Please name field with {} `{}`'.format(data_name, data_name)) data = add_label_fields_to_data(data_name, params.get('label_fields', []), data) rows, cols = data['image'].shape[:2] if params['format'] == 'albumentations': check_fn(data[data_name]) else: data[data_name] = convert_fn(data[data_name], params['format'], rows, cols, check_validity=True) return data def add_label_fields_to_data(data_name, label_fields, data): for field in label_fields: data_with_added_field = [] for d, field_value in zip(data[data_name], data[field]): data_with_added_field.append(list(d) + [field_value]) data[data_name] = data_with_added_field return data def remove_label_fields_from_data(data_name, label_fields, data): for idx, field in enumerate(label_fields): field_values = [] for bbox in data[data_name]: field_values.append(bbox[4 + idx]) data[field] = field_values if label_fields: data[data_name] = [d[:4] for d in data[data_name]] return data
[docs]class OneOf(BaseCompose): """Select on of transforms to apply Args: transforms (list): list of transformations to compose. p (float): probability of applying selected transform. Default: 0.5. """ def __init__(self, transforms, p=0.5): super(OneOf, self).__init__(transforms, p) transforms_ps = [t.p for t in transforms] s = sum(transforms_ps) self.transforms_ps = [t / s for t in transforms_ps] def __call__(self, force_apply=False, **data): if force_apply or random.random() < self.p: random_state = np.random.RandomState(random.randint(0, 2 ** 32 - 1)) t = random_state.choice(self.transforms, p=self.transforms_ps) data = t(force_apply=True, **data) return data
class OneOrOther(BaseCompose): def __init__(self, first, second, p=0.5): super(OneOrOther, self).__init__([first, second], p) def __call__(self, force_apply=False, **data): if random.random() < self.p: return self.transforms[0](force_apply=True, **data) else: return self.transforms[-1](force_apply=True, **data)