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Source code for flash.image.segmentation.input

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np

from flash.core.data.io.input import DataKeys, Input
from flash.core.data.utilities.loading import IMG_EXTENSIONS, load_image, NP_EXTENSIONS
from flash.core.data.utilities.paths import filter_valid_files, PATH_TYPE
from flash.core.data.utilities.samples import to_samples
from flash.core.integrations.fiftyone.utils import FiftyOneLabelUtilities
from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import
from flash.image.data import ImageFilesInput, ImageNumpyInput, ImageTensorInput
from flash.image.segmentation.output import SegmentationLabelsOutput

if _FIFTYONE_AVAILABLE:
    fo = lazy_import("fiftyone")
    SampleCollection = "fiftyone.core.collections.SampleCollection"
else:
    fo = None
    SampleCollection = None


[docs]class SemanticSegmentationInput(Input): num_classes: int labels_map: Dict[int, Tuple[int, int, int]] def load_labels_map( self, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None ) -> None: if num_classes is not None: self.num_classes = num_classes labels_map = labels_map or SegmentationLabelsOutput.create_random_labels_map(num_classes) if labels_map is not None: self.labels_map = labels_map
[docs]class SemanticSegmentationTensorInput(SemanticSegmentationInput, ImageTensorInput): def load_data( self, tensor: Any, masks: Any = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) return to_samples(tensor, masks) def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]: if DataKeys.TARGET in sample: sample[DataKeys.TARGET] = sample[DataKeys.TARGET].numpy() return super().load_sample(sample)
[docs]class SemanticSegmentationNumpyInput(SemanticSegmentationInput, ImageNumpyInput): def load_data( self, array: Any, masks: Any = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) return to_samples(array, masks)
[docs]class SemanticSegmentationFilesInput(SemanticSegmentationInput, ImageFilesInput): def load_data( self, files: Union[PATH_TYPE, List[PATH_TYPE]], mask_files: Optional[Union[PATH_TYPE, List[PATH_TYPE]]] = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) if mask_files is None: files = filter_valid_files(files, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS) else: files, mask_files = filter_valid_files(files, mask_files, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS) return to_samples(files, mask_files) def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]: if DataKeys.TARGET in sample: sample[DataKeys.TARGET] = np.array(load_image(sample[DataKeys.TARGET])).transpose((2, 0, 1))[:, :, 0] return super().load_sample(sample)
[docs]class SemanticSegmentationFolderInput(SemanticSegmentationFilesInput): def load_data( self, folder: PATH_TYPE, mask_folder: Optional[PATH_TYPE] = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) files = os.listdir(folder) files.sort() if mask_folder is not None: mask_files = {os.path.splitext(file)[0]: file for file in os.listdir(mask_folder)} file_names = [os.path.splitext(file)[0] for file in files] if len(set(file_names) - mask_files.keys()) != 0: raise ValueError( f"Found inconsistent files in input folder: {folder} and mask folder: {mask_folder}. All input " f"files must have a corresponding mask file with the same name." ) files = [os.path.join(folder, file) for file in files] mask_files = [os.path.join(mask_folder, mask_files[file_name]) for file_name in file_names] return super().load_data(files, mask_files) return super().load_data([os.path.join(folder, file) for file in files])
[docs]class SemanticSegmentationFiftyOneInput(SemanticSegmentationFilesInput): label_field: str def load_data( self, sample_collection: SampleCollection, label_field: str = "ground_truth", num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) self.label_field = label_field label_utilities = FiftyOneLabelUtilities(label_field, fo.Segmentation) label_utilities.validate(sample_collection) self._fo_dataset_name = sample_collection.name return to_samples(sample_collection.values("filepath")) def predict_load_data( self, sample_collection: SampleCollection, ) -> List[Dict[str, Any]]: return to_samples(sample_collection.values("filepath")) def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]: filepath = sample[DataKeys.INPUT] sample = super().load_sample(sample) if not self.predicting: fo_dataset = fo.load_dataset(self._fo_dataset_name) fo_sample = fo_dataset[filepath] sample[DataKeys.TARGET] = fo_sample[self.label_field].mask return sample

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