Source code for flash.image.data

# 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 base64
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List

import torch

import flash
from flash.core.data.io.input import DataKeys, Input, ServeInput
from flash.core.data.utilities.loading import IMG_EXTENSIONS, NP_EXTENSIONS, load_image
from flash.core.data.utilities.paths import PATH_TYPE, filter_valid_files
from flash.core.data.utilities.samples import to_samples
from flash.core.utilities.imports import _TORCHVISION_AVAILABLE, Image, requires

if _TORCHVISION_AVAILABLE:
    from torchvision.transforms.functional import to_pil_image


class ImageDeserializer(ServeInput):
    @requires("image")
    def serve_load_sample(self, data: str) -> Dict:
        encoded_with_padding = (data + "===").encode("ascii")
        img = base64.b64decode(encoded_with_padding)
        buffer = BytesIO(img)
        img = Image.open(buffer, mode="r")
        w, h = img.size
        return {
            DataKeys.INPUT: img,
            DataKeys.METADATA: {
                "size": (h, w),
            },
        }

    @property
    def example_input(self) -> str:
        with (Path(flash.ASSETS_ROOT) / "fish.jpg").open("rb") as f:
            return base64.b64encode(f.read()).decode("UTF-8")


class ImageInput(Input):
    @requires("image")
    def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        w, h = sample[DataKeys.INPUT].size  # W x H
        if DataKeys.METADATA not in sample:
            sample[DataKeys.METADATA] = {}
        sample[DataKeys.METADATA].update(
            {
                "size": (h, w),
                "height": h,
                "width": w,
            }
        )
        return sample


class ImageFilesInput(ImageInput):
    def load_data(self, files: List[PATH_TYPE]) -> List[Dict[str, Any]]:
        files = filter_valid_files(files, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS)
        return to_samples(files)

    def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        filepath = sample[DataKeys.INPUT]
        sample[DataKeys.INPUT] = load_image(filepath)
        sample = super().load_sample(sample)
        sample[DataKeys.METADATA]["filepath"] = filepath
        return sample


class ImageTensorInput(ImageInput):
    def load_data(self, tensor: Any) -> List[Dict[str, Any]]:
        return to_samples(tensor)

    def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        img = to_pil_image(sample[DataKeys.INPUT])
        sample[DataKeys.INPUT] = img
        return super().load_sample(sample)


class ImageNumpyInput(ImageInput):
    def load_data(self, array: Any) -> List[Dict[str, Any]]:
        return to_samples(array)

    def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        img = to_pil_image(torch.from_numpy(sample[DataKeys.INPUT]))
        sample[DataKeys.INPUT] = img
        return super().load_sample(sample)