feat: added load_percentage parameter to ImageList.from_files to load a subset of the given files

src/safeds/data/image/containers/_image.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import io
+import os.path
 import sys
 import warnings
 from pathlib import Path
@@ -76,12 +77,14 @@ def from_file(path: str | Path) -> Image:
         FileNotFoundError
             If the file of the path cannot be found
         """
-        from PIL.Image import open as pil_image_open
-        from torchvision.transforms.functional import pil_to_tensor
+        from torchvision.io import read_image
 
         _init_default_device()
 
-        return Image(image_tensor=pil_to_tensor(pil_image_open(path)))
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"No such file or directory: '{path}'")
+
+        return Image(image_tensor=read_image(str(path)).to(_get_device()))
 
     @staticmethod
     def from_bytes(data: bytes) -> Image:

src/safeds/data/image/containers/_image_list.py

@@ -3,12 +3,15 @@
 import io
 import math
 import os
+import random
 from abc import ABCMeta, abstractmethod
 from pathlib import Path
+from threading import Thread
 from typing import TYPE_CHECKING, Literal, overload
 
 from safeds._config import _init_default_device
 from safeds.data.image.containers._image import Image
+from safeds.exceptions import OutOfBoundsError, ClosedBound
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
@@ -87,26 +90,61 @@ def from_files(path: str | Path | Sequence[str | Path]) -> ImageList: ...
 
     @staticmethod
     @overload
-    def from_files(path: str | Path | Sequence[str | Path], return_filenames: Literal[False]) -> ImageList: ...
+    def from_files(path: str | Path | Sequence[str | Path], *, load_percentage: float) -> ImageList: ...
+
+    @staticmethod
+    @overload
+    def from_files(path: str | Path | Sequence[str | Path], *, return_filenames: Literal[False]) -> ImageList: ...
+
+    @staticmethod
+    @overload
+    def from_files(
+        path: str | Path | Sequence[str | Path],
+        *,
+        return_filenames: Literal[False],
+        load_percentage: float,
+    ) -> ImageList: ...
 
     @staticmethod
     @overload
     def from_files(
         path: str | Path | Sequence[str | Path],
+        *,
         return_filenames: Literal[True],
     ) -> tuple[ImageList, list[str]]: ...
 
     @staticmethod
     @overload
     def from_files(
         path: str | Path | Sequence[str | Path],
+        *,
+        return_filenames: Literal[True],
+        load_percentage: float,
+    ) -> tuple[ImageList, list[str]]: ...
+
+    @staticmethod
+    @overload
+    def from_files(
+        path: str | Path | Sequence[str | Path],
+        *,
         return_filenames: bool,
     ) -> ImageList | tuple[ImageList, list[str]]: ...
 
     @staticmethod
+    @overload
     def from_files(
         path: str | Path | Sequence[str | Path],
+        *,
+        return_filenames: bool,
+        load_percentage: float,
+    ) -> ImageList | tuple[ImageList, list[str]]: ...
+
+    @staticmethod
+    def from_files(
+        path: str | Path | Sequence[str | Path],
+        *,
         return_filenames: bool = False,
+        load_percentage: float = 1.0,
     ) -> ImageList | tuple[ImageList, list[str]]:
         """
         Create an ImageList from a directory or a list of files.
@@ -119,6 +157,8 @@ def from_files(
             the path to the directory or a list of files
         return_filenames:
             if True the output will be a tuple which contains a list of the filenames in order of the images
+        load_percentage:
+            the percentage of the given data being loaded. If below 1 the files will be shuffled before loading
 
         Returns
         -------
@@ -129,22 +169,26 @@ def from_files(
         ------
         FileNotFoundError
             If the directory or one of the files of the path cannot be found
+        OutOfBoundsError
+            If load_percentage is not between 0 and 1
         """
         from PIL.Image import open as pil_image_open
-        from torchvision.transforms.v2.functional import pil_to_tensor
 
         _init_default_device()
 
         from safeds.data.image.containers._empty_image_list import _EmptyImageList
         from safeds.data.image.containers._multi_size_image_list import _MultiSizeImageList
         from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 
+        if load_percentage < 0 or load_percentage > 1:
+            raise OutOfBoundsError(
+                load_percentage, name="load_percentage", lower_bound=ClosedBound(0), upper_bound=ClosedBound(1)
+            )
+
         if isinstance(path, list) and len(path) == 0:
             return _EmptyImageList()
 
-        image_tensors = []
         file_names = []
-        fixed_size = True
 
         path_list: list[str | Path]
         if isinstance(path, Path | str):
@@ -155,30 +199,139 @@ def from_files(
             p = Path(path_list.pop(0))
             if p.is_dir():
                 path_list += sorted([p / name for name in os.listdir(p)])
-            else:
-                image_tensors.append(pil_to_tensor(pil_image_open(p)))
+            elif p.is_file():
                 file_names.append(str(p))
-                if fixed_size and (
-                    image_tensors[0].size(dim=2) != image_tensors[-1].size(dim=2)
-                    or image_tensors[0].size(dim=1) != image_tensors[-1].size(dim=1)
-                ):
-                    fixed_size = False
+            else:
+                raise FileNotFoundError(f"No such file or directory: '{path}'")
 
-        if len(image_tensors) == 0:
-            return _EmptyImageList()
+        if load_percentage < 1:
+            random.shuffle(file_names)
+            file_names = file_names[: max(round(len(file_names) * load_percentage), 1) if load_percentage > 0 else 0]
 
-        indices = list(range(len(image_tensors)))
+        num_of_files = len(file_names)
 
-        if fixed_size:
-            image_list = _SingleSizeImageList._create_image_list(image_tensors, indices)
+        if num_of_files == 0:
+            return _EmptyImageList()
+
+        image_sizes: dict[tuple[int, int], dict[int, list[str]]] = {}
+        image_indices: dict[tuple[int, int], dict[int, list[int]]] = {}
+        image_count: dict[tuple[int, int], int] = {}
+        max_channel = -1
+
+        for i, filename in enumerate(file_names):
+            im = pil_image_open(filename)
+            im_channel = len(im.getbands())
+            im_size = (im.width, im.height)
+            if im_channel > max_channel:
+                max_channel = im_channel
+            if im_size not in image_sizes:
+                image_sizes[im_size] = {im_channel: [filename]}
+                image_indices[im_size] = {im_channel: [i]}
+                image_count[im_size] = 1
+            elif im_channel not in image_sizes[im_size]:
+                image_sizes[im_size][im_channel] = [filename]
+                image_indices[im_size][im_channel] = [i]
+                image_count[im_size] += 1
+            else:
+                image_sizes[im_size][im_channel].append(filename)
+                image_indices[im_size][im_channel].append(i)
+                image_count[im_size] += 1
+
+        num_of_threads = min(math.ceil(num_of_files / 1000), 100)
+        num_of_files_per_thread = math.ceil(num_of_files / num_of_threads)
+
+        single_sized_image_lists = []
+        thread_packages = []
+        for size, image_files in image_sizes.items():
+            im_list, packages = _SingleSizeImageList._create_image_list_from_files(
+                image_files,
+                image_count[size],
+                max_channel,
+                size[0],
+                size[1],
+                image_indices[size],
+                num_of_files_per_thread,
+            )
+            single_sized_image_lists.append(im_list._as_single_size_image_list())
+            thread_packages += packages
+        thread_packages.sort(key=lambda x: len(x), reverse=True)
+
+        threads: list[ImageList._FromImageThread] = []
+        for thread_index in range(num_of_threads):
+            current_thread_workload = 0
+            current_thread_packages = []
+            while current_thread_workload < num_of_files_per_thread and len(thread_packages) > 0:
+                next_package = thread_packages.pop()
+                current_thread_packages.append(next_package)
+                current_thread_workload += len(next_package)
+            if thread_index == num_of_threads - 1 and len(thread_packages) > 0:
+                current_thread_packages += thread_packages  # pragma: no cover
+            thread = ImageList._FromImageThread(current_thread_packages)
+            threads.append(thread)
+            thread.start()
+        for thread in threads:
+            thread.join()
+
+        if len(single_sized_image_lists) == 1:
+            image_list: ImageList = single_sized_image_lists[0]
         else:
-            image_list = _MultiSizeImageList._create_image_list(image_tensors, indices)
+            image_list = _MultiSizeImageList._create_from_single_sized_image_lists(single_sized_image_lists)
 
         if return_filenames:
             return image_list, file_names
         else:
             return image_list
 
+    class _FromFileThreadPackage:
+
+        def __init__(
+            self,
+            im_files: list[str],
+            im_channel: int,
+            to_channel: int,
+            im_width: int,
+            im_height: int,
+            tensor: Tensor,
+            start_index: int,
+        ) -> None:
+            self._im_files = im_files
+            self._im_channel = im_channel
+            self._to_channel = to_channel
+            self._im_width = im_width
+            self._im_height = im_height
+            self._tensor = tensor
+            self._start_index = start_index
+
+        def load_files(self) -> None:
+            import torch
+            from torchvision.io import read_image
+
+            _init_default_device()
+
+            num_of_files = len(self._im_files)
+            tensor_channel = max(self._im_channel, min(self._to_channel, 3))
+            for index, im in enumerate(self._im_files):
+                self._tensor[index + self._start_index, 0:tensor_channel] = read_image(im)
+            if self._to_channel == 4 and self._im_channel < 4:
+                torch.full(
+                    (num_of_files, 1, self._im_height, self._im_width),
+                    255,
+                    out=self._tensor[self._start_index : self._start_index + num_of_files, 3:4],
+                )
+
+        def __len__(self) -> int:
+            return len(self._im_files)
+
+    class _FromImageThread(Thread):
+
+        def __init__(self, packages: list[ImageList._FromFileThreadPackage]) -> None:
+            super().__init__()
+            self._packages = packages
+
+        def run(self) -> None:
+            for pck in self._packages:
+                pck.load_files()
+
     @abstractmethod
     def _clone(self) -> ImageList:
         """

src/safeds/data/image/containers/_multi_size_image_list.py

@@ -45,6 +45,45 @@ def __init__(self) -> None:
         self._image_list_dict: dict[tuple[int, int], ImageList] = {}  # {image_size: image_list}
         self._indices_to_image_size_dict: dict[int, tuple[int, int]] = {}  # {index: image_size}
 
+    @staticmethod
+    def _create_from_single_sized_image_lists(single_size_image_lists: list[_SingleSizeImageList]) -> ImageList:
+        from safeds.data.image.containers._empty_image_list import _EmptyImageList
+
+        if len(single_size_image_lists) == 0:
+            return _EmptyImageList()
+        elif len(single_size_image_lists) == 1:
+            return single_size_image_lists[0]
+
+        different_channels: bool = False
+        max_channel: None | int = None
+
+        image_list = _MultiSizeImageList()
+        for single_size_image_list in single_size_image_lists:
+            image_size = (single_size_image_list.widths[0], single_size_image_list.heights[0])
+            image_list._image_list_dict[image_size] = single_size_image_list
+            image_list._indices_to_image_size_dict.update(
+                zip(
+                    single_size_image_list._indices_to_tensor_positions.keys(),
+                    [image_size] * len(single_size_image_list),
+                    strict=False,
+                )
+            )
+            if max_channel is None:
+                max_channel = single_size_image_list.channel
+            elif max_channel < single_size_image_list.channel:
+                different_channels = True
+                max_channel = single_size_image_list.channel
+            elif max_channel > single_size_image_list.channel:
+                different_channels = True
+
+        if different_channels:
+            for size in image_list._image_list_dict:
+                if max_channel is not None and image_list._image_list_dict[size].channel != max_channel:
+                    image_list._image_list_dict[size] = image_list._image_list_dict[size].change_channel(
+                        int(max_channel)
+                    )
+        return image_list
+
     @staticmethod
     def _create_image_list(images: list[Tensor], indices: list[int]) -> ImageList:
         from safeds.data.image.containers._empty_image_list import _EmptyImageList