%global _empty_manifest_terminate_build 0 Name: python-split-folders Version: 0.5.1 Release: 1 Summary: Split folders with files (e.g. images) into training, validation and test (dataset) folders. License: MIT URL: https://github.com/jfilter/split-folders Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a7/4c/32d2d49b82ea5baf0ff1a55de88c7fb8a0bf2aab02763c8501b2a51bf55f/split_folders-0.5.1.tar.gz BuildArch: noarch Requires: python3-tqdm %description # `split-folders` [![Build Status](https://img.shields.io/github/workflow/status/jfilter/split-folders/Test)](https://github.com/jfilter/split-folders/actions/workflows/test.yml) [![PyPI](https://img.shields.io/pypi/v/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/split-folders)](https://pypistats.org/packages/split-folders) Split folders with files (e.g. images) into **train**, **validation** and **test** (dataset) folders. The input folder should have the following format: ``` input/ class1/ img1.jpg img2.jpg ... class2/ imgWhatever.jpg ... ... ``` In order to give you this: ``` output/ train/ class1/ img1.jpg ... class2/ imga.jpg ... val/ class1/ img2.jpg ... class2/ imgb.jpg ... test/ class1/ img3.jpg ... class2/ imgc.jpg ... ``` This should get you started to do some serious deep learning on your data. [Read here](https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set) why it's a good idea to split your data intro three different sets. - Split files into a training set and a validation set (and optionally a test set). - Works on any file types. - The files get shuffled. - A [seed](https://docs.python.org/3/library/random.html#random.seed) makes splits reproducible. - Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets. - Optionally group files by prefix. - (Should) work on all operating systems. ## Install This package is Python only and there are no external dependencies. ```bash pip install split-folders ``` Optionally, you may install [tqdm](https://github.com/tqdm/tqdm) to get get a progress bar when moving files. ```bash pip install split-folders[full] ``` ## Usage You can use `split-folders` as Python module or as a Command Line Interface (CLI). If your datasets is balanced (each class has the same number of samples), choose `ratio` otherwise `fixed`. NB: oversampling is turned off by default. Oversampling is only applied to the _train_ folder since having duplicates in _val_ or _test_ would be considered cheating. ### Module ```python import splitfolders # Split with a ratio. # To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`. splitfolders.ratio("input_folder", output="output", seed=1337, ratio=(.8, .1, .1), group_prefix=None, move=False) # default values # Split val/test with a fixed number of items, e.g. `(100, 100)`, for each set. # To only split into training and validation set, use a single number to `fixed`, i.e., `10`. # Set 3 values, e.g. `(300, 100, 100)`, to limit the number of training values. splitfolders.fixed("input_folder", output="output", seed=1337, fixed=(100, 100), oversample=False, group_prefix=None, move=False) # default values ``` Occasionally, you may have things that comprise more than a single file (e.g. picture (.png) + annotation (.txt)). `splitfolders` lets you split files into equally-sized groups based on their prefix. Set `group_prefix` to the length of the group (e.g. `2`). But now _all_ files should be part of groups. Set `move=True` if you want to move the files instead of copying. ### CLI ``` Usage: splitfolders [--output] [--ratio] [--fixed] [--seed] [--oversample] [--group_prefix] [--move] folder_with_images Options: --output path to the output folder. defaults to `output`. Get created if non-existent. --ratio the ratio to split. e.g. for train/val/test `.8 .1 .1 --` or for train/val `.8 .2 --`. --fixed set the absolute number of items per validation/test set. The remaining items constitute the training set. e.g. for train/val/test `100 100` or for train/val `100`. Set 3 values, e.g. `300 100 100`, to limit the number of training values. --seed set seed value for shuffling the items. defaults to 1337. --oversample enable oversampling of imbalanced datasets, works only with --fixed. --group_prefix split files into equally-sized groups based on their prefix --move move the files instead of copying Example: splitfolders --ratio .8 .1 .1 -- folder_with_images ``` Because of some [Python quirks](https://github.com/jfilter/split-folders/issues/19) you have to prepend ` --` afer using `--ratio`. Instead of the command `splitfolders` you can also use `split_folders` or `split-folders`. ## Development Install and use [poetry](https://python-poetry.org/). ## Contributing If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/split-folders/issues). **Pull requests** are especially welcomed when they fix bugs or improve the code quality. ## License MIT %package -n python3-split-folders Summary: Split folders with files (e.g. images) into training, validation and test (dataset) folders. Provides: python-split-folders BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-split-folders # `split-folders` [![Build Status](https://img.shields.io/github/workflow/status/jfilter/split-folders/Test)](https://github.com/jfilter/split-folders/actions/workflows/test.yml) [![PyPI](https://img.shields.io/pypi/v/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/split-folders)](https://pypistats.org/packages/split-folders) Split folders with files (e.g. images) into **train**, **validation** and **test** (dataset) folders. The input folder should have the following format: ``` input/ class1/ img1.jpg img2.jpg ... class2/ imgWhatever.jpg ... ... ``` In order to give you this: ``` output/ train/ class1/ img1.jpg ... class2/ imga.jpg ... val/ class1/ img2.jpg ... class2/ imgb.jpg ... test/ class1/ img3.jpg ... class2/ imgc.jpg ... ``` This should get you started to do some serious deep learning on your data. [Read here](https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set) why it's a good idea to split your data intro three different sets. - Split files into a training set and a validation set (and optionally a test set). - Works on any file types. - The files get shuffled. - A [seed](https://docs.python.org/3/library/random.html#random.seed) makes splits reproducible. - Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets. - Optionally group files by prefix. - (Should) work on all operating systems. ## Install This package is Python only and there are no external dependencies. ```bash pip install split-folders ``` Optionally, you may install [tqdm](https://github.com/tqdm/tqdm) to get get a progress bar when moving files. ```bash pip install split-folders[full] ``` ## Usage You can use `split-folders` as Python module or as a Command Line Interface (CLI). If your datasets is balanced (each class has the same number of samples), choose `ratio` otherwise `fixed`. NB: oversampling is turned off by default. Oversampling is only applied to the _train_ folder since having duplicates in _val_ or _test_ would be considered cheating. ### Module ```python import splitfolders # Split with a ratio. # To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`. splitfolders.ratio("input_folder", output="output", seed=1337, ratio=(.8, .1, .1), group_prefix=None, move=False) # default values # Split val/test with a fixed number of items, e.g. `(100, 100)`, for each set. # To only split into training and validation set, use a single number to `fixed`, i.e., `10`. # Set 3 values, e.g. `(300, 100, 100)`, to limit the number of training values. splitfolders.fixed("input_folder", output="output", seed=1337, fixed=(100, 100), oversample=False, group_prefix=None, move=False) # default values ``` Occasionally, you may have things that comprise more than a single file (e.g. picture (.png) + annotation (.txt)). `splitfolders` lets you split files into equally-sized groups based on their prefix. Set `group_prefix` to the length of the group (e.g. `2`). But now _all_ files should be part of groups. Set `move=True` if you want to move the files instead of copying. ### CLI ``` Usage: splitfolders [--output] [--ratio] [--fixed] [--seed] [--oversample] [--group_prefix] [--move] folder_with_images Options: --output path to the output folder. defaults to `output`. Get created if non-existent. --ratio the ratio to split. e.g. for train/val/test `.8 .1 .1 --` or for train/val `.8 .2 --`. --fixed set the absolute number of items per validation/test set. The remaining items constitute the training set. e.g. for train/val/test `100 100` or for train/val `100`. Set 3 values, e.g. `300 100 100`, to limit the number of training values. --seed set seed value for shuffling the items. defaults to 1337. --oversample enable oversampling of imbalanced datasets, works only with --fixed. --group_prefix split files into equally-sized groups based on their prefix --move move the files instead of copying Example: splitfolders --ratio .8 .1 .1 -- folder_with_images ``` Because of some [Python quirks](https://github.com/jfilter/split-folders/issues/19) you have to prepend ` --` afer using `--ratio`. Instead of the command `splitfolders` you can also use `split_folders` or `split-folders`. ## Development Install and use [poetry](https://python-poetry.org/). ## Contributing If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/split-folders/issues). **Pull requests** are especially welcomed when they fix bugs or improve the code quality. ## License MIT %package help Summary: Development documents and examples for split-folders Provides: python3-split-folders-doc %description help # `split-folders` [![Build Status](https://img.shields.io/github/workflow/status/jfilter/split-folders/Test)](https://github.com/jfilter/split-folders/actions/workflows/test.yml) [![PyPI](https://img.shields.io/pypi/v/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/split-folders)](https://pypistats.org/packages/split-folders) Split folders with files (e.g. images) into **train**, **validation** and **test** (dataset) folders. The input folder should have the following format: ``` input/ class1/ img1.jpg img2.jpg ... class2/ imgWhatever.jpg ... ... ``` In order to give you this: ``` output/ train/ class1/ img1.jpg ... class2/ imga.jpg ... val/ class1/ img2.jpg ... class2/ imgb.jpg ... test/ class1/ img3.jpg ... class2/ imgc.jpg ... ``` This should get you started to do some serious deep learning on your data. [Read here](https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set) why it's a good idea to split your data intro three different sets. - Split files into a training set and a validation set (and optionally a test set). - Works on any file types. - The files get shuffled. - A [seed](https://docs.python.org/3/library/random.html#random.seed) makes splits reproducible. - Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets. - Optionally group files by prefix. - (Should) work on all operating systems. ## Install This package is Python only and there are no external dependencies. ```bash pip install split-folders ``` Optionally, you may install [tqdm](https://github.com/tqdm/tqdm) to get get a progress bar when moving files. ```bash pip install split-folders[full] ``` ## Usage You can use `split-folders` as Python module or as a Command Line Interface (CLI). If your datasets is balanced (each class has the same number of samples), choose `ratio` otherwise `fixed`. NB: oversampling is turned off by default. Oversampling is only applied to the _train_ folder since having duplicates in _val_ or _test_ would be considered cheating. ### Module ```python import splitfolders # Split with a ratio. # To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`. splitfolders.ratio("input_folder", output="output", seed=1337, ratio=(.8, .1, .1), group_prefix=None, move=False) # default values # Split val/test with a fixed number of items, e.g. `(100, 100)`, for each set. # To only split into training and validation set, use a single number to `fixed`, i.e., `10`. # Set 3 values, e.g. `(300, 100, 100)`, to limit the number of training values. splitfolders.fixed("input_folder", output="output", seed=1337, fixed=(100, 100), oversample=False, group_prefix=None, move=False) # default values ``` Occasionally, you may have things that comprise more than a single file (e.g. picture (.png) + annotation (.txt)). `splitfolders` lets you split files into equally-sized groups based on their prefix. Set `group_prefix` to the length of the group (e.g. `2`). But now _all_ files should be part of groups. Set `move=True` if you want to move the files instead of copying. ### CLI ``` Usage: splitfolders [--output] [--ratio] [--fixed] [--seed] [--oversample] [--group_prefix] [--move] folder_with_images Options: --output path to the output folder. defaults to `output`. Get created if non-existent. --ratio the ratio to split. e.g. for train/val/test `.8 .1 .1 --` or for train/val `.8 .2 --`. --fixed set the absolute number of items per validation/test set. The remaining items constitute the training set. e.g. for train/val/test `100 100` or for train/val `100`. Set 3 values, e.g. `300 100 100`, to limit the number of training values. --seed set seed value for shuffling the items. defaults to 1337. --oversample enable oversampling of imbalanced datasets, works only with --fixed. --group_prefix split files into equally-sized groups based on their prefix --move move the files instead of copying Example: splitfolders --ratio .8 .1 .1 -- folder_with_images ``` Because of some [Python quirks](https://github.com/jfilter/split-folders/issues/19) you have to prepend ` --` afer using `--ratio`. Instead of the command `splitfolders` you can also use `split_folders` or `split-folders`. ## Development Install and use [poetry](https://python-poetry.org/). ## Contributing If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/split-folders/issues). **Pull requests** are especially welcomed when they fix bugs or improve the code quality. ## License MIT %prep %autosetup -n split-folders-0.5.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-split-folders -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 0.5.1-1 - Package Spec generated