%global _empty_manifest_terminate_build 0
Name: python-autocrop
Version: 1.3.0
Release: 1
Summary: Automatically crops faces from batches of pictures
License: BSD 2-Clause
URL: https://github.com/leblancfg/autocrop
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/26/2398b55ee12628b7b80f25fcf583de528b1ff948e9387119074b4a47476f/autocrop-1.3.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-opencv-python-headless
Requires: python3-Pillow
%description
# autocrop
[](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml) [](https://codecov.io/gh/leblancfg/autocrop) [](https://leblancfg.com/autocrop) [](https://badge.fury.io/py/autocrop) [](https://pepy.tech/project/autocrop)
Perfect for profile picture processing for your website or batch work for ID cards, autocrop will output images centered around the biggest face detected.
# Installation
Simple!
~~~sh
pip install autocrop
~~~
# Use
Autocrop can be used [from the command line](#from-the-command-line) or directly [from Python API](#from-python).
## From Python
Import the `Cropper` class, set some parameters (optional), and start cropping.
The `crop` method accepts filepaths or `np.ndarray`, and returns Numpy arrays. These are easily handled with [PIL](https://pillow.readthedocs.io/) or [Matplotlib](https://matplotlib.org/).
~~~python
from PIL import Image
from autocrop import Cropper
cropper = Cropper()
# Get a Numpy array of the cropped image
cropped_array = cropper.crop('portrait.png')
# Save the cropped image with PIL if a face was detected:
if cropped_array:
cropped_image = Image.fromarray(cropped_array)
cropped_image.save('cropped.png')
~~~
Further examples and use cases are found in the [accompanying Jupyter Notebook](https://github.com/leblancfg/autocrop/blob/master/examples/visual_tests.ipynb).
## From the command line
usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-e EXTENSION] [-v]
Automatically crops faces from batches of pictures
optional arguments:
-h, --help
Show this help message and exit
-o, --output, -p, --path
Folder where cropped images will be placed.
Default: current working directory
-r, --reject
Folder where images without detected faces will be placed.
Default: same as output directory
-i, --input
Folder where images to crop are located.
Default: current working directory
-w, --width
Width of cropped files in px. Default=500
-H, --height
Height of cropped files in px. Default=500
--facePercent
Zoom factor. Percentage of face height to image height.
-e, --extension
Enter the image extension which to save at output.
Default: Your current image extension
-v, --version
Show program's version number and exit
### Examples
* Crop every image in the `pics` folder, resize them to 400 px squares, and output them in the `crop` directory:
- `autocrop -i pics -o crop -w 400 -H 400`.
- Images where a face can't be detected will be left in `crop`.
* Same as above, but output the images with undetected faces to the `reject` directory:
- `autocrop -i pics -o crop -r reject -w 400 -H 400`.
* Same as above but the image extension will be `png`:
- `autocrop -i pics -o crop -w 400 -H 400 -e png`
If no output folder is added, asks for confirmation and destructively crops images in-place.
### Detecting faces from video files
You can use autocrop to detect faces in frames extracted from a video. A great way to [perform the frame extraction step is with `ffmpeg`](https://ffmpeg.org/download.html):
```sh
mkdir frames faces
# Extract one frame per second
ffmpeg -i input.mp4 -filter:v fps=fps=1/60 frames/ffmpeg_%0d.bmp
# Crop faces as jpg
autocrop -i frames -o faces -e jpg
```
# Supported file types
The following file types are supported:
- EPS files (`.eps`)
- GIF files (`.gif`) (only the first frame of an animated GIF is used)
- JPEG 2000 files (`.j2k`, `.j2p`, `.jp2`, `.jpx`)
- JPEG files (`.jpeg`, `.jpg`, `.jpe`)
- LabEye IM files (`.im`)
- macOS ICNS files (`.icns`)
- Microsoft Paint bitmap files (`.msp`)
- PCX files (`.pcx`)
- Portable Network Graphics (`.png`)
- Portable Pixmap files (`.pbm`, `.pgm`, `.ppm`)
- SGI files (`.sgi`)
- SPIDER files (`.spi`)
- TGA files (`.tga`)
- TIFF files (`.tif`, `.tiff`)
- WebP (`.webp`)
- Windows bitmap files (`.bmp`, `.dib`)
- Windows ICO files (`.ico`)
- X bitmap files (`.xbm`)
# Misc
### Installing directly
In some cases, you may wish the package directly, instead of through [PyPI](https://pypi.python.org/pypi):
~~~
cd ~
git clone https://github.com/leblancfg/autocrop
cd autocrop
pip install .
~~~
### conda
Development of a `conda-forge` package for the [Anaconda Python distribution](https://www.anaconda.com/download/) is currently stalled due to the complexity of setting up the workflow with OpenCV. Please leave feedback on [issue #7](https://github.com/leblancfg/autocrop/issues/7) to see past attempts if you are insterested in helping out!
### Requirements
Best practice for your projects is of course to [use virtual environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/). At the very least, you will need to [have pip installed](https://pip.pypa.io/en/stable/installing/).
Autocrop is [currently being tested on](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml):
* Python 3.7 to 3.10
* OS:
- Linux
- macOS
- Windows
# More Info
Check out:
* http://docs.opencv.org/master/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0
* http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html#gsc.tab=0
Adapted from:
* http://photo.stackexchange.com/questions/60411/how-can-i-batch-crop-based-on-face-location
### Contributing
Although autocrop is essentially a CLI wrapper around a single OpenCV function, it is actively developed. It has active users throughout the world.
If you would like to contribute, please consult the [contribution docs](CONTRIBUTING.md).
%package -n python3-autocrop
Summary: Automatically crops faces from batches of pictures
Provides: python-autocrop
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-autocrop
# autocrop
[](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml) [](https://codecov.io/gh/leblancfg/autocrop) [](https://leblancfg.com/autocrop) [](https://badge.fury.io/py/autocrop) [](https://pepy.tech/project/autocrop)
Perfect for profile picture processing for your website or batch work for ID cards, autocrop will output images centered around the biggest face detected.
# Installation
Simple!
~~~sh
pip install autocrop
~~~
# Use
Autocrop can be used [from the command line](#from-the-command-line) or directly [from Python API](#from-python).
## From Python
Import the `Cropper` class, set some parameters (optional), and start cropping.
The `crop` method accepts filepaths or `np.ndarray`, and returns Numpy arrays. These are easily handled with [PIL](https://pillow.readthedocs.io/) or [Matplotlib](https://matplotlib.org/).
~~~python
from PIL import Image
from autocrop import Cropper
cropper = Cropper()
# Get a Numpy array of the cropped image
cropped_array = cropper.crop('portrait.png')
# Save the cropped image with PIL if a face was detected:
if cropped_array:
cropped_image = Image.fromarray(cropped_array)
cropped_image.save('cropped.png')
~~~
Further examples and use cases are found in the [accompanying Jupyter Notebook](https://github.com/leblancfg/autocrop/blob/master/examples/visual_tests.ipynb).
## From the command line
usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-e EXTENSION] [-v]
Automatically crops faces from batches of pictures
optional arguments:
-h, --help
Show this help message and exit
-o, --output, -p, --path
Folder where cropped images will be placed.
Default: current working directory
-r, --reject
Folder where images without detected faces will be placed.
Default: same as output directory
-i, --input
Folder where images to crop are located.
Default: current working directory
-w, --width
Width of cropped files in px. Default=500
-H, --height
Height of cropped files in px. Default=500
--facePercent
Zoom factor. Percentage of face height to image height.
-e, --extension
Enter the image extension which to save at output.
Default: Your current image extension
-v, --version
Show program's version number and exit
### Examples
* Crop every image in the `pics` folder, resize them to 400 px squares, and output them in the `crop` directory:
- `autocrop -i pics -o crop -w 400 -H 400`.
- Images where a face can't be detected will be left in `crop`.
* Same as above, but output the images with undetected faces to the `reject` directory:
- `autocrop -i pics -o crop -r reject -w 400 -H 400`.
* Same as above but the image extension will be `png`:
- `autocrop -i pics -o crop -w 400 -H 400 -e png`
If no output folder is added, asks for confirmation and destructively crops images in-place.
### Detecting faces from video files
You can use autocrop to detect faces in frames extracted from a video. A great way to [perform the frame extraction step is with `ffmpeg`](https://ffmpeg.org/download.html):
```sh
mkdir frames faces
# Extract one frame per second
ffmpeg -i input.mp4 -filter:v fps=fps=1/60 frames/ffmpeg_%0d.bmp
# Crop faces as jpg
autocrop -i frames -o faces -e jpg
```
# Supported file types
The following file types are supported:
- EPS files (`.eps`)
- GIF files (`.gif`) (only the first frame of an animated GIF is used)
- JPEG 2000 files (`.j2k`, `.j2p`, `.jp2`, `.jpx`)
- JPEG files (`.jpeg`, `.jpg`, `.jpe`)
- LabEye IM files (`.im`)
- macOS ICNS files (`.icns`)
- Microsoft Paint bitmap files (`.msp`)
- PCX files (`.pcx`)
- Portable Network Graphics (`.png`)
- Portable Pixmap files (`.pbm`, `.pgm`, `.ppm`)
- SGI files (`.sgi`)
- SPIDER files (`.spi`)
- TGA files (`.tga`)
- TIFF files (`.tif`, `.tiff`)
- WebP (`.webp`)
- Windows bitmap files (`.bmp`, `.dib`)
- Windows ICO files (`.ico`)
- X bitmap files (`.xbm`)
# Misc
### Installing directly
In some cases, you may wish the package directly, instead of through [PyPI](https://pypi.python.org/pypi):
~~~
cd ~
git clone https://github.com/leblancfg/autocrop
cd autocrop
pip install .
~~~
### conda
Development of a `conda-forge` package for the [Anaconda Python distribution](https://www.anaconda.com/download/) is currently stalled due to the complexity of setting up the workflow with OpenCV. Please leave feedback on [issue #7](https://github.com/leblancfg/autocrop/issues/7) to see past attempts if you are insterested in helping out!
### Requirements
Best practice for your projects is of course to [use virtual environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/). At the very least, you will need to [have pip installed](https://pip.pypa.io/en/stable/installing/).
Autocrop is [currently being tested on](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml):
* Python 3.7 to 3.10
* OS:
- Linux
- macOS
- Windows
# More Info
Check out:
* http://docs.opencv.org/master/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0
* http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html#gsc.tab=0
Adapted from:
* http://photo.stackexchange.com/questions/60411/how-can-i-batch-crop-based-on-face-location
### Contributing
Although autocrop is essentially a CLI wrapper around a single OpenCV function, it is actively developed. It has active users throughout the world.
If you would like to contribute, please consult the [contribution docs](CONTRIBUTING.md).
%package help
Summary: Development documents and examples for autocrop
Provides: python3-autocrop-doc
%description help
# autocrop
[](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml) [](https://codecov.io/gh/leblancfg/autocrop) [](https://leblancfg.com/autocrop) [](https://badge.fury.io/py/autocrop) [](https://pepy.tech/project/autocrop)
Perfect for profile picture processing for your website or batch work for ID cards, autocrop will output images centered around the biggest face detected.
# Installation
Simple!
~~~sh
pip install autocrop
~~~
# Use
Autocrop can be used [from the command line](#from-the-command-line) or directly [from Python API](#from-python).
## From Python
Import the `Cropper` class, set some parameters (optional), and start cropping.
The `crop` method accepts filepaths or `np.ndarray`, and returns Numpy arrays. These are easily handled with [PIL](https://pillow.readthedocs.io/) or [Matplotlib](https://matplotlib.org/).
~~~python
from PIL import Image
from autocrop import Cropper
cropper = Cropper()
# Get a Numpy array of the cropped image
cropped_array = cropper.crop('portrait.png')
# Save the cropped image with PIL if a face was detected:
if cropped_array:
cropped_image = Image.fromarray(cropped_array)
cropped_image.save('cropped.png')
~~~
Further examples and use cases are found in the [accompanying Jupyter Notebook](https://github.com/leblancfg/autocrop/blob/master/examples/visual_tests.ipynb).
## From the command line
usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-e EXTENSION] [-v]
Automatically crops faces from batches of pictures
optional arguments:
-h, --help
Show this help message and exit
-o, --output, -p, --path
Folder where cropped images will be placed.
Default: current working directory
-r, --reject
Folder where images without detected faces will be placed.
Default: same as output directory
-i, --input
Folder where images to crop are located.
Default: current working directory
-w, --width
Width of cropped files in px. Default=500
-H, --height
Height of cropped files in px. Default=500
--facePercent
Zoom factor. Percentage of face height to image height.
-e, --extension
Enter the image extension which to save at output.
Default: Your current image extension
-v, --version
Show program's version number and exit
### Examples
* Crop every image in the `pics` folder, resize them to 400 px squares, and output them in the `crop` directory:
- `autocrop -i pics -o crop -w 400 -H 400`.
- Images where a face can't be detected will be left in `crop`.
* Same as above, but output the images with undetected faces to the `reject` directory:
- `autocrop -i pics -o crop -r reject -w 400 -H 400`.
* Same as above but the image extension will be `png`:
- `autocrop -i pics -o crop -w 400 -H 400 -e png`
If no output folder is added, asks for confirmation and destructively crops images in-place.
### Detecting faces from video files
You can use autocrop to detect faces in frames extracted from a video. A great way to [perform the frame extraction step is with `ffmpeg`](https://ffmpeg.org/download.html):
```sh
mkdir frames faces
# Extract one frame per second
ffmpeg -i input.mp4 -filter:v fps=fps=1/60 frames/ffmpeg_%0d.bmp
# Crop faces as jpg
autocrop -i frames -o faces -e jpg
```
# Supported file types
The following file types are supported:
- EPS files (`.eps`)
- GIF files (`.gif`) (only the first frame of an animated GIF is used)
- JPEG 2000 files (`.j2k`, `.j2p`, `.jp2`, `.jpx`)
- JPEG files (`.jpeg`, `.jpg`, `.jpe`)
- LabEye IM files (`.im`)
- macOS ICNS files (`.icns`)
- Microsoft Paint bitmap files (`.msp`)
- PCX files (`.pcx`)
- Portable Network Graphics (`.png`)
- Portable Pixmap files (`.pbm`, `.pgm`, `.ppm`)
- SGI files (`.sgi`)
- SPIDER files (`.spi`)
- TGA files (`.tga`)
- TIFF files (`.tif`, `.tiff`)
- WebP (`.webp`)
- Windows bitmap files (`.bmp`, `.dib`)
- Windows ICO files (`.ico`)
- X bitmap files (`.xbm`)
# Misc
### Installing directly
In some cases, you may wish the package directly, instead of through [PyPI](https://pypi.python.org/pypi):
~~~
cd ~
git clone https://github.com/leblancfg/autocrop
cd autocrop
pip install .
~~~
### conda
Development of a `conda-forge` package for the [Anaconda Python distribution](https://www.anaconda.com/download/) is currently stalled due to the complexity of setting up the workflow with OpenCV. Please leave feedback on [issue #7](https://github.com/leblancfg/autocrop/issues/7) to see past attempts if you are insterested in helping out!
### Requirements
Best practice for your projects is of course to [use virtual environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/). At the very least, you will need to [have pip installed](https://pip.pypa.io/en/stable/installing/).
Autocrop is [currently being tested on](https://github.com/leblancfg/autocrop/actions/workflows/ci.yml):
* Python 3.7 to 3.10
* OS:
- Linux
- macOS
- Windows
# More Info
Check out:
* http://docs.opencv.org/master/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0
* http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html#gsc.tab=0
Adapted from:
* http://photo.stackexchange.com/questions/60411/how-can-i-batch-crop-based-on-face-location
### Contributing
Although autocrop is essentially a CLI wrapper around a single OpenCV function, it is actively developed. It has active users throughout the world.
If you would like to contribute, please consult the [contribution docs](CONTRIBUTING.md).
%prep
%autosetup -n autocrop-1.3.0
%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-autocrop -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot - 1.3.0-1
- Package Spec generated