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https://github.com/leblancfg/autocrop
:relieved: Automatically detects and crops faces from batches of pictures.
https://github.com/leblancfg/autocrop
autocrop computer-vision crop face face-detection facedetect opencv pictures python
Last synced: about 1 month ago
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:relieved: Automatically detects and crops faces from batches of pictures.
- Host: GitHub
- URL: https://github.com/leblancfg/autocrop
- Owner: leblancfg
- License: other
- Created: 2015-11-05T15:48:44.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-01-17T16:26:03.000Z (almost 2 years ago)
- Last Synced: 2024-09-29T00:21:41.591Z (about 1 month ago)
- Topics: autocrop, computer-vision, crop, face, face-detection, facedetect, opencv, pictures, python
- Language: Python
- Homepage: https://leblancfg.com/autocrop/
- Size: 21.4 MB
- Stars: 631
- Watchers: 16
- Forks: 117
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - leblancfg/autocrop - :relieved: Automatically detects and crops faces from batches of pictures. (Python)
README
# 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: autocrop [-h] [-v] [--no-confirm] [-n] [-i INPUT] [-o OUTPUT] [-r REJECT] [-w WIDTH] [-H HEIGHT] [--facePercent FACEPERCENT]
[-e EXTENSION]
Automatically crops faces from batches of pictures
options:
-h, --help Show this help message and exit
-v, --version Show program's version number and exit
--no-confirm, --skip-prompt
Bypass any confirmation prompts
-n, --no-resize Do not resize images to the specified width and height, but instead use the original image's pixels.
-i, --input INPUT
Folder where images to crop are located. Default: current working directory
-o, -p, --output, --path OUTPUT
Folder where cropped images will be moved to. Default: current working directory, meaning images are cropped in
place.
-r, --reject REJECT
Folder where images that could not be cropped will be moved to. Default: current working directory, meaning images
that are not cropped will be left in place.
-w, --width WIDTH
Width of cropped files in px. Default=500
-H, --height HEIGHT
Height of cropped files in px. Default=500
--facePercent FACEPERCENT
Percentage of face to image height
-e, --extension EXTENSION
Enter the image extension which to save at output
### 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`
* Crop every image in the `pics` folder and output to the `crop` directory, but keep the original pixels from the images:
- `autocrop -i pics -o crop --no-resize`
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).