Profile Photo¶
Center + Crop Image to create a Profile Pic or Headshot.
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Install¶
Local tests:
The
[all]extra installsboto3, which is excluded by default - this assumes an AWS environment.
$ pip install profile-photo[all]
AWS Lambda deployment:
$ pip install profile-photo
Features¶
Exports a helper function,
create_headshot, to create a close-up or headshot of the primary face in a photo or image.Leverages Amazon Rekognition to detect bounding boxes of a person’s Face and relevant Labels, such as Person.
Exposes helper methods to save the result image (cropped) as well as API responses to a local folder.
Usage¶
Basic usage, with a sample image:
from urllib.request import urlopen
from profile_photo import create_headshot
# Set the $AWS_PROFILE environment variable instead
aws_profile = 'my-profile'
im_url = 'https://raw.githubusercontent.com/rnag/profile-photo/main/examples/woman-2.jpeg'
im_bytes = urlopen(im_url).read()
photo = create_headshot(im_bytes, profile=aws_profile)
photo.show()
An example with a local image, and saving the result image and API responses to a folder:
from __future__ import annotations
from profile_photo import create_headshot
# customize local file location for API responses
def get_filename(file_name: str | None, api: str):
return f'responses/{file_name}_{api}.json'
photo = create_headshot('/path/to/image')
# this saves image and API responses to a results/ folder
# can also be achieved by passing `output_dir` above
photo.save_all('results', get_response_filename=get_filename)
# display before-and-after images
photo.show()
Lastly, an example with an image on S3, and passing in cached Rekognition API responses for the image:
from pathlib import Path
from profile_photo import create_headshot
s3_image_path = Path('path/to/image.jpg')
responses_dir = Path('./my/responses')
_photo = create_headshot(bucket='my-bucket',
key=str(s3_image_path),
profile='my-profile',
faces=responses_dir / f'{s3_image_path.stem}_DetectFaces.json',
labels=responses_dir / f'{s3_image_path.stem}_DetectLabels.json',
debug=True)
Examples¶
Check out example images on GitHub for sample use cases and results.
How It Works¶
This library currently makes calls to the Amazon Rekognition APIs to detect bounding boxes on a Face and Person in a photo.
It then uses custom, in-house logic to determine the X/Y coordinates for cropping. This mainly involves “blowing up” or enlarging the Face bounding box, but then correcting the coordinates as needed by the Person box. This logic has been fine-tuned based on what I have found provide the best overall results for generic images (not necessary profile photos).
In the future, other ideas other than Rekognition might be considered
– such as existing machine learning approaches or even a solution with
the opencv library in Python alone.
Future Ideas¶
Support background removal with
rembg.Investigate other (alternate) approaches to Rekognition for detecting a face and person in a photo.
Credits¶
This package was created with Cookiecutter and the rnag/cookiecutter-pypackage project template.
Buy me a coffee¶
Liked some of my work? Buy me a coffee (or more likely a beer)
License¶
Copyright (c) 2023-present Ritvik Nag
Licensed under MIT License
