profile-photo 0.1.0

Center + Crop Image to create a Profile Pic or Headshot

Profile Photo

Center + Crop Image to create a Profile Pic or Headshot.

Check out example images on GitHub for sample use cases and results.

Features

• Exports a helper function, create_headshot used 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)

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.

Credits

This package was created with Cookiecutter and the rnag/cookiecutter-pypackage project template.

History

0.1.0 (2023-03-08)

• First release on PyPI.