HivisionIDPhoto

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🤩Project Update

Online Demo:
2023.12.1: Update API deployment (based on fastapi)
2023.6.20: Update Preset size menu
2023.6.19: Update Layout photos
2023.6.13: Update Center gradient color
2023.6.11: Update Top and bottom gradient color
2023.6.8: Update Custom size
2023.6.4: Update Custom background color, face detection bug notification
2023.5.10: Update Change the background without changing the size

Overview

🚀Thank you for your interest in our work. You may also want to check out our other achievements in the field of image processing. Please feel free to contact us at [email protected].

HivisionIDPhoto aims to develop a practical intelligent algorithm for producing ID photos. It uses a complete set of model workflows to recognize various user photo scenarios, perform image segmentation, and generate ID photos.

HivisionIDPhoto can:

Perform lightweight image segmentation
Generate standard ID photos and six-inch layout photos according to different size specifications
Provide beauty features (waiting)
Provide intelligent formal wear replacement (waiting)

If HivisionIDPhoto is helpful to you, please star this repo or recommend it to your friends to solve the problem of emergency ID photo production!

🔧Environment Dependencies and Installation

Python >= 3.7（The main test of the project is in Python 3.10.）
onnxruntime
OpenCV
Option: Linux, Windows, MacOS

Installation

Clone repo

git clone https://github.com/Zeyi-Lin/HivisionIDPhotos.git
cd  HivisionIDPhotos

Install dependent packages

pip install -r requirements.txt

3. Download Pretrain file

Download the weight file hivision_modnet.onnx from our Release and save it to the root directory.

Gradio Demo

python app.py

Running the program will generate a local web page, where operations and interactions with ID photos can be completed.

Deploy API service

python deploy_api.py

Request API service (Python)

Use Python to send a request to the service:

ID photo production (input 1 photo, get 1 standard ID photo and 1 high-definition ID photo 4-channel transparent png):

python requests_api.py -u http://127.0.0.1:8080 -i test.jpg -o ./idphoto.png -s '(413,295)'

Add background color (input 1 4-channel transparent png, get 1 image with added background color):

python requests_api.py -u http://127.0.0.1:8080 -t add_background -i ./idphoto.png -o ./idhoto_ab.jpg -c '(0,0,0)'

Get a six-inch layout photo (input a 3-channel photo, get a six-inch layout photo):

python requests_api.py -u http://127.0.0.1:8080 -t generate_layout_photos -i ./idhoto_ab.jpg -o ./idhoto_layout.jpg -s '(413,295)'

🐳Docker deployment

After ensuring that the model weight file hivision_modnet.onnx is placed in the root directory, execute in the root directory:

docker build -t hivision_idphotos .

After the image is packaged, run the following command to start the API service:

docker run -p 8080:8080 hivision_idphotos

Reference Projects

MTCNN: https://github.com/ipazc/mtcnn
ModNet: https://github.com/ZHKKKe/MODNet

📧Contact

If you have any questions, please email [email protected]