-
2024.9.2: Update Adjusted photo KB size
-
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
🚀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 (Only CPU is needed for fast inference.)
- 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!
- Python >= 3.7 (The main test of the project is in Python 3.10.)
- onnxruntime
- OpenCV
- Option: Linux, Windows, MacOS
- 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.
python app.py
Running the program will generate a local web page, where operations and interactions with ID photos can be completed.
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 images/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)' -k 30
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)' -k 200
Pull Image from DockerHub
This image is built on a machine with ARM architecture (e.g. Mac M1). If you want to use it on a machine with x86 architecture, please use Dockerfile.
docker pull linzeyi/hivision_idphotos:v1
Build Image
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 packaging is completed, run the following command to start the Gradio Demo service:
docker run -p 7860:7860 hivision_idphotos
You can access it locally at http://127.0.0.1:7860.
docker run -p 8080:8080 hivision_idphotos python3 deploy_api.py
- MTCNN: https://github.com/ipazc/mtcnn
- ModNet: https://github.com/ZHKKKe/MODNet
1. How to modify the preset size?
After modifying size_list_CN.csv, run app.py again, where the first column is the size name, the second column is height, and the third column is width.
If you have any questions, please email [email protected]
Copyright © 2023, ZeYiLin. All Rights Reserved.
Zeyi-Lin、SAKURA-CAT、Feudalman、swpfY、Kaikaikaifang、ShaohonChen、KashiwaByte