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HivisionIDPhoto

English / 中文 / 日本語 / 한국어

GitHub GitHub stars SwanHub Demo zhihu Spaces

Zeyi-Lin%2FHivisionIDPhotos | Trendshift

🤩Project Update

  • Online Demo: SwanHub Demo、Spaces

  • 2024.9.6: Add a new image matting model modnet_photographic_portrait_matting.onnx

  • 2024.9.2: Update Adjusted photo KB size，DockerHub

  • 2023.12.1: Update API deployment (based on fastapi)

  • 2023.6.20: Update Preset size menu

  • 2023.6.19: Update Layout photos


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 zeyi.lin@swanhub.co.

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:

  1. Perform lightweight image segmentation (Only CPU is needed for fast inference.)
  2. Generate standard ID photos and six-inch layout photos according to different size specifications
  3. Provide beauty features (waiting)
  4. 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

  1. Clone repo
git clone https://github.com/Zeyi-Lin/HivisionIDPhotos.git
cd  HivisionIDPhotos
  1. (Important) Install dependent packages

It is recommended to create a Python 3.10 virtual environment with conda and then execute the following command.

pip install -r requirements.txt
pip install -r requirements-app.txt

3. Download Pretrain file

Download the weight file hivision_modnet.onnx from our Release and save it to the hivision/creator/weights directory.

Expand matting model weights (all in the hivision/creator/weights 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.


🚀 Python Inference

1. ID Photo Production

Input 1 photo, get 1 standard ID photo and 1 HD ID photo in a transparent PNG with 4 channels.

python inference.py -i demo/images/test.jpg -o ./idphoto.png --height 413 --width 295

2. Add Background Color

Input 1 transparent PNG with 4 channels, get an image with added background color.

python inference.py -t add_background -i ./idphoto.png -o ./idhoto_ab.jpg  -c 000000 -k 30

3. Obtain Six-Inch Layout Photo

Input 1 photo with 3 channels, obtain one six-inch layout photo.

python inference.py -t generate_layout_photos -i ./idhoto_ab.jpg -o ./idhoto_layout.jpg  --height 413 --width 295 -k 200

⚡️ Deploy API service

Start backend

python deploy_api.py

Request API Service - Python Request

Please refer to the API documentation for the request method, including examples of requests using cURL, Python, Java, and Javascript.

1. ID Photo Creation

Input 1 photo, receive 1 standard ID photo and 1 high-definition ID photo in 4-channel transparent PNG format.

import requests

url = "http://127.0.0.1:8080/idphoto"
input_image_path = "images/test.jpg"

files = {"input_image": open(input_image_path, "rb")}
data = {"height": 413, "width": 295}

response = requests.post(url, files=files, data=data).json()

# response is a JSON dictionary containing status, image_base64_standard, and image_base64_hd
print(response)

2. Add Background Color

Input 1 4-channel transparent PNG, receive 1 image with added background color.

import requests

url = "http://127.0.0.1:8080/add_background"
input_image_path = "test.png"

files = {"input_image": open(input_image_path, "rb")}
data = {"color": '638cce', 'kb': None}

response = requests.post(url, files=files, data=data).json()

# response is a JSON dictionary containing status and image_base64
print(response)

3. Get 6-inch Layout Photo

Input 1 3-channel photo, receive 1 6-inch layout photo.

import requests

url = "http://127.0.0.1:8080/generate_layout_photos"
input_image_path = "test.jpg"

files = {"input_image": open(input_image_path, "rb")}
data = {"height": 413, "width": 295, "kb": 200}

response = requests.post(url, files=files, data=data).json()

# response is a JSON dictionary containing status and image_base64
print(response)

🐳 Docker deployment

1. Pull or Build Image

Choose one of the following three methods

Method 1 - Pull Image from DockerHub:

docker pull linzeyi/hivision_idphotos:v1
docker tag linzeyi/hivision_idphotos:v1 hivision_idphotos

Method 2 - Build Image:

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

docker build -t hivision_idphotos .

Method 3 - Docker Compose:

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

docker compose build

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

docker compose up -d

2. Run the Gradio Demo

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.

3. Run API backend service

docker run -p 8080:8080 hivision_idphotos python3 deploy_api.py

🌲 Friendship link


📖 Reference Projects

  1. MTCNN:
@software{ipazc_mtcnn_2021,
    author = {ipazc},
    title = {{MTCNN}},
    url = {https://github.com/ipazc/mtcnn},
    year = {2021},
    publisher = {GitHub}
}
  1. ModNet:
@software{zhkkke_modnet_2021,
    author = {ZHKKKe},
    title = {{ModNet}},
    url = {https://github.com/ZHKKKe/MODNet},
    year = {2021},
    publisher = {GitHub}
}

💻 Development Tips

1. How to modify the preset size?

After modifying demo/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.


📧 Contact

If you have any questions, please email Zeyi.lin@swanhub.co

Copyright © 2023, ZeYiLin. All Rights Reserved.


Contributor

Zeyi-Lin、SAKURA-CAT、Feudalman、swpfY、Kaikaikaifang、ShaohonChen、KashiwaByte


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