9.4 KiB
🤩Project Update
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Online Demo:
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2024.9.6: Add a new image matting model modnet_photographic_portrait_matting.onnx
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2024.9.2: Update Adjusted photo KB size，DockerHub
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2023.12.1: Update API deployment (based on fastapi)
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2023.6.20: Update Preset size menu
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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:
- 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!
🔧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
- (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
- MTCNN:
@software{ipazc_mtcnn_2021,
author = {ipazc},
title = {{MTCNN}},
url = {https://github.com/ipazc/mtcnn},
year = {2021},
publisher = {GitHub}
}
- 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