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API Docs

English / 中文 / 日本語 / 한국어

Table of Contents

Getting Started: Start the Backend Service

Before making API requests, please start the backend service

python deploy_api.py

API Function Descriptions

1. Generate ID Photo (Transparent Background)

API Name: idphoto

The logic of the Generate ID Photo API is to send an RGB image and output a standard ID photo and a high-definition ID photo:

  • High-definition ID Photo: The ID photo created based on the aspect ratio of size, with the filename being output_image_dir with the _hd suffix added.
  • Standard ID Photo: The size is equal to size, scaled from the high-definition ID photo, with the filename being output_image_dir.

It is important to note that both generated photos are transparent (RGBA four-channel images). To generate a complete ID photo, the following Add Background Color API is needed.

Q: Why is it designed this way?
A: Because in actual products, users often switch background colors to preview effects frequently. Providing a transparent background image allows for a better experience with color composition done by frontend JS code.

2. Add Background Color

API Name: add_background

The logic of the Add Background Color API is to send an RGBA image and add a background color based on color, resulting in a JPG image.

3. Generate Six-Inch Layout Photo

API Name: generate_layout_photos

The logic of the Generate Six-Inch Layout Photo API is to send an RGB image (typically the ID photo after adding background color) and arrange the photos based on size, resulting in a six-inch layout photo.

4. Human Matting

API Name: human_matting

The logic of the Human Matting API is to send an RGB image and output a standard matting portrait and a high-definition matting portrait (with no background fill).


cURL Request Examples

cURL is a command-line tool for transferring data using various network protocols. Below are examples of using cURL to call these APIs.

1. Generate ID Photo (Transparent Background)

curl -X POST "http://127.0.0.1:8080/idphoto" \
-F "input_image=@demo/images/test0.jpg" \
-F "height=413" \
-F "width=295" \
-F "human_matting_model=hivision_modnet" \
-F "face_detect_model=mtcnn"

2. Add Background Color

curl -X POST "http://127.0.0.1:8080/add_background" \
-F "[email protected]" \
-F "color=638cce" \
-F "kb=200" \
-F "render=0"

3. Generate Six-Inch Layout Photo

curl -X POST "http://127.0.0.1:8080/generate_layout_photos" \
-F "[email protected]" \
-F "height=413" \
-F "width=295" \
-F "kb=200"

4. Human Matting

curl -X POST "http://127.0.0.1:8080/human_matting" \
-F "input_image=@demo/images/test0.jpg" \
-F "human_matting_model=hivision_modnet"

Python Request Examples

1️⃣ Python Requests Method

1. Generate ID Photo (Transparent Background)

import requests

url = "http://127.0.0.1:8080/idphoto"
input_image_path = "images/test0.jpg"

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

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

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

2. Add 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, "render": 0}

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

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

3. Generate Six-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 formatted dictionary containing status and image_base64
print(response)

4. Human Matting

import requests

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

files = {"input_image": open(input_image_path, "rb")}
data = {"human_matting_model": "hivision_modnet"}

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

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

2️⃣ Python Script Method

python requests_api.py -u <URL> -t <TYPE> -i <INPUT_IMAGE_DIR> -o <OUTPUT_IMAGE_DIR> [--height <HEIGHT>] [--width <WIDTH>] [-c <COLOR>] [-k <KB>]

Parameter Descriptions

Basic Parameters

  • -u, --url

    • Description: The URL of the API service.
    • Default Value: http://127.0.0.1:8080

  • -t, --type

    • Description: The type of API request.
    • Default Value: idphoto

  • -i, --input_image_dir

    • Description: The path to the input image.
    • Required: Yes
    • Example: ./input_images/photo.jpg

  • -o, --output_image_dir

    • Description: The path to save the image.
    • Required: Yes
    • Example: ./output_images/processed_photo.jpg
Optional Parameters

  • --face_detect_model

    • Description: Face detection model
    • Default Value: mtcnn

  • --human_matting_model

    • Description: Human matting model
    • Default Value: hivision_modnet

  • --height

    • Description: The height of the output size for the standard ID photo.
    • Default Value: 413

  • --width

    • Description: The width of the output size for the standard ID photo.
    • Default Value: 295

  • -c, --color

    • Description: Add background color to the transparent image, format as Hex (e.g., #638cce), only effective when type is add_background
    • Default Value: 638cce

  • -k, --kb

    • Description: The KB value of the output photo, only effective when type is add_background or generate_layout_photos, no setting when the value is None.
    • Default Value: None
    • Example: 50

  • -r, --render

    • Description: The rendering method for adding background color to the transparent image, only effective when type is add_background or generate_layout_photos
    • Default Value: 0

1. Generate ID Photo (Transparent Background)

python requests_api.py  \
    -u http://127.0.0.1:8080 \
    -t idphoto \
    -i ./photo.jpg \
    -o ./idphoto.png \
    --height 413 \
    --width 295 \
    --face_detect_model mtcnn \
    --human_matting_model hivision_modnet

2. Add Background Color

python requests_api.py  \
    -u http://127.0.0.1:8080  \
    -t add_background  \
    -i ./idphoto.png  \
    -o ./idphoto_with_background.jpg  \
    -c 638cce  \
    -k 50 \
    -r 0

3. Generate Six-Inch Layout Photo

python requests_api.py  \
    -u http://127.0.0.1:8080  \
    -t generate_layout_photos  \
    -i ./idphoto_with_background.jpg  \
    -o ./layout_photo.jpg  \
    --height 413  \
    --width 295 \
    -k 200

4. Human Matting

python requests_api.py  \
    -u http://127.0.0.1:8080  \
    -t human_matting  \
    -i ./photo.jpg  \
    -o ./photo_matting.png \
    --human_matting_model hivision_modnet

Failure Cases

  • If more than one face is detected in the photo, the request will fail.

Java Request Examples

Add Maven Dependencies

<dependency>
    <groupId>cn.hutool</groupId>
    <artifactId>hutool-all</artifactId>
    <version>5.8.16</version>
</dependency>

<dependency>
    <groupId>commons-io</groupId>
    <artifactId>commons-io</artifactId>
    <version>2.6</version>
</dependency>

Run Code

1. Generate ID Photo (Transparent Background)

/**
    * Generate ID Photo (Transparent Background) /idphoto API
    * @param inputImageDir File path
    * @return
    * @throws IOException
    */
public static String requestIdPhoto(String inputImageDir) throws IOException {
    String url = BASE_URL+"/idphoto";
    // Create file object
    File inputFile = new File(inputImageDir);
    Map<String, Object> paramMap=new HashMap<>();
    paramMap.put("input_image",inputFile);
    paramMap.put("height","413");
    paramMap.put("width","295");
    // Contains status, image_base64_standard, and image_base64_hd
    return HttpUtil.post(url, paramMap);
}

2. Add Background Color

/**
    * Add Background Color /add_background API
    * @param inputImageDir File path
    * @return
    * @throws IOException
    */
public static String requestAddBackground(String inputImageDir) throws IOException {
    String url = BASE_URL+"/add_background";
    // Create file object
    File inputFile = new File(inputImageDir);
    Map<String, Object> paramMap=new HashMap<>();
    paramMap.put("input_image",inputFile);
    paramMap.put("color","638cce");
    paramMap.put("kb","200");
    // response is a JSON formatted dictionary containing status and image_base64
    return HttpUtil.post(url, paramMap);
}

3. Generate Six-Inch Layout Photo

/**
    * Generate Six-Inch Layout Photo /generate_layout_photos API
    * @param inputImageDir File path
    * @return
    * @throws IOException
    */
public static String requestGenerateLayoutPhotos(String inputImageDir) throws IOException {
    String url = BASE_URL+"/generate_layout_photos";
    // Create file object
    File inputFile = new File(inputImageDir);
    Map<String, Object> paramMap=new HashMap<>();
    paramMap.put("input_image",inputFile);
    paramMap.put("height","413");
    paramMap.put("width","295");
    paramMap.put("kb","200");
    // response is a JSON formatted dictionary containing status and image_base64
    return HttpUtil.post(url, paramMap);
}

4. Human Matting

/**
    * Generate Human Matting Photo /human_matting API
    * @param inputImageDir File path
    * @return
    * @throws IOException
    */
public static String requestHumanMattingPhotos(String inputImageDir) throws IOException {
    String url = BASE_URL+"/human_matting";
    // Create file object
    File inputFile = new File(inputImageDir);
    Map<String, Object> paramMap=new HashMap<>();
    paramMap.put("input_image",inputFile);
    // Contains status and image_base64
    return HttpUtil.post(url, paramMap);
}

JavaScript Request Examples

In JavaScript, we can use the fetch API to send HTTP requests. Below are examples of how to call these APIs using JavaScript.

1. Generate ID Photo (Transparent Background)

async function generateIdPhoto(inputImagePath, height, width) {
    const url = "http://127.0.0.1:8080/idphoto";
    const formData = new FormData();
    formData.append("input_image", new File([await fetch(inputImagePath).then(res => res.blob())], "test.jpg"));
    formData.append("height", height);
    formData.append("width", width);

    const response = await fetch(url, {
        method: 'POST',
        body: formData
    });

    const result = await response.json();
    console.log(result);
    return result;
}

// Example call
generateIdPhoto("images/test0.jpg", 413, 295).then(response => {
    console.log(response);
});

2. Add Background Color

async function addBackground(inputImagePath, color, kb) {
    const url = "http://127.0.0.1:8080/add_background";
    const formData = new FormData();
    formData.append("input_image", new File([await fetch(inputImagePath).then(res => res.blob())], "test.png"));
    formData.append("color", color);
    formData.append("kb", kb);

    const response = await fetch(url, {
        method: 'POST',
        body: formData
    });

    const result = await response.json();
    console.log(result);
    return result;
}

// Example call
addBackground("test.png", "638cce", 200).then(response => {
    console.log(response);
});

3. Generate Six-Inch Layout Photo

async function generateLayoutPhotos(inputImagePath, height, width, kb) {
    const url = "http://127.0.0.1:8080/generate_layout_photos";
    const formData = new FormData();
    formData.append("input_image", new File([await fetch(inputImagePath).then(res => res.blob())], "test.jpg"));
    formData.append("height", height);
    formData.append("width", width);
    formData.append("kb", kb);

    const response = await fetch(url, {
        method: 'POST',
        body: formData
    });

    const result = await response.json();
    console.log(result);
    return result;
}

// Example call
generateLayoutPhotos("test.jpg", 413, 295, 200).then(response => {
    console.log(response);
});

4. Human Matting

async function uploadImage(inputImagePath) {
    const url = "http://127.0.0.1:8080/human_matting";
    const formData = new FormData();
    formData.append("input_image", new File([await fetch(inputImagePath).then(res => res.blob())], "test.jpg"));

    const response = await fetch(url, {
        method: 'POST',
        body: formData
    });

    const result = await response.json(); // Assume the response is in JSON format
    console.log(result);
    return result;
}

// Example call
uploadImage("demo/images/test0.jpg").then(response => {
    console.log(response);
});