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Image Generation

Model basic information

This model is encapsulated from [the paddlepaddle version of the photo2cartoon project of Xiaoshi Technology] (https://github.com/minivision-ai/photo2cartoon-paddle).

Reference：https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.2/modules/image/Image_gan/style_transfer/Photo2Cartoon

Example

Image Source (https://www.pexels.com)

face

face

Let's try it out now

Prerequisite

1. environment dependent

Please visit dependencies

2. mobilenet_v2_animals dependent

  • paddlepaddle >= 2.0.0

  • paddlehub >= 2.0.0

3. Download the model

hub install Photo2Cartoon

Serve the Model

Install Pinferencia

First, let's install Pinferencia.

pip install "pinferencia[streamlit]"

Create app.py

Let's save our predict function into a file app.py and add some lines to register it.

app.py
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import base64
from io import BytesIO

import paddlehub as hub
from PIL import Image

from pinferencia import Server, task
from pinferencia.tools import base64_str_to_cv2

image_generation = hub.Module(name="Photo2Cartoon")


def predict(base64_img_str: str) -> str:
    result = image_generation.Cartoon_GEN(
        images=[base64_str_to_cv2(base64_img_str)], visualization=True, output_dir="./"
    )
    pil_img = Image.fromarray(result[0])
    buff = BytesIO()
    pil_img.save(buff, format="JPEG")
    return base64.b64encode(buff.getvalue()).decode("utf-8")


service = Server()
service.register(
    model_name="image_generation",
    model=predict,
    metadata={"task": task.IMAGE_TO_IMAGE},
)

Run the service, and wait for it to load the model and start the server:

$ uvicorn app:service --reload
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [xxxxx] using statreload
INFO:     Started server process [xxxxx]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

$ pinfer app:service --reload

Pinferencia: Frontend component streamlit is starting...
Pinferencia: Backend component uvicorn is starting...

Test the service

Open http://127.0.0.1:8501, and the template Url Image To Image will be selected automatically.

png

Request

curl --location --request POST \
    'http://127.0.0.1:8000/v1/models/image_generation/predict' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "data": "base64 image string"
    }'

Response

{
    "model_name": "image_generation",
    "model_version": "default",
    "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a..."
}

Create the test.py.

test.py
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import requests


response = requests.post(
    url="http://localhost:8000/v1/models/image_generation/predict",
    headers = {"Content-type": "application/json"},
    json={"data": "base64 image string"}
)
print(response.json())
Run the script and check the result.

$ python test.py
{
    "model_name": "image_generation",
    "model_version": "default",
    "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a..."
}

Even cooler, go to http://127.0.0.1:8000, and you will have a full documentation of your APIs.

You can also send predict requests just there!