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Web Service for Face2Face

image of openapi server

Starting the Web Service

In your cmd start the server with:

  • python -m face2face.server

Note: The first time you start the server, it will download the models. This can take a while.

How it works:

The Webservice is built with FastTaskAPI. In this regard, for each request it will create a task and return a job id. You can then check the status of the job and retrieve the result.

We recommend using fastSDK and FastTaskAPI as illustrated for easy file transfers.

Read the documentations of fastSDK, FastTaskAPI and media-toolkit to get the most out of the service and to familiarize yourself with the concepts.

Configuration

You can configure some settings via environment variables:

ENV Variable Description
MODELS_DIR Path to the folder where the models are stored. For example the inswapper and the GPEN models are downloaded and stored in this folder.
EMBEDDINGS_DIR Path to the folder where the face embeddings are stored. Stored faces can be reused by the api.

Deployment, Runpod, Docker, file uploads and more

For more settings and how to deploy the service check-out FastTaskAPI.

For example it allows you to deploy the service with Runpod out of the box. Checkout the DOCKERFILE for runpod.

Usage

The webservice provides enpdoints for the swap, add_face and enhance_face functionality. You can send requests to the endpoints with any http client, e.g. requests, httpx, curl, etc.

Using fastSDK is the most convenient way to interact with the webservice. You can find an implementation of an SDK generated for fastSDK in the socaity SDK documentation.

With plain web requests

Send requests

First encode the image as bytes.

# load images from disc
with open("myimage.jpg", "rb") as image:
    my_image = image.read()

Then send a post request to the endpoint.

my_job = requests.post("http://localhost:8020/api/add_face", files={"media": my_image, "faces": "biden"})

Parse the results

The response is a json that includes the job id and meta information. By sending then a request to the job endpoint you can check the status and progress of the job. If the job is finished, you will get the result, including the swapped image.

import cv2
from io import BytesIO
# check status of job
response = requests.get(f"http://localhost:8020/api/status?job_id={my_job['job_id']}")
# convert result to image file
swapped = cv2.imread(BytesIO(response.json()['result']))