Face Detection

Model basic information¶

Pyramid-Lite is a lightweight model developed by Baidu in 2018 in PyramBox of Computer Vision id 2018 ECCV. It is based on the main network FaceBoxes, measurement, environment, expression changes, meeting changes and other common problem models. The PaddleHub module is based on WIDER FACE data It can be used for face detection based on self-collected face datasets and Baidu self-collected face datasets, which supports prediction.

Reference：https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.2/modules/image/face_detection/pyramidbox_lite_server

Sample result example¶

Enter the file path and the model will give its predictions：

Let's try it out now

Prerequisite¶

1、environment dependent¶

Please visit dependencies

2、pyramidbox_lite_server dependent¶

• paddlepaddle >= 1.6.2

• paddlehub >= 1.6.0

3、Download the model¶

hub install pyramidbox_lite_server

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.

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import base64

import cv2
import numpy as np
import paddlehub as hub

from pinferencia import Server

face_detector = hub.Module(name="pyramidbox_lite_server")


def base64_str_to_cv2(base64_str: str) -> np.ndarray:
    return cv2.imdecode(
        np.fromstring(base64.b64decode(base64_str), np.uint8), cv2.IMREAD_COLOR
    )


def predict(base64_img_str: str):
    return face_detector.face_detection(
        images=[base64_str_to_cv2(base64_img_str)], visualization=True, output_dir="./"
    )


service = Server()
service.register(model_name="face_detector", model=predict)

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

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

Test the service¶

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

{
    "model_name": "face_detector",
    "model_version": "default",
    "data": [
        {
            "data": [
                {
                    "confidence": 0.9984221458435059,
                    "left": 519,
                    "top": 447,
                    "right": 755,
                    "bottom": 750
                }
            ],
            "path": "ndarray_time=1655802174713885.0"
        }
    ]
}

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import requests


response = requests.post(
    url="http://localhost:8000/v1/models/face_detector/predict",
    headers = {"Content-type": "application/json"},
    json={"data": "{base64 encoded image}"},
)
print(response.json())

$ python test.py
{
    "model_name": "face_detector",
    "model_version": "default",
    "data": [
        {
            "data": [
                {
                    "confidence": 0.9984221458435059,
                    "left": 519,
                    "top": 447,
                    "right": 755,
                    "bottom": 750
                }
            ],
            "path": "ndarray_time=1655802174713885.0"
        }
    ]
}