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语义分割

模型基本信息

基于 ExtremeC3 模型实现的轻量化人像分割模型, 更多详情请参考： ExtremeC3_Portrait_Segmentation 项目。

参考：https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.2/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation

样例结果示例

image

image

image

现在就来试试吧

先决条件

1、环境依赖

请访问 依赖项

2、ExtremeC3_Portrait_Segmentation 依赖

  • paddlepaddle >= 2.0.0

  • paddlehub >= 2.0.0

3、下载模型

hub install ExtremeC3_Portrait_Segmentation

服务模型

安装 Pinferencia

首先，让我们安装 Pinferencia。

pip install "pinferencia[streamlit]"

创建app.py

让我们将我们的预测函数保存到一个文件 app.py 中并添加一些行来注册它。

app.py
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import base64
from io import BytesIO

import cv2
import numpy as np
import paddlehub as hub
from PIL import Image

from pinferencia import Server, task

semantic_segmentation = hub.Module(name="ExtremeC3_Portrait_Segmentation")


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


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

运行服务，等待它加载模型并启动服务器：

$ 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...

测试服务

打开http://127.0.0.1:8501，模板 Url Image To Image 会自动选中。

png

请求

curl --location --request POST \
    'http://127.0.0.1:8000/v1/models/semantic_segmentation/predict' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "data": "/9j/4AAQSkZJRgABAQEA/..."
    }'

响应

{
    "model_name": "semantic_segmentation",
    "model_version": "default",
    "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRo..."
}

创建test.py。

test.py
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import requests


response = requests.post(
    url="http://localhost:8000/v1/models/semantic_segmentation/predict",
    headers = {"Content-type": "application/json"},
    json={"data": "/9j/4AAQSkZJRgABAQEA/..."},
)
print(response.json())
运行脚本并检查结果。

$ python test.py
{
    "model_name": "semantic_segmentation",
    "model_version": "default",
    "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRo..."
}

更酷的是，访问 http://127.0.0.1:8000，您将拥有一个完整的 API 文档。

您甚至也可以在那里发送预测请求！