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File size: 1,067 Bytes
96c502a 5f1458d a816c37 5f1458d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | from fastapi import FastAPI, UploadFile, File
import onnxruntime as ort
import numpy as np
from PIL import Image
import io
app = FastAPI()
MODEL_PATH = "/app/w600k_r50.onnx"
session = ort.InferenceSession(
MODEL_PATH,
providers=["CPUExecutionProvider"]
)
input_name = session.get_inputs()[0].name
def preprocess(img):
img = img.resize((112, 112))
img = np.array(img).astype("float32")
img = (img - 127.5) / 128.0
img = np.transpose(img, (2, 0, 1))
return np.expand_dims(img, axis=0)
@app.post("/embed")
async def embed_face(file: UploadFile = File(...)):
img = Image.open(io.BytesIO(await file.read())).convert("RGB")
inp = preprocess(img)
emb = session.run(None, {input_name: inp})[0][0]
return {"embedding": emb.tolist()}
if __name__ == "__main__":
import uvicorn
uvicorn.run(
"app:app",
host="0.0.0.0",
port=7860,
reload=False
)
det_sess = ort.InferenceSession(
"/app/det_10g.onnx",
providers=["CPUExecutionProvider"]
)
det_input = det_sess.get_inputs()[0].name
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