File size: 1,046 Bytes
e259e75 1fc541a e259e75 1fc541a e259e75 1fc541a e259e75 1fc541a e259e75 | 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 45 46 | from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import cv2
import numpy as np
from ultralytics import YOLO
app = FastAPI()
# Allow CORS for GitHub Pages
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
model = YOLO("helmet.pt")
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
image_bytes = await file.read()
np_img = np.frombuffer(image_bytes, np.uint8)
img = cv2.imdecode(np_img, cv2.IMREAD_COLOR)
results = model(img)[0]
detections = []
for box in results.boxes:
cls = int(box.cls[0])
conf = float(box.conf[0])
label = model.names[cls]
detections.append({
"label": label,
"confidence": round(conf, 3)
})
return {
"count": len(detections),
"detections": detections
}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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