predict.py

from fastapi import FastAPI, File, UploadFile
import cv2
import numpy as np
from ultralytics import YOLO
from fastapi.responses import FileResponse

app = FastAPI()
yolo_model_path = 'best.pt'
yolo = YOLO(yolo_model_path)

def detect_wounds(image):
    results = yolo(image)
    boxes = results[0].boxes.xyxy.tolist()
    return boxes

def draw_boxes(image, boxes):
    for box in boxes:
        x1, y1, x2, y2 = map(int, box)
        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    return image

@app.post("/detect")
async def detect(image: UploadFile = File(...)):
    image_bytes = await image.read()
    image = np.frombuffer(image_bytes, np.uint8)
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
    wound_boxes = detect_wounds(image)
    image_with_boxes = draw_boxes(image, wound_boxes)
    result_path = 'esult.jpg'
    cv2.imwrite(result_path, image_with_boxes)
    return FileResponse(result_path, media_type='image/jpeg')