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| from fastapi import FastAPI, File, UploadFile, HTTPException | |
| from fastapi.responses import JSONResponse | |
| from typing import List, Dict | |
| import cv2 | |
| from PIL import Image | |
| import numpy as np | |
| from io import BytesIO | |
| import base64 | |
| app = FastAPI() | |
| def detect_eyes(image: np.ndarray) -> List[Dict[str, str]]: | |
| eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| eyes = eye_cascade.detectMultiScale(gray, | |
| scaleFactor=1.1, | |
| minNeighbors=5, | |
| minSize=(30, 30) | |
| ) | |
| print("Detected eyes:", eyes, type(eyes)) | |
| if len(eyes) == 0: | |
| return [] | |
| detections = [] | |
| for (x, y, w, h) in eyes: | |
| # para dibujar rectangulos en ojos detectados | |
| cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2) | |
| detections.append({ | |
| "label": "eye", | |
| "bounding_box": { | |
| "x": int(x), | |
| "y": int(y), | |
| "width": int(w), | |
| "height": int(h) | |
| } | |
| }) | |
| return detections | |
| async def predict(file: UploadFile = File(...)) -> JSONResponse: | |
| try: | |
| image = Image.open(BytesIO(await file.read())).convert("RGB") | |
| image_np = np.array(image) | |
| detections = detect_eyes(image_np) | |
| if len(detections) == 0: | |
| return JSONResponse(content={ | |
| "detections": detections, | |
| "count": 0, | |
| "image_with_detections": None | |
| }) | |
| _, buffer = cv2.imencode('.jpg', image_np) | |
| image_base64 = base64.b64encode(buffer).decode('utf-8') | |
| return JSONResponse(content={ | |
| "detections": detections, | |
| "count": len(detections), | |
| "image_with_detections": image_base64 | |
| }) | |
| except Exception as e: | |
| raise HTTPException( | |
| status_code=500, | |
| detail=f"Error al procesar la imagen: {str(e)}") | |