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from fastapi import FastAPI, File, UploadFile, HTTPException |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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import io |
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import base64 |
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app = FastAPI() |
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@app.post("/detect/") |
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async def detect_face(file: UploadFile = File(...)): |
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try: |
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image_bytes = await file.read() |
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image = Image.open(io.BytesIO(image_bytes)) |
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img_np = np.array(image) |
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if img_np.shape[2] == 4: |
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGRA2BGR) |
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') |
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gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) |
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) |
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if len(faces) == 0: |
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raise HTTPException(status_code=404, detail="No se detectaron rostros en la imagen.") |
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for (x, y, w, h) in faces: |
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cv2.rectangle(img_np, (x, y), (x+w, y+h), (255, 0, 0), 2) |
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result_image = Image.fromarray(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)) |
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img_byte_arr = io.BytesIO() |
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result_image.save(img_byte_arr, format='JPEG') |
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img_byte_arr = img_byte_arr.getvalue() |
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return { |
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"message": "Rostros detectados", |
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"rostros": len(faces), |
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"imagen_base64": base64.b64encode(img_byte_arr).decode('utf-8') |
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} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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