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3a24a7b 55365d8 3a24a7b 55365d8 | 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 47 48 49 50 51 52 53 54 55 56 57 58 59 | import os
import cv2
import numpy as np
from keras.models import load_model
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODEL_PATH = os.path.join(BASE_DIR, 'data', 'model', 'retina.h5')
IMAGE_SIZE = (224, 224) # Standard per la maggior parte delle CNN (VGG/ResNet)
model = None
def load_resources():
global model
if model is None and os.path.exists(MODEL_PATH):
try:
print(f"Caricamento modello Retina da {MODEL_PATH}...")
model = load_model(MODEL_PATH, compile=False)
print("✅ Modello Retina caricato.")
except Exception as e:
print(f"❌ Errore caricamento modello Retina: {e}")
# Caricamento all'avvio
load_resources()
def predict_diabetic_retinopathy(image_array):
# Restituiamo sempre DUE valori (Diagnosi, Percentuale) anche in caso di errore
if model is None:
return "❌ Errore: Modello non trovato", "0%"
if image_array is None:
return "⚠️ Nessuna immagine caricata", "0%"
try:
# 2. PREPROCESSING
img_resized = cv2.resize(image_array, IMAGE_SIZE)
img_normalized = img_resized.astype('float32') / 255.0
img_batch = np.expand_dims(img_normalized, axis=0)
# 3. PREDIZIONE
prediction_score = model.predict(img_batch, verbose=0)[0][0]
positive_prob = float(prediction_score)
# 4. LOGICA DIAGNOSI
if positive_prob > 0.5:
diagnosis = "⚠️ Presenza Retinopatia Diabetica"
else:
diagnosis = "✅ Nessuna Retinopatia Diabetica"
# 5. FORMATTAZIONE PERCENTUALE
percentage = positive_prob * 100
probability = f"{percentage:.2f}%" # Formattazione f-string più pulita
return diagnosis, probability
except Exception as e:
print(f"Errore analisi retina: {e}")
return "❌ Errore durante l'analisi", "0%"
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