new

pages/Model_Evaluation.py

@@ -60,37 +60,32 @@ def apply_gaussian_filter(image, kernel_size=(5, 5), sigma=1.0):
     return cv2.GaussianBlur(image, kernel_size, sigma)
 
 # ---- Custom Dataset ----
-class DDRDataset(Dataset):
-    def __init__(self, csv_path, transform=None):
-        self.data = pd.read_csv(csv_path)
-        self.image_paths = self.data['new_path'].tolist()
-        self.labels = self.data['label'].tolist()
-        self.transform = transform
+def __getitem__(self, idx):
+    img_url = self.image_paths[idx]
+    label = int(self.labels[idx])
 
-    def __len__(self):
-        return len(self.image_paths)
-
-    def __getitem__(self, idx):
-        img_url = self.image_paths[idx]
-        label = int(self.labels[idx])
-
-        # Load image from URL
+    try:
+        print(f"Downloading: {img_url}")
         response = requests.get(img_url)
-        image = Image.open(BytesIO(response.content)).convert('RGB')
-        image = np.array(image)  # Convert PIL image to NumPy array for OpenCV functions
+        if response.status_code != 200:
+            raise ValueError(f"Failed to download image from {img_url} - status code: {response.status_code}")
+
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+    except Exception as e:
+        raise RuntimeError(f"Error loading image from {img_url}: {e}")
 
-        # Apply OpenCV preprocessing
-        image = apply_median_filter(image)
-        image = apply_clahe(image)
-        image = apply_gamma_correction(image)
-        image = apply_gaussian_filter(image)
+    image = np.array(image)
 
-        image = Image.fromarray(image)  # Back to PIL for transforms
+    image = apply_median_filter(image)
+    image = apply_clahe(image)
+    image = apply_gamma_correction(image)
+    image = apply_gaussian_filter(image)
 
-        if self.transform:
-            image = self.transform(image)
+    image = Image.fromarray(image)
+    if self.transform:
+        image = self.transform(image)
 
-        return image, torch.tensor(label, dtype=torch.long)
+    return image, torch.tensor(label, dtype=torch.long)
     
 # ---- Image Transforms ----
 val_transform = transforms.Compose([