🐛 fix(image processing): ensure image consistency for gradient and minmax preprocessors

- added type check for PIL Image and conversion to NumPy array in min_max preprocessing
- 【Refactor】also converted PIL Image to NumPy array in image prediction module

💄 style(image processing): optimize ELA image list

- removed ela_img_4 as it did not add significant insights and kept the uncommented portion orderly

app.py

@@ -231,17 +231,17 @@ def predict_image_with_html(img, confidence_threshold, augment_methods, rotate_d
     else:
         img_pil = img
     img_pil, results = predict_image(img_pil, confidence_threshold)
-    
-    gradient_image = gradient_processing(img_pil)  # Added gradient processing
-    minmax_image = minmax_preprocess(img_pil)  # Added MinMax processing
+    img_np = np.array(img_pil)  # Convert PIL Image to NumPy array
+
+    gradient_image = gradient_processing(img_np)  # Added gradient processing
+    minmax_image = minmax_preprocess(img_np)  # Added MinMax processing
 
     # Generate ELA images with different presets
     ela_img_1 = ELA(img_pil, scale=100, alpha=0.66)
     ela_img_2 = ELA(img_pil, scale=75, alpha=0.8)
     ela_img_3 = ELA(img_pil, scale=50, alpha=0.5)
-    ela_img_4 = ELA(img_pil, scale=25, alpha=0.85)
     
-    ela_images = [ela_img_1, ela_img_2, ela_img_3, ela_img_4, gradient_image, minmax_image]
+    ela_images = [ela_img_1, ela_img_2, ela_img_3, gradient_image, minmax_image]
     
     html_content = generate_results_html(results)
     return img_pil, ela_images, html_content

utils/minmax.py

@@ -27,8 +27,9 @@ def blk_filter(img, radius):
             )
     return cv.normalize(result, None, 0, 127, cv.NORM_MINMAX, cv.CV_8UC1)
 
-
 def preprocess(image, channel=0, radius=0):
+    if not isinstance(image, np.ndarray):
+        image = np.array(image)  # Ensure image is a NumPy array
     if channel == 0:
         img = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
     elif channel == 4: