Refactor preprocessing pipeline to include brightness normalization and streamline image processing

app.py

@@ -41,6 +41,22 @@ CLASS_NAMES = [
     "Salak",
 ]
 
+def normalize_brightness(img):
+    """Normalisasi brightness dan contrast untuk konsistensi"""
+    # Convert ke LAB color space
+    lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
+    l, a, b = cv2.split(lab)
+    
+    # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) pada channel L
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    l = clahe.apply(l)
+    
+    # Merge kembali
+    lab = cv2.merge([l, a, b])
+    normalized = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
+    
+    return normalized
+
 def extract_color_histogram(img, bins=(8, 8, 8)):
     hist = cv2.calcHist([img], [0, 1, 2], None, bins, [0, 256, 0, 256, 0, 256])
     hist = cv2.normalize(hist, hist).flatten()
@@ -57,17 +73,10 @@ def extract_lbp_features(gray_img, P=8, R=1, method="uniform"):
 
 
 def preprocessing_pipeline(pil_img):
-    """
-    Pipeline preprocessing sesuai dengan data training:
-    - Convert ke RGB
-    - Resize ke 100x100
-    - Extract color histogram & LBP features
-    """
-    # Convert ke RGB dan resize ke 100x100 (sesuai dataset training)
     img = np.array(pil_img.convert('RGB'))
     img = cv2.resize(img, (100, 100), interpolation=cv2.INTER_AREA)
 
-    img_float = img.astype(np.float32) / 255.0
+    img_float = normalize_brightness(img)
     img_uint8 = (img_float * 255).astype(np.uint8)
 
     feat_color = extract_color_histogram(img_uint8)
@@ -102,10 +111,8 @@ def index():
             try:
                 image = Image.open(file.stream)
                 
-                # Preprocessing sesuai dengan data training (hanya resize 100x100)
                 features = preprocessing_pipeline(image)
                 
-                # Untuk tampilan, resize ke 100x100
                 preprocessed_img = cv2.resize(np.array(image.convert('RGB')), (100, 100), interpolation=cv2.INTER_AREA)
                 preprocessed_pil = Image.fromarray(preprocessed_img)
                 
@@ -114,16 +121,13 @@ def index():
                 encoded_img = base64.b64encode(img_io.getvalue()).decode("ascii")
                 img_data = f"data:image/png;base64, {encoded_img}"
                 
-                # Prediksi dengan probabilitas
                 pred_index = model.predict(features)[0]
                 prediction_text = CLASS_NAMES[int(pred_index)]
                 
-                # Dapatkan probabilitas untuk semua kelas
                 if hasattr(model, 'predict_proba'):
                     probabilities = model.predict_proba(features)[0]
                     confidence = float(probabilities[int(pred_index)]) * 100
                     
-                    # Dapatkan top 3 prediksi
                     top_3_indices = probabilities.argsort()[-3:][::-1]
                     top_3_predictions = [
                         {
@@ -133,7 +137,6 @@ def index():
                         for idx in top_3_indices
                     ]
                 else:
-                    # Jika model tidak support predict_proba
                     confidence = None
                     top_3_predictions = None