Remove brightness normalization function and update preprocessing pipeline to directly use normalized image data

app.py

@@ -41,22 +41,6 @@ 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()
@@ -76,7 +60,7 @@ def preprocessing_pipeline(pil_img):
     img = np.array(pil_img.convert('RGB'))
     img = cv2.resize(img, (100, 100), interpolation=cv2.INTER_AREA)
 
-    img_float = normalize_brightness(img)
+    img_float = img.astype(np.float32) / 255.0
     img_uint8 = (img_float * 255).astype(np.uint8)
 
     feat_color = extract_color_histogram(img_uint8)