Update utils.py

utils.py

@@ -116,8 +116,7 @@ class WatermarkProcessor:
           except Exception as e:
                return image_path, f"Error in encoding: {str(e)}"
 
-
-    def decode(self, image_path):
+     def decode(self, image_path):
           """Decode watermark using simple LSB steganography"""
           try:
                # Try PNG metadata method first
@@ -186,3 +185,40 @@ class WatermarkProcessor:
           except Exception as e:
                return f"Error in decoding: {str(e)}"
 
+     def analyze_quality(self, original_path, watermarked_path):
+          """Analyze watermark quality"""
+          try:
+               original = cv2.imread(original_path)
+               watermarked = cv2.imread(watermarked_path)
+               
+               if original is None or watermarked is None:
+                    raise ValueError("Could not read image files")
+               
+               # Calculate PSNR
+               mse = np.mean((original - watermarked) ** 2)
+               if mse == 0:
+                    psnr = float('inf')
+               else:
+                    psnr = 20 * np.log10(255.0 / np.sqrt(mse))
+               
+               # Calculate histogram similarity
+               hist_original = cv2.calcHist([original], [0], None, [256], [0, 256])
+               hist_watermarked = cv2.calcHist([watermarked], [0], None, [256], [0, 256])
+               hist_correlation = cv2.compareHist(hist_original, hist_watermarked, cv2.HISTCMP_CORREL)
+               
+               # Count modified pixels
+               diff = cv2.bitwise_xor(original, watermarked)
+               modified_pixels = np.count_nonzero(diff)
+               
+               report = {
+                    'psnr': round(psnr, 2),
+                    'histogram_similarity': round(hist_correlation, 4),
+                    'modified_pixels': modified_pixels,
+                    'image_size': original.shape,
+                    'quality_score': round((psnr / 50) * 100, 2) if psnr != float('inf') else 100
+               }
+               
+               return json.dumps(report, indent=2)
+               
+          except Exception as e:
+               return f"Error in quality analysis: {str(e)}"