Update README.md

README.md

@@ -110,8 +110,8 @@ def denoise_image(image_path, model_path, patch_size=256, num_threads=4, overlap
     pad_bottom = patch_size - (height % patch_size) if height % patch_size != 0 else 0
     
     # Add padding with reflection instead of zeros
-    padded_width = width + pad_right + overlap
-    padded_height = height + pad_bottom + overlap
+    padded_width = width + pad_right
+    padded_height = height + pad_bottom
     
     # Create padded image using reflection padding
     padded_image = Image.new("RGB", (padded_width, padded_height))
@@ -136,9 +136,9 @@ def denoise_image(image_path, model_path, patch_size=256, num_threads=4, overlap
     # Generate patches with positions
     patches = []
     positions = []
-    for i in range(0, padded_height - patch_size + 1, patch_size - overlap):
-        for j in range(0, padded_width - patch_size + 1, patch_size - overlap):
-            patch = padded_image.crop((j, i, j + patch_size, i + patch_size))
+    for i in range(0, padded_height, patch_size - overlap):
+        for j in range(0, padded_width, patch_size - overlap):
+            patch = padded_image.crop((j, i, min(j + patch_size, padded_width), min(i + patch_size, padded_height)))
             patches.append(patch)
             positions.append((i, j))
     
@@ -151,21 +151,25 @@ def denoise_image(image_path, model_path, patch_size=256, num_threads=4, overlap
     weight_map = np.zeros((padded_height, padded_width), dtype=np.float32)
     
     # Create smooth blending weights
-    patch_weights = np.ones((patch_size, patch_size), dtype=np.float32)
-    if overlap > 0:
-        # Create smooth transition in overlap regions
-        fade = np.cos(np.linspace(np.pi, 2*np.pi, overlap)) * 0.5 + 0.5
-        patch_weights[:overlap, :] *= fade[:, np.newaxis]
-        patch_weights[-overlap:, :] *= fade[::-1][:, np.newaxis]
-        patch_weights[:, :overlap] *= fade[np.newaxis, :]
-        patch_weights[:, -overlap:] *= fade[::-1][np.newaxis, :]
-    
-    # Blend patches
     for (i, j), denoised_patch in zip(positions, denoised_patches):
-        denoised_image[i:i + patch_size, j:j + patch_size] += (
+        patch_height, patch_width, _ = denoised_patch.shape
+        patch_weights = np.ones((patch_height, patch_width), dtype=np.float32)
+        if i > 0:
+            patch_weights[:overlap, :] *= np.linspace(0, 1, overlap)[:, np.newaxis]
+        if j > 0:
+            patch_weights[:, :overlap] *= np.linspace(0, 1, overlap)[np.newaxis, :]
+        if i + patch_height < padded_height:
+            patch_weights[-overlap:, :] *= np.linspace(1, 0, overlap)[:, np.newaxis]
+        if j + patch_width < padded_width:
+            patch_weights[:, -overlap:] *= np.linspace(1, 0, overlap)[np.newaxis, :]
+        
+        # Clip the patch values to prevent very bright pixels
+        denoised_patch = np.clip(denoised_patch, 0, 255)
+        
+        denoised_image[i:i + patch_height, j:j + patch_width] += (
             denoised_patch * patch_weights[:, :, np.newaxis]
         )
-        weight_map[i:i + patch_size, j:j + patch_size] += patch_weights
+        weight_map[i:i + patch_height, j:j + patch_width] += patch_weights
     
     # Normalize by weights
     mask = weight_map > 0
@@ -177,12 +181,15 @@ def denoise_image(image_path, model_path, patch_size=256, num_threads=4, overlap
     
     # Save the result
     denoised_image_path = os.path.splitext(image_path)[0] + "_denoised.png"
+    print(f"Saving denoised image to {denoised_image_path}")
+    
     Image.fromarray(denoised_image).save(denoised_image_path)
 
 if __name__ == "__main__":
-    image_path = r"path/to/image"
+    image_path = input("Enter the path of the image: ")
     model_path = r"path/to/model.pkl"
-    denoise_image(image_path, model_path, num_threads=12  #Use the number of threads your processor has.)
+    denoise_image(image_path, model_path, num_threads=12)
+    print("Denoising completed.")  # Use the number of threads your processor has.)
 ```
 
 
@@ -230,4 +237,5 @@ Vericu de Buget
 
 ## Model Card Contact
 
-[[email protected]](mailto:[email protected])
+[[email protected]](mailto:[email protected])
+[ConvoLite](https://convolite.github.io/selector.html)