From 8f49cdd14481484d4b625debb9e13148df78a47f Mon Sep 17 00:00:00 2001
From: Sebastian Olter <qduaty@gmail.com>
Date: Sun, 30 Jun 2024 15:44:53 +0200
Subject: [PATCH] fixed mono models code indentation

---
 QualityScaler.py | 100 +++++++++++++++++++++++------------------------
 1 file changed, 50 insertions(+), 50 deletions(-)

diff --git a/QualityScaler.py b/QualityScaler.py
index 3d2d7fd..99d2b96 100644
--- a/QualityScaler.py
+++ b/QualityScaler.py
@@ -299,21 +299,21 @@ def AI_upscale(
                     output_image[:,:,2] = numpy.clip(output_imageYCbCr[:,:,0] + 1.772 * (output_imageYCbCr[:,:,1] - 0.5), 0, 1)
 
                 else:
-                imageR = image[:, :, 0]
-                imageR = numpy_expand_dims(imageR, axis=2)
-                imageR = preprocess_image(imageR)
-                output_image = process_image_with_AI_model(AI_model, imageR)
-                output_image = numpy.repeat(output_image, 3, 2)
-
-                imageG = image[:, :, 1]
-                imageG = numpy_expand_dims(imageG, axis=2)
-                imageG = preprocess_image(imageG)
-                output_image[:,:,1] = process_image_with_AI_model(AI_model, imageG).squeeze()
-
-                imageB = image[:, :, 2]
-                imageB = numpy_expand_dims(imageB, axis=2)
-                imageB = preprocess_image(imageB)
-                output_image[:,:,2] = process_image_with_AI_model(AI_model, imageB).squeeze()
+                    imageR = image[:, :, 0]
+                    imageR = numpy_expand_dims(imageR, axis=2)
+                    imageR = preprocess_image(imageR)
+                    output_image = process_image_with_AI_model(AI_model, imageR)
+                    output_image = numpy.repeat(output_image, 3, 2)
+
+                    imageG = image[:, :, 1]
+                    imageG = numpy_expand_dims(imageG, axis=2)
+                    imageG = preprocess_image(imageG)
+                    output_image[:,:,1] = process_image_with_AI_model(AI_model, imageG).squeeze()
+
+                    imageB = image[:, :, 2]
+                    imageB = numpy_expand_dims(imageB, axis=2)
+                    imageB = preprocess_image(imageB)
+                    output_image[:,:,2] = process_image_with_AI_model(AI_model, imageB).squeeze()
 
                 return postprocess_output(output_image, range)
 
@@ -348,41 +348,41 @@ def AI_upscale(
                 output_image = output_image.squeeze()
                 return postprocess_output(output_image, range)
     else:
-    match image_mode:
-        case "RGB":
-            image = preprocess_image(image)
-            output_image = process_image_with_AI_model(AI_model, image)
-            return postprocess_output(output_image, range)
-        
-        case "RGBA":
-            alpha = image[:, :, 3]
-            image = image[:, :, :3]
-            image = opencv_cvtColor(image, COLOR_BGR2RGB)
-            alpha = opencv_cvtColor(alpha, COLOR_GRAY2RGB)
-
-            image = image.astype(float32)
-            alpha = alpha.astype(float32)
-
-            # Image
-            image = preprocess_image(image)
-            output_image = process_image_with_AI_model(AI_model, image)
-            output_image = opencv_cvtColor(output_image, COLOR_RGB2BGRA)
-
-            # Alpha
-            alpha = preprocess_image(alpha)
-            output_alpha = process_image_with_AI_model(AI_model, alpha)
-            output_alpha = opencv_cvtColor(output_alpha, COLOR_RGB2GRAY)
-
-            # Fusion Image + Alpha
-            output_image[:, :, 3] = output_alpha
-            return postprocess_output(output_image, range)
-        
-        case "Grayscale":
-            image = opencv_cvtColor(image, COLOR_GRAY2RGB)
-            image = preprocess_image(image)
-            output_image = process_image_with_AI_model(AI_model, image)
-            output_image = opencv_cvtColor(output_image, COLOR_RGB2GRAY)
-            return postprocess_output(output_image, range)
+        match image_mode:
+            case "RGB":
+                image = preprocess_image(image)
+                output_image = process_image_with_AI_model(AI_model, image)
+                return postprocess_output(output_image, range)
+
+            case "RGBA":
+                alpha = image[:, :, 3]
+                image = image[:, :, :3]
+                image = opencv_cvtColor(image, COLOR_BGR2RGB)
+                alpha = opencv_cvtColor(alpha, COLOR_GRAY2RGB)
+
+                image = image.astype(float32)
+                alpha = alpha.astype(float32)
+
+                # Image
+                image = preprocess_image(image)
+                output_image = process_image_with_AI_model(AI_model, image)
+                output_image = opencv_cvtColor(output_image, COLOR_RGB2BGRA)
+
+                # Alpha
+                alpha = preprocess_image(alpha)
+                output_alpha = process_image_with_AI_model(AI_model, alpha)
+                output_alpha = opencv_cvtColor(output_alpha, COLOR_RGB2GRAY)
+
+                # Fusion Image + Alpha
+                output_image[:, :, 3] = output_alpha
+                return postprocess_output(output_image, range)
+
+            case "Grayscale":
+                image = opencv_cvtColor(image, COLOR_GRAY2RGB)
+                image = preprocess_image(image)
+                output_image = process_image_with_AI_model(AI_model, image)
+                output_image = opencv_cvtColor(output_image, COLOR_RGB2GRAY)
+                return postprocess_output(output_image, range)
 
 def get_image_mode(image: numpy_ndarray) -> str:
     match image.shape: