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app.py

@@ -93,16 +93,15 @@ def run(content_image, style_name, style_strength=10):
         else:
             future_all = executor.submit(run_inference, False)
             future_bg = executor.submit(run_inference, True)
-        generated_img_all, _ = future_all.result()
-        generated_img_bg, bg_ratio = future_bg.result()
+        generated_img_all = future_all.result()
+        generated_img_bg = future_bg.result()
 
     et = time.time()
     print('TIME TAKEN:', et-st)
     
     yield (
         (content_image, postprocess_img(generated_img_all, original_size)),
-        (content_image, postprocess_img(generated_img_bg, original_size)),
-        f'{bg_ratio:.2f}'
+        (content_image, postprocess_img(generated_img_bg, original_size))
     )
 
 def set_slider(value):
@@ -116,13 +115,13 @@ css = """
 """
 
 with gr.Blocks(css=css) as demo:
-    gr.HTML("<h1 style='text-align: center; padding: 10px'>🖼️ Neural Style Transfer w/ Salient Object Masking")
+    gr.HTML("<h1 style='text-align: center; padding: 10px'>🖼️ Neural Style Transfer w/ Salient Region Preservation")
     with gr.Row(elem_id='container'):
         with gr.Column():
             content_image = gr.Image(label='Content', type='pil', sources=['upload', 'webcam', 'clipboard'], format='jpg', show_download_button=False)
             style_dropdown = gr.Radio(choices=list(style_options.keys()), label='Style', value='Starry Night', type='value')
             with gr.Group():
-                style_strength_slider = gr.Slider(label='Style Strength', minimum=1, maximum=10, step=1, value=5, info='Higher values add artistic flair, lower values add a realistic feel.')
+                style_strength_slider = gr.Slider(label='Style Strength', minimum=1, maximum=10, step=1, value=10, info='Higher values add artistic flair, lower values add a realistic feel.')
             submit_button = gr.Button('Submit', variant='primary')
             
             examples = gr.Examples(
@@ -139,7 +138,6 @@ with gr.Blocks(css=css) as demo:
             download_button_1 = gr.DownloadButton(label='Download Styled Image', visible=False)
             with gr.Group():
                 output_image_background = ImageSlider(position=0.15, label='Styled Background', type='pil', interactive=False, show_download_button=False)
-                bg_ratio_label = gr.Label(label='Background Ratio')
             download_button_2 = gr.DownloadButton(label='Download Styled Background', visible=False)
 
     def save_image(img_tuple1, img_tuple2):
@@ -156,7 +154,7 @@ with gr.Blocks(css=css) as demo:
     submit_button.click(
         fn=run, 
         inputs=[content_image, style_dropdown, style_strength_slider], 
-        outputs=[output_image_all, output_image_background, bg_ratio_label]
+        outputs=[output_image_all, output_image_background]
     ).then(
         fn=save_image,
         inputs=[output_image_all, output_image_background],

inference.py

@@ -55,17 +55,12 @@ def inference(
         content_features = model(content_image)
 
         resized_bg_masks = []
-        salient_object_ratio = None
         if apply_to_background:
             segmentation_output = sod_model(content_image_norm)[0]
             segmentation_output = torch.sigmoid(segmentation_output)
             segmentation_mask = (segmentation_output > 0.7).float()
             background_mask = (segmentation_mask == 0).float()
             foreground_mask = 1 - background_mask
-            
-            salient_object_pixel_count = foreground_mask.sum().item()
-            total_pixel_count = segmentation_mask.numel()
-            salient_object_ratio = salient_object_pixel_count / total_pixel_count
 
             for cf in content_features:
                 _, _, h_i, w_i = cf.shape
@@ -93,4 +88,4 @@ def inference(
                 foreground_mask_resized = F.interpolate(foreground_mask.unsqueeze(1), size=generated_image.shape[2:], mode='nearest')
                 generated_image.data = generated_image.data * (1 - foreground_mask_resized) + content_image.data * foreground_mask_resized
                 
-    return generated_image, salient_object_ratio
+    return generated_image