Upload app.py

app.py

@@ -63,7 +63,7 @@ def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
 
     # Restorer Class
     upsampler = RealESRGANer(
-        scale=netscale, 
+        scale=netscale,
         model_path=model_path,
         dni_weight=dni_weight,
         model=model,
@@ -103,7 +103,7 @@ def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
             extension = 'png'
         else:
             extension = 'jpg'
-        
+
         out_filename = f"output_{rnd_string(8)}.{extension}"
         cv2.imwrite(out_filename, output)
         global last_file
@@ -111,18 +111,18 @@ def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
         return out_filename
 
 
-def rnd_string(number):
-    """Returns a string of 'number' random characters
+def rnd_string(x):
+    """Returns a string of 'x' random characters
     """
     characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
-    result = "".join((random.choice(characters)) for x in range(number))
+    result = "".join((random.choice(characters)) for i in range(x))
     return result
 
 
 def reset():
-    """Resets the Image components of the Gradio interface and deletes 
+    """Resets the Image components of the Gradio interface and deletes
     the last processed image
-    """ 
+    """
     global last_file
     if last_file:
         print(f"Deleting {last_file} ...")
@@ -132,12 +132,12 @@ def reset():
 
 
 def has_transparency(img):
-    """This function works by first checking to see if a "transparency" property is defined 
+    """This function works by first checking to see if a "transparency" property is defined
     in the image's info -- if so, we return "True". Then, if the image is using indexed colors
-    (such as in GIFs), it gets the index of the transparent color in the palette 
-    (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas 
-    (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in 
-    it, but it double-checks by getting the minimum and maximum values of every color channel 
+    (such as in GIFs), it gets the index of the transparent color in the palette
+    (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
+    (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
+    it, but it double-checks by getting the minimum and maximum values of every color channel
     (img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
     https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
     """
@@ -169,46 +169,58 @@ def image_properties(img):
         return properties
 
 
-# Gradio Interface
-with gr.Blocks(title="Real-ESRGAN Gradio Demo") as demo:
-
-    gr.Markdown(
-    """# <div align="center"> Real-ESRGAN Demo for Image Restoration and Upscaling </div>
-    <div align="center"><img width="200" height="74" src="https://github.com/xinntao/Real-ESRGAN/raw/master/assets/realesrgan_logo.png"></div>
+def main():
+    # Gradio Interface
+    with gr.Blocks(title="Real-ESRGAN Gradio Demo") as demo:
+
+        gr.Markdown(
+            """# <div align="center"> Real-ESRGAN Demo for Image Restoration and Upscaling </div>
+        <div align="center"><img width="200" height="74" src="https://github.com/xinntao/Real-ESRGAN/raw/master/assets/realesrgan_logo.png"></div>  
+
+        This Gradio Demo was built as my Final Project for **CS50's Introduction to Programming with Python**.  
+        Please visit the [Real-ESRGAN GitHub page](https://github.com/xinntao/Real-ESRGAN) for detailed information about the project.
+        """
+        )
+
+        with gr.Accordion("Options/Parameters"):
+            with gr.Row():
+                model_name = gr.Dropdown(label="Real-ESRGAN inference model to be used",
+                                         choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B",
+                                                  "RealESRGAN_x2plus", "realesr-general-x4v3"],
+                                         value="realesr-general-x4v3", show_label=True)
+                denoise_strength = gr.Slider(label="Denoise Strength (Used only with the realesr-general-x4v3 model)",
+                                             minimum=0, maximum=1, step=0.1, value=0.5)
+                outscale = gr.Slider(label="Image Upscaling Factor",
+                                     minimum=1, maximum=10, step=1, value=2, show_label=True)
+                face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)",
+                                           value=False, show_label=True)
 
-    This Gradio Demo was built as my Final Project for **CS50's Introduction to Programming with Python**.
-    Please visit the [Real-ESRGAN GitHub page](https://github.com/xinntao/Real-ESRGAN) for detailed information about the project.
-    """
-    )
-    
-    with gr.Accordion("Options/Parameters"):
         with gr.Row():
-            model_name = gr.Dropdown(label="Real-ESRGAN model to be used", 
-                choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", 
-                    "RealESRGAN_x2plus", "realesr-general-x4v3"], 
-                value="realesr-general-x4v3", show_label=True)
-            denoise_strength = gr.Slider(label="Denoise Strength (Used only with the realesr-general-x4v3 model)", 
-                minimum=0, maximum=1, step=0.1, value=0.5)
-            outscale = gr.Slider(label="Image Upscaling Factor",
-                minimum=1, maximum=10, step=1, value=2, show_label=True)
-            face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)", 
-                value=False, show_label=True)
-    
-    with gr.Row():
-        with gr.Group():
-            input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
-            input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
-        output_image = gr.Image(label="Restored Image", image_mode="RGBA")
-    with gr.Row():
-        restore_btn = gr.Button("Restore Image")
-        reset_btn = gr.Button("Reset")
-
-    # Event listeners:
-    input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
-    restore_btn.click(fn=realesrgan, 
-        inputs=[input_image, model_name, denoise_strength, face_enhance, outscale], outputs=output_image)
-    reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
-    # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
-    # Undocumented method to clear a component's value using Javascript
-
-demo.launch()
+            with gr.Group():
+                input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
+                input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
+            output_image = gr.Image(label="Restored Image", image_mode="RGBA")
+        with gr.Row():
+            restore_btn = gr.Button("Restore Image")
+            reset_btn = gr.Button("Reset")
+
+        # Event listeners:
+        input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
+        restore_btn.click(fn=realesrgan,
+                          inputs=[input_image, model_name, denoise_strength, face_enhance, outscale],
+                          outputs=output_image)
+        reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
+        # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
+        # Undocumented method to clear a component's value using Javascript
+
+        gr.Markdown(
+            """*Please note that support for animated GIFs is not yet implemented. Should an animated GIF is chosen for restoration, 
+            the demo will output only the first frame saved in PNG format (to preserve probable transparency).*
+        """
+        )
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()