Update app.py

app.py

@@ -13,12 +13,17 @@ import matplotlib.pyplot as plt
 # Definir el tamaño de la imagen constante
 IMAGE_SIZE = 640  # Asignar el valor constante para image_size
 
-def process_image(image, model_id, sat_factor, DO, t, vertical):
+def process_image(image, model_id, sat_factor, selected_method):
     # Definir valores fijos dentro de la función
     conf_threshold = 0.85
     correction = 1.0
     kernel_size = 7  # Definir kernel_size como constante
     
+    # Valores fijos para DO, t y vertical
+    DO = 1
+    t = 0.7
+    vertical = True
+    
     original_image = np.array(image)
     original_image = original_image - original_image.min()
     original_image = original_image / original_image.max()
@@ -42,7 +47,7 @@ def process_image(image, model_id, sat_factor, DO, t, vertical):
     clipped_annotated, clipped_detections = yolov10_inference((clipped_image*255.0).astype(np.uint8), "yolov10n", IMAGE_SIZE, conf_threshold)
     wrapped_annotated, wrapped_detections = yolov10_inference(wrapped_image, model_id, IMAGE_SIZE, conf_threshold)
 
-    recon_image = recons(img_tensor, DO=1, L=1.0, vertical=(vertical == "True"), t=t)
+    recon_image = recons(img_tensor, DO=DO, L=1.0, vertical=vertical, t=t)
     recon_image_pil = transforms.ToPILImage()(recon_image.squeeze(0))
     recon_image_np = np.array(recon_image_pil).astype(np.uint8)
 
@@ -86,12 +91,12 @@ def app():
     }}
     .custom-button {{
         display: inline-block;
-        padding: 10px 20px;
-        font-size: 16px;
+        padding: 5px 10px; /* Ajustar el tamaño del botón */
+        font-size: 12px; /* Reducir el tamaño de la fuente */
         font-weight: bold;
         color: white;
         border-radius: 5px;
-        margin: 10px;
+        margin: 5px; /* Ajustar el margen */
         text-decoration: none;
         text-align: center;
     }}
@@ -101,6 +106,10 @@ def app():
     .btn-red {{
         background-color: #e94e42;
     }}
+    .gr-examples img {{
+        width: 200px; /* Ajusta este valor al doble del tamaño original */
+        height: 200px; /* Ajusta este valor al doble del tamaño original */
+    }}
     """) as demo:
         gr.Markdown("## Modulo Imaging for Computer Vision", elem_id="centered-title")
 
@@ -113,34 +122,49 @@ def app():
                 gr.Markdown("### Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications", elem_id="centered-text")
                 gr.HTML('<a href="https://cvlai.net/aim/2024/" target="_blank" class="custom-button btn-red">Article</a>')
 
-        image = gr.Image(type="pil", label="Upload Image")
+        image = gr.Image(type="pil", label="Upload Image", interactive=True)
+
         model_id = gr.Dropdown(label="Model", choices=["yolov10n", "yolov10s", "yolov10m", "yolov10b", "yolov10l", "yolov10x"], value="yolov10x")
-        
         sat_factor = gr.Slider(label="Saturation Factor", minimum=1.0, maximum=5.0, step=0.1, value=2.0)
-        DO = gr.Radio(label="DO", choices=["1", "2"], value="1")
-        t = gr.Slider(label="t", minimum=0.0, maximum=1.0, step=0.1, value=0.7)
-        vertical = gr.Radio(label="Vertical", choices=["True", "False"], value="True")
 
-        with gr.Row():
-            spud_button = gr.Button("SPUD", elem_id="spud-button")
-            ahfd_button = gr.Button("AHFD", elem_id="ahfd-button")
+        selected_method = gr.Radio(
+            label="Select Method",
+            choices=["SPUD", "AHFD"],
+            value="SPUD"
+        )
         
         process_button = gr.Button("Process Image")
 
+                # Agregar las imágenes de ejemplo al final con tamaño más grande
+        examples = [
+            ["imagen1.png"],
+            ["imagen2.png"],
+            ["imagen3.png"],
+            ["imagen4.png"]
+        ]
+
+        gr.Examples(
+            examples=examples,
+            inputs=[image],
+            label="Choose an Example Image"
+        )
+
         with gr.Row():
             with gr.Column():
-                output_original = gr.Image(label="Original + blur")
-                output_clip = gr.Image(label="Clipped")
+                output_original = gr.Image(label="Real")
+                output_wrap = gr.Image(label="Modulo")
             with gr.Column():
-                output_wrap = gr.Image(label="Wrapped")
-                output_recons = gr.Image(label="Reconstructed")
+                output_clip = gr.Image(label="Saturated")
+                output_recons = gr.Image(label="Recovery")
 
         process_button.click(
             fn=process_image,
-            inputs=[image, model_id, sat_factor, DO, t, vertical],
+            inputs=[image, model_id, sat_factor, selected_method],
             outputs=[output_original, output_clip, output_wrap, output_recons]
         )
 
+
+
     return demo
 
 if __name__ == "__main__":