app

app.py

@@ -0,0 +1,69 @@
+import gradio as gr
+
+def app():
+    # Variables para ajustar el tamaño de las imágenes
+    image_scaler = 0.55  # Cambia este valor para ajustar el tamaño de las imágenes
+    image_width = int(600 * image_scaler)  # Ancho de la imagen
+    image_height = int(200 * image_scaler)  # Alto de la imagen
+
+    with gr.Blocks(css=f"""
+    .fixed-size-image img {{
+        width: {image_width}px;
+        height: {image_height}px;
+        object-fit: cover;
+    }}
+    .gr-row {{
+        display: flex;
+        justify-content: left;
+        align-items: center;
+    }}
+    .gr-column {{
+        padding: 0 !important;
+        margin: 0 !important;
+        flex: 0 0 auto !important;
+        width: {image_width}px !important;
+    }}
+    """) as demo:
+        gr.Markdown("## High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving")
+        
+        gr.Markdown("### Authors:")
+
+        with gr.Row():
+            with gr.Column(elem_classes="gr-column"):
+                gr.Image("autores.png", show_download_button=False, elem_classes="fixed-size-image")
+                gr.Markdown("Kebin Contreras *, Brayan Monrroy ~ and Jorge Bacca ~")
+                gr.Markdown("*Department of Physics, Universidad del Cauca, ~Department of Computer Engineer, Universidad Industrial de Santander  ")
+                
+        
+            #with gr.Column():
+                #gr.Markdown("*Department of Physics, Universidad del Cauca ")
+                #gr.Markdown("~Department of Computer Engineer, Universidad Industrial de Santander ")
+        
+        # Agregar la funcionalidad original de imágenes y controles
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil", label="Upload Image")
+                model_id = gr.Dropdown(label="Model", choices=["yolov10n", "yolov10s", "yolov10m", "yolov10b", "yolov10l", "yolov10x"], value="yolov10x")
+                image_size = gr.Slider(label="Image Size", minimum=320, maximum=1280, step=32, value=640)
+                conf_threshold = gr.Slider(label="Confidence Threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.85)
+                correction = gr.Slider(label="Correction Factor", minimum=0, maximum=1.0, step=0.1, value=1.0)
+                sat_factor = gr.Slider(label="Saturation Factor", minimum=1.0, maximum=5.0, step=0.1, value=2.0)
+                kernel_size = gr.Slider(label="Blur Kernel Size", minimum=1, maximum=7, step=1, value=7)
+                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=0.5)
+                vertical = gr.Radio(label="Vertical", choices=["True", "False"], value="True")
+                process_button = gr.Button("Process Image")
+
+            with gr.Column():
+                output_original = gr.Image(label="Original + blur")
+                output_clip = gr.Image(label="Clipped ")
+                output_wrap = gr.Image(label="Wrapped")
+                output_recons = gr.Image(label="Reconstructed")
+                metrics_clip = gr.Textbox(label="Metrics for Clipped Image")
+                metrics_wrap = gr.Textbox(label="Metrics for Wrapped Image")
+                metrics_recons = gr.Textbox(label="Metrics for Reconstructed Image")
+
+    return demo
+
+if __name__ == "__main__":
+    app().launch()