Update app.py

app.py

@@ -1,25 +1,25 @@
 import gradio as gr
-from image_processing import apply_blur, clip_image, wrap_image
-from detection import yolov10_inference, calculate_detection_metrics
+from Scripts.image_processing import apply_blur, clip_image, wrap_image
+from Scripts.detection import yolov10_inference, calculate_detection_metrics
 from PIL import Image
 import numpy as np
 import torchvision.transforms as transforms
 import torch
-from utils import *
-from utils import modulo
+from Scripts.utils import *
+from Scripts.utils import modulo
+from Scripts.utils_spud import *  # Asegúrate de que las funciones necesarias estén importadas aquí
+
 import cv2
 import matplotlib.pyplot as plt
+import packaging
 
-# 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, selected_method):
-    # Definir valores fijos dentro de la función
     conf_threshold = 0.85
     correction = 1.0
-    kernel_size = 7  # Definir kernel_size como constante
+    kernel_size = 7
     
-    # Valores fijos para DO, t y vertical
     DO = 1
     t = 0.7
     vertical = True
@@ -30,12 +30,11 @@ def process_image(image, model_id, sat_factor, selected_method):
     original_image = original_image * 255.0
     original_image = original_image.astype(np.uint8)
 
-    # scaling factor
     scaling = 1.0
     original_image = cv2.resize(original_image, (0, 0), fx=scaling, fy=scaling)
 
     blurred_image = apply_blur(original_image / 255.0, kernel_size)
-    clipped_image = clip_image(blurred_image, correction, sat_factor) 
+    clipped_image = clip_image(blurred_image, correction, sat_factor)
 
     img_tensor = torch.tensor(blurred_image, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
     img_tensor = modulo(img_tensor * sat_factor, L=1.0)
@@ -47,7 +46,13 @@ def process_image(image, model_id, sat_factor, selected_method):
     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=DO, L=1.0, vertical=vertical, t=t)
+    if selected_method == "SPUD":
+        # Uso de la función personalizada de SPUD
+        recon_image = recons_spud(img_tensor, threshold=0.1, mx=1.0)
+    else:
+        # Método por defecto AHFD
+        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)
 
@@ -64,16 +69,6 @@ def app():
     image_width = int(600 * image_scaler)  
     image_height = int(200 * image_scaler)
 
-    bibtex_citation = """
-    @inproceedings{contreras2024high,
-      title={High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving},
-      author={Kebin Contreras, Brayan Monroy and Jorge Bacca},
-      booktitle={ECCV 2024},
-      year={2024},
-      organization={European Conference on Computer Vision (ECCV)}
-    }
-    """
-
     with gr.Blocks(css=f"""
     .fixed-size-image img {{
         width: {image_width}px;
@@ -87,7 +82,7 @@ def app():
     }}
     .gr-column {{
         display: flex;
-        flex-direction: column;
+        flex-direction: row;
         align-items: center;
         padding: 0 !important;
         margin: 0 !important;
@@ -101,12 +96,13 @@ def app():
     }}
     .custom-button {{
         display: inline-block;
-        padding: 5px 10px; /* Ajustar el tamaño del botón */
-        font-size: 12px; /* Reducir el tamaño de la fuente */
+        padding: 5px 10px;
+        font-size: 12px;
         font-weight: bold;
         color: white;
         border-radius: 5px;
-        margin: 5px; /* Ajustar el margen */
+        margin-right: 5px;  /* Reducir el margen entre botones */
+        margin-bottom: 0px;
         text-decoration: none;
         text-align: center;
     }}
@@ -120,8 +116,8 @@ def app():
         background-color: #007bff;
     }}
     .gr-examples img {{
-        width: 200px; /* Ajusta este valor al doble del tamaño original */
-        height: 200px; /* Ajusta este valor al doble del tamaño original */
+        width: 200px;
+        height: 200px;
     }}
     """) as demo:
         gr.Markdown("## Modulo Imaging for Computer Vision", elem_id="centered-title")
@@ -129,13 +125,15 @@ def app():
         with gr.Row():
             with gr.Column():
                 gr.Markdown("### High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving", elem_id="centered-text")
-                gr.HTML('<a href="https://openreview.net/pdf?id=2GqZFx2I7s" target="_blank" class="custom-button btn-grey">Article</a>')
-                gr.HTML('<a href="https://github.com/kebincontreras/Modulo_images.git" target="_blank" class="custom-button btn-blue">GitHub</a>')
-            
+                with gr.Row():
+                    gr.HTML('<a href="https://openreview.net/pdf?id=2GqZFx2I7s" target="_blank" class="custom-button btn-grey">Article</a>')
+                    gr.HTML('<a href="https://github.com/kebincontreras/Modulo_images.git" target="_blank" class="custom-button btn-blue">GitHub</a>')
+
             with gr.Column():
                 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>')
-                gr.HTML('<a href="https://github.com/bemc22/AHFD" target="_blank" class="custom-button btn-blue">GitHub</a>')
+                with gr.Row():
+                    gr.HTML('<a href="https://cvlai.net/aim/2024/" target="_blank" class="custom-button btn-red">Article</a>')
+                    gr.HTML('<a href="https://github.com/bemc22/AHFD" target="_blank" class="custom-button btn-blue">GitHub</a>')
 
         image = gr.Image(type="pil", label="Upload Image", interactive=True)
 
@@ -144,20 +142,19 @@ def app():
 
         selected_method = gr.Radio(
             label="Select Method",
-            choices=["SPUD", "AHFD"],
+            choices=["AHFD","SPUD"],
             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.jpg"],
-            ["imagen4.jpg"],
-            ["imagen5.jpg"],
-            ["imagen6.png"]
+            ["Add_ons/imagen1.png"],
+            ["Add_ons/imagen2.png"],
+            ["Add_ons/imagen3.jpg"],
+            ["Add_ons/imagen4.jpg"],
+            ["Add_ons/imagen5.jpg"],
+            ["Add_ons/imagen6.png"]
         ]
 
         gr.Examples(
@@ -180,15 +177,6 @@ def app():
             outputs=[output_original, output_clip, output_wrap, output_recons]
         )
 
-        # Añadir un botón pequeño para copiar la cita en BibTeX
-        def copy_bibtex():
-            return bibtex_citation
-
-        with gr.Row():
-            with gr.Column():
-                gr.Button("Copy BibTeX", elem_classes="custom-button btn-blue", size="small").click(fn=copy_bibtex, outputs=[gr.Markdown("")])
-                gr.Markdown("", visible=False)
-
     return demo
 
 if __name__ == "__main__":