Mejorar la visualización de la tabla de Registered Faces y manejar errores de imágenes

streamlit_app.py

@@ -1737,157 +1737,127 @@ def main():
                                 st.error("Failed to extract embeddings. Please try again with a clearer image.")
             
             # Mostrar tabla de rostros registrados
-            st.subheader("Registered Faces")
-            
-            # Debug de contenido de base de datos
-            st.sidebar.write(f"Face database contains {len(st.session_state.face_database)} entries at display time")
-            
-            if 'face_database' in st.session_state and st.session_state.face_database:
-                # Inicializar variables para la tabla
+            def display_registered_faces():
+                # Display registered faces in a table
+                st.subheader("Registered Faces")
+                
+                if not st.session_state.face_database:
+                    st.info("No faces registered yet. Use the form above to register a face.")
+                    return
+                
+                # Prepare data for the table
                 data = []
                 
-                # Preparar datos para la tabla
                 for name, info in st.session_state.face_database.items():
-                    # Determinar el número de embeddings
-                    if 'embeddings' in info:
-                        num_embeddings = len(info['embeddings'])
-                        models = ', '.join(info['models'])
-                    else:
-                        num_embeddings = 1
-                        models = 'VGG-Face'  # Modelo por defecto para formato antiguo
+                    # Extract face image if available
+                    face_img = None
+                    if 'image' in info and info['image'] is not None:
+                        try:
+                            if is_valid_image(info['image']):
+                                face_img = info['image']
+                            else:
+                                print(f"Invalid face image for {name}")
+                        except Exception as e:
+                            print(f"Error accessing face image for {name}: {str(e)}")
+                    
+                    # Count embeddings and determine models used
+                    num_embeddings = 0
+                    models_used = []
                     
-                    # Determinar el número de imágenes
-                    num_images = info.get('count', 1)
+                    if 'embeddings' in info and info['embeddings']:
+                        num_embeddings = len(info['embeddings'])
+                        # Extract models from embeddings
+                        for emb in info['embeddings']:
+                            if 'model' in emb and emb['model'] not in models_used:
+                                models_used.append(emb['model'])
                     
-                    # Añadir a los datos
+                    # Add row to data
                     data.append({
                         "Name": name,
-                        "Images": num_images,
+                        "Face": face_img,
+                        "Images": 1 if face_img is not None else 0,
                         "Embeddings": num_embeddings,
-                        "Models": models,
-                        "Face": info.get('face_image', None)
+                        "Models": ", ".join(models_used) if models_used else "None"
                     })
                 
-                # Debug de los datos procesados
-                st.sidebar.write(f"Processed {len(data)} entries for display")
+                # Create table with custom layout
+                col_thumb, col1, col2, col3, col4, col5 = st.columns([2, 3, 2, 2, 4, 2])
                 
-                # Verificar si hay datos para mostrar
-                if data:
-                    # Crear cabeceras de la tabla
+                with col_thumb:
+                    st.write("**Thumbnail**")
+                with col1:
+                    st.write("**Name**")
+                with col2:
+                    st.write("**Images**")
+                with col3:
+                    st.write("**Embeddings**")
+                with col4:
+                    st.write("**Models**")
+                with col5:
+                    st.write("**Actions**")
+                
+                # Mostrar tabla con botones de eliminación
+                for i, row in enumerate(data):
                     col_thumb, col1, col2, col3, col4, col5 = st.columns([2, 3, 2, 2, 4, 2])
                     
+                    # Mostrar miniatura si está disponible
                     with col_thumb:
-                        st.write("**Thumbnail**")
+                        if row["Face"] is not None:
+                            try:
+                                # Redimensionar para crear miniatura
+                                face_img = row["Face"]
+                                if is_valid_image(face_img):
+                                    h, w = face_img.shape[:2]
+                                    thumbnail = safe_resize(face_img, (w//4, h//4))
+                                    if thumbnail is not None:
+                                        st.image(cv2.cvtColor(thumbnail, cv2.COLOR_BGR2RGB), width=50)
+                                    else:
+                                        st.write("Invalid image")
+                                else:
+                                    st.write("Invalid image")
+                            except Exception as e:
+                                print(f"Error displaying thumbnail for {row['Name']}: {str(e)}")
+                                st.write("Error")
+                        else:
+                            st.write("No image")
+                            
                     with col1:
-                        st.write("**Name**")
+                        st.write(row["Name"])
                     with col2:
-                        st.write("**Images**")
+                        st.write(row["Images"])
                     with col3:
-                        st.write("**Embeddings**")
+                        st.write(row["Embeddings"])
                     with col4:
-                        st.write("**Models**")
+                        st.write(row["Models"])
                     with col5:
-                        st.write("**Actions**")
-                    
-                    # Mostrar tabla con botones de eliminación
-                    for i, row in enumerate(data):
-                        col_thumb, col1, col2, col3, col4, col5 = st.columns([2, 3, 2, 2, 4, 2])
-                        
-                        # Mostrar miniatura si está disponible
-                        with col_thumb:
-                            if row["Face"] is not None:
-                                # Redimensionar para crear miniatura
-                                face_img = row["Face"]
-                                h, w = face_img.shape[:2]
-                                thumbnail = cv2.resize(face_img, (w//4, h//4))
-                                st.image(cv2.cvtColor(thumbnail, cv2.COLOR_BGR2RGB), width=50)
-                            else:
-                                st.write("No image")
+                        if st.button("Delete", key=f"delete_{row['Name']}"):
+                            # Eliminar el registro
+                            if row["Name"] in st.session_state.face_database:
+                                del st.session_state.face_database[row["Name"]]
                                 
-                        with col1:
-                            st.write(row["Name"])
-                        with col2:
-                            st.write(row["Images"])
-                        with col3:
-                            st.write(row["Embeddings"])
-                        with col4:
-                            st.write(row["Models"])
-                        with col5:
-                            if st.button("Delete", key=f"delete_{row['Name']}"):
-                                # Eliminar el registro
-                                if row["Name"] in st.session_state.face_database:
-                                    del st.session_state.face_database[row["Name"]]
-                                    
-                                    # Guardar la base de datos actualizada
-                                    if DATABASE_UTILS_AVAILABLE:
-                                        save_face_database(st.session_state.face_database)
-                                    
-                                    st.success(f"Deleted {row['Name']} from the database.")
-                                    st.rerun()
-                    
-                    # Botón para eliminar todos los registros
-                    if st.button("Delete All Registered Faces"):
-                        # Mostrar confirmación
-                        if 'confirm_delete_all' not in st.session_state:
-                            st.session_state.confirm_delete_all = False
-                        
-                        if not st.session_state.confirm_delete_all:
-                            st.warning("Are you sure you want to delete all registered faces? This action cannot be undone.")
-                            col1, col2 = st.columns(2)
-                            with col1:
-                                if st.button("Yes, delete all"):
-                                    st.session_state.face_database = {}
-                                    
-                                    # Guardar la base de datos vacía
-                                    if DATABASE_UTILS_AVAILABLE:
-                                        save_face_database({})
-                                    
-                                    st.session_state.confirm_delete_all = False
-                                    st.success("All registered faces have been deleted.")
-                                    st.rerun()
-                            with col2:
-                                if st.button("Cancel"):
-                                    st.session_state.confirm_delete_all = False
-                                    st.rerun()
-                else:
-                    st.info("No faces registered yet. Use the form above to register faces.")
-            else:
-                st.info("No faces registered yet. Use the form above to register faces.")
-            
-            # Añadir botones para importar/exportar la base de datos
-            if DATABASE_UTILS_AVAILABLE:
-                st.subheader("Database Management")
-                col1, col2 = st.columns(2)
-                
-                with col1:
-                    # Exportar base de datos
-                    if st.button("Export Face Database") and st.session_state.face_database:
-                        export_file = export_database_json()
-                        if export_file:
-                            with open(export_file, "rb") as f:
-                                st.download_button(
-                                    label="Download JSON Database",
-                                    data=f,
-                                    file_name="face_database.json",
-                                    mime="application/json"
-                                )
+                                # Guardar la base de datos actualizada
+                                if DATABASE_UTILS_AVAILABLE:
+                                    save_face_database(st.session_state.face_database)
+                                
+                                st.success(f"Deleted {row['Name']} from the database.")
+                                st.rerun()
                 
-                with col2:
-                    # Importar base de datos
-                    uploaded_json = st.file_uploader("Import Face Database", type=["json"], key="import_database")
-                    if uploaded_json is not None:
-                        if st.button("Process Import"):
-                            with st.spinner("Importing database..."):
-                                imported_db = import_database_json(uploaded_json)
-                                if imported_db:
-                                    # Actualizar la base de datos actual
-                                    st.session_state.face_database.update(imported_db)
-                                    
-                                    # Guardar la base de datos actualizada
-                                    if save_face_database(st.session_state.face_database):
-                                        st.success("Database imported and saved successfully!")
-                                        st.rerun()
-        
+                # Botón para eliminar todos los registros
+                if st.button("Delete All Registered Faces"):
+                    # Mostrar confirmación
+                    confirm_delete = st.checkbox("Are you sure you want to delete all registered faces? This action cannot be undone.")
+                    
+                    if confirm_delete:
+                        # Resetear la base de datos
+                        st.session_state.face_database = {}
+                        
+                        # Guardar la base de datos vacía
+                        if DATABASE_UTILS_AVAILABLE:
+                            save_face_database(st.session_state.face_database)
+                        
+                        st.success("All registered faces have been deleted.")
+                        st.rerun()
+
         with tab2:
             st.header("Image Recognition")
             
@@ -2265,10 +2235,10 @@ def main():
                             scale_factor = 0.5
                             h, w = img.shape[:2]
                             
-                            try:
-                                small_img = cv2.resize(img, (int(w * scale_factor), int(h * scale_factor)))
-                            except Exception as e:
-                                print(f"Error al redimensionar: {e}")
+                            small_img = safe_resize(img, (int(w * scale_factor), int(h * scale_factor)))
+                            if small_img is None:
+                                # Si no se puede redimensionar, usar el frame original (solo para diagnóstico)
+                                print("No se pudo redimensionar la imagen para procesamiento")
                                 return av.VideoFrame.from_ndarray(img, format="bgr24")
                             
                             # Detect faces - la función ahora devuelve directamente los bboxes