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app.py

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+import gradio as gr
+import cv2
+import os
+import random
+import zipfile
+from PIL import Image
+from datetime import datetime
+
+def procesar_video(video_path):
+    try:
+        # Configurar directorio temporal
+        temp_dir = f"temp_{datetime.now().strftime('%Y%m%d%H%M%S')}"
+        os.makedirs(temp_dir, exist_ok=True)
+        
+        # Leer el video
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        # Seleccionar 4 fotogramas aleatorios
+        frame_indices = sorted(random.sample(range(total_frames), 4))
+        selected_frames = []
+        
+        for idx in frame_indices:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+            ret, frame = cap.read()
+            if ret:
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                img = Image.fromarray(frame_rgb)
+                img_path = os.path.join(temp_dir, f"frame_{idx}.jpg")
+                img.save(img_path)
+                selected_frames.append(img_path)
+        
+        cap.release()
+        
+        # Crear collage
+        images = [Image.open(img) for img in selected_frames]
+        collage = Image.new('RGB', (images[0].width * 2, images[0].height * 2))
+        
+        collage.paste(images[0], (0, 0))
+        collage.paste(images[1], (images[0].width, 0))
+        collage.paste(images[2], (0, images[0].height))
+        collage.paste(images[3], (images[0].width, images[0].height))
+        
+        collage_path = os.path.join(temp_dir, "collage.jpg")
+        collage.save(collage_path)
+        
+        # Crear archivo ZIP
+        zip_path = os.path.join(temp_dir, "frames.zip")
+        with zipfile.ZipFile(zip_path, 'w') as zipf:
+            for img in selected_frames:
+                zipf.write(img, os.path.basename(img))
+        
+        return collage_path, zip_path, temp_dir
+    
+    except Exception as e:
+        raise gr.Error(f"Error al procesar el video: {str(e)}")
+
+def limpiar_cache(temp_dir):
+    if temp_dir and os.path.exists(temp_dir):
+        for file in os.listdir(temp_dir):
+            os.remove(os.path.join(temp_dir, file))
+        os.rmdir(temp_dir)
+
+with gr.Blocks(title="Extracción de Fotogramas Forenses") as demo:
+    gr.Markdown("# Herramienta de Extracción de Fotogramas Forenses")
+    gr.Markdown("**Carga un video para extraer 4 fotogramas aleatorios y generar un collage.**")
+    gr.Markdown("Desarrollado por José R. Leonett para el Grupo de Peritos Forenses Digitales de Guatemala - [www.forensedigital.gt](https://www.forensedigital.gt)")
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(label="Subir Video", interactive=True)
+            procesar_btn = gr.Button("Procesar Fotogramas", interactive=False)
+        
+        with gr.Column():
+            gallery_output = gr.Image(label="Collage de Fotogramas")
+            download_btn = gr.Button("DESCARGAR FOTOGRAMAS", interactive=False)
+    
+    temp_dir_state = gr.State()
+    
+    def habilitar_procesar(video):
+        return gr.Button(interactive=True) if video else gr.Button(interactive=False)
+    
+    def procesar_y_mostrar(video):
+        limpiar_cache(temp_dir_state.value)
+        collage_path, zip_path, temp_dir = procesar_video(video)
+        return collage_path, zip_path, temp_dir, gr.Button(interactive=True)
+    
+    def actualizar_descarga(temp_dir):
+        return gr.Button(interactive=bool(temp_dir))
+    
+    video_input.change(
+        fn=habilitar_procesar,
+        inputs=video_input,
+        outputs=procesar_btn,
+        queue=False
+    )
+    
+    procesar_btn.click(
+        fn=procesar_y_mostrar,
+        inputs=video_input,
+        outputs=[gallery_output, download_btn, temp_dir_state, download_btn],
+    )
+    
+    download_btn.click(
+        fn=lambda temp_dir: os.path.join(temp_dir, "frames.zip"),
+        inputs=temp_dir_state,
+        outputs=gr.File(label="Frames descargados"),
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+opencv-python-headless
+pillow