import gradio as gr import cv2 import os import zipfile from PIL import Image, ImageOps from datetime import datetime import hashlib def procesar_video(video): try: if isinstance(video, dict): original_name = video.get("name", "video") video_path = video.get("file", video.get("data")) else: original_name = os.path.basename(video) video_path = video allowed_extensions = ('.mp4', '.avi', '.mov', '.mkv') if not original_name.lower().endswith(allowed_extensions): raise gr.Error("Solo se permiten archivos de video (mp4, avi, mov, mkv)") timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') temp_dir = f"temp_{datetime.now().strftime('%Y%m%d%H%M%S')}" os.makedirs(temp_dir, exist_ok=True) # Extracción de todos los fotogramas cap = cv2.VideoCapture(video_path) frame_count = 0 frame_paths = [] while True: ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) img = Image.fromarray(frame_rgb) img_path = os.path.join(temp_dir, f"frame_{frame_count:04d}.jpg") img.save(img_path) frame_paths.append(img_path) frame_count += 1 cap.release() if frame_count == 0: raise gr.Error("No se pudieron extraer fotogramas del video") # Selección estratégica de 4 fotogramas equidistantes n_seleccion = 4 step = max(1, frame_count // (n_seleccion + 1)) selected_indices = [step * (i+1) for i in range(n_seleccion)] selected_frames = [frame_paths[min(i, len(frame_paths)-1)] for i in selected_indices] # Creación de collage profesional images = [] for img_path in selected_frames: img = Image.open(img_path) bordered_img = ImageOps.expand(img, border=2, fill='white') # Borde blanco images.append(bordered_img) # Configuración del diseño img_w, img_h = images[0].size margin = 30 border_size = 20 shadow_offset = 5 collage_width = (img_w * 2) + margin + (border_size * 2) collage_height = (img_h * 2) + margin + (border_size * 2) collage = Image.new('RGB', (collage_width, collage_height), (230, 230, 230)) # Fondo gris claro # Posiciones con efecto de profundidad positions = [ (border_size, border_size), (border_size + img_w + margin, border_size), (border_size, border_size + img_h + margin), (border_size + img_w + margin, border_size + img_h + margin) ] # Pegar imágenes con sombra for i, img in enumerate(images): # Sombra shadow = Image.new('RGBA', (img_w + shadow_offset, img_h + shadow_offset), (0,0,0,50)) collage.paste(shadow, (positions[i][0]+shadow_offset, positions[i][1]+shadow_offset), shadow) # Imagen principal collage.paste(img, positions[i]) collage_path = os.path.join(temp_dir, "collage_forense.jpg") collage.save(collage_path, quality=95, dpi=(300, 300)) # Generación del ZIP con cadena de custodia base_name = os.path.splitext(original_name)[0] zip_filename = f"{base_name}_Fotogramas.zip" final_zip_path = os.path.join(temp_dir, zip_filename) with zipfile.ZipFile(final_zip_path, mode="w") as zipf: # Añadir todos los frames for img_path in frame_paths: zipf.write(img_path, os.path.basename(img_path)) # Archivo TXT con formato profesional with open(video_path, "rb") as f: video_hash = hashlib.md5(f.read()).hexdigest() chain_content = ( "=== CADENA DE CUSTODIA DIGITAL ===\r\n\r\n" f"• Archivo original: {original_name}\r\n" f"• Fecha de procesamiento: {timestamp}\r\n" f"• Fotogramas totales: {frame_count}\r\n" f"• Hash MD5 video: {video_hash}\r\n" f"• Fotogramas muestra: {', '.join([f'#{i+1}' for i in selected_indices])}\r\n\r\n" "Este documento certifica la integridad del proceso de extracción.\n" "Sistema Certificado por Peritos Forenses Digitales de Guatemala. \n" "www.forensedigital.gt" ) zipf.writestr("00_CADENA_CUSTODIA.txt", chain_content) return collage_path, final_zip_path, temp_dir except Exception as e: raise gr.Error(f"Error en procesamiento: {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="Extractior Forense de Fotogramas") as demo: gr.Markdown("# 📷 Extractor Forense de Fotogramas de Videos") gr.Markdown("**Herramienta certificada para extracción forense de fotogramas de videos** (No se guarda ninguna información") 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="CARGAR VIDEO", sources=["upload"], format="mp4", interactive=True ) procesar_btn = gr.Button("🔍 INICIAR ANÁLISIS", interactive=False) with gr.Column(): # gr.Markdown("## Resultados:") gallery_output = gr.Image(label="COLLAGE DE REFERENCIA", height=400) download_file = gr.File(label="DESCARGAR EVIDENCIAS", visible=True) temp_dir_state = gr.State() zip_path_state = gr.State() def habilitar_procesado(video): return gr.update(interactive=True) if video else gr.update(interactive=False) video_input.change( fn=habilitar_procesado, inputs=video_input, outputs=procesar_btn, queue=False ) def procesar_y_mostrar(video): if temp_dir_state.value: limpiar_cache(temp_dir_state.value) collage, zip_path, temp_dir = procesar_video(video) return collage, zip_path, temp_dir, zip_path procesar_btn.click( fn=procesar_y_mostrar, inputs=video_input, outputs=[gallery_output, download_file, temp_dir_state, zip_path_state] ) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860)