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app.py

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+
+import gradio as gr
+import cv2
+import os
+import zipfile
+from PIL import Image, ImageOps
+from datetime import datetime
+import hashlib
+import shutil
+
+TEMP_CACHE = None
+
+def procesar_video(video_path):
+    try:
+        original_name = os.path.basename(video_path)
+        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)
+
+        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")
+
+        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]
+
+        images = []
+        for img_path in selected_frames:
+            img = Image.open(img_path)
+            bordered_img = ImageOps.expand(img, border=2, fill='white')
+            images.append(bordered_img)
+
+        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))
+        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)
+        ]
+
+        for i, img in enumerate(images):
+            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)
+            collage.paste(img, positions[i])
+
+        collage_path = os.path.join(temp_dir, "collage_forense.jpg")
+        collage.save(collage_path, quality=95, dpi=(300, 300))
+
+        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:
+            for img_path in frame_paths:
+                zipf.write(img_path, os.path.basename(img_path))
+
+            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)
+
+        global TEMP_CACHE
+        TEMP_CACHE = temp_dir
+
+        return collage_path, final_zip_path
+
+    except Exception as e:
+        raise gr.Error(f"Error en procesamiento: {str(e)}")
+
+def limpiar_cache():
+    global TEMP_CACHE
+    if TEMP_CACHE and os.path.exists(TEMP_CACHE):
+        shutil.rmtree(TEMP_CACHE)
+    TEMP_CACHE = None
+
+with gr.Blocks(title="Extractor 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(scale=1):
+            video_input = gr.Video(
+                label="🎞️ VIDEO CARGADO",
+                format="mp4",
+                interactive=True,
+                height=320
+            )
+            procesar_btn = gr.Button("🔍 INICIAR ANÁLISIS", interactive=False)
+        with gr.Column(scale=1):
+            gallery_output = gr.Image(label="📸 COLLAGE DE REFERENCIA", height=400)
+            download_file = gr.File(label="📂 DESCARGAR EVIDENCIAS", visible=True)
+
+    def habilitar_procesado(video):
+        limpiar_cache()
+        return gr.update(interactive=True), None, None
+
+    video_input.change(
+        fn=habilitar_procesado,
+        inputs=video_input,
+        outputs=[procesar_btn, gallery_output, download_file],
+        queue=False
+    )
+
+    def procesar_y_mostrar(video):
+        collage, zip_path = procesar_video(video)
+        return collage, zip_path
+
+    procesar_btn.click(
+        fn=procesar_y_mostrar,
+        inputs=video_input,
+        outputs=[gallery_output, download_file]
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+gradio>=4.0.0
+numpy>=1.26.0
+opencv-python-headless>=4.8.0
+opencv-python>=4.8.0
+pillow>=10.0.0
+matplotlib>=3.7.0