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"}}},{"cell_type":"markdown","source":"#### **Library in python**","metadata":{}},{"cell_type":"code","source":"!pip install imagecodecs\n!pip install tifffile","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:51:28.412213Z","iopub.execute_input":"2026-03-03T12:51:28.412537Z","iopub.status.idle":"2026-03-03T12:51:37.651007Z","shell.execute_reply.started":"2026-03-03T12:51:28.412513Z","shell.execute_reply":"2026-03-03T12:51:37.649819Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==============================================================================\n# 1. SISTEMA, RUTAS Y UTILIDADES GENERALES\n# ==============================================================================\nimport os\nimport glob\nimport random\nfrom pathlib import Path\n\n# ==============================================================================\n# 2. COMPUTACIÓN CIENTÍFICA Y MATEMÁTICAS (DATA ENGINEERING CORE)\n# ==============================================================================\nimport numpy as np\nimport pandas as pd\nfrom scipy.spatial import cKDTree\nfrom scipy.ndimage import distance_transform_edt\nfrom numba import jit  # Compilación Just-In-Time para alto rendimiento\nfrom scipy import ndimage as ndi\nfrom scipy import stats\n\n# ==============================================================================\n# 3. DEEP LEARNING (PYTORCH ECOSYSTEM)\n# ==============================================================================\nimport torch\nimport torch.nn.functional as F\n\n# ==============================================================================\n# 4. PROCESAMIENTO DE IMÁGENES Y VISIÓN ARTIFICIAL (IO & OPENCV)\n# ==============================================================================\nimport cv2          # OpenCV: Procesamiento de alto nivel y tiempo real\nimport tifffile     # Específico para formatos TIFF científicos de alta profundidad\nimport imageio      # Versatilidad en lectura/escritura de diversos formatos\nfrom PIL import Image, ImageSequence  # Manipulación de imágenes y secuencias (GIF/TIFF)\n\n# ==============================================================================\n# 5. SCIKIT-IMAGE (PROCESAMIENTO AVANZADO Y MORFOLOGÍA)\n# ==============================================================================\nfrom skimage import exposure, filters, segmentation, feature, morphology\nfrom skimage.exposure import rescale_intensity\nfrom skimage.filters import (\n    frangi, meijering, sato, unsharp_mask, sobel, \n    difference_of_gaussians, threshold_sauvola, threshold_otsu\n)\nfrom skimage.filters.rank import entropy\nfrom skimage.feature import (\n    local_binary_pattern, hessian_matrix, \n    hessian_matrix_eigvals, peak_local_max\n)\nfrom skimage.morphology import (\n    white_tophat, skeletonize, medial_axis, \n    remove_small_objects, remove_small_holes, disk, dilation\n)\nfrom skimage.segmentation import watershed\n\nfrom skimage import morphology as morph\nfrom skimage.morphology import skeletonize\nfrom skimage.segmentation import watershed\nfrom skimage.feature import peak_local_max\n\n# ==============================================================================\n# 6. TEORÍA DE GRAFOS Y ANÁLISIS ESTRUCTURAL\n# ==============================================================================\nimport networkx as nx  # Para análisis de esqueletos y redes vasculares/neuronales\n\n# ==============================================================================\n# 7. VISUALIZACIÓN E INTERFAZ DE USUARIO (GUI)\n# ==============================================================================\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap\nfrom matplotlib.widgets import Slider\nimport ipywidgets as widgets\nfrom IPython.display import display, clear_output, Image as IPyImage\n\n# ==============================================================================\n# 8. GESTIÓN DE PROGRESO\n# ==============================================================================\nfrom tqdm.auto import tqdm  # Selecciona automáticamente entre consola y notebook\nfrom tqdm import tqdm\n\n\n\nIMG_DIR = '/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images'\nLBL_DIR = '/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_labels'\nINPUT_PATH = '/kaggle/working/'\nINPUT_BASE = '/kaggle/input/competitions/vesuvius-challenge-surface-detection'\nINPUT_DIR = \"/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images\"\nOUTPUT_DIR = \"/kaggle/input/datasets/guillermopetcho/esqueleto-slice\"\nCSV_FILE = \"dataset_limpio_320x320.csv\" \n\n# ==========================================================\n# CONFIG\n# ==========================================================\nNPZ_PATH = \"/kaggle/input/datasets/guillermopetcho/esqueleto-slice/1004283650_skeleton.npz\"\nassert os.path.exists(NPZ_PATH), \"No se encontró el archivo NPZ\"\n\ndata = np.load(NPZ_PATH)\nskeleton_volume = data[\"skeleton\"].astype(np.uint8)\n\nnum_slices = skeleton_volume.shape[0]\n\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\n#### pruebas\n\n# Ajusta esta ruta si es necesario\nTIFF_PATH = \"/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/1004283650.tif\"\nfile_path_rot = '/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/1004283650.tif'\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1004283650.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1004283650.tif'\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1006462223.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1013184726.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/102536988.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1029212680.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1033784946.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1044587645.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/105068588.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/105796630.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1059332280.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1061356924.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1075217434.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1079776201.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1083486419.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1108059824.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/110997297.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1113943087.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1127903126.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1128635125.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/114235076.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/11460685.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1154777170.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1156808983.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1157445126.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1159818141.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/11630450.tif\n    # /kaggle/input/vesuvius-challenge-surface-detection/train_images/1168011639.tif\n    \ndef get_sample_image():\n    for path in IMG_DIR:\n        p = Path(IMG_DIR)\n        if p.exists():\n            files = list(p.glob(\"*.tif\"))\n            if files: return files[0]\n    return None\n\ndef get_sample_image_path():\n    # Intenta encontrar una imagen real\n\n    for p in IMG_DIR:\n        path = Path(IMG_DIR)\n        if path.is_file(): return path\n        if path.is_dir():\n            files = list(path.glob(\"*.tif\"))\n            if files: return files[0]\n    return None\n\n\ndef read_robust(filepath):\n    \"\"\"\n    Intenta leer con tifffile. Si falla por compresión LZW, usa PIL.\n    Devuelve siempre un array de NumPy.\n    \"\"\"\n    try:\n        # Intento A: Tifffile (Rápido)\n        return tifffile.imread(str(filepath))\n    except Exception as e:\n        # Si el error menciona compresión, activamos el Plan B\n        error_msg = str(e).lower()\n        if \"compression\" in error_msg or \"lzw\" in error_msg:\n            # Intento B: PIL (Compatible)\n            return np.array(Image.open(str(filepath)))\n        else:\n            raise e # Si es otro error (ej. archivo no encontrado), que falle.\n\nclass Config:\n    file_path = '/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/1013184726.tif'\n\n    # PARÁMETROS DE FILTRADO INTELIGENTE (La ventaja de Medial Axis)\n    # Solo conservaremos estructuras cuyo grosor esté entre estos valores (en píxeles)\n    MIN_THICKNESS = 1.5   # Elimina ruido de sal y pimienta (muy fino)\n    MAX_THICKNESS = 15.0  # Elimina manchas grandes de tinta o burbujas\n    \n    # PARÁMETROS VISUALES\n    UPSCALE_FACTOR = 1.0       # Mantener 1.0 para velocidad, 2.0 para super-resolución\n    VISUAL_CONTRAST = 1.2      # Aumentar contraste para ver mejor las fibras\n    BINARY_THRESH = 0.30       # Umbral base para definir qué es \"materia\"\n    \n    # SALIDA\n    OUTPUT_FILENAME = 'analisis_medial_axis_smart.gif'\n    FPS = 15\n    GIF_SCALE = 2            # Reducir tamaño final del GIF al 50% para que no pese 100MB","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:51:37.652693Z","iopub.execute_input":"2026-03-03T12:51:37.652981Z","iopub.status.idle":"2026-03-03T12:51:45.521739Z","shell.execute_reply.started":"2026-03-03T12:51:37.652948Z","shell.execute_reply":"2026-03-03T12:51:45.519814Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def find_vesuvius_data(start_path='/kaggle/input'):\n    \"\"\"\n    Rastrea el directorio de entrada y busca dónde están realmente\n    las carpetas 'train_images' y 'train_labels'.\n    \"\"\"\n    print(f\"🕵️ Buscando datos en: {start_path} ...\\n\")\n    \n    found_images = False\n    found_labels = False\n    \n    # Recorremos el árbol de directorios\n    for root, dirs, files in os.walk(start_path):\n        # Buscamos carpetas clave\n        if 'train_images' in root:\n            print(f\" ENCONTRADO IMÁGENES: {root}\")\n            # Vemos qué hay dentro para confirmar\n            if len(files) > 0:\n                print(f\"   └── Contiene {len(files)} archivos (Ej: {files[0]})\")\n            elif len(dirs) > 0:\n                print(f\"   └── Contiene subcarpetas: {dirs}\")\n            found_images = True\n            \n        if 'train_labels' in root:\n            print(f\"ENCONTRADO ETIQUETAS: {root}\")\n            if len(files) > 0:\n                print(f\"   └── Contiene {len(files)} archivos (Ej: {files[0]})\")\n            elif len(dirs) > 0:\n                print(f\"   └── Contiene subcarpetas: {dirs}\")\n            found_labels = True\n\n    if not found_images and not found_labels:\n        print(\"\\n No encontré nada con nombre 'train_images' o 'train_labels'.\")\n        print(\"Listando todo el contenido de nivel 1 para depurar:\")\n        try:\n            print(os.listdir(start_path))\n        except:\n            print(f\"No puedo leer {start_path}\")\n\n# --- EJECUCIÓN ---\nfind_vesuvius_data()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:51:45.522852Z","iopub.execute_input":"2026-03-03T12:51:45.523513Z","iopub.status.idle":"2026-03-03T12:51:48.167343Z","shell.execute_reply.started":"2026-03-03T12:51:45.523463Z","shell.execute_reply":"2026-03-03T12:51:48.166304Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!rm dataset_metadata_completo.csv","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:51:48.168884Z","iopub.execute_input":"2026-03-03T12:51:48.169140Z","iopub.status.idle":"2026-03-03T12:51:48.305058Z","shell.execute_reply.started":"2026-03-03T12:51:48.169120Z","shell.execute_reply":"2026-03-03T12:51:48.303656Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Aumentamos el límite de píxeles de PIL por seguridad\n\nfrom scipy import ndimage\nimport os\nimport numpy as np\nimport pandas as pd\nimport tifffile\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom PIL import Image\nfrom scipy import ndimage\nfrom scipy import stats\n\n\nImage.MAX_IMAGE_PIXELS = None \n\ndef extract_deep_metadata(directory_path, output_csv=\"dataset_metadata_completo.csv\"):\n    \"\"\"\n    EXTRACTOR FORENSE DE METADATOS V2.0\n    -----------------------------------\n    Extrae no solo estadísticas de píxeles, sino metadatos físicos del TIFF,\n    topología de la imagen y flags de calidad para Machine Learning.\n    \"\"\"\n    path = Path(directory_path)\n    \n    # Busqueda recursiva por si hay subcarpetas\n    files = sorted(list(path.rglob(\"*.tif\")))\n    \n    if not files:\n        print(\"No se encontraron archivos TIFF en la ruta (ni subcarpetas).\")\n        return\n\n    print(f\" Iniciando Deep Scan Forense en {len(files)} volúmenes...\")\n    print(\"   -> Buscando: Tags Físicos, Topología, Metadatos ImageJ y Anomalías.\")\n    \n    data_registry = []\n\n    # --- HELPER: LECTURA HÍBRIDA ---\n    def read_image_robust(filepath):\n        try:\n            # Tifffile es mejor para metadatos\n            with tifffile.TiffFile(str(filepath)) as tif:\n                page = tif.pages[0]\n                img = page.asarray()\n                \n                # Extracción de Tags Ocultos\n                meta = {}\n                # Tags comunes: 282 (XRes), 283 (YRes), 296 (Unit), 270 (Desc)\n                tags = page.tags\n                meta['x_res'] = tags[282].value if 282 in tags else 0\n                meta['y_res'] = tags[283].value if 283 in tags else 0\n                meta['unit']  = tags[296].value if 296 in tags else 0\n                meta['compression'] = page.compression.name if hasattr(page, 'compression') else \"Unknown\"\n                \n                # A veces ImageJ guarda info en la descripción\n                desc = tags[270].value if 270 in tags else \"\"\n                meta['imagej_meta'] = str(desc)[:50] if desc else \"None\" # Cortamos para no ensuciar CSV\n                \n            return img, meta\n            \n        except Exception:\n            # Fallback a PIL si Tifffile falla por codecs raros\n            try:\n                pil_img = Image.open(str(filepath))\n                img = np.array(pil_img)\n                # PIL info básica\n                meta = {\n                    'x_res': pil_img.info.get('dpi', (0,0))[0], \n                    'y_res': pil_img.info.get('dpi', (0,0))[1],\n                    'unit': 'PIL_DPI',\n                    'compression': pil_img.info.get('compression', 'PIL_Unknown'),\n                    'imagej_meta': \"Recovered via PIL\"\n                }\n                return img, meta\n            except Exception as e:\n                raise e\n\n    # --- BUCLE PRINCIPAL ---\n    for f in tqdm(files, desc=\"Analizando Topología\"):\n        try:\n            # 1. Lectura\n            img, tags = read_image_robust(f)\n            \n            # 2. Datos Físicos Básicos\n            h, w = img.shape\n            file_size_kb = os.path.getsize(f) / 1024\n\n            # 3. Estadística Descriptiva (Feature Engineering)\n            # Convertimos a float64 para precisión matemática\n            img_flat = img.flatten().astype(np.float64)\n            \n            min_val = np.min(img_flat)\n            max_val = np.max(img_flat)\n            mean_val = np.mean(img_flat)\n            std_val = np.std(img_flat)\n            \n            # 4. Análisis Topológico (La \"Forma\" de los datos)\n            # Centro de Masa: ¿Dónde está \"pesada\" la imagen? (Top-Left, Center, etc.)\n            # Esto ayuda a saber si el papiro está centrado o desviado.\n            cy, cx = ndimage.center_of_mass(img) if max_val > 0 else (0, 0)\n            \n            # Skewness (Asimetría): \n            # > 0: Cola hacia la derecha (mucho fondo oscuro, pocos brillos) -> Típico de CT Scan bueno\n            # = 0: Normal\n            # < 0: Cola izquierda (imagen muy blanca/quemada)\n            try:\n                skew_val = stats.skew(img_flat)\n            except:\n                skew_val = 0\n\n            # 5. Métricas de Sparsity (Vacío)\n            non_zero = np.count_nonzero(img)\n            info_ratio = non_zero / (h * w)\n\n            # 6. Lógica del Competidor (Detección de Capas Muertas)\n            # Si la Std Dev es bajísima (< 5), es probable que sea una capa de aire puro o ruido térmico.\n            is_likely_blank = std_val < 5.0\n            \n            # 7. Compilación del Registro\n            record = {\n                \"filename\": f.name,\n                \"filepath\": str(f), # Guardamos ruta completa por seguridad\n                \"z_index\": f.stem,  # ID numérico\n                \n                # Dimensiones\n                \"height\": h,\n                \"width\": w,\n                \"size_kb\": round(file_size_kb, 2),\n                \n                # Estadísticas Píxel\n                \"min\": int(min_val),\n                \"max\": int(max_val),\n                \"mean\": round(mean_val, 2),\n                \"std\": round(std_val, 2),\n                \n                # Topología Matemática\n                \"center_mass_y\": round(cy, 1),\n                \"center_mass_x\": round(cx, 1),\n                \"skewness\": round(skew_val, 3), # Topología del histograma\n                \n                # Metadatos Físicos (TIFF Tags)\n                \"x_res\": tags['x_res'],\n                \"y_res\": tags['y_res'],\n                \"res_unit\": tags['unit'],\n                \"compression\": tags['compression'],\n                \n                # Diagnóstico\n                \"info_ratio\": round(info_ratio, 4),\n                \"is_blank_layer\": is_likely_blank, # TRUE si deberíamos ignorarla en training\n                \"error\": \"None\"\n            }\n            \n            data_registry.append(record)\n\n        except Exception as e:\n            # Captura de errores silenciosa\n            data_registry.append({\n                \"filename\": f.name,\n                \"error\": str(e)[:100],\n                \"is_blank_layer\": True\n            })\n\n    # 8. Guardado y Post-Procesamiento\n    df = pd.DataFrame(data_registry)\n    \n    # Intentar ordenar numéricamente\n    try:\n        # Extraer números del nombre (ej: \"001.tif\" -> 1)\n        #df['sort_id'] = df['z_index'].str.extract('(\\d+)').astype(float)\n        df['sort_id'] = df['z_index'].str.extract(r'(\\d+)').astype(float)\n        df = df.sort_values('sort_id').drop('sort_id', axis=1)\n    except:\n        pass # Orden por defecto\n\n    df.to_csv(output_csv, index=False)\n    \n    print(\"\\n\" + \"=\"*60)\n    print(f\"REPORTE DE INTELIGENCIA DE DATOS\")\n    print(\"=\"*60)\n    \n    if not df.empty and 'std' in df.columns:\n        valid = df[df['error'] == \"None\"]\n        \n        # Análisis de los hallazgos del Competidor\n        blanks = valid['is_blank_layer'].sum()\n        resolutions = valid['x_res'].unique()\n        \n        print(f\"Procesados: {len(df)}\")\n        print(f\"Capas detectadas como 'Basura/Vacías' (Std<5): {blanks}\")\n        print(f\"   -> Recomendación: Eliminar estas {blanks} imágenes del set de entrenamiento.\")\n        \n        print(f\"Resoluciones Detectadas: {resolutions}\")\n        if len(resolutions) > 1:\n            print(\"ALERTA: ¡Tienes resoluciones mezcladas! Debes reescalar antes de entrenar.\")\n        else:\n            print(\"Consistencia Física: OK (Todas las imágenes tienen la misma escala).\")\n            \n        print(f\"\\n Archivo Maestro guardado en: {output_csv}\")\n\n# --- EJECUCIÓN ---\n# Ajusta esta ruta a tu carpeta real\n#ruta_dataset = '/kaggle/input/vesuvius-challenge-surface-detection/train_images'\n\n# Ejecutamos el extractor\nextract_deep_metadata(IMG_DIR, \"dataset_metadata_completo.csv\")\n\n# ==========================================\n# MÓDULO DE LIMPIEZA Y TRANSFORMACIÓN (ETL)\n# ==========================================\ndef generate_clean_manifest(input_csv, output_csv):\n    \"\"\"\n    Filtra el manifiesto crudo para conservar solo tensores válidos (320x320).\n    Esto evita el 'Shape Mismatch' durante el entrenamiento.\n    \"\"\"\n    print(f\"Cargando metadatos crudos desde: {input_csv}...\")\n    \n    try:\n        # 1. Carga (Extract)\n        # Intentamos cargar con el nombre que generaste en el paso anterior\n        df = pd.read_csv(input_csv)\n        \n        # 2. Validación de Esquema\n        required_cols = ['filename', 'height', 'width']\n        if not all(col in df.columns for col in required_cols):\n            raise ValueError(f\"El CSV no tiene las columnas requeridas: {required_cols}\")\n\n        # 3. Transformación (Filter)\n        # Regla de Negocio: Solo aceptamos tensores de 320x320 píxeles.\n        # Las imágenes de 256x256 o 384x384 son ruido/errores de ingestión.\n        mask_valid_dim = (df['height'] == 320) & (df['width'] == 320)\n        \n        df_clean = df[mask_valid_dim].copy()\n        n_dropped = len(df) - len(df_clean)\n        \n        # 4. Carga (Load/Save)\n        df_clean.to_csv(output_csv, index=False)\n        \n        # --- REPORTE DE INGENIERÍA ---\n        print(\"-\" * 40)\n        print(f\"MANIFIESTO LIMPIO GENERADO: {output_csv}\")\n        print(\"-\" * 40)\n        print(f\"Total Original:   {len(df)}\")\n        print(f\"Descartados:      {n_dropped} (Dimensiones incorrectas)\")\n        print(f\"Total Training:   {len(df_clean)} (Tensores 320x320 puros)\")\n        print(\"-\" * 40)\n        \n        return df_clean\n\n    except FileNotFoundError:\n        print(f\"ERROR: No se encuentra el archivo '{input_csv}'.\")\n        print(\"   Asegúrate de haber ejecutado la celda de 'EXTRACTOR FORENSE' primero.\")\n        return None\n\n# --- EJECUCIÓN ---\n# Ajusta el nombre 'input_file' si tu archivo se llama diferente (ej: dataset_metadata_completo(1).csv)\ninput_file = \"dataset_metadata_completo.csv\"\noutput_file = \"dataset_limpio_320x320.csv\"\n\ndf_final = generate_clean_manifest(input_file, output_file)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:51:48.306381Z","iopub.execute_input":"2026-03-03T12:51:48.306726Z","iopub.status.idle":"2026-03-03T12:52:00.772933Z","shell.execute_reply.started":"2026-03-03T12:51:48.306650Z","shell.execute_reply":"2026-03-03T12:52:00.772042Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_debug = pd.read_csv(\"dataset_metadata_completo.csv\")\nprint(\"Columnas:\", df_debug.columns.tolist())\nprint(\"Primeras filas:\")\nprint(df_debug.head())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:52:00.773931Z","iopub.execute_input":"2026-03-03T12:52:00.774198Z","iopub.status.idle":"2026-03-03T12:52:00.791972Z","shell.execute_reply.started":"2026-03-03T12:52:00.774177Z","shell.execute_reply":"2026-03-03T12:52:00.791230Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Extractor de Metadatos Híbrido\nEste código es un **Extractor de Metadatos Híbrido** muy sofisticado. No solo lee la imagen como una matriz de números, sino que interroga al encabezado (header) del archivo TIFF para sacar información sobre el escáner y la física del archivo.\n\nAquí tienes el cuadro detallado de los **Tipos de Datos** que genera tu script y cómo se estructuran en el CSV de salida (`df`). He clasificado los datos según su origen (Sistema de Archivos, Matriz de Píxeles o Tags TIFF).\n\n**Tabla de Especificación de Datos de Salida:**\n\n| Nombre del Campo (CSV) | Tipo de Dato (Python/Pandas) | Origen del Dato | Descripción Técnica y Uso en ML |\n| --- | --- | --- | --- |\n| **`filename`** | `str` (String) | `pathlib` | Nombre del archivo (ej. `001.tif`). Útil para mapear el orden del stack. |\n| **`filepath`** | `str` (String) | `pathlib` | Ruta absoluta. Crítico para la trazabilidad si mueves los datasets. |\n| **`z_index`** | `str` / `obj` | Nombre del archivo | Identificador de la capa en el eje Z (profundidad). Aunque es numérico, se trata como ID. |\n| **`height`** | `int64` | `img.shape[0]` | Altura en píxeles. Fundamental para definir tensores de entrada `(H, W)`. |\n| **`width`** | `int64` | `img.shape[1]` | Anchura en píxeles. Debe ser consistente en todo el stack. |\n| **`size_kb`** | `float64` | `os.path.getsize` | Peso en KB. Útil para detectar archivos corruptos (peso 0 o muy bajo). |\n| **`min`** | `int` (o `float`) | `np.min` | Valor mínimo de intensidad. Si es `0`, hay negro absoluto. |\n| **`max`** | `int` (o `float`) | `np.max` | Valor máximo. Define el rango dinámico. En uint16 suele llegar a 65535. |\n| **`mean`** | `float64` | `np.mean` | Promedio de intensidad. Si fluctúa mucho entre capas, indica cambios de iluminación. |\n| **`std`** | `float64` | `np.std` | **Dato Crítico:** Desviación estándar. Mide el contraste. `std < 5` indica capa vacía. |\n| **`center_mass_y`** | `float64` | `scipy.ndimage` | Coordenada Y ponderada por intensidad. Detecta si el papiro se desplaza (drift). |\n| **`center_mass_x`** | `float64` | `scipy.ndimage` | Coordenada X ponderada. Junto con Y, forma el vector de desplazamiento. |\n| **`skewness`** | `float64` | `scipy.stats` | Asimetría del histograma. Indica si la imagen es \"oscura\" (cola derecha) o \"quemada\". |\n| **`x_res`** | `float` / `rational` | **TIFF Tag 282** | Resolución horizontal física del escáner. Vital para saber la escala real (micras/px). |\n| **`y_res`** | `float` / `rational` | **TIFF Tag 283** | Resolución vertical. Si `x_res != y_res`, los píxeles no son cuadrados (anisotropía). |\n| **`res_unit`** | `int` / `str` | **TIFF Tag 296** | Unidad de medida (ej. Pulgadas, Centímetros). Define qué significan `x_res` e `y_res`. |\n| **`compression`** | `str` | **TIFF Header** | Método de compresión (ej. `LZW`, `None`). Afecta la velocidad de carga en el Dataloader. |\n| **`info_ratio`** | `float64` | Cálculo | Proporción de píxeles no-cero `(≠0)`. Mide la \"densidad de información\" útil. |\n| **`is_blank_layer`** | `bool` | Lógica (`std < 5`) | **Flag de Ingeniería:** Verdadero si la capa es ruido/aire. Usado para filtrar el dataset. |\n| **`error`** | `str` | `try/except` | Mensaje de depuración si la lectura falla. Permite no detener el script por un archivo malo. |\n\n---\n\n","metadata":{}},{"cell_type":"markdown","source":"```python\n# ==============================================================\n# EXTRACTOR FORENSE 4.1 — GPU (CuPy) PROFESIONAL Y ROBUSTO\n# Soporta TIFF 2D/3D | Métricas GPU | Axial Consistency | Z-check\n# Fallback automático a NumPy\n# ==============================================================\n\n\nimport os\nimport re\nimport gc\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport tifffile\nfrom scipy import ndimage\nfrom PIL import Image\n\nImage.MAX_IMAGE_PIXELS = None\nplt.style.use(\"dark_background\")\n\n# --------------------------------------------------------------\n# GPU DETECTION\n# --------------------------------------------------------------\ntry:\n    import cupy as cp\n    GPU_AVAILABLE = True\nexcept Exception:\n    GPU_AVAILABLE = False\n\n\n# --------------------------------------------------------------\n# UTILS\n# --------------------------------------------------------------\ndef _safe_numeric_stem(stem: str):\n    nums = re.findall(r\"\\d+\", stem)\n    if nums:\n        try:\n            return float(nums[-1])\n        except Exception:\n            return None\n    return None\n\n\ndef _compute_entropy_gpu(flat_gpu):\n    hist = cp.histogram(flat_gpu, bins=256)[0]\n    prob = hist / (cp.sum(hist) + 1e-8)\n    return float((-cp.sum(prob * cp.log2(prob + 1e-8))).get())\n\n\ndef _compute_entropy_cpu(flat):\n    hist, _ = np.histogram(flat, bins=256)\n    prob = hist / (hist.sum() + 1e-8)\n    return float(-np.sum(prob * np.log2(prob + 1e-8)))\n\n\ndef _robust_blank(std_val, info_ratio, entropy):\n    return (\n        std_val < 5.0 and\n        info_ratio < 0.01 and\n        entropy < 1.0\n    )\n\n\n# --------------------------------------------------------------\n# MAIN EXTRACTOR\n# --------------------------------------------------------------\ndef extract_deep_metadata_v41_gpu(\n    directory_path,\n    output_csv=\"dataset_master_gpu_v41.csv\",\n    compute_axial=True,\n    free_gpu_memory_every=50\n):\n\n    directory_path = Path(directory_path)\n    files = sorted(directory_path.rglob(\"*.tif\"))\n\n    if not files:\n        raise FileNotFoundError(\"No TIFF files found in directory.\")\n\n    data_registry = []\n    previous_gpu = None\n    previous_cpu = None\n\n    for idx, f in enumerate(tqdm(files, desc=\"GPU Forensic Scan\")):\n\n        try:\n            with tifffile.TiffFile(str(f)) as tif:\n                img_cpu = tif.asarray()\n\n                page0 = tif.pages[0]\n                tags = page0.tags\n\n                x_res = tags[282].value if 282 in tags else 0\n                y_res = tags[283].value if 283 in tags else 0\n                unit = tags[296].value if 296 in tags else 0\n                compression = (\n                    page0.compression.name\n                    if hasattr(page0, \"compression\")\n                    else \"Unknown\"\n                )\n\n            # --------------------------------------------------\n            # DIMENSIONS\n            # --------------------------------------------------\n            if img_cpu.ndim == 3:\n                depth, height, width = img_cpu.shape\n            elif img_cpu.ndim == 2:\n                depth = 1\n                height, width = img_cpu.shape\n            else:\n                raise ValueError(\"Unsupported image dimensionality.\")\n\n            file_size_kb = os.path.getsize(f) / 1024\n            z_numeric = _safe_numeric_stem(f.stem)\n\n            # ==================================================\n            # GPU BRANCH\n            # ==================================================\n            if GPU_AVAILABLE:\n                img_gpu = cp.asarray(img_cpu, dtype=cp.float32)\n                flat_gpu = img_gpu.ravel()\n\n                min_val = float(cp.min(flat_gpu).get())\n                max_val = float(cp.max(flat_gpu).get())\n                mean_val = float(cp.mean(flat_gpu).get())\n                std_val = float(cp.std(flat_gpu).get())\n\n                non_zero = int(cp.count_nonzero(img_gpu).get())\n                info_ratio = non_zero / flat_gpu.size\n\n                entropy = _compute_entropy_gpu(flat_gpu)\n\n                if compute_axial and previous_gpu is not None:\n                    if img_gpu.shape == previous_gpu.shape:\n                        axial_diff = float(\n                            cp.mean(cp.abs(img_gpu - previous_gpu)).get()\n                        )\n                    else:\n                        axial_diff = -1.0\n                else:\n                    axial_diff = 0.0\n\n                previous_gpu = img_gpu\n\n            # ==================================================\n            # CPU FALLBACK\n            # ==================================================\n            else:\n                img_cpu = img_cpu.astype(np.float32)\n                flat = img_cpu.ravel()\n\n                min_val = float(np.min(flat))\n                max_val = float(np.max(flat))\n                mean_val = float(np.mean(flat))\n                std_val = float(np.std(flat))\n\n                non_zero = int(np.count_nonzero(img_cpu))\n                info_ratio = non_zero / flat.size\n\n                entropy = _compute_entropy_cpu(flat)\n\n                if compute_axial and previous_cpu is not None:\n                    if img_cpu.shape == previous_cpu.shape:\n                        axial_diff = float(\n                            np.mean(np.abs(img_cpu - previous_cpu))\n                        )\n                    else:\n                        axial_diff = -1.0\n                else:\n                    axial_diff = 0.0\n\n                previous_cpu = img_cpu\n\n            # --------------------------------------------------\n            # CENTER OF MASS (CPU)\n            # --------------------------------------------------\n            if max_val > min_val:\n                norm = (img_cpu - min_val) / (max_val - min_val + 1e-8)\n                if depth == 1:\n                    cy, cx = ndimage.center_of_mass(norm)\n                else:\n                    cy, cx = ndimage.center_of_mass(norm[depth // 2])\n            else:\n                cy, cx = 0.0, 0.0\n\n            # --------------------------------------------------\n            # BLANK DETECTION\n            # --------------------------------------------------\n            is_blank = _robust_blank(std_val, info_ratio, entropy)\n\n            # --------------------------------------------------\n            # RECORD\n            # --------------------------------------------------\n            record = {\n                \"filename\": f.name,\n                \"filepath\": str(f),\n                \"z_numeric\": z_numeric,\n\n                \"depth\": depth,\n                \"height\": height,\n                \"width\": width,\n                \"size_kb\": round(file_size_kb, 3),\n\n                \"min\": round(min_val, 5),\n                \"max\": round(max_val, 5),\n                \"mean\": round(mean_val, 5),\n                \"std\": round(std_val, 5),\n                \"entropy\": round(entropy, 5),\n\n                \"info_ratio\": round(info_ratio, 6),\n                \"axial_difference\": round(axial_diff, 5),\n\n                \"center_mass_y\": round(float(cy), 5),\n                \"center_mass_x\": round(float(cx), 5),\n\n                \"x_res\": x_res,\n                \"y_res\": y_res,\n                \"res_unit\": unit,\n                \"compression\": compression,\n\n                \"is_blank_layer\": bool(is_blank),\n                \"error\": \"None\"\n            }\n\n            data_registry.append(record)\n\n            # --------------------------------------------------\n            # GPU MEMORY CONTROL\n            # --------------------------------------------------\n            if GPU_AVAILABLE and (idx % free_gpu_memory_every == 0):\n                cp.get_default_memory_pool().free_all_blocks()\n                gc.collect()\n\n        except Exception as e:\n            data_registry.append({\n                \"filename\": f.name,\n                \"filepath\": str(f),\n                \"error\": str(e)[:200],\n                \"is_blank_layer\": True\n            })\n\n    # --------------------------------------------------------------\n    # POST-PROCESSING\n    # --------------------------------------------------------------\n    df = pd.DataFrame(data_registry)\n\n    if \"z_numeric\" in df.columns:\n        df = df.sort_values(\"z_numeric\", na_position=\"last\")\n\n        z_vals = df[\"z_numeric\"].dropna().values\n        if len(z_vals) > 1:\n            diffs = np.diff(z_vals)\n            df[\"z_gap_flag\"] = False\n            gap_indices = np.where(diffs != 1)[0]\n            for gi in gap_indices:\n                df.loc[df.index[gi], \"z_gap_flag\"] = True\n        else:\n            df[\"z_gap_flag\"] = False\n\n    df.to_csv(output_csv, index=False)\n\n    return df\n\n# EJECUCIÓN REAL (OBLIGATORIA)\n\nIMG_DIR = \"/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images\"  # <-- CAMBIAR ESTO\n\nprint(\"Iniciando extractor 4.1...\")\ndf_result = extract_deep_metadata_v41_gpu(\n    directory_path=IMG_DIR,\n    output_csv=\"dataset.csv\",\n    compute_axial=True\n)\n\nprint(\"Finalizado.\")\nprint(\"Filas procesadas:\", len(df_result))\n\n\n\n# -----------------------------------------------------------------------------\n\n\n\nplt.style.use(\"dark_background\")\n\n\n# =====================================================================================\n# UTILIDADES\n# =====================================================================================\n\ndef safe_numeric_df(df: pd.DataFrame) -> pd.DataFrame:\n    \"\"\"Devuelve solo columnas numéricas válidas.\"\"\"\n    numeric_df = df.select_dtypes(include=[np.number]).copy()\n    numeric_df = numeric_df.replace([np.inf, -np.inf], np.nan)\n    numeric_df = numeric_df.dropna(axis=1, how=\"all\")\n    return numeric_df\n\n\ndef print_section(title: str):\n    print(\"\\n\" + \"=\" * 80)\n    print(title)\n    print(\"=\" * 80)\n\n\n# =====================================================================================\n# RESUMEN BÁSICO\n# =====================================================================================\n\ndef show_basic_summary(df: pd.DataFrame):\n    print_section(\"INFORMACIÓN GENERAL\")\n    print(\"Filas:\", df.shape[0])\n    print(\"Columnas:\", df.shape[1])\n    print(\"\\nColumnas:\")\n    print(df.dtypes)\n\n    print_section(\"RESUMEN ESTADÍSTICO\")\n    try:\n        print(df.describe(include=\"all\"))\n    except Exception:\n        print(\"No se pudo generar describe().\")\n\n\n# =====================================================================================\n# DISTRIBUCIONES NUMÉRICAS\n# =====================================================================================\n\ndef plot_distributions(df: pd.DataFrame):\n    numeric_df = safe_numeric_df(df)\n\n    if numeric_df.empty:\n        print(\"\\n[INFO] No hay columnas numéricas para graficar distribuciones.\")\n        return\n\n    for col in numeric_df.columns:\n        plt.figure(figsize=(8, 4))\n        sns.histplot(numeric_df[col].dropna(), bins=40, kde=True)\n        plt.title(f\"Distribución - {col}\")\n        plt.tight_layout()\n        plt.show()\n\n\n# =====================================================================================\n# MATRIZ DE CORRELACIÓN (ULTRA SEGURA)\n# =====================================================================================\n\ndef plot_correlation_matrix(df: pd.DataFrame):\n    numeric_df = safe_numeric_df(df)\n\n    if numeric_df.shape[1] < 2:\n        print(\"\\n[INFO] No hay suficientes columnas numéricas para correlación.\")\n        return\n\n    corr = numeric_df.corr()\n\n    if corr.size == 0 or corr.isna().all().all():\n        print(\"\\n[INFO] Matriz de correlación vacía o inválida.\")\n        return\n\n    plt.figure(figsize=(10, 8))\n    sns.heatmap(corr, cmap=\"coolwarm\", annot=False)\n    plt.title(\"Correlation Matrix\")\n    plt.tight_layout()\n    plt.show()\n\n\n# =====================================================================================\n# DETECCIÓN DE OUTLIERS\n# =====================================================================================\n\ndef detect_outliers(df: pd.DataFrame):\n    numeric_df = safe_numeric_df(df)\n\n    if numeric_df.empty:\n        print(\"\\n[INFO] No hay columnas numéricas para detectar outliers.\")\n        return\n\n    print_section(\"DETECCIÓN DE OUTLIERS (Z-SCORE > 3)\")\n\n    for col in numeric_df.columns:\n        values = numeric_df[col].dropna()\n        if len(values) < 5:\n            continue\n\n        z = (values - values.mean()) / values.std()\n        outliers = values[np.abs(z) > 3]\n\n        print(f\"{col}: {len(outliers)} outliers\")\n\n\n# =====================================================================================\n# TENDENCIAS POR ORDEN\n# =====================================================================================\n\ndef plot_index_trends(df: pd.DataFrame):\n    numeric_df = safe_numeric_df(df)\n\n    if numeric_df.empty:\n        print(\"\\n[INFO] No hay columnas numéricas para graficar tendencias.\")\n        return\n\n    for col in numeric_df.columns:\n        plt.figure(figsize=(10, 4))\n        plt.plot(numeric_df[col].values)\n        plt.title(f\"Tendencia por índice - {col}\")\n        plt.tight_layout()\n        plt.show()\n\n\n# =====================================================================================\n# VISUALIZADOR PRINCIPAL\n# =====================================================================================\n\ndef visualize_forensic_dataset(csv_path: str):\n\n    if not os.path.exists(csv_path):\n        raise FileNotFoundError(f\"No existe el archivo: {csv_path}\")\n\n    df = pd.read_csv(csv_path)\n\n    show_basic_summary(df)\n    plot_distributions(df)\n    plot_correlation_matrix(df)\n    plot_index_trends(df)\n    detect_outliers(df)\n\n    print(\"\\nANÁLISIS COMPLETADO.\")\n    return df\n\n\n# ==============================================================\n# EJECUCIÓN\n# ==============================================================\n\nCSV_PATH = \"/kaggle/working/dataset.csv\"  # <-- Ajustar si es necesario\n\ndf_analysis = visualize_forensic_dataset(CSV_PATH)\n\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-03T03:48:39.359456Z","iopub.execute_input":"2026-03-03T03:48:39.360142Z","iopub.status.idle":"2026-03-03T03:48:39.494544Z","shell.execute_reply.started":"2026-03-03T03:48:39.360111Z","shell.execute_reply":"2026-03-03T03:48:39.493785Z"}}},{"cell_type":"markdown","source":"---\n\n#### Analizando las imagenes:","metadata":{}},{"cell_type":"code","source":"# ==========================================\n# 1. UTILIDADES DE INGENIERÍA DE DATOS\n# ==========================================\n\"\"\"\ndef read_robust(path):\n    # Lectura a prueba de fallos de compresión.\n    try:\n        return tifffile.imread(str(path))\n    except:\n        return np.array(Image.open(str(path)))\n\n\"\"\"\n\n# ==========================================\n# 2. BIBLIOTECA DE FILTROS (THE TOOLKIT)\n# ==========================================\n\n# --- GRUPO A: MEJORA DE IMAGEN ---\ndef apply_clahe(image):\n    if image.dtype != np.uint8:\n        image = ((image - image.min()) / (image.max() - image.min()) * 255).astype(np.uint8)\n    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))\n    return clahe.apply(image)\n\ndef apply_gamma(image, gamma=0.5):\n    img_float = image.astype(np.float32) / image.max()\n    return exposure.adjust_gamma(img_float, gamma)\n\ndef apply_sharpening(image):\n    kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])\n    return cv2.filter2D(image, -1, kernel)\n\ndef apply_unsharp_mask(image):\n    img_float = image.astype(np.float32) / image.max()\n    return unsharp_mask(img_float, radius=2.0, amount=2.0)\n\n# --- GRUPO B: BORDES Y ESTRUCTURAS ---\ndef apply_canny(image):\n    if image.dtype != np.uint8:\n        image = ((image - image.min()) / (image.max() - image.min()) * 255).astype(np.uint8)\n    return cv2.Canny(image, 50, 150)\n\ndef apply_sobel(image):\n    return sobel(image)\n\ndef apply_dog(image):\n    return difference_of_gaussians(image, low_sigma=1, high_sigma=5)\n\ndef apply_bilateral(image):\n    img_float = image.astype(np.float32)\n    return cv2.bilateralFilter(img_float, d=9, sigmaColor=75, sigmaSpace=75)\n\n# --- GRUPO C: RIDGE DETECTION (Detectores de Pliegues) ---\ndef apply_frangi(image):\n    return frangi(image, sigmas=range(1, 4))\n\ndef apply_sato(image):\n    return sato(image, sigmas=range(1, 4), black_ridges=False)\n\ndef apply_meijering(image):\n    return meijering(image, sigmas=range(1, 4), black_ridges=False)\n\ndef apply_hessian_curvature(image):\n    # CORREGIDO: use_gaussian_derivatives=False y paso de lista única\n    H_elems = hessian_matrix(image, sigma=1, order='rc', use_gaussian_derivatives=False)\n    l1, l2 = hessian_matrix_eigvals(H_elems)\n    return l2\n\n# --- GRUPO D: TEXTURA Y MORFOLOGÍA ---\ndef apply_tophat(image):\n    selem = disk(5)\n    return white_tophat(image, selem)\n\ndef apply_local_entropy(image):\n    if image.dtype != np.uint8:\n        image = ((image - image.min()) / (image.max() - image.min()) * 255).astype(np.uint8)\n    return entropy(image, disk(5))\n\ndef apply_lbp(image):\n    radius = 3; n_points = 8 * radius\n    return local_binary_pattern(image, n_points, radius, method='uniform')\n\ndef apply_gabor_bank(image):\n    gabor_all = np.zeros_like(image, dtype=np.float32)\n    ksize = 15; sigma = 3; lambd = 5; gamma = 0.5; psi = 0\n    for theta in np.arange(0, np.pi, np.pi / 4):\n        kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, lambd, gamma, psi, ktype=cv2.CV_32F)\n        fimg = cv2.filter2D(image.astype(np.float32), cv2.CV_32F, kernel)\n        np.maximum(gabor_all, fimg, gabor_all)\n    return gabor_all\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:52:00.792929Z","iopub.execute_input":"2026-03-03T12:52:00.793557Z","iopub.status.idle":"2026-03-03T12:52:00.811112Z","shell.execute_reply.started":"2026-03-03T12:52:00.793527Z","shell.execute_reply":"2026-03-03T12:52:00.809872Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Filtro dinamico:","metadata":{}},{"cell_type":"code","source":"# CORE IMPORTS (EJECUTAR SIEMPRE PRIMERO)\n# LECTOR ROBUSTO TIFF (2D o 3D)\n\ndef read_robust(filepath):\n    with tifffile.TiffFile(filepath) as tif:\n        img = tif.asarray()\n    return img\n\n# MOTOR DE INGENIERÍA TOPOLÓGICA\n\ndef apply_topological_analysis(image, binary_thresh, separation_distance, min_object_size):\n    \"\"\"\n    Pipeline 2D:\n    1. Normaliza\n    2. Binariza\n    3. Limpia ruido\n    4. Distance transform\n    5. Skeleton\n    6. Watershed\n    \"\"\"\n\n    # A. Normalización segura\n    img_float = image.astype(np.float32)\n    min_val = img_float.min()\n    max_val = img_float.max()\n\n    if max_val - min_val == 0:\n        img_norm = np.zeros_like(img_float)\n    else:\n        img_norm = (img_float - min_val) / (max_val - min_val)\n\n    # B. Binarización\n    binary_mask = img_norm > binary_thresh\n\n    # C. Limpieza de ruido\n    clean_mask = morph.remove_small_objects(binary_mask, min_size=min_object_size)\n\n    # D. Transformada de distancia\n    distance_map = ndi.distance_transform_edt(clean_mask)\n\n    # E. Skeleton 2D\n    skeleton = skeletonize(clean_mask)\n\n    # F. Watershed\n    coords = peak_local_max(\n        distance_map,\n        min_distance=separation_distance,\n        labels=clean_mask\n    )\n\n    mask_peaks = np.zeros(distance_map.shape, dtype=bool)\n\n    if len(coords) > 0:\n        mask_peaks[tuple(coords.T)] = True\n\n    markers, _ = ndi.label(mask_peaks)\n\n    labels = watershed(-distance_map, markers, mask=clean_mask)\n\n    return clean_mask, distance_map, skeleton, labels\n\n# CARGA DE IMAGEN\n\nimg_path = \"/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/105068588.tif\"\n\noriginal = None\n\nif os.path.exists(img_path):\n    original = read_robust(img_path)\n\n    # Si es volumen 3D → usamos slice central\n    if original.ndim == 3:\n        print(\"Volumen detectado:\", original.shape)\n        original = original[original.shape[0] // 2]\n        print(\"Usando slice central:\", original.shape)\n\nelif os.path.exists(img_path):\n    print(\"Ruta inválida.\")\nelse:\n    print(\"No se encontró la imagen.\")\n\n# 3. DASHBOARD INTERACTIVO\n\ndef dashboard_topology(thresh, sep_dist, noise_px):\n\n    if original is None:\n        return\n\n    mask, dist_map, skeleton, result = apply_topological_analysis(\n        original,\n        thresh,\n        sep_dist,\n        noise_px\n    )\n\n    fig, axes = plt.subplots(1, 4, figsize=(24, 6))\n\n    # 1️⃣ Máscara limpia\n    axes[0].imshow(mask, cmap='gray')\n    axes[0].set_title(f\"1. Máscara Limpia (> {noise_px}px)\", fontsize=11)\n\n    # 2️⃣ Mapa topológico\n    im = axes[1].imshow(dist_map, cmap='magma')\n    axes[1].set_title(\"2. Elevación Topológica\", fontsize=11)\n\n    # 3️⃣ Esqueleto\n    skel_vis = morph.dilation(skeleton, morph.disk(1))\n\n    axes[2].imshow(mask, cmap='gray', alpha=0.1)\n    axes[2].imshow(skel_vis, cmap='spring')\n    axes[2].set_title(\"3. Esqueleto Limpio\", fontsize=11, fontweight='bold')\n\n    # 4️⃣ Watershed\n    axes[3].imshow(result, cmap='nipy_spectral')\n    axes[3].set_title(\"4. Watershed Final\", fontsize=11)\n\n    for ax in axes:\n        ax.axis(\"off\")\n\n    plt.tight_layout()\n    plt.show()\n\n# 4. PANEL DE CONTROL\nif original is not None:\n\n    print(\"\\n LABORATORIO DE LIMPIEZA Y ESQUELETIZACIÓN\")\n    print(\"Instrucciones:\")\n    print(\"• Umbral: Define la fibra.\")\n    print(\"• Limpieza (px): Elimina objetos pequeños.\")\n    print(\"  -> Rango recomendado: 20 - 100\")\n\n    style = {'description_width': 'initial'}\n    layout_slider = widgets.Layout(width='70%')\n\n    widgets.interact(\n        dashboard_topology,\n\n        thresh=widgets.FloatSlider(\n            min=0.0, max=0.6, step=0.01, value=0.30,\n            description='1. Umbral Binario',\n            style=style, layout=layout_slider\n        ),\n\n        sep_dist=widgets.IntSlider(\n            min=1, max=10, step=1, value=1,\n            description='2. Distancia Watershed',\n            style=style, layout=layout_slider\n        ),\n\n        noise_px=widgets.IntSlider(\n            min=0, max=500, step=10, value=50,\n            description='3. Limpieza de Ruido (Area Min)',\n            style=style, layout=layout_slider\n        )\n    )\n\nelse:\n    print(\"No se encontró imagen para cargar.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:52:00.812603Z","iopub.execute_input":"2026-03-03T12:52:00.813096Z","iopub.status.idle":"2026-03-03T12:52:02.367261Z","shell.execute_reply.started":"2026-03-03T12:52:00.813038Z","shell.execute_reply":"2026-03-03T12:52:02.366367Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ... (Tu clase Config se mantiene igual, agrega PRUNE_THRESHOLD = 15) ...\nclass Config:\n    UPSCALE_FACTOR = 1.0\n    BINARY_THRESH = 0.30\n    MIN_THICKNESS = 1\n    MAX_THICKNESS = 10\n    PRUNE_THRESHOLD = 15 # Nivel agresividad de la poda (en píxeles)\n    GIF_SCALE = 1.0\n    FPS = 2\n    OUTPUT_FILENAME = 'topologia_auditoria_output.gif'\n    file_path = '/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/1013184726.tif'\n\n# ==========================================\n# 2. MOTOR TOPOLÓGICO: ALGORITMO DE GRAFOS (NUEVO)\n# ==========================================\ndef prune_skeleton_networkx(skel_matrix, prune_thresh=15):\n    \"\"\"\n    Transforma el tensor booleano en un Grafo Euclidiano y poda los espolones.\n    Complejidad Espacial: O(N_pixeles_esqueleto)\n    Complejidad Temporal: O(N log N) gracias a cKDTree\n    \"\"\"\n    # 1. Extracción de Coordenadas (Tensor a Vector Espacial)\n    y_coords, x_coords = np.nonzero(skel_matrix)\n    if len(y_coords) == 0:\n        return skel_matrix # Retorno de seguridad si la máscara está vacía\n        \n    points = list(zip(y_coords, x_coords))\n    \n    # 2. Indexación Espacial de Alta Velocidad\n    tree = cKDTree(points)\n    \n    # Radio 1.5 cubre la 8-conectividad (distancia diagonal sqrt(2) ≈ 1.414)\n    pairs = tree.query_pairs(r=1.5)\n    \n    # 3. Construcción del Grafo Estructurado\n    G = nx.Graph()\n    G.add_nodes_from(points)\n    G.add_edges_from([(points[i], points[j]) for i, j in pairs])\n    \n    # 4. Análisis Topológico de Grados\n    # Grado 1: Puntos ciegos (Extremos)\n    # Grado > 2: Intersecciones (Junctions)\n    endpoints = [node for node, degree in G.degree() if degree == 1]\n    nodes_to_remove = set()\n    \n    # 5. Motor de Poda (Graph Traversal)\n    for ep in endpoints:\n        if ep in nodes_to_remove: \n            continue # Evitar re-procesar ramas ya marcadas para muerte\n            \n        path = [ep]\n        curr = ep\n        \n        # Random Walk Determinista hacia el núcleo\n        while True:\n            # Buscar vecinos que no hayamos visitado ya en esta ruta\n            neighbors = [n for n in G.neighbors(curr) if n not in path]\n            \n            if not neighbors:\n                break # Rama aislada flotante\n                \n            next_node = neighbors[0] # Al ser rama recta, típicamente hay 1 solo vecino válido\n            \n            if G.degree(next_node) > 2:\n                # ¡Impacto! Llegamos a una bifurcación principal (columna vertebral)\n                break\n                \n            path.append(next_node)\n            curr = next_node\n            \n        # 6. Evaluación Condicional Matemátca\n        if len(path) <= prune_thresh:\n            nodes_to_remove.update(path) # Marcar toda la rama para aniquilación\n\n    # 7. Ejecución de la Poda\n    G.remove_nodes_from(nodes_to_remove)\n    \n    # 8. Reconstrucción Tensorial (Grafo a Matriz 2D)\n    pruned_skel = np.zeros_like(skel_matrix, dtype=bool)\n    for y, x in G.nodes():\n        pruned_skel[y, x] = True\n        \n    return pruned_skel\n\n# ==========================================\n# 3. MOTOR TOPOLÓGICO: PIPELINE PRINCIPAL (ACTUALIZADO)\n# ==========================================\ndef process_layer_medial_axis(image, layer_idx, thresh=0.30, noise_px=1000, watershed_dist=1):\n    # ... (Sección A y B se mantienen EXACTAMENTE IGUAL) ...\n    \n    # --- A. PREPROCESAMIENTO Y ESTABILIZACIÓN ---\n    img_float = image.astype(np.float32)\n    min_v, max_v = img_float.min(), img_float.max()\n    epsilon = 1e-8\n    if max_v > min_v:\n        img_norm = (img_float - min_v) / (max_v - min_v + epsilon)\n    else:\n        img_norm = np.zeros_like(img_float)\n    img_input = img_norm\n    viz_raw = (img_input * 255).astype(np.uint8)\n    viz_raw = cv2.cvtColor(viz_raw, cv2.COLOR_GRAY2BGR)\n\n    # --- B. BINARIZACIÓN Y TAMIZ ---\n    binary_mask = img_input > thresh\n    clean_mask = remove_small_objects(binary_mask, min_size=noise_px)\n    clean_mask = remove_small_holes(clean_mask, area_threshold=noise_px)\n\n    # --- C. SEPARACIÓN TOPOLÓGICA (WATERSHED SUAVIZADO) ---\n    distance_map = ndi.distance_transform_edt(clean_mask)\n    \n    # APLICACIÓN DE FILTRO GAUSSIANO AL MAPA DE DISTANCIAS\n    # Suaviza el terreno matemáticamente antes de buscar los picos.\n    smooth_distance_map = ndi.gaussian_filter(distance_map, sigma=1.0)\n    \n    coords = peak_local_max(smooth_distance_map, min_distance=watershed_dist, labels=clean_mask)\n    mask_peaks = np.zeros(smooth_distance_map.shape, dtype=bool)\n    mask_peaks[tuple(coords.T)] = True\n    \n    markers, _ = ndi.label(mask_peaks)\n    watershed_labels = watershed(-smooth_distance_map, markers, mask=clean_mask)\n    final_mask = watershed_labels > 0\n\n    # --- D. CÁLCULO Y PODA DEL EJE MEDIO (NUEVA ARQUITECTURA) ---\n    # 1. Extraemos el esqueleto crudo y su mapa de grosores\n    raw_skel, dist_map_medial = medial_axis(final_mask, return_distance=True)\n    \n    # 2. ¡EL PASO MÁGICO! Inyectamos el esqueleto al Grafo para podarlo\n    prune_val = getattr(Config, 'PRUNE_THRESHOLD', 15)\n    pruned_skel = prune_skeleton_networkx(raw_skel, prune_thresh=prune_val)\n    \n    # --- E. FILTRO DIMENSIONAL (SMART FILTER APLICADO AL GRAFO LIMPIO) ---\n    # Usamos el esqueleto podado en lugar del crudo\n    skeleton_thickness = dist_map_medial * pruned_skel \n    \n    min_t = getattr(Config, 'MIN_THICKNESS', 1)\n    max_t = getattr(Config, 'MAX_THICKNESS', 10)\n    \n    filtered_skeleton = (skeleton_thickness >= min_t) & (skeleton_thickness <= max_t)\n\n    # ... (Sección F y G de Renderizado se mantienen igual) ...\n    \n    viz_dist = (dist_map_medial / (dist_map_medial.max() + 1e-5) * 255).astype(np.uint8)\n    viz_dist = cv2.applyColorMap(viz_dist, cv2.COLORMAP_JET)\n    \n    skel_vis = dilation(filtered_skeleton, disk(1))\n    viz_result = np.zeros_like(viz_dist)\n    viz_result[:] = [30, 30, 30] \n    viz_result[skel_vis] = [0, 255, 255] \n    \n    combined = np.hstack((viz_raw, viz_dist, viz_result))\n    \n    h, w = combined.shape[:2]\n    w_third = w // 3\n    cv2.putText(combined, \"1. ESCANEO RAW\", (15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)\n    cv2.putText(combined, \"2. TOPOLOGIA 3D\", (w_third + 15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)\n    cv2.putText(combined, \"3. EJE MEDIAL LIMPIO\", (2 * w_third + 15, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)\n    \n    info_txt = f\"Z:{layer_idx} | Poda:{prune_val}px | Grosor:{min_t}-{max_t}px\"\n    cv2.rectangle(combined, (0, h - 35), (w, h), (0, 0, 0), -1)\n    cv2.putText(combined, info_txt, (15, h - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)\n    \n    return combined\n\ndef run_pipeline():\n    path = getattr(Config, 'file_path', None)\n    if not path or not os.path.exists(path):\n        print(f\" [!] ERROR: Archivo no encontrado en la ruta: {path}\")\n        return\n\n    print(f\"Iniciando Pipeline Topológico con Auditoría Visual...\")\n    print(f\"Parámetros Inyectados: Umbral=0.30 | Limpieza=50px | Watershed Dist=1\")\n    \n    frames = []\n    try:\n        # Cargador Perezoso (Lazy Loader) para evitar OOM (Out of Memory)\n        img = Image.open(path)\n        total_layers = getattr(img, 'n_frames', 320)\n        \n        for i in tqdm(range(total_layers), desc=\"Computando y Ensamblando Paneles\", unit=\"capas\"):\n            try:\n                img.seek(i)\n                layer_data = np.array(img)\n                \n                # Inyección de hiperparámetros de control\n                viz_frame = process_layer_medial_axis(\n                    image=layer_data, \n                    layer_idx=i, \n                    thresh=0.30, \n                    noise_px=100, \n                    watershed_dist=1\n                )\n                frames.append(viz_frame)\n                \n            except EOFError:\n                break\n                \n        if frames:\n            output_name = getattr(Config, 'OUTPUT_FILENAME', 'topologia_auditoria_output.gif')\n            fps_val = getattr(Config, 'FPS', 10)\n            print(f\"Renderizando GIF Tripartito ({len(frames)} frames) a {fps_val} FPS...\")\n            imageio.mimsave(output_name, frames, fps=fps_val, loop=0)\n            print(f\"Pipeline Exitoso! Guardado en: {output_name}\")\n            \n            try:\n                from IPython.display import display, Image as IPyImage\n                display(IPyImage(filename=output_name))\n            except ImportError:\n                print(\" Entorno no admite IPyImage. Revisa el archivo generado.\")\n        else:\n            print(\"El tensor de frames está vacío. Verifica los cortes de entrada.\")\n            \n    except Exception as e:\n        import traceback\n        print(f\"ERROR FATAL DE KERNEL: {e}\")\n        traceback.print_exc()\n\nif __name__ == \"__main__\":\n    run_pipeline()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:52:02.368422Z","iopub.execute_input":"2026-03-03T12:52:02.368726Z","iopub.status.idle":"2026-03-03T12:53:08.758930Z","shell.execute_reply.started":"2026-03-03T12:52:02.368696Z","shell.execute_reply":"2026-03-03T12:53:08.758042Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## VESUVIUS CHALLENGE – KAGGLE SAFE VERSION (LZW FIX DEFINITIVO)\n### Carga TIFF LZW sin usar tifffile.asarray()","metadata":{}},{"cell_type":"code","source":"# INSTALAR DEPENDENCIA CORRECTAMENTE\n#import sys\n\n#!pip install tifffile\n#!{sys.executable} -m pip install -q --upgrade imagecodecs\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:53:08.760806Z","iopub.execute_input":"2026-03-03T12:53:08.761052Z","iopub.status.idle":"2026-03-03T12:53:08.765154Z","shell.execute_reply.started":"2026-03-03T12:53:08.761029Z","shell.execute_reply":"2026-03-03T12:53:08.764073Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"```python\n# PATH DATASET\nTIFF_PATH = \"/kaggle/input/competitions/vesuvius-challenge-surface-detection/train_images/1059332280.tif\"\nassert os.path.exists(TIFF_PATH), \"No se encontró el TIFF\"\n\n# CARGA ULTRA ROBUSTA CON OPENCV (LEE LZW SIN PROBLEMAS)\nvolume = cv2.imreadmulti(TIFF_PATH, flags=cv2.IMREAD_UNCHANGED)[1]\n\nif volume is None or len(volume) == 0:\n    raise RuntimeError(\"No se pudo leer el TIFF\")\n\nvolume = np.array(volume, dtype=np.float32)\n\n# NORMALIZACIÓN ROBUSTA\np1, p99 = np.percentile(volume, (1, 99))\nvolume = rescale_intensity(volume, in_range=(p1, p99), out_range=(0, 1))\n\nnum_slices, H, W = volume.shape\n\n# ESQUELETO 2D SLICE-BY-SLICE\nbinary_volume = np.zeros_like(volume, dtype=np.uint8)\n\nfor i in range(num_slices):\n    img = volume[i]\n\n    try:\n        thresh = threshold_otsu(img)\n    except:\n        thresh = 0.5\n\n    binary = img > thresh\n    skel = skeletonize(binary)\n    binary_volume[i] = skel.astype(np.uint8)\n\n# COLORMAP PROFESIONAL\ncmap_skeleton = LinearSegmentedColormap.from_list(\n    \"vesuvius_skeleton\",\n    [(0, 0, 0, 0), (1, 0.2, 0.1, 1)]\n)\n\n# VISUALIZADOR PROFESIONAL\ndef launch_viewer(volume, skeleton):\n\n    fig, ax = plt.subplots(figsize=(8, 8))\n    plt.subplots_adjust(bottom=0.15)\n\n    img_display = ax.imshow(volume[0], cmap=\"gray\")\n    skel_overlay = ax.imshow(skeleton[0], cmap=cmap_skeleton, alpha=0.9)\n\n    ax.set_title(f\"Slice 0/{num_slices-1}\", fontsize=14)\n    ax.axis(\"off\")\n\n    ax_slider = plt.axes([0.2, 0.05, 0.6, 0.03])\n    slider = Slider(ax_slider, \"Slice\", 0, num_slices-1, valinit=0, valstep=1)\n\n    def update(val):\n        idx = int(slider.val)\n        img_display.set_data(volume[idx])\n        skel_overlay.set_data(skeleton[idx])\n        ax.set_title(f\"Slice {idx}/{num_slices-1}\", fontsize=14)\n        fig.canvas.draw_idle()\n\n    slider.on_changed(update)\n    plt.show()\n\n\nlaunch_viewer(volume, binary_volume)\n\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-02T11:47:00.071570Z","iopub.execute_input":"2026-03-02T11:47:00.072820Z","iopub.status.idle":"2026-03-02T11:47:05.487866Z","shell.execute_reply.started":"2026-03-02T11:47:00.072779Z","shell.execute_reply":"2026-03-02T11:47:05.486383Z"}}},{"cell_type":"code","source":"# ==========================================================\n# PATH DATASET\n# ==========================================================\nassert os.path.exists(TIFF_PATH), \"No se encontró el TIFF\"\n\n# ==========================================================\n# CARGA CON OPENCV (LZW SAFE)\n# ==========================================================\nsuccess, pages = cv2.imreadmulti(TIFF_PATH, flags=cv2.IMREAD_UNCHANGED)\n\nif not success or pages is None or len(pages) == 0:\n    raise RuntimeError(\"No se pudo leer el TIFF\")\n\nvolume = np.array(pages, dtype=np.float32)\n\n# ==========================================================\n# NORMALIZACIÓN\n# ==========================================================\np1, p99 = np.percentile(volume, (1, 99))\nvolume = rescale_intensity(volume, in_range=(p1, p99), out_range=(0, 1))\n\nnum_slices, H, W = volume.shape\n\n# ==========================================================\n# ESQUELETO 2D\n# ==========================================================\nbinary_volume = np.zeros_like(volume, dtype=np.uint8)\n\nfor i in range(num_slices):\n    img = volume[i]\n    try:\n        thresh = threshold_otsu(img)\n    except:\n        thresh = 0.5\n    binary_volume[i] = skeletonize(img > thresh).astype(np.uint8)\n\n# ==========================================================\n# COLORMAP\n# ==========================================================\ncmap_skeleton = LinearSegmentedColormap.from_list(\n    \"vesuvius_skeleton\",\n    [(0, 0, 0, 0), (1, 0.2, 0.1, 1)]\n)\n\n# ==========================================================\n# VISUALIZADOR PROFESIONAL CON ipywidgets (FUNCIONA EN KAGGLE)\n# ==========================================================\n\nfig, ax = plt.subplots(figsize=(8, 8))\nplt.close(fig)  # evita doble render\n\ndef show_slice(idx):\n    ax.clear()\n    ax.imshow(volume[idx], cmap=\"gray\")\n    ax.imshow(binary_volume[idx], cmap=cmap_skeleton, alpha=0.9)\n    ax.set_title(f\"Slice {idx}/{num_slices-1}\", fontsize=14)\n    ax.axis(\"off\")\n    display(fig)\n\nslider = widgets.IntSlider(\n    value=0,\n    min=0,\n    max=num_slices - 1,\n    step=1,\n    description=\"Slice:\",\n    continuous_update=True\n)\n\nwidgets.interact(show_slice, idx=slider)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:53:08.766259Z","iopub.execute_input":"2026-03-03T12:53:08.766543Z","iopub.status.idle":"2026-03-03T12:53:12.530400Z","shell.execute_reply.started":"2026-03-03T12:53:08.766513Z","shell.execute_reply":"2026-03-03T12:53:12.529626Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Escanea TODOS los .tif y guarda SOLO los esqueletos\n","metadata":{}},{"cell_type":"markdown","source":"``` python\n# VESUVIUS CHALLENGE – BATCH SKELETON GENERATOR (OPTIMIZADO)\n# Escanea TODOS los .tif y guarda SOLO los esqueletos\n# Formato de salida: NPZ comprimido (ultra eficiente)\n# Compatible Kaggle\n\n\n# FUNCIÓN: PROCESAR UN TIFF → ESQUELETO 3D\ndef process_tiff_to_skeleton(tiff_path):\n\n    success, pages = cv2.imreadmulti(str(tiff_path), flags=cv2.IMREAD_UNCHANGED)\n    if not success or pages is None or len(pages) == 0:\n        raise RuntimeError(f\"No se pudo leer {tiff_path}\")\n\n    volume = np.array(pages, dtype=np.float32)\n\n    # Normalización robusta\n    p1, p99 = np.percentile(volume, (1, 99))\n    volume = rescale_intensity(volume, in_range=(p1, p99), out_range=(0, 1))\n\n    num_slices = volume.shape[0]\n\n    # Usamos bool para ahorrar memoria\n    skeleton_volume = np.zeros_like(volume, dtype=np.bool_)\n\n    for i in range(num_slices):\n        img = volume[i]\n\n        try:\n            thresh = threshold_otsu(img)\n        except:\n            thresh = 0.5\n\n        binary = img > thresh\n        skeleton_volume[i] = skeletonize(binary)\n\n    return skeleton_volume\n\n# ==========================================================\n# PROCESAMIENTO MASIVO\n# ==========================================================\ntiff_files = sorted(Path(INPUT_DIR).glob(\"*.tif\"))\n\nprint(f\"Se encontraron {len(tiff_files)} archivos TIFF\")\n\nfor tiff_path in tqdm(tiff_files):\n\n    try:\n        skeleton_volume = process_tiff_to_skeleton(tiff_path)\n\n        output_path = Path(OUTPUT_DIR) / (tiff_path.stem + \"_skeleton.npz\")\n\n        # Guardado ultra eficiente (compresión ZIP interna)\n        np.savez_compressed(\n            output_path,\n            skeleton=skeleton_volume.astype(np.uint8)  # 0/1\n        )\n\n    except Exception as e:\n        print(f\"Error en {tiff_path.name}: {e}\")\n\nprint(\"PROCESAMIENTO COMPLETADO\")\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-02T14:13:26.952209Z","iopub.execute_input":"2026-03-02T14:13:26.952537Z","iopub.status.idle":"2026-03-02T15:37:04.956374Z","shell.execute_reply.started":"2026-03-02T14:13:26.952510Z","shell.execute_reply":"2026-03-02T15:37:04.953314Z"}}},{"cell_type":"markdown","source":"Se encontraron 786 archivos TIFF\n\n100%|██████████| 786/786 [1:23:37<00:00,  6.38s/it]\n\nPROCESAMIENTO COMPLETADO\n","metadata":{}},{"cell_type":"markdown","source":"``` python\n## .ZIP PARA DESCARGAR\nimport shutil\n\n# Estructura: shutil.make_archive('nombre_del_zip', 'zip', 'ruta_de_la_carpeta')\nshutil.make_archive('esqueleto_slice', 'zip', '/kaggle/working/esqueleto')\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-02T15:39:36.811043Z","iopub.execute_input":"2026-03-02T15:39:36.814458Z","iopub.status.idle":"2026-03-02T15:41:06.244364Z","shell.execute_reply.started":"2026-03-02T15:39:36.814363Z","shell.execute_reply":"2026-03-02T15:41:06.243291Z"}}},{"cell_type":"code","source":"# VISUALIZADOR DEFINITIVO (KAGGLE 100% FUNCIONAL)\n# Sin widgets rotos, sin backend interactivo\n# Usa interact + output controlado\n\n# ==========================================================\n# COLORMAP\n# ==========================================================\ncmap_skeleton = LinearSegmentedColormap.from_list(\n    \"skeleton_only\",\n    [(0, 0, 0, 1), (0.9, 0.2, 0.1, 1)]\n)\n\n# ==========================================================\n# VISUALIZADOR ESTABLE\n# ==========================================================\noutput = widgets.Output()\n\ndef show_slice(idx):\n    with output:\n        clear_output(wait=True)\n        plt.figure(figsize=(8,8))\n        plt.imshow(skeleton_volume[idx], cmap=cmap_skeleton)\n        plt.title(f\"Skeleton Slice {idx}/{num_slices-1}\")\n        plt.axis(\"off\")\n        plt.show()\n\nslider = widgets.IntSlider(\n    value=0,\n    min=0,\n    max=num_slices-1,\n    step=1,\n    description=\"Slice:\",\n    continuous_update=True\n)\n\nwidgets.interactive_output(show_slice, {\"idx\": slider})\n\ndisplay(slider, output)\n\nshow_slice(0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-03T12:53:12.531400Z","iopub.execute_input":"2026-03-03T12:53:12.531643Z","iopub.status.idle":"2026-03-03T12:53:12.810729Z","shell.execute_reply.started":"2026-03-03T12:53:12.531622Z","shell.execute_reply":"2026-03-03T12:53:12.809785Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Topología, Geometría Diferencial, Fractales, Análisis Espectral y Tensores de Estructura.","metadata":{}},{"cell_type":"markdown","source":"``` python\n# Instalación rápida de CuPy (ajustada a la versión de CUDA común en 2026)\n!pip install cupy-cuda12x \n\n# Opcional: Si el entorno es muy específico, usa la versión genérica:\n!pip install cupy\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-02T16:04:26.259101Z","iopub.execute_input":"2026-03-02T16:04:26.259821Z","iopub.status.idle":"2026-03-02T16:04:30.226595Z","shell.execute_reply.started":"2026-03-02T16:04:26.259786Z","shell.execute_reply":"2026-03-02T16:04:30.225687Z"}}},{"cell_type":"markdown","source":"``` python\n# GESTIÓN DE DEPENDENCIAS GPU (CUPY)\ntry:\n    import cupy as cp\n    from cupyx.scipy.sparse import coo_matrix as sparse_coo_gpu\n    from cupyx.scipy.sparse.linalg import eigsh as eigsh_gpu\n    CUPY_AVAILABLE = True\nexcept ImportError:\n    CUPY_AVAILABLE = False\n    print(\" ADVERTENCIA: CuPy no detectado. El análisis espectral se omitirá.\")\n\nclass GeometryFeatureEngine:\n    def __init__(self, device='cuda', sigma_axial=1.5):\n        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')\n        self.sigma_axial = sigma_axial\n        \n        # Kernel 3D para conectividad de 26 vecinos\n        self.topo_kernel = torch.ones((1, 1, 3, 3, 3), device=self.device)\n        self.topo_kernel[0, 0, 1, 1, 1] = 0\n        \n        print(f\"Engine inicializado en: {self.device}\")\n\n    # 1. TOPOLOGÍA (CONVOLUCIÓN 3D)\n    def _extract_topology(self, vol_tensor):\n        with torch.no_grad():\n            # Grado local = suma de vecinos en vecindario 3x3x3\n            degree_map = F.conv3d(vol_tensor, self.topo_kernel, padding=1) * vol_tensor\n            total_voxels = torch.sum(vol_tensor).item()\n            if total_voxels == 0: return 0, 0, 0\n            \n            endpoints = torch.sum(degree_map == 1).item()\n            bifurcations = torch.sum(degree_map > 2).item()\n            avg_degree = torch.sum(degree_map).item() / (total_voxels + 1e-6)\n        return endpoints, bifurcations, avg_degree\n\n    # 2. DIMENSIÓN FRACTAL (BOX-COUNTING VECTORIZADO)\n    def _extract_fractal(self, vol_tensor):\n        z, y, x = vol_tensor.shape[2:]\n        # Escalas en potencias de 2 para optimizar kernels CUDA\n        max_s = int(np.log2(min(z,y,x))) - 1\n        scales = 2 ** np.arange(1, max_s)\n        n_counts, eps = [], []\n        \n        for s in scales:\n            s = int(s)\n            # max_pool3d detecta si la caja de s^3 \"toca\" el esqueleto\n            count = torch.sum(F.max_pool3d(vol_tensor, s, s) > 0).item()\n            if count > 0:\n                n_counts.append(count)\n                eps.append(s)\n        \n        if len(n_counts) < 2: return 0.0\n        # D = pendiente de log(N) vs log(1/eps)\n        return float(np.polyfit(np.log(1.0/np.array(eps)), np.log(np.array(n_counts)), 1)[0])\n\n    # 3. TENSOR DE ESTRUCTURA (ANISOTROPÍA LOCAL) \n    def _extract_structure_tensor(self, vol_tensor):\n        with torch.no_grad():\n            # Gradientes espaciales discretos\n            dz, dy, dx = torch.gradient(vol_tensor.squeeze())\n            # Promedio local de productos de gradientes (ventana de integración)\n            Ixx = F.avg_pool3d((dx*dx).unsqueeze(0).unsqueeze(0), 3, 1, 1).squeeze()\n            Iyy = F.avg_pool3d((dy*dy).unsqueeze(0).unsqueeze(0), 3, 1, 1).squeeze()\n            \n            # Linealidad: Cuanto más domina un eje sobre otro (fibra vs ruido)\n            linearity = torch.abs(Ixx - Iyy) / (Ixx + Iyy + 1e-6)\n            return linearity.mean().item()\n\n    # 4. VALOR DE FIEDLER (CONECTIVIDAD ESPECTRAL)\n    def _extract_spectral_fiedler(self, volume_np):\n        if not CUPY_AVAILABLE: return 0.0\n        \n        coords = cp.array(np.argwhere(volume_np == 1))\n        n = coords.shape[0]\n        if n < 10: return 0.0\n        \n        # Hashing espacial para búsqueda de vecinos en O(N log N)\n        z_max, y_max, x_max = volume_np.shape\n        s_hash = coords[:,0]*(y_max*x_max) + coords[:,1]*x_max + coords[:,2]\n        s_idx = cp.argsort(s_hash)\n        sorted_h = s_hash[s_idx]\n        \n        r, c = [], []\n        off = cp.array([[dz,dy,dx] for dz in [-1,0,1] for dy in [-1,0,1] for dx in [-1,0,1] if not (dz==dy==dx==0)])\n        \n        for o in off:\n            n_c = coords + o\n            mask = (n_c[:,0]>=0)&(n_c[:,0]<z_max)&(n_c[:,1]>=0)&(n_c[:,1]<y_max)&(n_c[:,2]>=0)&(n_c[:,2]<x_max)\n            n_h = n_c[mask,0]*(y_max*x_max) + n_c[mask,1]*x_max + n_c[mask,2]\n            idx = cp.searchsorted(sorted_h, n_h)\n            v = (idx < n) & (sorted_h[idx] == n_h)\n            if cp.any(v):\n                r.append(cp.where(mask)[0][v])\n                c.append(s_idx[idx[v]])\n\n        # Matriz Laplaciana dispersa L = D - A\n        A = sparse_coo_gpu((cp.ones(len(cp.concatenate(r))), (cp.concatenate(r), cp.concatenate(c))), shape=(n, n))\n        L = sparse_coo_gpu((A.sum(axis=1).flatten(), (cp.arange(n), cp.arange(n))), shape=(n, n)) - A\n        \n        try:\n            # El segundo autovalor más pequeño es la conectividad algebraica\n            vals = eigsh_gpu(L.tocsr(), k=2, which='SM', tol=1e-3, maxiter=500)\n            return float(cp.sort(vals[0])[1])\n        except: return 0.0\n\n    # 5. CONTINUIDAD AXIAL (DISTANCE TRANSFORM)\n    def _extract_axial(self, volume_np):\n        z_dim = volume_np.shape[0]\n        c_vals = []\n        for z in range(z_dim - 1):\n            s1, s2 = volume_np[z], volume_np[z+1]\n            if np.any(s1) and np.any(s2):\n                # Campo de distancia al esqueleto del siguiente slice\n                dt = distance_transform_edt(1 - s2)\n                dists = dt[s1 == 1]\n                # Decaimiento exponencial: $C = e^{-d^2/\\sigma^2}$\n                c_vals.append(np.mean(np.exp(-(dists**2)/(self.sigma_axial**2))))\n        return (np.mean(c_vals), np.var(c_vals)) if c_vals else (0.0, 0.0)\n\n    # 6. GEOMETRÍA DIFERENCIAL (CURVATURA Y ENERGÍA) \n    def _extract_differential(self, volume_np):\n        coords = np.argwhere(volume_np == 1)\n        if len(coords) < 10: return 0.0, 0.0\n        \n        # PCA Global del volumen para estimar curvatura media\n        coords_centered = coords - coords.mean(axis=0)\n        cov = np.cov(coords_centered.T)\n        eigvals = np.sort(np.linalg.eigvalsh(cov))[::-1]\n        \n        # Curvatura normalizada\n        curvature = eigvals[2] / (np.sum(eigvals) + 1e-6)\n        energy = eigvals[0] # Magnitud del eje principal\n        return float(curvature), float(energy)\n\n    # PROCESAMIENTO PRINCIPAL\n    def run(self, input_path, output_csv):\n        files = sorted(glob.glob(os.path.join(input_path, \"*.npz\")))\n        if not files:\n            print(f\" No se encontraron archivos en {input_path}\")\n            return\n\n        data_list = []\n        for f in tqdm(files, desc=\"Procesando\"):\n            try:\n                # 1. Carga\n                vol = np.load(f)[\"skeleton\"].astype(np.uint8)\n                t_vol = torch.from_numpy(vol).float().to(self.device).unsqueeze(0).unsqueeze(0)\n                \n                # 2. Ejecución de Métricas\n                ep, bif, adeg = self._extract_topology(t_vol)\n                fd = self._extract_fractal(t_vol)\n                lin = self._extract_structure_tensor(t_vol)\n                fiedler = self._extract_spectral_fiedler(vol)\n                ax_m, ax_v = self._extract_axial(vol)\n                curv, energy = self._extract_differential(vol)\n                \n                # 3. Registro\n                data_list.append({\n                    \"id\": os.path.basename(f),\n                    \"endpoints\": ep, \"bifurcations\": bif, \"avg_degree\": adeg,\n                    \"fractal_dim\": fd, \"linearity\": lin, \"fiedler\": fiedler,\n                    \"axial_mean\": ax_m, \"axial_var\": ax_v, \n                    \"curvature\": curv, \"structural_energy\": energy\n                })\n                \n                # 4. Limpieza de memoria\n                del t_vol\n                if self.device.type == 'cuda':\n                    torch.cuda.empty_cache()\n                    if CUPY_AVAILABLE: cp.get_default_memory_pool().free_all_blocks()\n                    \n            except Exception as e:\n                print(f\"Error procesando {f}: {e}\")\n\n        df = pd.DataFrame(data_list)\n        df.to_csv(output_csv, index=False)\n        print(f\"\\n Dataset generado: {output_csv}\")\n\n# ==========================================================\n# ==========================================================\n# ==========================================================\n# ==========================================================\n# ==========================================================\n# ==========================================================\n# ==========================================================\n\n# Engine 2026 con py extract \n## extract de geometry feature\nengine = GeometryFeatureEngine()\nengine.run(\"/kaggle/working/esqueleto/\", \"features_vesuvius.csv\")\n\n```","metadata":{"execution":{"iopub.status.busy":"2026-03-02T16:04:32.719611Z","iopub.execute_input":"2026-03-02T16:04:32.720042Z","iopub.status.idle":"2026-03-02T16:46:34.871503Z","shell.execute_reply.started":"2026-03-02T16:04:32.720009Z","shell.execute_reply":"2026-03-02T16:46:34.870807Z"}}},{"cell_type":"markdown","source":"Engine inicializado en: cuda\n\nProcesando: 100%|██████████| 786/786 [41:56<00:00,  3.20s/it]\n\nDataset generado: features_vesuvius.csv","metadata":{}}]}