Refactor HVideoTool to exclusively use YOLO for detection and DeepMosaics for restoration: removed classic CV and composite detectors, updated configuration and UI accordingly. Enhanced documentation in README and CLAUDE.md to reflect these changes, including new batch processing capabilities and device diagnostics.

hvideotool/core/torch_info.py

@@ -1,18 +1,66 @@
-"""Probe the PyTorch / CUDA situation, so the UI can show a device badge.
+"""Diagnose the PyTorch / CUDA situation so the UI can explain *why* it's on CPU.
 
-Pure (no Qt). ``gather()`` imports torch (slow / heavy) — call it off the GUI
-thread. The rest are tiny formatters the UI uses to explain *why* it's on CPU and
-how to enable the GPU.
+Pure (no Qt). ``gather()`` is the heavy part — it imports torch and shells out to
+``nvidia-smi`` — so call it off the GUI thread. ``analyze()`` is fast formatting on
+the gathered dict and figures out the most likely cause + concrete fix.
 """
 
 from __future__ import annotations
 
-# pip index for the CUDA build (matches the README).
-CUDA_WHEEL_INDEX = "https://download.pytorch.org/whl/cu121"
+import re
+import shutil
+import subprocess
+import sys
+
+# pip indexes for the CUDA builds (see README). cu121 needs a driver with CUDA >= 12.1.
+CUDA_WHEELS = {
+    "cu121": "https://download.pytorch.org/whl/cu121",
+    "cu118": "https://download.pytorch.org/whl/cu118",
+}
+
+_NO_WINDOW = 0x08000000 if sys.platform == "win32" else 0  # CREATE_NO_WINDOW
+
+
+def _run_nvidia_smi() -> dict:
+    """Probe the NVIDIA driver/GPU via nvidia-smi. Never raises."""
+    out: dict = {"found": False, "gpus": [], "driver_version": None, "cuda_driver": None}
+    exe = shutil.which("nvidia-smi")
+    if not exe and sys.platform == "win32":
+        candidate = r"C:\Windows\System32\nvidia-smi.exe"
+        exe = candidate if shutil.os.path.isfile(candidate) else None
+    if not exe:
+        return out
+    # GPU names + driver version (robust CSV form).
+    try:
+        r = subprocess.run(
+            [exe, "--query-gpu=name,driver_version", "--format=csv,noheader,nounits"],
+            capture_output=True, text=True, timeout=10, creationflags=_NO_WINDOW,
+        )
+        if r.returncode == 0:
+            out["found"] = True
+            for line in r.stdout.strip().splitlines():
+                parts = [p.strip() for p in line.split(",")]
+                if parts and parts[0]:
+                    out["gpus"].append(parts[0])
+                    if len(parts) > 1 and parts[1]:
+                        out["driver_version"] = parts[1]
+    except Exception:  # noqa: BLE001 - any failure => "not found"
+        return out
+    # Max CUDA version the driver supports (only in the plain header).
+    try:
+        r2 = subprocess.run(
+            [exe], capture_output=True, text=True, timeout=10, creationflags=_NO_WINDOW,
+        )
+        m = re.search(r"CUDA Version:\s*([\d.]+)", r2.stdout)
+        if m:
+            out["cuda_driver"] = m.group(1)
+    except Exception:  # noqa: BLE001
+        pass
+    return out
 
 
 def gather() -> dict:
-    """Collect torch/CUDA facts. Never raises — missing torch is a valid result."""
+    """Collect torch + NVIDIA facts. Never raises — missing torch/GPU are valid."""
     info: dict = {
         "installed": False,
         "version": None,        # torch.__version__ (e.g. "2.12.0+cpu")
@@ -20,27 +68,38 @@ def gather() -> dict:
         "cuda_available": False,
         "device_name": None,    # the active GPU's name, if any
         "import_error": None,
+        # filled by nvidia-smi:
+        "nvidia_smi": False,
+        "gpus": [],
+        "driver_version": None,
+        "cuda_driver": None,
     }
     try:
         import torch
-    except Exception as exc:  # noqa: BLE001 - report any import failure, not just ImportError
+    except Exception as exc:  # noqa: BLE001 - report any import failure
         info["import_error"] = str(exc)
-        return info
-    info["installed"] = True
-    info["version"] = getattr(torch, "__version__", None)
-    try:
-        info["built_cuda"] = torch.version.cuda
-    except Exception:  # noqa: BLE001
-        info["built_cuda"] = None
-    try:
-        info["cuda_available"] = bool(torch.cuda.is_available())
-    except Exception:  # noqa: BLE001
-        info["cuda_available"] = False
-    if info["cuda_available"]:
+    else:
+        info["installed"] = True
+        info["version"] = getattr(torch, "__version__", None)
         try:
-            info["device_name"] = torch.cuda.get_device_name(0)
+            info["built_cuda"] = torch.version.cuda
         except Exception:  # noqa: BLE001
-            info["device_name"] = None
+            info["built_cuda"] = None
+        try:
+            info["cuda_available"] = bool(torch.cuda.is_available())
+        except Exception:  # noqa: BLE001
+            info["cuda_available"] = False
+        if info["cuda_available"]:
+            try:
+                info["device_name"] = torch.cuda.get_device_name(0)
+            except Exception:  # noqa: BLE001
+                info["device_name"] = None
+
+    smi = _run_nvidia_smi()
+    info["nvidia_smi"] = smi["found"]
+    info["gpus"] = smi["gpus"]
+    info["driver_version"] = smi["driver_version"]
+    info["cuda_driver"] = smi["cuda_driver"]
     return info
 
 
@@ -48,33 +107,117 @@ def device_label(info: dict) -> str:
     return "CUDA" if info.get("cuda_available") else "CPU"
 
 
-def reason(info: dict) -> str:
-    """One-sentence human explanation of the current device choice."""
-    if not info.get("installed"):
-        return ("PyTorch не установлен — детектор YOLO и восстановление DeepMosaics "
-                "работают на CPU (классический детектор torch не требует).")
-    if info.get("cuda_available"):
-        name = info.get("device_name") or "GPU"
-        return f"PyTorch использует CUDA: {name}. Вычисления идут на видеокарте."
-    version = info.get("version") or "?"
-    built = info.get("built_cuda")
-    if not built:
-        return (f"Установлена CPU-сборка PyTorch ({version}) — без поддержки CUDA, "
-                "поэтому вычисления идут на процессоре (медленно).")
-    return (f"PyTorch собран с CUDA {built} ({version}), но GPU недоступен: нет "
-            "NVIDIA-видеокарты, не установлен/устарел драйвер, либо версия CUDA "
-            "несовместима с драйвером.")
+def _ver_tuple(v: str | None) -> tuple[int, ...]:
+    try:
+        return tuple(int(x) for x in str(v).split(".")[:2])
+    except (ValueError, AttributeError):
+        return ()
 
 
-def install_hint() -> str:
-    """Steps to enable the GPU (shown when running on CPU)."""
+def recommend_channel(info: dict) -> str:
+    """Pick the pip CUDA wheel index that matches the driver (cu118 for older)."""
+    cd = _ver_tuple(info.get("cuda_driver"))
+    if cd and cd < (12, 1):
+        return "cu118"
+    return "cu121"
+
+
+def install_command(channel: str = "cu121") -> str:
+    """The pip commands to (re)install the chosen CUDA build.
+
+    Targets the **running interpreter** (``sys.executable -m pip``) so the command
+    hits the same venv that runs the app — not whatever ``pip`` is on PATH. (A common
+    trap: running bare ``pip`` in a global shell while torch lives in the project venv.)
+    """
+    index = CUDA_WHEELS.get(channel, CUDA_WHEELS["cu121"])
+    py = sys.executable or "python"
+    q = f'"{py}"' if " " in py else py
     return (
-        "Как включить GPU (NVIDIA):\n"
-        "1. Нужна видеокарта NVIDIA и свежий драйвер (проверка в консоли: nvidia-smi).\n"
-        "2. Переустановите PyTorch со сборкой CUDA:\n\n"
-        "   pip uninstall -y torch torchvision\n"
-        f"   pip install torch torchvision --index-url {CUDA_WHEEL_INDEX}\n\n"
-        "3. Перезапустите приложение.\n\n"
-        "Классический детектор работает и без CUDA. На CPU детекция и расцензуривание "
-        "просто медленнее."
+        f"{q} -m pip uninstall -y torch torchvision\n"
+        f"{q} -m pip install torch torchvision --index-url {index}"
     )
+
+
+def analyze(info: dict) -> dict:
+    """Turn the raw facts into {summary, details[list], steps, command}."""
+    installed = info.get("installed")
+    cuda = info.get("cuda_available")
+    built = info.get("built_cuda")
+    gpus = info.get("gpus") or []
+    smi = info.get("nvidia_smi")
+    driver = info.get("driver_version")
+    cuda_driver = info.get("cuda_driver")
+    channel = recommend_channel(info)
+    command = install_command(channel)
+
+    if not installed:
+        build_str = "не установлен"
+    elif built:
+        build_str = f"CUDA {built}"
+    else:
+        build_str = "CPU-only (+cpu)"
+    gpu_str = (
+        ", ".join(gpus) if gpus
+        else ("не обнаружена" if smi or info.get("nvidia_smi") is False else "nvidia-smi не найден")
+    )
+    if not gpus and not smi:
+        gpu_str = "nvidia-smi не найден (нет драйвера NVIDIA?)"
+    details = [
+        f"PyTorch: {info.get('version') or 'не установлен'}",
+        f"Сборка PyTorch: {build_str}",
+        f"CUDA доступна в PyTorch: {'да' if cuda else 'нет'}",
+        f"Видеокарта (nvidia-smi): {gpu_str}",
+        f"Драйвер NVIDIA: {driver or '—'}",
+        f"Макс. CUDA драйвера: {cuda_driver or '—'}",
+        f"Интерпретатор (venv): {sys.executable}",
+    ]
+    if info.get("import_error"):
+        details.append(f"Ошибка импорта torch: {info['import_error']}")
+
+    if not installed:
+        summary = "PyTorch не установлен — детекция YOLO и DeepMosaics идут на CPU."
+        steps = ("Установите PyTorch (CUDA-сборку, если есть NVIDIA-видеокарта):\n\n"
+                 + command + "\n\nДля YOLO также: pip install -e \".[yolo]\"")
+    elif cuda:
+        gpu = info.get("device_name") or (gpus[0] if gpus else "GPU")
+        summary = f"Всё в порядке: PyTorch использует CUDA. Активный GPU: {gpu}."
+        steps = "GPU уже задействован — ничего делать не нужно."
+    elif not built:  # CPU-only torch build — the usual case
+        if gpus:
+            summary = (
+                "Главная причина: установлена CPU-сборка PyTorch (+cpu) — она физически "
+                f"не умеет в CUDA. Видеокарта ({gpus[0]}) и драйвер {driver or '?'} на месте, "
+                "поэтому достаточно переустановить PyTorch со сборкой CUDA."
+            )
+            steps = ("Переустановите PyTorch под CUDA, затем перезапустите приложение:\n\n"
+                     + command)
+        else:
+            summary = (
+                "Установлена CPU-сборка PyTorch (+cpu), и видеокарта NVIDIA не обнаружена "
+                "(nvidia-smi не отвечает). Либо нет NVIDIA GPU, либо не установлен драйвер."
+            )
+            steps = ("1. Проверьте видеокарту и драйвер: в консоли выполните  nvidia-smi\n"
+                     "2. Если NVIDIA GPU есть — переустановите PyTorch под CUDA:\n\n"
+                     + command +
+                     "\n\nБез NVIDIA GPU всё работает на CPU — просто медленнее.")
+    else:  # CUDA-enabled torch build, but CUDA still not available
+        if not gpus:
+            summary = (
+                f"PyTorch собран под CUDA {built}, но видеокарта/драйвер NVIDIA не найдены. "
+                "Скорее всего не установлен драйвер NVIDIA или нет GPU."
+            )
+            steps = "Установите свежий драйвер NVIDIA и перезапустите. Проверка: nvidia-smi."
+        elif cuda_driver and _ver_tuple(cuda_driver) < _ver_tuple(built):
+            summary = (
+                f"Драйвер поддерживает CUDA {cuda_driver}, а PyTorch собран под CUDA {built} — "
+                "версия драйвера слишком старая."
+            )
+            steps = ("Вариант A — обновите драйвер NVIDIA (рекомендуется).\n"
+                     "Вариант B — поставьте PyTorch под CUDA вашего драйвера:\n\n" + command)
+        else:
+            summary = (
+                f"PyTorch собран под CUDA {built}, GPU ({gpus[0]}) есть, но CUDA недоступна — "
+                "вероятен конфликт версий или повреждённая установка."
+            )
+            steps = "Переустановите PyTorch под CUDA:\n\n" + command
+    return {"summary": summary, "details": details, "steps": steps, "command": command}