Enhance HVideoTool with background processing and device detection: implemented a background job system for detection and restoration tasks, updated the UI to display CUDA/CPU status, and improved device diagnostics. Documentation in README and CLAUDE.md reflects these changes.

hvideotool/core/detection/yolo.py

@@ -54,15 +54,20 @@ class YoloDetector(Detector):
                 "(и PyTorch с CUDA отдельно — см. README)."
             ) from exc
 
-        # Resolve the device: explicit override, else CUDA when available.
+        # Resolve the device: CUDA only when actually available, else CPU. A CPU-only
+        # torch build raises "Torch not compiled with CUDA enabled" if asked for cuda,
+        # so we never request it without a working GPU (even on an explicit override).
+        try:
+            import torch
+
+            cuda_ok = torch.cuda.is_available()
+        except Exception:  # noqa: BLE001
+            cuda_ok = False
         device = self.cfg.yolo_device
         if device is None:
-            try:
-                import torch
-
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            except Exception:  # noqa: BLE001
-                device = "cpu"
+            device = "cuda" if cuda_ok else "cpu"
+        elif "cuda" in str(device) and not cuda_ok:
+            device = "cpu"
         self._device = device
         self._model = YOLO(model_path)
 

hvideotool/core/restore/deepmosaics.py

@@ -118,6 +118,14 @@ class DeepMosaicsRestorer(Restorer):
             return
         if str(_VENDOR) not in sys.path:
             sys.path.insert(0, str(_VENDOR))  # so vendored `from models/util import …` resolve
+
+        # Fall back to CPU when CUDA isn't available: DeepMosaics calls `.cuda()`
+        # whenever gpu_id != "-1", which raises "Torch not compiled with CUDA enabled"
+        # on a CPU-only torch build.
+        import torch  # noqa: E402
+        if self._gpu != "-1" and not torch.cuda.is_available():
+            self._gpu = "-1"
+
         from models import loadmodel, runmodel  # type: ignore  # noqa: E402
         import util.image_processing as impro    # type: ignore  # noqa: E402
 

hvideotool/core/torch_info.py

@@ -0,0 +1,80 @@
+"""Probe the PyTorch / CUDA situation, so the UI can show a device badge.
+
+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.
+"""
+
+from __future__ import annotations
+
+# pip index for the CUDA build (matches the README).
+CUDA_WHEEL_INDEX = "https://download.pytorch.org/whl/cu121"
+
+
+def gather() -> dict:
+    """Collect torch/CUDA facts. Never raises — missing torch is a valid result."""
+    info: dict = {
+        "installed": False,
+        "version": None,        # torch.__version__ (e.g. "2.12.0+cpu")
+        "built_cuda": None,     # torch.version.cuda (None on a CPU-only build)
+        "cuda_available": False,
+        "device_name": None,    # the active GPU's name, if any
+        "import_error": None,
+    }
+    try:
+        import torch
+    except Exception as exc:  # noqa: BLE001 - report any import failure, not just ImportError
+        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"]:
+        try:
+            info["device_name"] = torch.cuda.get_device_name(0)
+        except Exception:  # noqa: BLE001
+            info["device_name"] = None
+    return info
+
+
+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 install_hint() -> str:
+    """Steps to enable the GPU (shown when running on CPU)."""
+    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."
+    )