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.

2026-06-07 05:24:06 +03:00
parent e27dfdf518
commit cc518cc3e6
7 changed files with 497 additions and 129 deletions
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+"""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."
+    )