{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 21, "status": "ok", "timestamp": 1787619221155, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "yO5a2QDwuwx3", "outputId": "a8282fc7-1c7d-4bec-e2fd-4603b5156cc8" }, "outputs": [], "source": [ "# #获取需要的库 \n", "# %%writefile /content/requirements.txt\n", "# # transformers==5.7.0\n", "# accelerate\n", "# safetensors\n", "# pillow\n", "# numpy<2\n", "# torchvision\n", "# opencv-python-headless<4.12\n", "# codec-video-prep\n", "from pathlib import Path\n", "\n", "\n", "Path(\"/content/requirements.txt\").write_text(\n", "\"\"\"transformers==5.7.0\n", "accelerate\n", "safetensors\n", "pillow\n", "numpy<2\n", "torchvision\n", "opencv-python-headless<4.12\n", "codec-video-prep\n", "\"\"\",\n", "encoding=\"utf-8\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python --version" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1820, "status": "ok", "timestamp": 1787619235573, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "_vF76oN3uk_e", "outputId": "7079eabd-43d2-403f-900e-7172736febe0" }, "outputs": [], "source": [ "# 1. 自动克隆官方 Demo 仓库（包含纯 PyTorch 版本的 mamba_ssm 文件夹）\n", "!git clone https://huggingface.co/spaces/microsoft/mage-vl-demo /content/mage-vl-demo\n", "\n", "# 2. 将包含 mamba_ssm 的目录加入 Python 模块搜索路径，确保 import mamba_ssm 永不报错\n", "import sys\n", "\n", "if \"/content/mage-vl-demo\" not in sys.path:\n", " sys.path.append(\"/content/mage-vl-demo\")\n", "\n", "print(\"mamba_ssm 路径加载成功！\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 233, "status": "ok", "timestamp": 1787619239776, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "LSKj4-71SO8H" }, "outputs": [], "source": [ "#移动目录\n", "!mv /content/mage-vl-demo/mamba_ssm /content/\n", "!mv /content/mage-vl-demo/examples/soccer-broadcast.mp4 /content/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 581, "status": "ok", "timestamp": 1787619244608, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "IePDcWu59e1G", "outputId": "61107950-f0fa-4be8-a14f-e2bd8447046d" }, "outputs": [], "source": [ "# !wget http://172.245.243.238:8000/test.mp4\n", "!wget http://172.245.243.238:8000/test02.mp4\n", "!wget http://172.245.243.238:8000/test03.mp4\n", "!wget http://172.245.243.238:8000/test04.mp4\n", "!wget http://172.245.243.238:8000/test05.mp4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 17882, "status": "ok", "timestamp": 1787619266366, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "BBNwlkbfiu89", "outputId": "cf3a98a7-400f-40fe-e130-b7223e411286" }, "outputs": [], "source": [ "!apt-get update && apt-get install -y ffmpeg" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "executionInfo": { "elapsed": 379491, "status": "ok", "timestamp": 1787619653638, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "I3NLQ1Rvq5Ii", "outputId": "084d334b-66ef-4ee7-979c-5d3df5351c75" }, "outputs": [], "source": [ "!pip install -r /content/requirements.txt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import login\n", "\n", "login(token=\"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\") " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#加载模型\n", "# ============================================================\n", "# 代码块 1：模型加载（只运行一次）\n", "# ============================================================\n", "import os, gc, time, subprocess, importlib, warnings, json, sys\n", "from pathlib import Path\n", "\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\", \"expandable_segments:True,max_split_size_mb:128\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "\n", "print(\"=\" * 60)\n", "print(\"正在加载模型（只加载一次）...\")\n", "print(\"=\" * 60)\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA GPU\")\n", "\n", "DEVICE = \"cuda\"\n", "GPU_NAME = torch.cuda.get_device_name(0)\n", "CUDA_VERSION = torch.version.cuda\n", "try:\n", " BF16_SUPPORTED = torch.cuda.is_bf16_supported()\n", "except Exception:\n", " BF16_SUPPORTED = False\n", "MODEL_DTYPE = torch.bfloat16 if BF16_SUPPORTED else torch.float16\n", "\n", "print(f\"[GPU] {GPU_NAME}\")\n", "print(f\"[CUDA] {CUDA_VERSION}\")\n", "print(f\"[BF16] {BF16_SUPPORTED}\")\n", "print(f\"[MODEL DTYPE] {MODEL_DTYPE}\")\n", "\n", "print(\"\\n[init] 加载 Processor...\")\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "print(\"[init] Processor 加载完成\")\n", "\n", "print(\"[init] 加载 Mage-VL 模型...\")\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", " low_cpu_mem_usage=True\n", ").to(DEVICE).eval()\n", "print(\"[init] Mage-VL 加载完成\")\n", "\n", "# Codec\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "codec_mod = importlib.import_module(_REMOTE_PKG + \".codec_video_processing_mage_vl\")\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "def get_codec_config(max_pixels=48000, target_canvas=20):\n", " override = {\"engine\": \"hevc\", \"target_canvas\": target_canvas, \"patch\": 16}\n", " kwargs = dict(processor._codec_config_defaults)\n", " dcvc = dict(kwargs.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", " kwargs[\"max_pixels\"] = max_pixels\n", " return CodecConfig(**kwargs), override\n", "\n", "# Gate\n", "print(\"[gate] 加载 Cognition Gate...\")\n", "try:\n", " gate = model.model._load_streammind_gate()\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "except Exception as e:\n", " gate = None\n", " print(\"[gate] 加载失败:\", type(e).__name__, e)\n", "\n", "if gate is not None and not BF16_SUPPORTED:\n", " try:\n", " gate = gate.float()\n", " model.model.streammind_gate = gate\n", " print(\"[gate] FP32 fallback 已启用\")\n", " except Exception as e:\n", " print(\"[gate] FP32 fallback 失败:\", e)\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"✅ 模型加载完成！请运行下面的【代码块 2】进行测试\")\n", "print(\"=\" * 60)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ============================================================\n# 代码块 2：工具函数（已优化）\n# ============================================================\nimport os, gc, time, subprocess, importlib, warnings, json, sys\nfrom pathlib import Path\n\ndef duration(path):\n try:\n r = subprocess.run(\n [\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration\",\n \"-of\", \"default=nw=1:nk=1\", str(path)],\n capture_output=True, text=True, check=True, timeout=60)\n return float(r.stdout.strip())\n except Exception:\n return 0.0\n\ndef make_prompt(prompt_template, previous_count, previous_colors, previous_occupancy):\n text = prompt_template.format(\n previous_count=previous_count,\n previous_colors=\", \".join(previous_colors) if previous_colors else \"暂无\",\n previous_occupancy=previous_occupancy)\n messages = [{\"role\": \"user\", \"content\": [\n {\"type\": \"video\"},\n {\"type\": \"text\", \"text\": text}\n ]}]\n return processor.apply_chat_template(\n messages, tokenize=False, add_generation_prompt=True)\n\ndef extract_clip(src, start, length, out):\n tmp = out.with_name(\".tmp_\" + out.name)\n if tmp.exists():\n try: tmp.unlink()\n except: pass\n subprocess.run([\n \"ffmpeg\", \"-y\", \"-loglevel\", \"error\",\n \"-ss\", f\"{start:.3f}\", \"-i\", str(src),\n \"-t\", f\"{length:.3f}\",\n \"-c:v\", \"libx264\", \"-preset\", \"ultrafast\",\n \"-crf\", \"26\", \"-an\", \"-pix_fmt\", \"yuv420p\", str(tmp)\n ], check=True, capture_output=True, timeout=60)\n tmp.replace(out)\n return out\n\ndef to_cuda(inputs):\n result = {}\n for k, v in inputs.items():\n if not hasattr(v, \"to\"): continue\n v = v.to(DEVICE)\n if k == \"pixel_values\":\n v = v.to(MODEL_DTYPE)\n result[k] = v\n return result\n\ndef cleanup(*objs):\n for x in objs:\n try: del x\n except: pass\n gc.collect()\n try:\n torch.cuda.empty_cache()\n torch.cuda.ipc_collect()\n torch.cuda.synchronize()\n except: pass\n\ndef gpu_mem():\n a = torch.cuda.memory_allocated() / 1024**3\n r = torch.cuda.memory_reserved() / 1024**3\n p = torch.cuda.max_memory_allocated() / 1024**3\n return f\"GPU={a:.2f}GB / reserved={r:.2f}GB / peak={p:.2f}GB\"\n\n@torch.inference_mode()\ndef run_gate(inputs):\n if gate is None:\n return 1.0\n feat = {}\n logits = None\n try:\n for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\"):\n if k not in inputs: continue\n v = inputs[k].to(DEVICE)\n if k == \"pixel_values\":\n v = v.to(MODEL_DTYPE)\n feat[k] = v\n\n if BF16_SUPPORTED:\n logits = model.model.streammind_gate_forward_segments([feat])\n else:\n visual_tokens = model.model._streammind_vision_tokens(\n feat[\"pixel_values\"],\n feat[\"image_grid_thw\"],\n patch_positions=feat.get(\"patch_positions\"))\n if not torch.isfinite(visual_tokens).all():\n return 0.0\n logits = gate(torch.cat([visual_tokens.float()], dim=1))\n\n if not isinstance(logits, torch.Tensor) or logits.ndim != 3:\n return 0.0\n\n gate_logits = logits[0]\n length = int(inputs[\"image_grid_thw\"][:, 0].sum().item())\n if length <= 0: return 0.0\n\n pos = min(length - 1, gate_logits.shape[0] - 1)\n last_logits = gate_logits[pos].float()\n if not torch.isfinite(last_logits).all():\n return 0.0\n\n prob = torch.exp(torch.log_softmax(last_logits, dim=-1)[1])\n return float(prob.item()) if torch.isfinite(prob) else 0.0\n except Exception as e:\n print(\"[gate] ERROR:\", type(e).__name__, e)\n return 0.0\n finally:\n cleanup(feat, logits)\n\n@torch.inference_mode()\ndef analyze_clip(clip, prompt, codec_override, max_pixels):\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n inputs = processor(\n text=[prompt],\n videos=[str(clip)],\n video_backend=\"codec\",\n max_pixels=max_pixels,\n codec_config=codec_override,\n return_tensors=\"pt\",\n padding=False)\n return inputs, run_gate(inputs)\n\n@torch.inference_mode()\ndef generate(inputs, max_new_tokens=128):\n out = model.generate(\n **inputs,\n max_new_tokens=max_new_tokens,\n do_sample=False,\n use_cache=True)\n n = inputs[\"input_ids\"].shape[1]\n text = processor.tokenizer.decode(out[0, n:], skip_special_tokens=True).strip()\n del out\n return text\n\ndef parse_json(text):\n if not text: return None\n text = text.strip().replace(\"```json\", \"\").replace(\"```\", \"\").strip()\n start = text.find(\"{\")\n end = text.rfind(\"}\")\n if start < 0 or end < 0: return None\n try:\n data = json.loads(text[start:end+1])\n return data if isinstance(data, dict) else None\n except:\n return None\n\n# ==================== 颜色归一化（大幅放宽） ====================\nVALID_COLORS = {\n \"黑色\", \"白色\", \"灰色\", \"蓝色\", \"深蓝色\", \"浅蓝色\",\n \"红色\", \"粉色\", \"黄色\", \"绿色\", \"棕色\", \"米色\", \"紫色\", \"橙色\",\n \"黑白\", \"灰白\", \"深灰\", \"浅灰\", \"咖色\", \"酒红\", \"深蓝\", \"浅蓝\"\n}\n\nCOLOR_MAP = {\n # 灰色系\n \"灰白\": \"灰色\", \"灰白色\": \"灰色\", \"浅灰\": \"灰色\", \"深灰\": \"灰色\",\n \"灰色花纹\": \"灰色\", \"灰花\": \"灰色\", \"花纹灰\": \"灰色\", \"灰白花纹\": \"灰色\",\n \"浅灰色\": \"灰色\", \"深灰色\": \"灰色\",\n # 黑白格纹\n \"黑白格纹\": \"黑白\", \"黑白格\": \"黑白\", \"格子\": \"黑白\", \"格纹\": \"黑白\",\n \"黑白花纹\": \"黑白\", \"黑白条纹\": \"黑白\",\n # 棕色系\n \"棕色\": \"棕色\", \"深棕色\": \"棕色\", \"咖啡色\": \"棕色\", \"咖色\": \"棕色\",\n \"褐色\": \"棕色\", \"深咖\": \"棕色\", \"酒红\": \"棕色\",\n # 其他\n \"白色印花\": \"白色\", \"白T\": \"白色\", \"白色T恤\": \"白色\",\n}\n\ndef normalize_colors(colors):\n if not isinstance(colors, list):\n return []\n result = []\n for c in colors:\n if not isinstance(c, str):\n continue\n c = c.strip()\n # 先精确匹配\n if c in VALID_COLORS and c not in result:\n result.append(c)\n continue\n # 模糊映射\n mapped = COLOR_MAP.get(c)\n if mapped and mapped not in result:\n result.append(mapped)\n continue\n # 包含关键词的粗匹配\n for key, val in COLOR_MAP.items():\n if key in c and val not in result:\n result.append(val)\n break\n return result\n\ndef normalize_state(data, previous_count, previous_colors, previous_occupancy, max_change=8):\n if not isinstance(data, dict):\n return {\n \"delta\": 0,\n \"current_count\": previous_count,\n \"colors\": previous_colors,\n \"occupancy\": previous_occupancy,\n \"confidence\": 0.0\n }\n\n try:\n delta = int(round(float(data.get(\"delta\", 0))))\n except:\n delta = 0\n delta = max(-max_change, min(max_change, delta))\n\n expected_count = max(0, previous_count + delta)\n current_count = expected_count\n\n # 模型输出的 current_count 仅作校验，不参与累计（消除跨窗口漂移）\n model_count = data.get(\"current_count\")\n if model_count is not None:\n try:\n if int(round(float(model_count))) != current_count:\n print(f\"[warn] 模型current_count={model_count} 与程序值 {current_count} 不一致，采用程序值\")\n except (TypeError, ValueError):\n pass\n\n colors = normalize_colors(data.get(\"colors\", previous_colors))\n if not colors:\n colors = previous_colors\n\n try:\n occupancy = int(round(float(data.get(\"occupancy\", previous_occupancy))))\n except:\n occupancy = previous_occupancy\n occupancy = max(0, min(100, occupancy))\n\n try:\n confidence = float(data.get(\"confidence\", 0.0))\n except:\n confidence = 0.0\n confidence = max(0.0, min(1.0, confidence))\n\n print(f\"[debug] 原始 delta={data.get('delta')} | 原始 current={data.get('current_count')} → 最终 delta={delta}, count={current_count}\")\n\n return {\n \"delta\": delta,\n \"current_count\": current_count,\n \"colors\": colors,\n \"occupancy\": occupancy,\n \"confidence\": confidence\n }\n\ndef state_to_text(state):\n d = state[\"delta\"]\n if d > 0: change = f\"放入 {d} 件\"\n elif d < 0: change = f\"取出 {abs(d)} 件\"\n else: change = \"数量无变化\"\n colors = \"、\".join(state[\"colors\"]) if state[\"colors\"] else \"未知\"\n return f\"{change} | 当前 {state['current_count']} 件 | 颜色={colors} | 占用={state['occupancy']}% | 置信度={state['confidence']:.2f}\"\n\n@torch.inference_mode()\ndef final_summary(history, final_state, final_max_tokens=128):\n if not history:\n return \"没有获得有效的衣物分析结果。\"\n\n prompt = FINAL_PROMPT.format(history=\"\\n\".join(history))\n prompt += f\"\"\"\n程序计算出的最终状态：\n数量：{final_state[\"current_count\"]} 件\n颜色：{\", \".join(final_state[\"colors\"]) if final_state[\"colors\"] else \"未知\"}\n空间占用：{final_state[\"occupancy\"]}%\n请以这个程序最终状态为主要依据。\n\"\"\"\n inputs = processor.tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=4096)\n inputs = {k: v.to(DEVICE) for k, v in inputs.items()}\n out = None\n try:\n out = model.generate(**inputs, max_new_tokens=final_max_tokens, do_sample=False, use_cache=True)\n n = inputs[\"input_ids\"].shape[1]\n return processor.tokenizer.decode(out[0, n:], skip_special_tokens=True).strip()\n finally:\n cleanup(inputs)\n try: del out\n except: pass\n\n# ------------------------------\n# 核心分析函数（已优化短片段保护 + 分辨率）\n# ------------------------------\ndef run_analysis(\n video_path=None,\n prompt_template=None,\n segment_sec=None,\n max_segments=None,\n gate_threshold=None,\n max_new_tokens=None,\n simulate_realtime=None,\n show_raw=None,\n max_change_per_segment=None,\n always_run_llm=None\n):\n video_path = video_path or DEFAULT_VIDEO\n prompt_template = prompt_template or PROMPT_TEMPLATE\n segment_sec = segment_sec if segment_sec is not None else DEFAULT_SEGMENT_SEC\n max_segments = max_segments if max_segments is not None else DEFAULT_MAX_SEGMENTS\n gate_threshold = gate_threshold if gate_threshold is not None else DEFAULT_GATE_THRESHOLD\n max_new_tokens = max_new_tokens if max_new_tokens is not None else DEFAULT_MAX_NEW_TOKENS\n simulate_realtime = simulate_realtime if simulate_realtime is not None else DEFAULT_SIMULATE_REALTIME\n show_raw = show_raw if show_raw is not None else DEFAULT_SHOW_RAW\n max_change = max_change_per_segment if max_change_per_segment is not None else DEFAULT_MAX_CHANGE_PER_SEGMENT\n always_run_llm = always_run_llm if always_run_llm is not None else DEFAULT_ALWAYS_RUN_LLM\n\n path = Path(video_path)\n if not path.exists():\n raise FileNotFoundError(f\"视频不存在: {video_path}\")\n\n total = duration(path)\n if total <= 0:\n raise RuntimeError(\"无法获取视频时长\")\n\n clip_dir = Path(os.environ[\"STREAMMIND_CLIP_CACHE\"])\n clip_dir.mkdir(parents=True, exist_ok=True)\n\n previous_count = 0\n previous_colors = []\n previous_occupancy = 0\n history = []\n results = []\n _, codec_override = get_codec_config(DEFAULT_CODEC_MAX_PIXELS, DEFAULT_CODEC_TARGET_CANVAS)\n\n print(f\"\\n开始分析 | 视频={path.name} | 时长={total:.1f}s | 窗口={segment_sec}s\")\n print(\"-\" * 60)\n\n # ============ 窗口规划 + 批量预提取（clip 保留磁盘，codec 缓存重跑可命中） ============\n windows = []\n cursor = 0.0\n while cursor < total - 0.05:\n if max_segments is not None and len(windows) >= max_segments:\n break\n end = min(total, cursor + segment_sec)\n length = end - cursor\n if length < 0.35:\n print(f\"**{cursor:.1f}–{end:.1f}s** → ⚠️ 片段过短 ({length:.2f}s)，跳过\")\n cursor = end\n continue\n windows.append((cursor, end, length))\n cursor = end\n\n clip_paths = []\n for w_index, (w_start, w_end, w_len) in enumerate(windows, 1):\n clip = clip_dir / f\"{path.stem}_{w_index:06d}_{w_start:.2f}_{w_end:.2f}.mp4\"\n try:\n extract_clip(path, w_start, w_len, clip)\n clip_paths.append(clip)\n except Exception as e:\n print(f\"**{w_start:.1f}–{w_end:.1f}s** → ❌ 片段提取失败 {type(e).__name__}: {e}\")\n clip_paths.append(None)\n n_ok = sum(c is not None for c in clip_paths)\n print(f\"[pre] 片段预提取 {n_ok}/{len(windows)} 成功（clip 保留磁盘，重跑时 codec 缓存复用）\")\n # ===================================================================\n\n index = 0\n for (start, end, length), clip in zip(windows, clip_paths):\n index += 1\n t0 = time.time()\n inputs = None\n cuda_inputs = None\n said = None\n\n if clip is None:\n line = f\"**{start:.1f}–{end:.1f}s** → ❌ 片段提取失败，跳过本窗口\"\n results.append(line)\n print(line)\n continue\n\n try:\n prompt = make_prompt(prompt_template, previous_count, previous_colors, previous_occupancy)\n inputs, prob = analyze_clip(clip, prompt, codec_override, DEFAULT_CODEC_MAX_PIXELS)\n\n head = f\"**{start:.1f}–{end:.1f}s** Gate=`{prob:.2f}`\"\n\n if not always_run_llm and prob < gate_threshold:\n line = f\"{head} → 🔇 无明显变化\"\n results.append(line)\n print(line)\n continue\n\n print(f\"{head} → 🧠 识别中...\")\n gt = time.time()\n\n cuda_inputs = to_cuda(inputs)\n said = generate(cuda_inputs, max_new_tokens=max_new_tokens)\n gen_time = time.time() - gt\n\n if show_raw:\n print(f\"[raw LLM] {said}\")\n\n raw_data = parse_json(said)\n if raw_data is None:\n truncated = said.strip().endswith(\"}\")\n print(f\"[warn] 窗口 {start:.1f}-{end:.1f}s JSON 解析失败（截断={truncated}），加大 token 重试一次\")\n said = generate(cuda_inputs, max_new_tokens=max_new_tokens * 2)\n if show_raw:\n print(f\"[raw LLM retry] {said}\")\n raw_data = parse_json(said)\n if raw_data is None:\n print(f\"[warn] 重试仍失败，本窗口按无变化处理（可能漏事件）\")\n state = normalize_state(raw_data, previous_count, previous_colors, previous_occupancy, max_change)\n\n previous_count = state[\"current_count\"]\n previous_colors = state[\"colors\"]\n previous_occupancy = state[\"occupancy\"]\n\n change = state[\"delta\"]\n if change > 0:\n change_text = f\"🟢 放入 {change} 件\"\n elif change < 0:\n change_text = f\"🔴 取出 {abs(change)} 件\"\n else:\n change_text = \"⚪ 数量无变化\"\n\n colors_text = \"、\".join(state[\"colors\"]) if state[\"colors\"] else \"未知\"\n elapsed = time.time() - t0\n speed = length / max(elapsed, 0.001)\n\n line = (\n f\"{head}\\n\"\n f\" {change_text} | 当前数量={state['current_count']}件\\n\"\n f\" 颜色={colors_text} | 占用={state['occupancy']}%\\n\"\n f\" 置信度={state['confidence']:.2f} | \"\n f\"Generate={gen_time:.2f}s | Total={elapsed:.2f}s | {speed:.2f}×\"\n )\n results.append(line)\n history.append(f\"{start:.1f}-{end:.1f}s：{state_to_text(state)}\")\n print(line)\n\n if simulate_realtime and elapsed < length:\n time.sleep(length - elapsed)\n\n except Exception as e:\n line = f\"**{start:.1f}–{end:.1f}s** → ❌ {type(e).__name__}: {e}\"\n results.append(line)\n print(line)\n\n finally:\n # L4 24GB 无 OOM 压力：仅释放张量引用，省去 empty_cache/synchronize 开销\n if isinstance(cuda_inputs, dict):\n cuda_inputs.clear()\n if isinstance(inputs, dict):\n inputs.clear()\n print(f\"[segment {index}] {end:.1f}/{total:.1f}s | {gpu_mem()}\", flush=True)\n\n # 结束统一释放（替代原逐窗口 empty_cache/synchronize）\n cleanup()\n\n # 最终结果\n final_state = {\n \"current_count\": previous_count,\n \"colors\": previous_colors,\n \"occupancy\": previous_occupancy\n }\n\n colors_text = \"、\".join(previous_colors) if previous_colors else \"未知\"\n if previous_count == 0:\n occupancy_state = \"空\"\n elif previous_occupancy <= 20:\n occupancy_state = \"少量\"\n elif previous_occupancy <= 50:\n occupancy_state = \"中等\"\n elif previous_occupancy <= 80:\n occupancy_state = \"较多\"\n else:\n occupancy_state = \"接近装满\"\n\n print(\"\\n\" + \"=\" * 60)\n print(\"🧺 最终衣物状态\")\n print(\"=\" * 60)\n print(f\"【最终数量】 {previous_count} 件\")\n print(f\"【最终颜色】 {colors_text}\")\n print(f\"【空间占用】 {previous_occupancy}%\")\n print(f\"【状态】 {occupancy_state}\")\n print(\"=\" * 60)\n\n try:\n final = final_summary(history, final_state)\n print(\"\\n🧠 最终模型校验：\")\n print(final)\n except Exception as e:\n print(f\"\\n❌ 最终模型校验失败: {type(e).__name__}: {e}\")\n\n print(f\"\\n✅ 完成，共分析 {index} 个片段\")\n return {\n \"final_count\": previous_count,\n \"final_colors\": previous_colors,\n \"final_occupancy\": previous_occupancy,\n \"history\": history,\n \"results\": results\n }\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ============================================================\n# 代码块 3：默认参数 + 优化Prompt + 调用\n# ============================================================\n\n# ------------------------------\n# 1. 默认参数（已针对准确率优化）\n# ------------------------------\nDEFAULT_VIDEO = \"/content/test02.mp4\" # 如果你本地路径不同请改这里\nDEFAULT_SEGMENT_SEC = 2.0\nDEFAULT_MAX_SEGMENTS = None\nDEFAULT_GATE_THRESHOLD = 0.40 # 稍微再降一点\nDEFAULT_MAX_NEW_TOKENS = 160 # 稍微增加，让模型多说一点\nDEFAULT_FINAL_MAX_NEW_TOKENS = 128\nDEFAULT_CODEC_MAX_PIXELS = 180000 # A/B 基线：稳定配置\nDEFAULT_CODEC_TARGET_CANVAS = 28\n# L4 24GB 高分辨率 A/B（每窗口慢 ~2-4s，小件/花纹细节更好），启用时改为下面两行：\n# DEFAULT_CODEC_MAX_PIXELS = 320000\n# DEFAULT_CODEC_TARGET_CANVAS = 32\nDEFAULT_SIMULATE_REALTIME = False\nDEFAULT_MAX_CHANGE_PER_SEGMENT = 10\nDEFAULT_ALWAYS_RUN_LLM = True # 强制每段都跑LLM\nDEFAULT_SHOW_RAW = True\n\n# ------------------------------\n# 2. 优化后的 Prompt（针对多件、花纹、取出加强）\n# ------------------------------\nPROMPT_TEMPLATE = \"\"\"\n你是一个专业的洗衣机滚筒衣物数量实时检测器，必须做到高精度计数。\n\n【核心任务】\n仔细观察当前视频片段，准确判断：\n1. 有没有新的衣物被【放入】滚筒？放了几件？\n2. 有没有衣物被【取出】滚筒？取了几件？\n3. 当前滚筒内可见的主要颜色/花纹是什么？\n4. 衣物大约占滚筒可见空间的百分比？\n\n【重要规则】\n- 一件衣服即使折叠、皱巴巴、有花纹/格纹，也算1件。\n- 只有当衣物在本片段结束前【完全进入】滚筒，才计为放入；衣物连续多件放入时，delta按总数累计。\n- 只有当衣物在本片段结束前【完全离开】滚筒，才计为取出。\n- 动作可能发生在上一个窗口的结尾、本窗口的开头：只在动作完成的那个窗口计数一次，避免重复计数。\n- 手伸进去拿出来，或明显把衣服往外拉，记为取出（delta为负）。\n- 衣服只是在滚筒里翻动、移位、露出更多，不算数量变化。\n- 无法确认是否发生放入/取出时，delta=0。\n- 颜色尽量使用：黑色、白色、灰色、棕色、黑白、灰白 等简单词。\n\n【上一时刻状态】\n数量：{previous_count}\n颜色：{previous_colors}\n占用：{previous_occupancy}%\n\n【输出格式（只输出纯JSON，不要其他文字）】\n{{\n \"delta\": 整数（正数=放入，负数=取出，0=无变化）,\n \"colors\": [\"颜色1\", \"颜色2\"],\n \"occupancy\": 0到100的整数（建议10的倍数）,\n \"confidence\": 0到1的小数\n}}\n\"\"\".strip()\n\nFINAL_PROMPT = \"\"\"\n你是洗衣机最终衣物状态统计模块。\n\n下面是视频播放过程中每个时间窗口得到的状态：\n\n{history}\n\n请根据以上过程，总结最终结果：\n\n【最终数量】\nX 件\n\n【最终颜色】\n颜色列表\n\n【空间占用】\nXX%\n\n【状态】\n空 / 少量 / 中等 / 较多 / 接近装满\n\n只输出以上内容，不要额外分析。\n\"\"\".strip()\n\n# ------------------------------\n# 5. 调用\n# ------------------------------\nresult = run_analysis()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#===================================================" ] } ], "metadata": { "accelerator": "GPU", "colab": { "authorship_tag": "ABX9TyMGAIXytPaUASdQOuPeQeFX", "gpuType": "T4", "provenance": [ { 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