add video analysis operator suite

runtime/ops/mapper/video_analysis_operator/__init__.py

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+# -*- coding: utf-8 -*-
+from datamate.core.base_op import OPERATORS
+
+# 注册该算子，确保 path 指向当前文件夹名 video_analysis_operator
+OPERATORS.register_module(
+    module_name='VideoAnalysisOperator',
+    module_path="ops.user.video_analysis_operator.process"
+)

runtime/ops/mapper/video_analysis_operator/metadata.yml

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+name: '视频处理套件'
+name_en: 'video_analysis_operator'
+description: '集成安全检查、视频切除、视频标注、摘要生成及视频分类的视频工程处理套件'
+description_en: 'An integrated suite for safety check, video cutting, annotation, summary, and classification for medical video engineering.'
+language: 'python'
+vendor: 'huawei'
+raw_id: 'VideoAnalysisOperator'
+version: '1.0.0'
+modal: 'video'
+types:
+  - 'cleaning'
+  - 'annotation'
+inputs: 'video'
+outputs: 'video'
+
+runtime:
+  cpu: '4.0'
+  memory: '32212254720' 
+  npu: '1.0'          
+  storage: '30GB'
+
+settings:
+  - name: '任务选择'
+    raw_id: 'taskType'
+    type: 'select'
+    defaultVal: 'safety_check'
+    options:
+      - label: '安全检查'
+        value: 'safety_check'
+      - label: '视频切除'
+        value: 'video_cut'
+      - label: '视频标注'
+        value: 'video_annot'
+      - label: '摘要生成'
+        value: 'summary_gen'
+      - label: '视频分类'
+        value: 'video_classify'
+
+  - name: 'Qwen权重绝对路径'
+    description: '昇腾910B服务器上模型存放的物理路径'
+    raw_id: 'modelPath'
+    type: 'input'
+    defaultVal: '/mnt/nvme0n1/home/gtx/Video_Analysis_System/models/qwen/Qwen/Qwen2.5-VL-7B-Instruct'

runtime/ops/mapper/video_analysis_operator/process.py

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+import os
+import sys
+import subprocess
+import re
+import json
+import cv2
+import numpy as np
+import mindspore
+from typing import Dict, Any
+from datamate.core.base_op import Mapper
+
+# 全局变量实现单例模式，防止显存 OOM
+GLOBAL_MODEL = None
+GLOBAL_PROCESSOR = None
+
+class VideoAnalysisOperator(Mapper):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # 1. 环境路径修复 (来自 Cell 1 & 2)
+        self._setup_env()
+
+        # 2. 从 UI 配置获取参数
+        self.task_type = kwargs.get("taskType", "safety_check")
+        self.model_path = kwargs.get("modelPath", "/mnt/nvme0n1/home/gtx/Video_Analysis_System/models/qwen/Qwen/Qwen2.5-VL-7B-Instruct")
+
+        # 3. 初始化加载模型
+        self._init_resources()
+
+        # 4. 任务 Prompt 库 (来自 Cell 3 & 4)
+        self.prompts = {
+            "audit": "分析视频中是否包含违规内容(色情/政治/国家领导人/战争/血腥)。仅返回区间数组如[[s,e]]，若无则返[]。禁言。",
+            "summary": "请详细描述这个视频里发生的主要内容，以‘这个视频’开头，30字以内",
+            "classify": "请分析视频内容，将其归类为以下类别中的【唯一一个】：\n日常生活, 影视剧集, 音乐舞蹈, 游戏电竞, 动漫, 新闻, 教育, 科技, 财经, 体育, 美食, 时尚, 汽车, 萌宠, 健康, 风光, 三农, 监控, 广告, 其他\n只输出类别名称。",
+            "extreme": "你现在是时序审计员。任务：将视频划分为5个以上的连续时间段，描述动作变化。格式必须为：[开始, 结束] 关键词。"
+        }
+
+    def _setup_env(self):
+        """路径穿透与修复"""
+        STD_PATH = "/mnt/nvme0n1/home/gtx/miniconda3/envs/video_ai/lib/python3.9/site-packages"
+        CANN_PATH = "/mnt/nvme0n1/home/gtx/my_env/cann/ascend-toolkit/8.3.RC2/python/site-packages"
+        for p in [STD_PATH, CANN_PATH]:
+            if os.path.exists(p) and p not in sys.path:
+                sys.path.insert(0, p)
+
+    def _init_resources(self):
+        """单例加载 Qwen2.5-VL"""
+        global GLOBAL_MODEL, GLOBAL_PROCESSOR
+        if GLOBAL_MODEL is None:
+            from mindone.transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+            from mindspore import context
+            context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend", device_id=0)
+
+            GLOBAL_MODEL = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+                self.model_path, mindspore_dtype=mindspore.bfloat16, 
+                trust_remote_code=False, local_files_only=True
+            )
+            GLOBAL_PROCESSOR = AutoProcessor.from_pretrained(self.model_path, trust_remote_code=False, local_files_only=True)
+        self.model = GLOBAL_MODEL
+        self.processor = GLOBAL_PROCESSOR
+
+    # --- 通用工具集 ---
+    def _get_duration(self, path):
+        cmd = ["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", path]
+        return float(subprocess.run(cmd, stdout=subprocess.PIPE).stdout)
+
+    def _read_frames(self, path, num_frames=8, start=None, end=None, res_limit=None):
+        cap = cv2.VideoCapture(path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        total_v_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        s_idx = int(start * fps) if start is not None else 0
+        e_idx = int(end * fps) if end is not None else total_v_frames
+        indices = np.linspace(s_idx, e_idx - 1, num_frames, dtype=int)
+        frames = []
+        for idx in indices:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+            ret, frame = cap.read()
+            if ret:
+                if res_limit:
+                    w, h = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+                    scale = np.sqrt(res_limit / (w * h))
+                    frame = cv2.resize(frame, (int(w*scale), int(h*scale)), interpolation=cv2.INTER_AREA)
+                frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        cap.release()
+        return frames
+
+    def _infer(self, frames, prompt, max_tokens=100):
+        messages = [{"role": "user", "content": [{"type": "video", "video": frames}, {"type": "text", "text": prompt}]}]
+        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text], videos=[frames], padding=True, return_tensors="ms", max_pixels=448*448)
+        for k, v in inputs.items():
+            if isinstance(v, mindspore.Tensor) and v.dtype == mindspore.float32:
+                inputs[k] = v.astype(mindspore.bfloat16)
+        gen_ids = self.model.generate(**inputs, max_new_tokens=max_tokens)
+        return self.processor.batch_decode(gen_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0].strip()
+
+    # --- 核心业务逻辑分流 ---
+    def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        video_path = sample.get('filePath')
+        if not video_path or not os.path.exists(video_path): return sample
+
+        if self.task_type == "safety_check":
+            sample['audit_result'] = self._run_safety_audit(video_path)
+        elif self.task_type == "video_cut":
+            violations = self._run_safety_audit(video_path, return_raw=True)
+            if violations:
+                sample['filePath'] = self._run_physical_cut(video_path, violations)
+        elif self.task_type == "video_annot":
+            sample['annotation'] = self._run_extreme_annot(video_path)
+        elif self.task_type == "summary_gen":
+            frames = self._read_frames(video_path, num_frames=16)
+            sample['summary'] = self._infer(frames, self.prompts["summary"])
+        elif self.task_type == "video_classify":
+            frames = self._read_frames(video_path, num_frames=8)
+            sample['category'] = self._infer(frames, self.prompts["classify"])
+
+        return sample
+
+    def _run_safety_audit(self, path, return_raw=False):
+        """高精审计 (Cell 3)"""
+        duration = self._get_duration(path)
+        violations, curr = [], 0.0
+        while curr < duration:
+            end = min(curr + 10, duration)
+            frames = self._read_frames(path, num_frames=12, start=curr, end=end)
+            res = self._infer(frames, self.prompts["audit"], max_tokens=40)
+            found = re.findall(r'\[\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', res)
+            for s, e in found: violations.append([curr + float(s), curr + float(e)])
+            curr += 8
+        if return_raw: return violations
+        return f"发现{len(violations)}处违规" if violations else "安全"
+
+    def _run_physical_cut(self, path, violations):
+        """FFMPEG 物理切除 (Cell 3)"""
+        out_path = path.replace(".mp4", "_cleaned.mp4")
+        # 构建 FFMPEG 保留区间逻辑... (此处填入你 Cell 3 的拼接代码)
+        return out_path
+
+    def _run_extreme_annot(self, path):
+        """极速标注 (Cell 4)"""
+        frames = self._read_frames(path, num_frames=12, res_limit=128*128)
+        return self._infer(frames, self.prompts["extreme"], max_tokens=256)

runtime/ops/mapper/video_analysis_operator/requirements.txt

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+mindspore==2.4.1
+mindone==0.5.0
+transformers>=4.46.1
+tokenizers>=0.20.0
+jinja2
+safetensors
+sentencepiece
+opencv-python-headless>=4.9.0.80
+numpy<2.0,>=1.24.0
+Pillow
+decord
+attrs
+decorator
+psutil
+cloudpickle
+ml-dtypes
+absl-py