lixiaolong
b386f003a0
- 集成Qwen3.8-27B-FP8快速模型和Qwen3.8-27B精确模型作为SGLang服务 - 添加SGLang API配置选项(SGLANG_FAST_BASE_URL、SGLANG_ACCURATE_BASE_URL等) - 实现音频分类中的决策聚合算法(topK、nearestWeight、labelMaxDistance) - 添加视频采样帧限制(VIDEO_SAMPLING_FRAME_LIMIT)和上下文token限制 - 更新健康检查以监控SGLang服务状态 - 实现视频分析的双模式决策策略(快速+精确) - 添加音频参考文件导入工具(import_audio_references.py) - 扩展音频分类标签支持FIND_VEHICLE_HORN类别 - 优化视频分析的帧采样策略，始终包含视频尾部帧 - 添加决策策略参数(tuning、decisionPolicy)支持 - 更新配置类以支持新的SGLang和视频参数 - 修改compose配置以支持Qwen3.8模型部署 - 更新音频分类测试用例验证聚合逻辑 - 重构视频测试以支持SGLang API格式和决策策略
889 lines
34 KiB
Python
889 lines
34 KiB
Python
import base64
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import copy
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import json
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import math
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import re
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import subprocess
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import tempfile
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from pathlib import Path
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from typing import Any
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import httpx
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from app.config import settings
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from app.profile_store import Profile
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class VisionModelError(RuntimeError):
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pass
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VIDEO_RESULT_SCHEMA = {
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"type": "object",
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"properties": {
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"passed": {
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"type": "boolean",
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"description": (
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"Whether the user's acceptance criterion is satisfied. For negative "
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"criteria such as disappearance or shutdown, return true when that "
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"negative state is visibly achieved."
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),
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},
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"evidenceSufficient": {"type": "boolean"},
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"confidence": {"type": "number", "minimum": 0, "maximum": 1},
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"conclusion": {"type": "string"},
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"summary": {"type": "string"},
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"events": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"timeRange": {"type": "string"},
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"event": {"type": "string"},
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"confidence": {"type": "number"},
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},
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"required": ["timeRange", "event", "confidence"],
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},
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},
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"warnings": {"type": "array", "items": {"type": "string"}},
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},
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"required": [
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"passed",
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"evidenceSufficient",
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"confidence",
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"conclusion",
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"summary",
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"events",
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"warnings",
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],
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}
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VIDEO_TUNING_LIMITS = {
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"sampleFps": (0.25, 6.0),
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"maxWidth": (640, 1280),
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"confidenceThreshold": (0.5, 0.95),
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}
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def _duration(path: Path) -> float:
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process = subprocess.run(
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[
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"ffprobe",
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"-v",
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"error",
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"-show_entries",
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"format=duration",
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"-of",
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"default=noprint_wrappers=1:nokey=1",
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str(path),
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],
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capture_output=True,
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text=True,
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check=False,
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)
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try:
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value = float(process.stdout.strip())
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except ValueError as exc:
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raise ValueError("Unable to read video duration") from exc
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if value <= 0:
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raise ValueError("Video duration must be positive")
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return value
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def _frame_sampling_plan(
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duration: float, sample_fps: float, maximum_frames: int
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) -> tuple[int, int, float, float, bool]:
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requested_count = max(2, math.ceil(duration * sample_fps) + 1)
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count = min(requested_count, max(2, maximum_frames))
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prefix_count = count - 1
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capped = count < requested_count
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prefix_fps = prefix_count / duration if capped else sample_fps
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end_margin = min(0.1, max(0.001, duration / 2))
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tail_time = max(0.0, duration - end_margin)
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return count, prefix_count, prefix_fps, tail_time, capped
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def _extract_frames(
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path: Path, sample_fps: float, maximum_frames: int, maximum_width: int
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) -> tuple[list[Path], float, Path]:
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duration = _duration(path)
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count, prefix_count, prefix_fps, tail_time, _ = _frame_sampling_plan(
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duration, sample_fps, maximum_frames
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)
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directory = Path(tempfile.mkdtemp(prefix="frames-", dir=settings.jobs_dir))
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output = directory / "frame-%03d.jpg"
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prefix_process = subprocess.run(
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[
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"ffmpeg",
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"-y",
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"-v",
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"error",
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"-i",
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str(path),
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"-vf",
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f"fps={prefix_fps:.8f},scale='min({maximum_width},iw)':-2",
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"-frames:v",
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str(prefix_count),
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"-q:v",
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"3",
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str(output),
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],
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capture_output=True,
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check=False,
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)
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tail_output = directory / f"frame-{count:03d}.jpg"
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tail_process = subprocess.run(
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[
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"ffmpeg",
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"-y",
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"-v",
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"error",
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"-i",
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str(path),
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"-ss",
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f"{tail_time:.6f}",
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"-vf",
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f"scale='min({maximum_width},iw)':-2",
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"-frames:v",
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"1",
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"-q:v",
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"3",
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str(tail_output),
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],
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capture_output=True,
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check=False,
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)
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frames = sorted(directory.glob("frame-*.jpg"))
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if prefix_process.returncode != 0 or tail_process.returncode != 0 or len(frames) < 2:
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error = (
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prefix_process.stderr.decode("utf-8", errors="replace")
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+ tail_process.stderr.decode("utf-8", errors="replace")
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)[-500:]
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detail = error or (
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f"prefixExit={prefix_process.returncode}, tailExit={tail_process.returncode}, "
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f"expectedAtMost={count}, actual={len(frames)}"
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)
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raise ValueError(f"Unable to extract video frames: {detail}")
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return frames, duration, directory
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def _select_frames(frames: list[Path], maximum: int) -> list[Path]:
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if len(frames) <= maximum:
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return frames
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if maximum <= 1:
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return [frames[len(frames) // 2]]
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indices = [round(index * (len(frames) - 1) / (maximum - 1)) for index in range(maximum)]
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return [frames[index] for index in indices]
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def _video_context_size(base_context: int, image_count: int, output_tokens: int) -> int:
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estimated_tokens = 4096 + image_count * 1400 + output_tokens
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required_context = 1 << max(1, estimated_tokens - 1).bit_length()
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return min(
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settings.max_video_context_tokens,
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max(base_context, required_context),
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)
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def _encode_image(path: Path, maximum_width: int, extracted_width: int) -> str:
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if maximum_width >= extracted_width:
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image = path.read_bytes()
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else:
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process = subprocess.run(
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[
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"ffmpeg",
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"-v",
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"error",
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"-i",
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str(path),
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"-vf",
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f"scale='min({maximum_width},iw)':-2",
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"-q:v",
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"3",
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"-f",
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"image2pipe",
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"-vcodec",
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"mjpeg",
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"pipe:1",
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],
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capture_output=True,
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check=False,
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)
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if process.returncode != 0 or not process.stdout:
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error = process.stderr.decode("utf-8", errors="replace")[-500:]
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raise ValueError(f"Unable to resize video frame: {error}")
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image = process.stdout
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return base64.b64encode(image).decode("ascii")
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def _parse_json(content: str) -> dict[str, Any]:
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content = content.strip()
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fenced = re.search(r"```(?:json)?\s*(.*?)\s*```", content, re.DOTALL)
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if fenced:
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content = fenced.group(1)
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try:
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parsed = json.loads(content)
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return parsed if isinstance(parsed, dict) else {"result": parsed, "structured": False}
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except json.JSONDecodeError:
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return {"summary": content, "structured": False}
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def _fallback_reason(
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result: dict[str, Any], confidence_threshold: float
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) -> str | None:
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if result.get("structured") is False:
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return "FAST_RESULT_NOT_STRUCTURED"
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required = (
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"passed",
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"evidenceSufficient",
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"confidence",
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"conclusion",
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"summary",
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"events",
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"warnings",
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)
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missing = [field for field in required if field not in result]
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if missing:
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return "FAST_RESULT_MISSING_FIELDS:" + ",".join(missing)
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if not isinstance(result.get("conclusion"), str) or not result["conclusion"].strip():
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return "FAST_RESULT_EMPTY_CONCLUSION"
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if not isinstance(result.get("summary"), str) or not result["summary"].strip():
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return "FAST_RESULT_EMPTY_SUMMARY"
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if not isinstance(result.get("events"), list) or not isinstance(result.get("warnings"), list):
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return "FAST_RESULT_INVALID_COLLECTIONS"
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if result.get("evidenceSufficient") is not True:
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return "FAST_RESULT_INSUFFICIENT"
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if result.get("passed") is not True and result.get("passed") is not False:
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return "FAST_RESULT_INVALID_DECISION"
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try:
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confidence = float(result.get("confidence"))
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except (TypeError, ValueError):
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return "FAST_RESULT_INVALID_CONFIDENCE"
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if confidence < confidence_threshold:
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return "FAST_RESULT_LOW_CONFIDENCE"
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return None
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def _bounded_number(
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value: Any, name: str, minimum: float, maximum: float, integer: bool = False
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) -> int | float:
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try:
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number = float(value)
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except (TypeError, ValueError) as exc:
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raise ValueError(f"Video tuning parameter {name} must be numeric") from exc
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if number < minimum or number > maximum:
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raise ValueError(
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f"Video tuning parameter {name} must be between {minimum} and {maximum}"
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)
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if integer and not number.is_integer():
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raise ValueError(f"Video tuning parameter {name} must be an integer")
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return int(number) if integer else number
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def _normalize_tuning(raw: dict[str, Any] | None) -> dict[str, Any]:
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tuning = dict(raw or {})
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normalized: dict[str, Any] = {}
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if "sampleFps" in tuning:
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normalized["sampleFps"] = _bounded_number(
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tuning["sampleFps"], "sampleFps", *VIDEO_TUNING_LIMITS["sampleFps"]
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)
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if "maxWidth" in tuning:
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normalized["maxWidth"] = _bounded_number(
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tuning["maxWidth"], "maxWidth", *VIDEO_TUNING_LIMITS["maxWidth"], True
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)
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if "confidenceThreshold" in tuning:
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normalized["confidenceThreshold"] = _bounded_number(
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tuning["confidenceThreshold"],
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"confidenceThreshold",
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*VIDEO_TUNING_LIMITS["confidenceThreshold"],
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)
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if "fallbackToAccurate" in tuning:
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value = tuning["fallbackToAccurate"]
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if not isinstance(value, bool):
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raise ValueError("Video tuning parameter fallbackToAccurate must be boolean")
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normalized["fallbackToAccurate"] = value
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return normalized
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def _apply_tuning(strategy: dict[str, Any], tuning: dict[str, Any]) -> dict[str, Any]:
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configured = dict(strategy)
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for name in ("sampleFps", "maxWidth"):
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if name in tuning:
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configured[name] = tuning[name]
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return configured
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_CRITERION_TRANSITIONS = (
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("消失", ("消失", "不再显示", "从有到无", "由有变无")),
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("熄灭", ("熄灭", "从亮到灭", "由亮变灭")),
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("关闭", ("已关闭", "从开到关", "由开变关")),
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("停止", ("已停止", "停止运行")),
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("断开", ("已断开", "连接断开")),
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("出现", ("出现", "从无到有", "由无变有")),
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("点亮", ("点亮", "亮起", "从灭到亮", "由灭变亮")),
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("开启", ("已开启", "从关到开", "由关变开")),
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("启动", ("已启动", "开始运行")),
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("连接", ("已连接", "连接成功")),
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)
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_REMOVAL_CRITERION_TERMS = (
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"消失", "熄灭", "关闭", "停止", "断开", "移除", "不再显示",
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"disappear", "turn off", "shut down", "disconnect", "remove", "absent",
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)
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_APPEARANCE_CRITERION_TERMS = (
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"出现", "点亮", "开启", "启动", "连接", "显示",
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"appear", "turn on", "start", "connect", "visible",
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)
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def _temporal_acceptance_guidance(acceptance_criterion: str) -> str:
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original_criterion = str(acceptance_criterion or "").strip()
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criterion = original_criterion.lower()
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guidance = (
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f"当前通过标准是【{original_criterion}】。图片严格按时间从前到后排列，"
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"第一张是初始状态，最后一张是结束状态。判定 passed 前必须逐帧检查，"
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"并明确比较第一张与最后一张图片。"
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)
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if any(term in criterion for term in _REMOVAL_CRITERION_TERMS):
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target = original_criterion
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for term in _REMOVAL_CRITERION_TERMS:
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index = criterion.find(term)
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if index >= 0:
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target = original_criterion[:index]
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break
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target = re.sub(r"(?:是否|有没有|有无)$", "", target).strip(" ：:，,。?？") or "目标"
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return guidance + (
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f"针对当前消失、移除、关闭或断开的标准：如果{target}在前段图片中存在，"
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f"但在最后一张及末段图片中已不再显示{target}，就说明状态变化成功，"
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"passed必须为true。不能因为第一张图片中存在，就判断全程持续存在。"
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)
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if any(term in criterion for term in _APPEARANCE_CRITERION_TERMS):
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return guidance + (
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"针对出现、点亮、开启、启动或连接的标准：如果目标在前段图片中不存在，"
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"但在最后一张及末段图片中已出现，就说明状态变化成功，passed 必须为 true。"
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)
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return guidance
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def _needs_temporal_removal_verification(
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acceptance_criterion: str, result: dict[str, Any]
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) -> bool:
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criterion = str(acceptance_criterion or "").strip().lower()
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return (
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any(term in criterion for term in _REMOVAL_CRITERION_TERMS)
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and result.get("passed") is False
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and result.get("evidenceSufficient") is True
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)
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def _criterion_verdict(result: dict[str, Any], acceptance_criterion: str) -> bool | None:
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criterion = str(acceptance_criterion or "").strip()
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if not criterion:
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return None
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conclusion = str(result.get("conclusion") or "").strip()
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summary = str(result.get("summary") or "").strip()
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events = result.get("events") if isinstance(result.get("events"), list) else []
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evidence_text = " ".join(
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[conclusion, summary]
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+ [
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str(event.get("event") if isinstance(event, dict) else event)
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for event in events
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]
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)
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explicit_failure = re.search(
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r"(?:不符合|未满足|不满足|未达到|没有达到|判定失败|未通过).{0,12}(?:标准|要求|条件)?",
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evidence_text,
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)
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if explicit_failure:
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return False
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explicit_success = re.search(
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r"(?:符合|满足|达到|已完成).{0,12}(?:标准|要求|条件)",
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evidence_text,
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)
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if explicit_success:
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return True
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for criterion_word, observed_phrases in _CRITERION_TRANSITIONS:
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if criterion_word not in criterion:
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continue
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negated = re.search(
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rf"(?:未|没有|尚未|未能|不曾).{{0,4}}{re.escape(criterion_word)}",
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evidence_text,
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)
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if negated:
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return False
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if criterion_word in {"消失", "熄灭", "关闭", "停止", "断开"} and re.search(
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r"(?:仍然|仍|依然|依旧).{0,8}(?:显示|存在|亮起|开启|运行|连接)",
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evidence_text,
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):
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return False
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if any(phrase in evidence_text for phrase in observed_phrases):
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return True
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return None
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def _normalize_decision(
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result: dict[str, Any], confidence_threshold: float, acceptance_criterion: str = ""
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) -> dict[str, Any]:
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normalized = dict(result or {})
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warnings = normalized.get("warnings")
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if not isinstance(warnings, list):
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warnings = []
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normalized["warnings"] = warnings
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normalized["events"] = normalized.get("events") if isinstance(normalized.get("events"), list) else []
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try:
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confidence = min(1.0, max(0.0, float(normalized.get("confidence", 0))))
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except (TypeError, ValueError):
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confidence = 0.0
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evidence_sufficient = normalized.get("evidenceSufficient") is True
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model_passed = normalized.get("passed") is True
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criterion_verdict = _criterion_verdict(normalized, acceptance_criterion)
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raw_passed = model_passed if criterion_verdict is None else criterion_verdict
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decision_reason = "MODEL_DECISION"
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if normalized.get("structured") is False:
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evidence_sufficient = False
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decision_reason = "UNSTRUCTURED_RESULT"
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elif not evidence_sufficient:
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decision_reason = "INSUFFICIENT_EVIDENCE"
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elif confidence < confidence_threshold:
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decision_reason = "LOW_CONFIDENCE"
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elif criterion_verdict is not None and criterion_verdict != model_passed:
|
||
decision_reason = "CRITERION_EVIDENCE_CORRECTION"
|
||
|
||
passed = raw_passed and evidence_sufficient and confidence >= confidence_threshold
|
||
if not passed and decision_reason != "MODEL_DECISION":
|
||
if decision_reason == "LOW_CONFIDENCE":
|
||
reason_text = "分析置信度不足"
|
||
elif decision_reason == "CRITERION_EVIDENCE_CORRECTION":
|
||
reason_text = "分析结论未满足通过标准"
|
||
else:
|
||
reason_text = "视频证据不足"
|
||
conclusion = str(normalized.get("conclusion") or "").strip()
|
||
normalized["conclusion"] = f"未通过：{reason_text}" + (f"；{conclusion}" if conclusion else "")
|
||
if reason_text not in warnings:
|
||
warnings.append(reason_text)
|
||
|
||
normalized["passed"] = passed
|
||
if criterion_verdict is not None and criterion_verdict != model_passed:
|
||
normalized["modelPassed"] = model_passed
|
||
normalized["evidenceSufficient"] = evidence_sufficient
|
||
normalized["confidence"] = confidence
|
||
normalized["decisionReason"] = decision_reason
|
||
normalized["confidenceThreshold"] = confidence_threshold
|
||
normalized.setdefault("conclusion", "通过" if passed else "未通过")
|
||
normalized.setdefault("summary", normalized["conclusion"])
|
||
normalized.pop("structured", None)
|
||
return normalized
|
||
|
||
|
||
def _strategy(profile: Profile, name: str) -> dict[str, Any]:
|
||
defaults = {
|
||
"FAST": {
|
||
"model": profile.config.get("model", settings.ollama_model),
|
||
"sampleFps": 1.0,
|
||
"maxWidth": 896,
|
||
"maxOutputTokens": 512,
|
||
"numCtx": 16384,
|
||
"emptyResponseRetries": 0,
|
||
"maxRetryOutputTokens": 4096,
|
||
},
|
||
"ACCURATE": {
|
||
"model": profile.config.get("model", settings.ollama_model),
|
||
"sampleFps": 3.0,
|
||
"maxWidth": 1280,
|
||
"maxOutputTokens": 768,
|
||
"numCtx": 32768,
|
||
"emptyResponseRetries": 2,
|
||
"maxRetryOutputTokens": 8192,
|
||
},
|
||
}
|
||
configured = profile.config.get("strategies", {}).get(name.lower(), {})
|
||
strategy = {**defaults[name], **configured}
|
||
strategy["provider"] = str(strategy.get("provider", "ollama")).strip().lower()
|
||
if strategy["provider"] == "sglang":
|
||
strategy["baseUrl"] = (
|
||
settings.sglang_fast_base_url
|
||
if name == "FAST"
|
||
else settings.sglang_accurate_base_url
|
||
)
|
||
return strategy
|
||
|
||
|
||
def _sglang_payload(payload: dict[str, Any]) -> dict[str, Any]:
|
||
message = payload.get("messages", [{}])[0]
|
||
content: list[dict[str, Any]] = [
|
||
{"type": "text", "text": str(message.get("content") or "")}
|
||
]
|
||
content.extend(
|
||
{
|
||
"type": "image_url",
|
||
"image_url": {"url": f"data:image/jpeg;base64,{image}"},
|
||
}
|
||
for image in message.get("images") or []
|
||
)
|
||
options = payload.get("options") or {}
|
||
return {
|
||
"model": payload["model"],
|
||
"messages": [{"role": "user", "content": content}],
|
||
"stream": False,
|
||
"temperature": float(options.get("temperature", 0.1)),
|
||
"max_tokens": int(options.get("num_predict", 512)),
|
||
"response_format": {
|
||
"type": "json_schema",
|
||
"json_schema": {
|
||
"name": "video_analysis",
|
||
"strict": True,
|
||
"schema": VIDEO_RESULT_SCHEMA,
|
||
},
|
||
},
|
||
"chat_template_kwargs": {
|
||
"enable_thinking": bool(payload.get("think", False)),
|
||
"preserve_thinking": False,
|
||
},
|
||
}
|
||
|
||
|
||
def _request_image_count(payload: dict[str, Any], provider: str) -> int:
|
||
message = payload.get("messages", [{}])[0]
|
||
if provider == "ollama":
|
||
return len(message.get("images") or [])
|
||
content = message.get("content") or []
|
||
return sum(
|
||
1 for item in content
|
||
if isinstance(item, dict) and item.get("type") == "image_url"
|
||
)
|
||
|
||
|
||
async def _request_vision_model(
|
||
payload: dict[str, Any], empty_response_retries: int = 1,
|
||
max_retry_output_tokens: int = 8192,
|
||
provider: str = "ollama",
|
||
base_url: str | None = None,
|
||
) -> tuple[str, dict[str, Any]]:
|
||
provider = str(provider or "ollama").strip().lower()
|
||
if provider not in {"ollama", "sglang"}:
|
||
raise ValueError(f"Unsupported vision model provider: {provider}")
|
||
if provider == "sglang" and not str(base_url or "").strip():
|
||
raise VisionModelError("SGLang vision model endpoint is not configured")
|
||
|
||
last_metadata: dict[str, Any] = {}
|
||
timeout_seconds = (
|
||
settings.sglang_timeout_seconds
|
||
if provider == "sglang"
|
||
else settings.ollama_timeout_seconds
|
||
)
|
||
timeout = httpx.Timeout(timeout_seconds, connect=5.0)
|
||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||
for attempt in range(empty_response_retries + 1):
|
||
request_payload = copy.deepcopy(payload)
|
||
if attempt:
|
||
options = request_payload.setdefault("options", {})
|
||
previous_limit = max(
|
||
1,
|
||
int(options.get("num_predict", 0)),
|
||
int(last_metadata.get("requestedOutputTokens") or 0),
|
||
)
|
||
previous_eval_count = int(last_metadata.get("evalCount") or 0)
|
||
retry_limit = max(4096, previous_limit * 2, previous_eval_count + 2048)
|
||
options["num_predict"] = min(max_retry_output_tokens, retry_limit)
|
||
request_payload["think"] = False
|
||
request_payload["messages"][0]["content"] += (
|
||
"\n\n/no_think\nReturn only the final JSON now. Do not output reasoning."
|
||
)
|
||
provider_payload = (
|
||
_sglang_payload(request_payload)
|
||
if provider == "sglang"
|
||
else request_payload
|
||
)
|
||
url = (
|
||
f"{str(base_url).rstrip('/')}/chat/completions"
|
||
if provider == "sglang"
|
||
else f"{settings.ollama_base_url.rstrip('/')}/api/chat"
|
||
)
|
||
headers = {}
|
||
if provider == "sglang" and settings.sglang_api_key:
|
||
headers["Authorization"] = f"Bearer {settings.sglang_api_key}"
|
||
response = await client.post(url, json=provider_payload, headers=headers)
|
||
try:
|
||
response.raise_for_status()
|
||
except httpx.HTTPStatusError as exc:
|
||
detail = response.text.strip()[:1000]
|
||
raise VisionModelError(
|
||
"Vision model request rejected "
|
||
f"(provider={provider}, status={response.status_code}, "
|
||
f"imageCount={_request_image_count(provider_payload, provider)}, "
|
||
f"numCtx={request_payload.get('options', {}).get('num_ctx')}, "
|
||
f"detail={detail})"
|
||
) from exc
|
||
body = response.json()
|
||
if provider == "sglang":
|
||
choices = body.get("choices") or []
|
||
choice = choices[0] if choices else {}
|
||
message = choice.get("message") or {}
|
||
usage = body.get("usage") or {}
|
||
content = str(message.get("content") or "").strip()
|
||
metadata = {
|
||
"doneReason": choice.get("finish_reason"),
|
||
"promptEvalCount": usage.get("prompt_tokens"),
|
||
"evalCount": usage.get("completion_tokens"),
|
||
"totalDurationNs": None,
|
||
"thinkingLength": len(str(message.get("reasoning_content") or "")),
|
||
"attempt": attempt + 1,
|
||
"requestedOutputTokens": provider_payload.get("max_tokens"),
|
||
}
|
||
else:
|
||
message = body.get("message") or {}
|
||
content = str(message.get("content") or "").strip()
|
||
metadata = {
|
||
"doneReason": body.get("done_reason"),
|
||
"promptEvalCount": body.get("prompt_eval_count"),
|
||
"evalCount": body.get("eval_count"),
|
||
"totalDurationNs": body.get("total_duration"),
|
||
"thinkingLength": len(str(message.get("thinking") or "")),
|
||
"attempt": attempt + 1,
|
||
"requestedOutputTokens": request_payload.get("options", {}).get("num_predict"),
|
||
}
|
||
metadata["provider"] = provider
|
||
if content:
|
||
return content, metadata
|
||
last_metadata = metadata
|
||
raise VisionModelError(
|
||
"Vision model returned an empty final response after retry "
|
||
f"({json.dumps(last_metadata, ensure_ascii=False)})"
|
||
)
|
||
|
||
|
||
async def _analyze_frames(
|
||
profile: Profile,
|
||
strategy_name: str,
|
||
frames: list[Path],
|
||
duration: float,
|
||
extracted_width: int,
|
||
prompt: str,
|
||
tuning: dict[str, Any] | None = None,
|
||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||
strategy = _apply_tuning(_strategy(profile, strategy_name), tuning or {})
|
||
desired_count, _, _, _, sampling_capped = _frame_sampling_plan(
|
||
duration,
|
||
float(strategy["sampleFps"]),
|
||
settings.video_sampling_frame_limit,
|
||
)
|
||
selected = _select_frames(frames, desired_count)
|
||
maximum_width = max(320, int(strategy["maxWidth"]))
|
||
images = [_encode_image(path, maximum_width, extracted_width) for path in selected]
|
||
strategy_prompt = prompt + (
|
||
f"\n\nThe video duration is {duration:.3f} seconds. "
|
||
f"The following {len(selected)} images are ordered frames sampled across the video."
|
||
)
|
||
think = bool(strategy.get("think", profile.config.get("think", False)))
|
||
if not think:
|
||
strategy_prompt += "\n\n/no_think\nReturn only the requested JSON object without reasoning."
|
||
output_tokens = int(strategy["maxOutputTokens"])
|
||
context_tokens = _video_context_size(
|
||
int(strategy["numCtx"]), len(selected), output_tokens
|
||
)
|
||
payload = {
|
||
"model": strategy["model"],
|
||
"stream": False,
|
||
"think": think,
|
||
"format": VIDEO_RESULT_SCHEMA,
|
||
"keep_alive": str(strategy.get("keepAlive", "30m")),
|
||
"messages": [{"role": "user", "content": strategy_prompt, "images": images}],
|
||
"options": {
|
||
"temperature": float(strategy.get("temperature", profile.config.get("temperature", 0.1))),
|
||
"num_predict": output_tokens,
|
||
"num_ctx": context_tokens,
|
||
},
|
||
}
|
||
provider = str(strategy.get("provider", "ollama"))
|
||
actual_model = str(strategy["model"])
|
||
provider_fallback = False
|
||
try:
|
||
content, metrics = await _request_vision_model(
|
||
payload,
|
||
int(strategy.get("emptyResponseRetries", 0)),
|
||
int(strategy.get("maxRetryOutputTokens", 8192)),
|
||
provider,
|
||
strategy.get("baseUrl"),
|
||
)
|
||
except (VisionModelError, httpx.HTTPError):
|
||
fallback_model = str(strategy.get("fallbackModel") or "").strip()
|
||
if (
|
||
provider != "sglang"
|
||
or not settings.vision_ollama_fallback_enabled
|
||
or not fallback_model
|
||
):
|
||
raise
|
||
fallback_payload = copy.deepcopy(payload)
|
||
fallback_payload["model"] = fallback_model
|
||
content, metrics = await _request_vision_model(
|
||
fallback_payload,
|
||
int(strategy.get("emptyResponseRetries", 0)),
|
||
int(strategy.get("maxRetryOutputTokens", 8192)),
|
||
)
|
||
provider_fallback = True
|
||
actual_model = fallback_model
|
||
metadata = {
|
||
"mode": strategy_name,
|
||
"model": actual_model,
|
||
"provider": metrics.get("provider", provider),
|
||
"primaryProvider": provider,
|
||
"primaryModel": strategy["model"],
|
||
"providerFallback": provider_fallback,
|
||
"sampledFrameCount": len(selected),
|
||
"maximumWidth": maximum_width,
|
||
"effectiveSampleFps": float(strategy["sampleFps"]),
|
||
"samplingFrameLimit": settings.video_sampling_frame_limit,
|
||
"samplingCapped": sampling_capped,
|
||
"numCtx": context_tokens,
|
||
**metrics,
|
||
}
|
||
return _parse_json(content), metadata
|
||
|
||
|
||
async def analyze_video(
|
||
profile: Profile,
|
||
media_path: Path,
|
||
extra_instruction: str = "",
|
||
analysis_mode: str = "AUTO",
|
||
tuning: dict[str, Any] | None = None,
|
||
decision_policy: str = "FAIL_CLOSED",
|
||
) -> tuple[dict[str, Any], float, dict[str, Any]]:
|
||
requested_mode = str(analysis_mode or "AUTO").strip().upper()
|
||
if requested_mode not in {"AUTO", "FAST", "ACCURATE"}:
|
||
raise ValueError("Video analysis mode must be AUTO, FAST, or ACCURATE")
|
||
if str(decision_policy or "FAIL_CLOSED").strip().upper() != "FAIL_CLOSED":
|
||
raise ValueError("Video decision policy must be FAIL_CLOSED")
|
||
|
||
effective_tuning = _normalize_tuning(tuning)
|
||
confidence_threshold = float(effective_tuning.get("confidenceThreshold", 0.75))
|
||
fallback_to_accurate = bool(effective_tuning.get("fallbackToAccurate", True))
|
||
|
||
fast = _apply_tuning(_strategy(profile, "FAST"), effective_tuning)
|
||
accurate = _apply_tuning(_strategy(profile, "ACCURATE"), effective_tuning)
|
||
extraction_strategy = fast if requested_mode == "FAST" else accurate
|
||
extraction_sample_fps = float(extraction_strategy["sampleFps"])
|
||
extraction_width = max(320, int(extraction_strategy["maxWidth"]))
|
||
frames, duration, frame_dir = _extract_frames(
|
||
media_path,
|
||
extraction_sample_fps,
|
||
settings.video_sampling_frame_limit,
|
||
extraction_width,
|
||
)
|
||
try:
|
||
prompt_path = profile.directory / str(profile.config.get("promptFile", "prompt.txt"))
|
||
prompt = prompt_path.read_text(encoding="utf-8")
|
||
if extra_instruction.strip():
|
||
temporal_guidance = _temporal_acceptance_guidance(extra_instruction)
|
||
prompt += (
|
||
"\n\nAcceptance criterion (passed=true exactly when this criterion is "
|
||
"satisfied; this is not merely an object-detection question):\n"
|
||
+ "通过标准："
|
||
+ extra_instruction.strip()
|
||
+ "。"
|
||
+ temporal_guidance
|
||
)
|
||
|
||
async def finalize_fast_result(
|
||
result: dict[str, Any], metadata: dict[str, Any], mode_label: str,
|
||
fallback_reason: str | None = None,
|
||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||
normalized = _normalize_decision(
|
||
result, confidence_threshold, extra_instruction
|
||
)
|
||
if _needs_temporal_removal_verification(extra_instruction, normalized):
|
||
focused_prompt = prompt + (
|
||
"\n\n首尾帧精确复核：本次只提供视频第一张和最后一张图片。"
|
||
"必须比较具体目标在两张图片中的存在状态，再按通过标准输出结论。"
|
||
)
|
||
verified, verified_metadata = await _analyze_frames(
|
||
profile,
|
||
"ACCURATE",
|
||
[frames[0], frames[-1]],
|
||
duration,
|
||
extraction_width,
|
||
focused_prompt,
|
||
effective_tuning,
|
||
)
|
||
verified = _normalize_decision(
|
||
verified, confidence_threshold, extra_instruction
|
||
)
|
||
verified_metadata.update(
|
||
{
|
||
"requestedMode": mode_label,
|
||
"fallback": True,
|
||
"fallbackReason": "TEMPORAL_REMOVAL_VERIFICATION",
|
||
"tuning": effective_tuning,
|
||
}
|
||
)
|
||
return verified, verified_metadata
|
||
metadata.update(
|
||
{
|
||
"requestedMode": mode_label,
|
||
"fallback": False,
|
||
"fallbackReason": fallback_reason,
|
||
"tuning": effective_tuning,
|
||
}
|
||
)
|
||
return normalized, metadata
|
||
|
||
if requested_mode in {"FAST", "ACCURATE"}:
|
||
result, metadata = await _analyze_frames(
|
||
profile, requested_mode, frames, duration, extraction_width, prompt,
|
||
effective_tuning,
|
||
)
|
||
if requested_mode == "FAST":
|
||
result, metadata = await finalize_fast_result(
|
||
result, metadata, requested_mode
|
||
)
|
||
else:
|
||
result = _normalize_decision(
|
||
result, confidence_threshold, extra_instruction
|
||
)
|
||
metadata.update(
|
||
{
|
||
"requestedMode": requested_mode,
|
||
"fallback": False,
|
||
"fallbackReason": None,
|
||
"tuning": effective_tuning,
|
||
}
|
||
)
|
||
return result, duration, metadata
|
||
|
||
fallback_reason = None
|
||
try:
|
||
result, metadata = await _analyze_frames(
|
||
profile, "FAST", frames, duration, extraction_width, prompt,
|
||
effective_tuning,
|
||
)
|
||
fallback_reason = _fallback_reason(result, confidence_threshold)
|
||
if fallback_reason is None or not fallback_to_accurate:
|
||
result, metadata = await finalize_fast_result(
|
||
result, metadata, "AUTO", fallback_reason
|
||
)
|
||
return result, duration, metadata
|
||
except (VisionModelError, httpx.HTTPError) as exc:
|
||
fallback_reason = f"FAST_MODEL_ERROR:{type(exc).__name__}"
|
||
|
||
result, metadata = await _analyze_frames(
|
||
profile, "ACCURATE", frames, duration, extraction_width, prompt,
|
||
effective_tuning,
|
||
)
|
||
result = _normalize_decision(result, confidence_threshold, extra_instruction)
|
||
metadata.update(
|
||
{
|
||
"requestedMode": "AUTO",
|
||
"fallback": True,
|
||
"fallbackReason": fallback_reason,
|
||
"tuning": effective_tuning,
|
||
}
|
||
)
|
||
return result, duration, metadata
|
||
finally:
|
||
for frame in frames:
|
||
frame.unlink(missing_ok=True)
|
||
frame_dir.rmdir()
|