040aada160
基于项目需求文档（PRD.md, FeatureSummary.md, DevelopmentPlan.md, UIDesign.md, User_Role_Interfaces.md）编写的 TDD 测试用例。 后端测试 (Python/pytest): - 单元测试: rule_engine, brief_parser, timestamp_alignment, video_auditor, validators - 集成测试: API Brief, Video, Review 端点 - AI 模块测试: ASR, OCR, Logo 检测服务 - 全局 fixtures 和 pytest 配置 前端测试 (TypeScript/Vitest): - 工具函数测试: utils.test.ts - 组件测试: Button, VideoPlayer, ViolationList - Hooks 测试: useVideoAudit, useVideoPlayer, useAppeal - MSW mock handlers 配置 E2E 测试 (Playwright): - 认证流程测试 - 视频上传流程测试 - 视频审核流程测试 - 申诉流程测试 所有测试当前使用 pytest.skip() / it.skip() 作为占位符， 遵循 TDD 红灯阶段 - 等待实现代码后运行。 验收标准覆盖: - ASR WER ≤ 10% - OCR 准确率 ≥ 95% - Logo F1 ≥ 0.85 - 时间戳误差 ≤ 0.5s - 频次统计准确率 ≥ 95% Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
366 lines
12 KiB
Python
366 lines
12 KiB
Python
"""
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多模态时间戳对齐模块单元测试
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TDD 测试用例 - 基于 DevelopmentPlan.md (F-14, F-45) 的验收标准
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验收标准：
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- 时长统计误差 ≤ 0.5秒
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- 频次统计准确率 ≥ 95%
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- 时间轴归一化精度 ≤ 0.1秒
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- 模糊匹配容差窗口 ±0.5秒
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"""
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import pytest
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from typing import Any
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# 导入待实现的模块（TDD 红灯阶段）
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# from app.utils.timestamp_align import (
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# TimestampAligner,
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# MultiModalEvent,
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# AlignmentResult,
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# )
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class TestTimestampAligner:
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"""
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时间戳对齐器测试
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验收标准：
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- 时间轴归一化精度 ≤ 0.1秒
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- 模糊匹配容差窗口 ±0.5秒
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"""
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@pytest.mark.unit
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@pytest.mark.parametrize("asr_ts,ocr_ts,cv_ts,tolerance,expected_merged,expected_ts", [
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# 完全对齐
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(1000, 1000, 1000, 500, True, 1000),
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# 容差范围内 - 应合并
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(1000, 1200, 1100, 500, True, 1100), # 中位数
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(1000, 1400, 1200, 500, True, 1200), # 中位数
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# 超出容差 - 不应合并
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(1000, 2000, 3000, 500, False, None),
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(1000, 1600, 1000, 500, False, None), # OCR 超出容差
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])
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def test_multimodal_event_alignment(
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self,
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asr_ts: int,
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ocr_ts: int,
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cv_ts: int,
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tolerance: int,
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expected_merged: bool,
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expected_ts: int | None,
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) -> None:
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"""测试多模态事件对齐"""
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events = [
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{"source": "asr", "timestamp_ms": asr_ts, "content": "测试文本"},
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{"source": "ocr", "timestamp_ms": ocr_ts, "content": "字幕内容"},
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{"source": "cv", "timestamp_ms": cv_ts, "content": "product_detected"},
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]
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# TODO: 实现 TimestampAligner
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# aligner = TimestampAligner(tolerance_ms=tolerance)
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# result = aligner.align_events(events)
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#
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# if expected_merged:
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# assert len(result.merged_events) == 1
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# assert abs(result.merged_events[0].timestamp_ms - expected_ts) <= 100
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# else:
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# # 未合并时，每个事件独立
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# assert len(result.merged_events) == 3
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pytest.skip("待实现：TimestampAligner")
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@pytest.mark.unit
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def test_timestamp_normalization_precision(self) -> None:
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"""
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测试时间戳归一化精度
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验收标准：精度 ≤ 0.1秒 (100ms)
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"""
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# 不同来源的时间戳格式
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asr_event = {"source": "asr", "timestamp_ms": 1500} # 毫秒
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cv_event = {"source": "cv", "frame": 45, "fps": 30} # 帧号 (45/30 = 1.5秒)
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ocr_event = {"source": "ocr", "timestamp_seconds": 1.5} # 秒
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# TODO: 实现时间戳归一化
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# aligner = TimestampAligner()
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# normalized = aligner.normalize_timestamps([asr_event, cv_event, ocr_event])
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#
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# # 所有归一化后的时间戳应在 100ms 误差范围内
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# timestamps = [e.timestamp_ms for e in normalized]
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# assert max(timestamps) - min(timestamps) <= 100
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pytest.skip("待实现：时间戳归一化")
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@pytest.mark.unit
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def test_fuzzy_matching_window(self) -> None:
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"""
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测试模糊匹配容差窗口
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验收标准：容差 ±0.5秒
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"""
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# TODO: 实现模糊匹配
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# aligner = TimestampAligner(tolerance_ms=500)
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#
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# # 1000ms 和 1499ms 应该匹配（差值 < 500ms）
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# assert aligner.is_within_tolerance(1000, 1499)
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#
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# # 1000ms 和 1501ms 不应匹配（差值 > 500ms）
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# assert not aligner.is_within_tolerance(1000, 1501)
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pytest.skip("待实现：模糊匹配容差")
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class TestDurationCalculation:
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"""
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时长统计测试
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验收标准 (FeatureSummary.md F-45):
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- 时长统计误差 ≤ 0.5秒
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"""
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@pytest.mark.unit
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@pytest.mark.parametrize("start_ms,end_ms,expected_duration_ms,tolerance_ms", [
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(0, 5000, 5000, 500),
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(1000, 6500, 5500, 500),
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(0, 10000, 10000, 500),
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(500, 3200, 2700, 500),
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])
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def test_duration_calculation_accuracy(
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self,
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start_ms: int,
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end_ms: int,
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expected_duration_ms: int,
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tolerance_ms: int,
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) -> None:
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"""测试时长计算准确性 - 误差 ≤ 0.5秒"""
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events = [
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{"timestamp_ms": start_ms, "type": "object_appear"},
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{"timestamp_ms": end_ms, "type": "object_disappear"},
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]
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# TODO: 实现时长计算
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# aligner = TimestampAligner()
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# duration = aligner.calculate_duration(events)
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#
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# assert abs(duration - expected_duration_ms) <= tolerance_ms
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pytest.skip("待实现：时长计算")
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@pytest.mark.unit
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def test_product_visible_duration(
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self,
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sample_cv_result: dict[str, Any],
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) -> None:
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"""测试产品可见时长统计"""
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# sample_cv_result 包含 start_frame=30, end_frame=180, fps=30
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# 预期时长: (180-30)/30 = 5 秒
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# TODO: 实现产品时长统计
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# aligner = TimestampAligner()
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# duration = aligner.calculate_object_duration(
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# sample_cv_result["detections"],
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# object_type="product"
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# )
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#
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# expected_duration_ms = 5000
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# assert abs(duration - expected_duration_ms) <= 500
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pytest.skip("待实现：产品可见时长统计")
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@pytest.mark.unit
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def test_multiple_segments_duration(self) -> None:
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"""测试多段时长累加"""
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# 产品在视频中多次出现
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segments = [
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{"start_ms": 0, "end_ms": 3000}, # 3秒
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{"start_ms": 10000, "end_ms": 12000}, # 2秒
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{"start_ms": 25000, "end_ms": 30000}, # 5秒
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]
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# 总时长应为 10秒
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# TODO: 实现多段时长累加
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# aligner = TimestampAligner()
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# total_duration = aligner.calculate_total_duration(segments)
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#
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# assert abs(total_duration - 10000) <= 500
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pytest.skip("待实现：多段时长累加")
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class TestFrequencyCount:
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"""
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频次统计测试
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验收标准 (FeatureSummary.md F-45):
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- 频次统计准确率 ≥ 95%
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"""
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@pytest.mark.unit
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def test_brand_mention_frequency(
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self,
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sample_asr_result: dict[str, Any],
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) -> None:
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"""测试品牌名提及频次统计"""
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# TODO: 实现频次统计
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# counter = FrequencyCounter()
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# count = counter.count_mentions(
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# sample_asr_result["segments"],
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# keyword="品牌"
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# )
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#
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# # 验证统计准确性
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# assert count >= 0
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pytest.skip("待实现：品牌名提及频次")
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@pytest.mark.unit
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@pytest.mark.parametrize("text_segments,keyword,expected_count", [
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# 简单情况
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(
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[{"text": "这个品牌真不错"}, {"text": "品牌介绍"}, {"text": "品牌故事"}],
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"品牌",
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3
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),
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# 无匹配
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(
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[{"text": "产品介绍"}, {"text": "使用方法"}],
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"品牌",
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0
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),
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# 同一句多次出现
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(
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[{"text": "品牌品牌品牌"}],
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"品牌",
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3
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),
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])
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def test_keyword_frequency_accuracy(
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self,
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text_segments: list[dict[str, str]],
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keyword: str,
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expected_count: int,
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) -> None:
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"""测试关键词频次准确性"""
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# TODO: 实现频次统计
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# counter = FrequencyCounter()
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# count = counter.count_keyword(text_segments, keyword)
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#
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# assert count == expected_count
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pytest.skip("待实现：关键词频次统计")
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@pytest.mark.unit
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def test_frequency_count_accuracy_rate(self) -> None:
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"""
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测试频次统计准确率
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验收标准：准确率 ≥ 95%
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"""
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# TODO: 使用标注测试集验证
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# test_cases = load_frequency_test_set()
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# counter = FrequencyCounter()
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#
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# correct = 0
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# for case in test_cases:
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# count = counter.count_keyword(case["segments"], case["keyword"])
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# if count == case["expected_count"]:
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# correct += 1
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#
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# accuracy = correct / len(test_cases)
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# assert accuracy >= 0.95
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pytest.skip("待实现：频次准确率测试")
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class TestMultiModalFusion:
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"""
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多模态融合测试
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"""
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@pytest.mark.unit
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def test_asr_ocr_cv_fusion(
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self,
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sample_asr_result: dict[str, Any],
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sample_ocr_result: dict[str, Any],
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sample_cv_result: dict[str, Any],
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) -> None:
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"""测试 ASR + OCR + CV 三模态融合"""
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# TODO: 实现多模态融合
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# aligner = TimestampAligner()
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# fused = aligner.fuse_multimodal(
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# asr_result=sample_asr_result,
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# ocr_result=sample_ocr_result,
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# cv_result=sample_cv_result,
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# )
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#
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# # 验证融合结果包含所有模态
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# assert fused.has_asr
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# assert fused.has_ocr
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# assert fused.has_cv
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#
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# # 验证时间轴统一
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# for event in fused.timeline:
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# assert event.timestamp_ms is not None
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pytest.skip("待实现：多模态融合")
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@pytest.mark.unit
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def test_cross_modality_consistency(self) -> None:
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"""测试跨模态一致性检测"""
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# ASR 说"产品名"，OCR 显示"产品名"，CV 检测到产品
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# 三者应该在时间上一致
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asr_event = {"source": "asr", "timestamp_ms": 5000, "content": "产品名"}
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ocr_event = {"source": "ocr", "timestamp_ms": 5100, "content": "产品名"}
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cv_event = {"source": "cv", "timestamp_ms": 5050, "content": "product"}
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# TODO: 实现一致性检测
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# aligner = TimestampAligner(tolerance_ms=500)
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# consistency = aligner.check_consistency([asr_event, ocr_event, cv_event])
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#
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# assert consistency.is_consistent
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# assert consistency.cross_modality_score >= 0.9
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pytest.skip("待实现：跨模态一致性")
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@pytest.mark.unit
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def test_handle_missing_modality(self) -> None:
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"""测试缺失模态处理"""
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# 视频无字幕时，OCR 结果为空
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asr_events = [{"source": "asr", "timestamp_ms": 1000, "content": "测试"}]
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ocr_events = [] # 无 OCR 结果
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cv_events = [{"source": "cv", "timestamp_ms": 1000, "content": "product"}]
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# TODO: 实现缺失模态处理
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# aligner = TimestampAligner()
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# result = aligner.align_events(asr_events + ocr_events + cv_events)
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#
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# # 应正常处理，不报错
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# assert result.status == "success"
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# assert result.missing_modalities == ["ocr"]
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pytest.skip("待实现：缺失模态处理")
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class TestTimestampOutput:
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"""
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时间戳输出格式测试
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"""
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@pytest.mark.unit
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def test_unified_timeline_format(self) -> None:
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"""测试统一时间轴输出格式"""
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# TODO: 实现时间轴输出
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# aligner = TimestampAligner()
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# timeline = aligner.get_unified_timeline(events)
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#
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# # 验证输出格式
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# for entry in timeline:
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# assert "timestamp_seconds" in entry
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# assert "multimodal_events" in entry
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# assert isinstance(entry["multimodal_events"], list)
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pytest.skip("待实现：统一时间轴格式")
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@pytest.mark.unit
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def test_violation_with_timestamp(self) -> None:
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"""测试违规项时间戳标注"""
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# TODO: 实现违规时间戳
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# violation = {
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# "type": "forbidden_word",
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# "content": "最好的",
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# "timestamp_start": 5.0,
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# "timestamp_end": 5.5,
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# }
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#
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# assert violation["timestamp_end"] > violation["timestamp_start"]
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pytest.skip("待实现：违规时间戳")
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