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