""" 视频审核模块单元测试 TDD 测试用例 - 基于 FeatureSummary.md (F-10~F-18) 的验收标准 验收标准： - 100MB 视频审核 ≤ 5 分钟 - 竞品 Logo F1 ≥ 0.85 - ASR 字错率 ≤ 10% - OCR 准确率 ≥ 95% """ import pytest from typing import Any # 导入待实现的模块（TDD 红灯阶段） # from app.services.video_auditor import VideoAuditor, AuditReport class TestVideoUpload: """ 视频上传测试 验收标准 (FeatureSummary.md F-10): - 支持 ≤ 100MB 视频 - 支持 MP4/MOV 格式 - 支持断点续传 """ @pytest.mark.unit @pytest.mark.parametrize("file_size_mb,expected_valid", [ (50, True), (100, True), (101, False), (200, False), ]) def test_file_size_validation(self, file_size_mb: int, expected_valid: bool) -> None: """测试文件大小验证 - 最大 100MB""" file_size_bytes = file_size_mb * 1024 * 1024 # TODO: 实现文件大小验证 # validator = VideoFileValidator() # result = validator.validate_size(file_size_bytes) # # assert result.is_valid == expected_valid # if not expected_valid: # assert "100MB" in result.error_message pytest.skip("待实现：文件大小验证") @pytest.mark.unit @pytest.mark.parametrize("file_format,mime_type,expected_valid", [ ("mp4", "video/mp4", True), ("mov", "video/quicktime", True), ("avi", "video/x-msvideo", False), ("mkv", "video/x-matroska", False), ("pdf", "application/pdf", False), ]) def test_file_format_validation( self, file_format: str, mime_type: str, expected_valid: bool, ) -> None: """测试文件格式验证 - 仅支持 MP4/MOV""" # TODO: 实现格式验证 # validator = VideoFileValidator() # result = validator.validate_format(file_format, mime_type) # # assert result.is_valid == expected_valid pytest.skip("待实现：文件格式验证") class TestASRAccuracy: """ ASR 语音识别测试 验收标准 (DevelopmentPlan.md): - 字错率 (WER) ≤ 10% """ @pytest.mark.unit def test_asr_output_format(self) -> None: """测试 ASR 输出格式""" # TODO: 实现 ASR 服务 # asr = ASRService() # result = asr.transcribe("test_audio.wav") # # assert "text" in result # assert "segments" in result # for segment in result["segments"]: # assert "word" in segment # assert "start_ms" in segment # assert "end_ms" in segment # assert "confidence" in segment # assert segment["end_ms"] >= segment["start_ms"] pytest.skip("待实现：ASR 输出格式") @pytest.mark.unit def test_asr_word_error_rate(self) -> None: """ 测试 ASR 字错率 验收标准：WER ≤ 10% """ # TODO: 使用标注测试集验证 # asr = ASRService() # test_set = load_asr_test_set() # 标注数据集 # # total_errors = 0 # total_words = 0 # # for sample in test_set: # result = asr.transcribe(sample["audio_path"]) # wer = calculate_wer(result["text"], sample["ground_truth"]) # total_errors += wer * len(sample["ground_truth"].split()) # total_words += len(sample["ground_truth"].split()) # # overall_wer = total_errors / total_words # assert overall_wer <= 0.10, f"WER {overall_wer:.2%} 超过阈值 10%" pytest.skip("待实现：ASR 字错率测试") @pytest.mark.unit def test_asr_timestamp_accuracy(self) -> None: """测试 ASR 时间戳准确性""" # TODO: 实现时间戳验证 # asr = ASRService() # result = asr.transcribe("test_audio.wav") # # # 时间戳应递增 # prev_end = 0 # for segment in result["segments"]: # assert segment["start_ms"] >= prev_end # prev_end = segment["end_ms"] pytest.skip("待实现：ASR 时间戳准确性") class TestOCRAccuracy: """ OCR 字幕识别测试 验收标准 (DevelopmentPlan.md): - 准确率 ≥ 95%（含复杂背景） """ @pytest.mark.unit def test_ocr_output_format(self) -> None: """测试 OCR 输出格式""" # TODO: 实现 OCR 服务 # ocr = OCRService() # result = ocr.extract_text("video_frame.jpg") # # assert "frames" in result # for frame in result["frames"]: # assert "timestamp_ms" in frame # assert "text" in frame # assert "confidence" in frame # assert "bbox" in frame pytest.skip("待实现：OCR 输出格式") @pytest.mark.unit def test_ocr_accuracy_rate(self) -> None: """ 测试 OCR 准确率 验收标准：准确率 ≥ 95% """ # TODO: 使用标注测试集验证 # ocr = OCRService() # test_set = load_ocr_test_set() # # correct = 0 # for sample in test_set: # result = ocr.extract_text(sample["image_path"]) # if result["text"] == sample["ground_truth"]: # correct += 1 # # accuracy = correct / len(test_set) # assert accuracy >= 0.95, f"准确率 {accuracy:.2%} 低于阈值 95%" pytest.skip("待实现：OCR 准确率测试") @pytest.mark.unit def test_ocr_complex_background(self) -> None: """测试复杂背景下的 OCR""" # TODO: 测试复杂背景 # ocr = OCRService() # # # 测试不同背景复杂度 # test_cases = [ # {"image": "simple_bg.jpg", "text": "测试文字"}, # {"image": "complex_bg.jpg", "text": "复杂背景"}, # {"image": "gradient_bg.jpg", "text": "渐变背景"}, # ] # # for case in test_cases: # result = ocr.extract_text(case["image"]) # assert result["text"] == case["text"] pytest.skip("待实现：复杂背景 OCR") class TestLogoDetection: """ 竞品 Logo 检测测试 验收标准 (FeatureSummary.md F-12): - F1 ≥ 0.85（含遮挡 30% 场景） """ @pytest.mark.unit def test_logo_detection_output_format(self) -> None: """测试 Logo 检测输出格式""" # TODO: 实现 Logo 检测服务 # detector = LogoDetector() # result = detector.detect("video_frame.jpg") # # assert "detections" in result # for detection in result["detections"]: # assert "logo_id" in detection # assert "confidence" in detection # assert "bbox" in detection # assert detection["confidence"] >= 0 and detection["confidence"] <= 1 pytest.skip("待实现：Logo 检测输出格式") @pytest.mark.unit def test_logo_detection_f1_score(self) -> None: """ 测试 Logo 检测 F1 值 验收标准：F1 ≥ 0.85 """ # TODO: 使用标注测试集验证 # detector = LogoDetector() # test_set = load_logo_test_set() # ≥ 200 张图片 # # predictions = [] # ground_truths = [] # # for sample in test_set: # result = detector.detect(sample["image_path"]) # predictions.append(result["detections"]) # ground_truths.append(sample["ground_truth_logos"]) # # f1 = calculate_f1(predictions, ground_truths) # assert f1 >= 0.85, f"F1 {f1:.2f} 低于阈值 0.85" pytest.skip("待实现：Logo F1 测试") @pytest.mark.unit def test_logo_detection_with_occlusion(self) -> None: """ 测试遮挡场景下的 Logo 检测 验收标准：30% 遮挡仍可检测 """ # TODO: 测试遮挡场景 # detector = LogoDetector() # # # 30% 遮挡的 Logo 图片 # result = detector.detect("logo_30_percent_occluded.jpg") # # assert len(result["detections"]) > 0 # assert result["detections"][0]["confidence"] >= 0.7 pytest.skip("待实现：遮挡场景 Logo 检测") @pytest.mark.unit def test_new_logo_instant_effect(self) -> None: """测试新 Logo 上传即刻生效""" # TODO: 测试动态添加 Logo # detector = LogoDetector() # # # 上传新 Logo # detector.add_logo("new_competitor_logo.png", brand="New Competitor") # # # 立即测试检测 # result = detector.detect("frame_with_new_logo.jpg") # assert any(d["brand"] == "New Competitor" for d in result["detections"]) pytest.skip("待实现：Logo 动态添加") class TestAuditPipeline: """ 审核流水线集成测试 """ @pytest.mark.unit def test_audit_processing_time(self) -> None: """ 测试审核处理时间 验收标准：100MB 视频 ≤ 5 分钟 """ # TODO: 实现处理时间测试 # import time # # auditor = VideoAuditor() # start_time = time.time() # # result = auditor.audit("100mb_test_video.mp4") # # processing_time = time.time() - start_time # assert processing_time <= 300, f"处理时间 {processing_time:.1f}s 超过 5 分钟" pytest.skip("待实现：处理时间测试") @pytest.mark.unit def test_audit_report_structure(self) -> None: """测试审核报告结构""" # TODO: 实现报告结构验证 # auditor = VideoAuditor() # report = auditor.audit("test_video.mp4") # # # 验证报告必需字段 # required_fields = [ # "report_id", "video_id", "processing_status", # "asr_results", "ocr_results", "cv_results", # "violations", "brief_compliance" # ] # for field in required_fields: # assert field in report pytest.skip("待实现：报告结构验证") @pytest.mark.unit def test_violation_with_evidence(self) -> None: """测试违规项包含证据""" # TODO: 实现证据验证 # auditor = VideoAuditor() # report = auditor.audit("video_with_violation.mp4") # # for violation in report["violations"]: # assert "evidence" in violation # assert violation["evidence"]["url"] is not None # assert violation["evidence"]["timestamp_start"] is not None pytest.skip("待实现：违规证据") class TestBriefCompliance: """ Brief 合规检查测试 验收标准 (FeatureSummary.md F-45): - 时长统计误差 ≤ 0.5秒 - 频次统计准确率 ≥ 95% """ @pytest.mark.unit def test_selling_point_coverage( self, sample_brief_rules: dict[str, Any], ) -> None: """测试卖点覆盖检测""" video_content = { "asr_text": "24小时持妆效果非常好，使用天然成分", "ocr_text": "24小时持妆", } # TODO: 实现卖点覆盖检测 # checker = BriefComplianceChecker() # result = checker.check_selling_points( # video_content, # sample_brief_rules["selling_points"] # ) # # # 应检测到 2/3 卖点覆盖 # assert result["coverage_rate"] >= 0.66 # assert "24小时持妆" in result["detected"] # assert "天然成分" in result["detected"] pytest.skip("待实现：卖点覆盖检测") @pytest.mark.unit def test_duration_requirement_check( self, sample_brief_rules: dict[str, Any], ) -> None: """测试时长要求检查""" cv_detections = [ {"object_type": "product", "start_ms": 0, "end_ms": 6000}, # 6秒 ] # 要求: 产品同框 > 5秒 # TODO: 实现时长检查 # checker = BriefComplianceChecker() # result = checker.check_duration( # cv_detections, # sample_brief_rules["timing_requirements"] # ) # # assert result["product_visible"]["status"] == "passed" # assert result["product_visible"]["detected_seconds"] == 6.0 pytest.skip("待实现：时长要求检查") @pytest.mark.unit def test_frequency_requirement_check( self, sample_brief_rules: dict[str, Any], ) -> None: """测试频次要求检查""" asr_segments = [ {"text": "品牌名产品"}, {"text": "这个品牌名很好"}, {"text": "推荐品牌名"}, ] # 要求: 品牌名提及 ≥ 3次 # TODO: 实现频次检查 # checker = BriefComplianceChecker() # result = checker.check_frequency( # asr_segments, # sample_brief_rules["timing_requirements"], # brand_keyword="品牌名" # ) # # assert result["brand_mention"]["status"] == "passed" # assert result["brand_mention"]["detected_count"] == 3 pytest.skip("待实现：频次要求检查")