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