feat: 添加全面的 TDD 测试套件框架
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>
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Claude Opus 4.5
Claude Opus 4.5
parent
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commit
040aada160
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"""
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ASR 服务单元测试
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TDD 测试用例 - 基于 DevelopmentPlan.md 的验收标准
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验收标准：
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- 字错率 (WER) ≤ 10%
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- 时间戳精度 ≤ 100ms
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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.ai.asr import ASRService, ASRResult, ASRSegment
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class TestASRService:
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"""ASR 服务测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_asr_service_initialization(self) -> None:
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"""测试 ASR 服务初始化"""
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# TODO: 实现 ASR 服务
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# service = ASRService()
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# assert service.is_ready()
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# assert service.model_name is not None
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pytest.skip("待实现：ASR 服务初始化")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_asr_transcribe_audio_file(self) -> None:
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"""测试音频文件转写"""
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# TODO: 实现音频转写
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# assert result.status == "success"
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# assert result.text is not None
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# assert len(result.text) > 0
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pytest.skip("待实现：音频转写")
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@pytest.mark.ai
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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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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# # 验证输出结构
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# assert hasattr(result, "text")
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# assert hasattr(result, "segments")
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# assert hasattr(result, "language")
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# assert hasattr(result, "duration_ms")
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#
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# # 验证 segment 结构
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# for segment in result.segments:
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# assert hasattr(segment, "text")
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# assert hasattr(segment, "start_ms")
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# assert hasattr(segment, "end_ms")
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# assert hasattr(segment, "confidence")
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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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class TestASRAccuracy:
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"""ASR 准确率测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_word_error_rate_threshold(self) -> None:
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"""
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测试字错率阈值
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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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# service = ASRService()
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# test_cases = load_asr_labeled_dataset()
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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 case in test_cases:
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# result = service.transcribe(case["audio_path"])
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# wer = calculate_word_error_rate(
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# result.text,
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# case["ground_truth"]
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# )
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# total_errors += wer * len(case["ground_truth"])
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# total_words += len(case["ground_truth"])
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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：WER 测试")
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@pytest.mark.ai
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@pytest.mark.unit
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@pytest.mark.parametrize("audio_type,expected_wer_threshold", [
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("clean_speech", 0.05), # 清晰语音 WER < 5%
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("background_music", 0.10), # 背景音乐 WER < 10%
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("multiple_speakers", 0.15), # 多人对话 WER < 15%
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("noisy_environment", 0.20), # 嘈杂环境 WER < 20%
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])
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def test_wer_by_audio_type(
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self,
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audio_type: str,
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expected_wer_threshold: float,
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) -> None:
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"""测试不同音频类型的 WER"""
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# TODO: 实现分类型 WER 测试
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# service = ASRService()
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# test_cases = load_asr_test_set_by_type(audio_type)
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#
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# wer = calculate_average_wer(service, test_cases)
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# assert wer <= expected_wer_threshold
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pytest.skip(f"待实现：{audio_type} WER 测试")
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class TestASRTimestamp:
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"""ASR 时间戳测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_monotonic_increase(self) -> None:
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"""测试时间戳单调递增"""
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# TODO: 实现时间戳验证
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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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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# f"时间戳不是单调递增: {segment.start_ms} < {prev_end}"
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# prev_end = segment.end_ms
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pytest.skip("待实现：时间戳单调递增")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_precision(self) -> None:
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"""
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测试时间戳精度
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验收标准：精度 ≤ 100ms
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"""
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# TODO: 使用标注测试集验证
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# service = ASRService()
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# test_cases = load_timestamp_labeled_dataset()
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#
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# total_error = 0
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# total_segments = 0
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#
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# for case in test_cases:
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# result = service.transcribe(case["audio_path"])
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# for i, segment in enumerate(result.segments):
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# if i < len(case["ground_truth_timestamps"]):
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# gt = case["ground_truth_timestamps"][i]
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# start_error = abs(segment.start_ms - gt["start_ms"])
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# end_error = abs(segment.end_ms - gt["end_ms"])
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# total_error += (start_error + end_error) / 2
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# total_segments += 1
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#
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# avg_error = total_error / total_segments if total_segments > 0 else 0
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# assert avg_error <= 100, f"平均时间戳误差 {avg_error:.0f}ms 超过阈值 100ms"
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pytest.skip("待实现：时间戳精度测试")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_timestamp_within_audio_duration(self) -> None:
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"""测试时间戳在音频时长范围内"""
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# TODO: 实现边界验证
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/sample.wav")
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#
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# for segment in result.segments:
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# assert segment.start_ms >= 0
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# assert segment.end_ms <= result.duration_ms
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pytest.skip("待实现：时间戳边界验证")
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class TestASRLanguage:
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"""ASR 语言处理测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_chinese_mandarin_recognition(self) -> None:
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"""测试普通话识别"""
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# TODO: 实现普通话测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/mandarin.wav")
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#
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# assert result.language == "zh-CN"
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# assert "你好" in result.text or len(result.text) > 0
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pytest.skip("待实现：普通话识别")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_mixed_language_handling(self) -> None:
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"""测试中英混合语音处理"""
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# TODO: 实现混合语言测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/mixed_cn_en.wav")
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#
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# # 应能识别中英文混合内容
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# assert result.status == "success"
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pytest.skip("待实现：中英混合识别")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_dialect_handling(self) -> None:
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"""测试方言处理"""
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# TODO: 实现方言测试
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# service = ASRService()
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#
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# # 方言可能降级处理或提示
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# result = service.transcribe("tests/fixtures/audio/cantonese.wav")
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#
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# if result.status == "success":
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# assert result.language in ["zh-CN", "zh-HK", "yue"]
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# else:
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# assert result.warning == "dialect_detected"
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pytest.skip("待实现：方言处理")
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class TestASRSpecialCases:
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"""ASR 特殊情况测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_silent_audio(self) -> None:
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"""测试静音音频"""
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# TODO: 实现静音测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/silent.wav")
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#
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# assert result.status == "success"
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# assert result.text == "" or result.segments == []
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pytest.skip("待实现：静音音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_very_short_audio(self) -> None:
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"""测试极短音频 (< 1秒)"""
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# TODO: 实现极短音频测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/short_500ms.wav")
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#
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# assert result.status == "success"
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pytest.skip("待实现：极短音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_long_audio(self) -> None:
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"""测试长音频 (> 5分钟)"""
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# TODO: 实现长音频测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/long_10min.wav")
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#
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# assert result.status == "success"
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# assert result.duration_ms >= 600000 # 10分钟
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pytest.skip("待实现：长音频处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_corrupted_audio_handling(self) -> None:
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"""测试损坏音频处理"""
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# TODO: 实现错误处理测试
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# service = ASRService()
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# result = service.transcribe("tests/fixtures/audio/corrupted.wav")
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#
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# assert result.status == "error"
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# assert "corrupted" in result.error_message.lower() or \
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# "invalid" in result.error_message.lower()
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pytest.skip("待实现：损坏音频处理")
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class TestASRPerformance:
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"""ASR 性能测试"""
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@pytest.mark.ai
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@pytest.mark.performance
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def test_transcription_speed(self) -> None:
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"""
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测试转写速度
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验收标准：实时率 ≤ 0.5 (转写时间 / 音频时长)
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"""
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# TODO: 实现性能测试
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# import time
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#
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# service = ASRService()
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#
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# # 60秒测试音频
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# start_time = time.time()
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# result = service.transcribe("tests/fixtures/audio/60s_sample.wav")
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# processing_time = time.time() - start_time
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#
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# audio_duration = result.duration_ms / 1000
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# real_time_factor = processing_time / audio_duration
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#
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# assert real_time_factor <= 0.5, \
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# f"实时率 {real_time_factor:.2f} 超过阈值 0.5"
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pytest.skip("待实现：转写速度测试")
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@pytest.mark.ai
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@pytest.mark.performance
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def test_concurrent_transcription(self) -> None:
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"""测试并发转写"""
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# TODO: 实现并发测试
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# import asyncio
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#
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# service = ASRService()
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#
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# async def transcribe_one(audio_path: str):
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# return await service.transcribe_async(audio_path)
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#
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# # 并发处理 5 个音频
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# tasks = [
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# transcribe_one(f"tests/fixtures/audio/sample_{i}.wav")
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# for i in range(5)
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# ]
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# results = await asyncio.gather(*tasks)
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#
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# assert all(r.status == "success" for r in results)
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pytest.skip("待实现：并发转写测试")
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@@ -0,0 +1,370 @@
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"""
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竞品 Logo 检测服务单元测试
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TDD 测试用例 - 基于 FeatureSummary.md F-12 的验收标准
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验收标准：
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- F1 ≥ 0.85（含遮挡 30% 场景）
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- 新 Logo 上传即刻生效
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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.ai.logo_detector import LogoDetector, LogoDetection
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class TestLogoDetector:
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"""Logo 检测器测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_logo_detector_initialization(self) -> None:
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"""测试 Logo 检测器初始化"""
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# TODO: 实现 Logo 检测器
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# detector = LogoDetector()
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# assert detector.is_ready()
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# assert detector.logo_count > 0 # 预加载的 Logo 数量
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pytest.skip("待实现：Logo 检测器初始化")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_detect_logo_in_image(self) -> None:
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"""测试图片中的 Logo 检测"""
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# TODO: 实现 Logo 检测
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
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#
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# assert result.status == "success"
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# assert len(result.detections) > 0
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pytest.skip("待实现：Logo 检测")
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@pytest.mark.ai
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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
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
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#
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# # 验证输出结构
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# assert hasattr(result, "detections")
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# for detection in result.detections:
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# assert hasattr(detection, "logo_id")
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# assert hasattr(detection, "brand_name")
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# assert hasattr(detection, "confidence")
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# assert hasattr(detection, "bbox")
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# assert 0 <= detection.confidence <= 1
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# assert len(detection.bbox) == 4
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pytest.skip("待实现：Logo 检测输出格式")
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class TestLogoDetectionAccuracy:
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"""Logo 检测准确率测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_f1_score_threshold(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
|
||||
"""
|
||||
# TODO: 使用标注测试集验证
|
||||
# detector = LogoDetector()
|
||||
# test_set = load_logo_labeled_dataset() # ≥ 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_score(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.ai
|
||||
@pytest.mark.unit
|
||||
def test_precision_recall(self) -> None:
|
||||
"""测试查准率和查全率"""
|
||||
# TODO: 使用标注测试集验证
|
||||
# detector = LogoDetector()
|
||||
# test_set = load_logo_labeled_dataset()
|
||||
#
|
||||
# precision, recall = calculate_precision_recall(detector, test_set)
|
||||
#
|
||||
# # 查准率和查全率都应该较高
|
||||
# assert precision >= 0.80
|
||||
# assert recall >= 0.80
|
||||
pytest.skip("待实现：查准率查全率测试")
|
||||
|
||||
|
||||
class TestLogoOcclusion:
|
||||
"""Logo 遮挡检测测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
@pytest.mark.parametrize("occlusion_percent,should_detect", [
|
||||
(0, True), # 无遮挡
|
||||
(10, True), # 10% 遮挡
|
||||
(20, True), # 20% 遮挡
|
||||
(30, True), # 30% 遮挡 - 边界
|
||||
(40, False), # 40% 遮挡 - 可能检测失败
|
||||
(50, False), # 50% 遮挡
|
||||
])
|
||||
def test_logo_detection_with_occlusion(
|
||||
self,
|
||||
occlusion_percent: int,
|
||||
should_detect: bool,
|
||||
) -> None:
|
||||
"""
|
||||
测试遮挡场景下的 Logo 检测
|
||||
|
||||
验收标准：30% 遮挡仍可检测
|
||||
"""
|
||||
# TODO: 实现遮挡测试
|
||||
# detector = LogoDetector()
|
||||
# image_path = f"tests/fixtures/images/logo_occluded_{occlusion_percent}pct.jpg"
|
||||
# result = detector.detect(image_path)
|
||||
#
|
||||
# if should_detect:
|
||||
# assert len(result.detections) > 0, \
|
||||
# f"{occlusion_percent}% 遮挡应能检测到 Logo"
|
||||
# # 置信度可能较低
|
||||
# assert result.detections[0].confidence >= 0.5
|
||||
pytest.skip(f"待实现：{occlusion_percent}% 遮挡 Logo 检测")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_partial_logo_detection(self) -> None:
|
||||
"""测试部分可见 Logo 检测"""
|
||||
# TODO: 实现部分可见测试
|
||||
# detector = LogoDetector()
|
||||
# result = detector.detect("tests/fixtures/images/logo_partial.jpg")
|
||||
#
|
||||
# # 部分可见的 Logo 应标记 partial=True
|
||||
# if len(result.detections) > 0:
|
||||
# assert result.detections[0].is_partial
|
||||
pytest.skip("待实现：部分可见 Logo 检测")
|
||||
|
||||
|
||||
class TestLogoDynamicUpdate:
|
||||
"""Logo 动态更新测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_add_new_logo_instant_effect(self) -> None:
|
||||
"""
|
||||
测试新 Logo 上传即刻生效
|
||||
|
||||
验收标准：新增竞品 Logo 应立即可检测
|
||||
"""
|
||||
# TODO: 实现动态添加测试
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# # 检测前应无法识别
|
||||
# result_before = detector.detect("tests/fixtures/images/with_new_logo.jpg")
|
||||
# assert not any(d.brand_name == "NewBrand" for d in result_before.detections)
|
||||
#
|
||||
# # 添加新 Logo
|
||||
# detector.add_logo(
|
||||
# logo_image="tests/fixtures/logos/new_brand_logo.png",
|
||||
# brand_name="NewBrand"
|
||||
# )
|
||||
#
|
||||
# # 检测后应能识别
|
||||
# result_after = detector.detect("tests/fixtures/images/with_new_logo.jpg")
|
||||
# assert any(d.brand_name == "NewBrand" for d in result_after.detections)
|
||||
pytest.skip("待实现：Logo 动态添加")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_remove_logo(self) -> None:
|
||||
"""测试移除 Logo"""
|
||||
# TODO: 实现 Logo 移除
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# # 移除前可检测
|
||||
# result_before = detector.detect("tests/fixtures/images/with_existing_logo.jpg")
|
||||
# assert any(d.brand_name == "ExistingBrand" for d in result_before.detections)
|
||||
#
|
||||
# # 移除 Logo
|
||||
# detector.remove_logo(brand_name="ExistingBrand")
|
||||
#
|
||||
# # 移除后不再检测
|
||||
# result_after = detector.detect("tests/fixtures/images/with_existing_logo.jpg")
|
||||
# assert not any(d.brand_name == "ExistingBrand" for d in result_after.detections)
|
||||
pytest.skip("待实现：Logo 移除")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_update_logo_variants(self) -> None:
|
||||
"""测试更新 Logo 变体"""
|
||||
# TODO: 实现 Logo 变体更新
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# # 添加多个变体
|
||||
# detector.add_logo_variant(
|
||||
# brand_name="Brand",
|
||||
# variant_image="tests/fixtures/logos/brand_variant_dark.png",
|
||||
# variant_type="dark_mode"
|
||||
# )
|
||||
#
|
||||
# # 应能检测新变体
|
||||
# result = detector.detect("tests/fixtures/images/with_dark_logo.jpg")
|
||||
# assert len(result.detections) > 0
|
||||
pytest.skip("待实现：Logo 变体更新")
|
||||
|
||||
|
||||
class TestLogoVideoProcessing:
|
||||
"""视频 Logo 检测测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_detect_logo_in_video_frames(self) -> None:
|
||||
"""测试视频帧中的 Logo 检测"""
|
||||
# TODO: 实现视频帧检测
|
||||
# detector = LogoDetector()
|
||||
# frame_paths = [
|
||||
# f"tests/fixtures/images/video_frame_{i}.jpg"
|
||||
# for i in range(30)
|
||||
# ]
|
||||
#
|
||||
# results = detector.batch_detect(frame_paths)
|
||||
#
|
||||
# assert len(results) == 30
|
||||
# # 至少部分帧应检测到 Logo
|
||||
# frames_with_logo = sum(1 for r in results if len(r.detections) > 0)
|
||||
# assert frames_with_logo > 0
|
||||
pytest.skip("待实现：视频帧 Logo 检测")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_logo_tracking_across_frames(self) -> None:
|
||||
"""测试跨帧 Logo 跟踪"""
|
||||
# TODO: 实现跨帧跟踪
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# # 检测连续帧
|
||||
# frame_results = []
|
||||
# for i in range(10):
|
||||
# result = detector.detect(f"tests/fixtures/images/tracking_frame_{i}.jpg")
|
||||
# frame_results.append(result)
|
||||
#
|
||||
# # 跟踪应返回相同的 track_id
|
||||
# track_ids = [
|
||||
# r.detections[0].track_id
|
||||
# for r in frame_results
|
||||
# if len(r.detections) > 0
|
||||
# ]
|
||||
# assert len(set(track_ids)) == 1 # 同一个 Logo
|
||||
pytest.skip("待实现：跨帧 Logo 跟踪")
|
||||
|
||||
|
||||
class TestLogoSpecialCases:
|
||||
"""Logo 检测特殊情况测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_no_logo_image(self) -> None:
|
||||
"""测试无 Logo 图片"""
|
||||
# TODO: 实现无 Logo 测试
|
||||
# detector = LogoDetector()
|
||||
# result = detector.detect("tests/fixtures/images/no_logo.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
# assert len(result.detections) == 0
|
||||
pytest.skip("待实现：无 Logo 图片处理")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_multiple_logos_detection(self) -> None:
|
||||
"""测试多 Logo 检测"""
|
||||
# TODO: 实现多 Logo 测试
|
||||
# detector = LogoDetector()
|
||||
# result = detector.detect("tests/fixtures/images/multiple_logos.jpg")
|
||||
#
|
||||
# assert len(result.detections) >= 2
|
||||
# # 每个检测应有唯一 ID
|
||||
# logo_ids = [d.logo_id for d in result.detections]
|
||||
# assert len(logo_ids) == len(set(logo_ids))
|
||||
pytest.skip("待实现：多 Logo 检测")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_similar_logo_distinction(self) -> None:
|
||||
"""测试相似 Logo 区分"""
|
||||
# TODO: 实现相似 Logo 区分
|
||||
# detector = LogoDetector()
|
||||
# result = detector.detect("tests/fixtures/images/similar_logos.jpg")
|
||||
#
|
||||
# # 应能区分相似但不同的 Logo
|
||||
# brand_names = [d.brand_name for d in result.detections]
|
||||
# assert "BrandA" in brand_names
|
||||
# assert "BrandB" in brand_names # 相似但不同
|
||||
pytest.skip("待实现：相似 Logo 区分")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_distorted_logo_detection(self) -> None:
|
||||
"""测试变形 Logo 检测"""
|
||||
# TODO: 实现变形 Logo 测试
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# # 测试不同变形
|
||||
# test_cases = [
|
||||
# "logo_stretched.jpg",
|
||||
# "logo_rotated.jpg",
|
||||
# "logo_skewed.jpg",
|
||||
# ]
|
||||
#
|
||||
# for image_name in test_cases:
|
||||
# result = detector.detect(f"tests/fixtures/images/{image_name}")
|
||||
# assert len(result.detections) > 0, f"变形 Logo {image_name} 应被检测"
|
||||
pytest.skip("待实现：变形 Logo 检测")
|
||||
|
||||
|
||||
class TestLogoPerformance:
|
||||
"""Logo 检测性能测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.performance
|
||||
def test_detection_speed(self) -> None:
|
||||
"""测试检测速度"""
|
||||
# TODO: 实现性能测试
|
||||
# import time
|
||||
#
|
||||
# detector = LogoDetector()
|
||||
#
|
||||
# start_time = time.time()
|
||||
# result = detector.detect("tests/fixtures/images/1080p_sample.jpg")
|
||||
# processing_time = time.time() - start_time
|
||||
#
|
||||
# # 单张图片应 < 200ms
|
||||
# assert processing_time < 0.2
|
||||
pytest.skip("待实现：Logo 检测速度测试")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.performance
|
||||
def test_batch_detection_speed(self) -> None:
|
||||
"""测试批量检测速度"""
|
||||
# TODO: 实现批量性能测试
|
||||
# import time
|
||||
#
|
||||
# detector = LogoDetector()
|
||||
# frame_paths = [
|
||||
# f"tests/fixtures/images/frame_{i}.jpg"
|
||||
# for i in range(30)
|
||||
# ]
|
||||
#
|
||||
# start_time = time.time()
|
||||
# results = detector.batch_detect(frame_paths)
|
||||
# processing_time = time.time() - start_time
|
||||
#
|
||||
# # 30 帧应在 2 秒内完成
|
||||
# assert processing_time < 2.0
|
||||
pytest.skip("待实现：批量 Logo 检测速度测试")
|
||||
@@ -0,0 +1,307 @@
|
||||
"""
|
||||
OCR 服务单元测试
|
||||
|
||||
TDD 测试用例 - 基于 DevelopmentPlan.md 的验收标准
|
||||
|
||||
验收标准：
|
||||
- 准确率 ≥ 95%（含复杂背景）
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from typing import Any
|
||||
|
||||
# 导入待实现的模块（TDD 红灯阶段）
|
||||
# from app.services.ai.ocr import OCRService, OCRResult, OCRDetection
|
||||
|
||||
|
||||
class TestOCRService:
|
||||
"""OCR 服务测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_service_initialization(self) -> None:
|
||||
"""测试 OCR 服务初始化"""
|
||||
# TODO: 实现 OCR 服务
|
||||
# service = OCRService()
|
||||
# assert service.is_ready()
|
||||
# assert service.model_name is not None
|
||||
pytest.skip("待实现：OCR 服务初始化")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_extract_text_from_image(self) -> None:
|
||||
"""测试从图片提取文字"""
|
||||
# TODO: 实现文字提取
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/text_sample.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
# assert len(result.detections) > 0
|
||||
pytest.skip("待实现：图片文字提取")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_output_format(self) -> None:
|
||||
"""测试 OCR 输出格式"""
|
||||
# TODO: 实现 OCR 服务
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/text_sample.jpg")
|
||||
#
|
||||
# # 验证输出结构
|
||||
# assert hasattr(result, "detections")
|
||||
# assert hasattr(result, "full_text")
|
||||
#
|
||||
# # 验证 detection 结构
|
||||
# for detection in result.detections:
|
||||
# assert hasattr(detection, "text")
|
||||
# assert hasattr(detection, "confidence")
|
||||
# assert hasattr(detection, "bbox")
|
||||
# assert len(detection.bbox) == 4 # [x1, y1, x2, y2]
|
||||
pytest.skip("待实现：OCR 输出格式")
|
||||
|
||||
|
||||
class TestOCRAccuracy:
|
||||
"""OCR 准确率测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_accuracy_threshold(self) -> None:
|
||||
"""
|
||||
测试 OCR 准确率阈值
|
||||
|
||||
验收标准：准确率 ≥ 95%
|
||||
"""
|
||||
# TODO: 使用标注测试集验证
|
||||
# service = OCRService()
|
||||
# test_cases = load_ocr_labeled_dataset()
|
||||
#
|
||||
# correct = 0
|
||||
# for case in test_cases:
|
||||
# result = service.extract_text(case["image_path"])
|
||||
# if normalize_text(result.full_text) == normalize_text(case["ground_truth"]):
|
||||
# correct += 1
|
||||
#
|
||||
# accuracy = correct / len(test_cases)
|
||||
# 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.ai
|
||||
@pytest.mark.unit
|
||||
@pytest.mark.parametrize("background_type,expected_accuracy", [
|
||||
("simple_white", 0.99), # 简单白底
|
||||
("solid_color", 0.98), # 纯色背景
|
||||
("gradient", 0.95), # 渐变背景
|
||||
("complex_image", 0.90), # 复杂图片背景
|
||||
("video_frame", 0.90), # 视频帧
|
||||
])
|
||||
def test_ocr_accuracy_by_background(
|
||||
self,
|
||||
background_type: str,
|
||||
expected_accuracy: float,
|
||||
) -> None:
|
||||
"""测试不同背景类型的 OCR 准确率"""
|
||||
# TODO: 实现分背景类型测试
|
||||
# service = OCRService()
|
||||
# test_cases = load_ocr_test_set_by_background(background_type)
|
||||
#
|
||||
# accuracy = calculate_ocr_accuracy(service, test_cases)
|
||||
# assert accuracy >= expected_accuracy
|
||||
pytest.skip(f"待实现：{background_type} OCR 准确率测试")
|
||||
|
||||
|
||||
class TestOCRChinese:
|
||||
"""中文 OCR 测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_simplified_chinese_recognition(self) -> None:
|
||||
"""测试简体中文识别"""
|
||||
# TODO: 实现简体中文测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/simplified_chinese.jpg")
|
||||
#
|
||||
# assert "测试" in result.full_text
|
||||
pytest.skip("待实现：简体中文识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_traditional_chinese_recognition(self) -> None:
|
||||
"""测试繁体中文识别"""
|
||||
# TODO: 实现繁体中文测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/traditional_chinese.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
pytest.skip("待实现：繁体中文识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_mixed_chinese_english(self) -> None:
|
||||
"""测试中英混合文字识别"""
|
||||
# TODO: 实现中英混合测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/mixed_cn_en.jpg")
|
||||
#
|
||||
# # 应能同时识别中英文
|
||||
# assert result.status == "success"
|
||||
pytest.skip("待实现：中英混合识别")
|
||||
|
||||
|
||||
class TestOCRVideoFrame:
|
||||
"""视频帧 OCR 测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_video_subtitle(self) -> None:
|
||||
"""测试视频字幕识别"""
|
||||
# TODO: 实现字幕识别
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/video_subtitle.jpg")
|
||||
#
|
||||
# assert len(result.detections) > 0
|
||||
# # 字幕通常在画面下方
|
||||
# subtitle_detection = result.detections[0]
|
||||
# assert subtitle_detection.bbox[1] > 0.6 # y 坐标在下半部分
|
||||
pytest.skip("待实现：视频字幕识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_watermark_detection(self) -> None:
|
||||
"""测试水印文字识别"""
|
||||
# TODO: 实现水印识别
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/with_watermark.jpg")
|
||||
#
|
||||
# # 应能检测到水印文字
|
||||
# watermark_found = any(
|
||||
# d.is_watermark for d in result.detections
|
||||
# )
|
||||
# assert watermark_found or len(result.detections) > 0
|
||||
pytest.skip("待实现：水印文字识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_ocr_batch_video_frames(self) -> None:
|
||||
"""测试批量视频帧 OCR"""
|
||||
# TODO: 实现批量处理
|
||||
# service = OCRService()
|
||||
# frame_paths = [
|
||||
# f"tests/fixtures/images/frame_{i}.jpg"
|
||||
# for i in range(10)
|
||||
# ]
|
||||
#
|
||||
# results = service.batch_extract(frame_paths)
|
||||
#
|
||||
# assert len(results) == 10
|
||||
# assert all(r.status == "success" for r in results)
|
||||
pytest.skip("待实现：批量视频帧 OCR")
|
||||
|
||||
|
||||
class TestOCRSpecialCases:
|
||||
"""OCR 特殊情况测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_rotated_text(self) -> None:
|
||||
"""测试旋转文字识别"""
|
||||
# TODO: 实现旋转文字测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/rotated_text.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
# assert len(result.detections) > 0
|
||||
pytest.skip("待实现：旋转文字识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_vertical_text(self) -> None:
|
||||
"""测试竖排文字识别"""
|
||||
# TODO: 实现竖排文字测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/vertical_text.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
pytest.skip("待实现：竖排文字识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_artistic_font(self) -> None:
|
||||
"""测试艺术字体识别"""
|
||||
# TODO: 实现艺术字体测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/artistic_font.jpg")
|
||||
#
|
||||
# # 艺术字体准确率可能较低，但应能识别
|
||||
# assert result.status == "success"
|
||||
pytest.skip("待实现：艺术字体识别")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_no_text_image(self) -> None:
|
||||
"""测试无文字图片"""
|
||||
# TODO: 实现无文字测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/no_text.jpg")
|
||||
#
|
||||
# assert result.status == "success"
|
||||
# assert len(result.detections) == 0
|
||||
# assert result.full_text == ""
|
||||
pytest.skip("待实现：无文字图片处理")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.unit
|
||||
def test_blurry_text(self) -> None:
|
||||
"""测试模糊文字识别"""
|
||||
# TODO: 实现模糊文字测试
|
||||
# service = OCRService()
|
||||
# result = service.extract_text("tests/fixtures/images/blurry_text.jpg")
|
||||
#
|
||||
# # 模糊文字可能识别失败或置信度低
|
||||
# if result.status == "success" and len(result.detections) > 0:
|
||||
# avg_confidence = sum(d.confidence for d in result.detections) / len(result.detections)
|
||||
# assert avg_confidence < 0.9 # 置信度应较低
|
||||
pytest.skip("待实现：模糊文字识别")
|
||||
|
||||
|
||||
class TestOCRPerformance:
|
||||
"""OCR 性能测试"""
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.performance
|
||||
def test_ocr_processing_speed(self) -> None:
|
||||
"""测试 OCR 处理速度"""
|
||||
# TODO: 实现性能测试
|
||||
# import time
|
||||
#
|
||||
# service = OCRService()
|
||||
#
|
||||
# # 标准 1080p 图片
|
||||
# start_time = time.time()
|
||||
# result = service.extract_text("tests/fixtures/images/1080p_sample.jpg")
|
||||
# processing_time = time.time() - start_time
|
||||
#
|
||||
# # 单张图片处理应 < 1 秒
|
||||
# assert processing_time < 1.0, \
|
||||
# f"处理时间 {processing_time:.2f}s 超过阈值 1s"
|
||||
pytest.skip("待实现：OCR 处理速度测试")
|
||||
|
||||
@pytest.mark.ai
|
||||
@pytest.mark.performance
|
||||
def test_ocr_batch_processing_speed(self) -> None:
|
||||
"""测试批量 OCR 处理速度"""
|
||||
# TODO: 实现批量性能测试
|
||||
# import time
|
||||
#
|
||||
# service = OCRService()
|
||||
# frame_paths = [
|
||||
# f"tests/fixtures/images/frame_{i}.jpg"
|
||||
# for i in range(30) # 30 帧 = 1 秒视频 @ 30fps
|
||||
# ]
|
||||
#
|
||||
# start_time = time.time()
|
||||
# results = service.batch_extract(frame_paths)
|
||||
# processing_time = time.time() - start_time
|
||||
#
|
||||
# # 30 帧应在 5 秒内处理完成
|
||||
# assert processing_time < 5.0
|
||||
pytest.skip("待实现：批量 OCR 处理速度测试")
|
||||
Reference in New Issue
Block a user