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>
371 lines
12 KiB
Python
371 lines
12 KiB
Python
"""
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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
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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_labeled_dataset() # ≥ 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_score(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.ai
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@pytest.mark.unit
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def test_precision_recall(self) -> None:
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"""测试查准率和查全率"""
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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_labeled_dataset()
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#
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# precision, recall = calculate_precision_recall(detector, test_set)
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#
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# # 查准率和查全率都应该较高
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# assert precision >= 0.80
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# assert recall >= 0.80
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pytest.skip("待实现：查准率查全率测试")
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class TestLogoOcclusion:
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"""Logo 遮挡检测测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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@pytest.mark.parametrize("occlusion_percent,should_detect", [
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(0, True), # 无遮挡
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(10, True), # 10% 遮挡
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(20, True), # 20% 遮挡
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(30, True), # 30% 遮挡 - 边界
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(40, False), # 40% 遮挡 - 可能检测失败
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(50, False), # 50% 遮挡
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])
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def test_logo_detection_with_occlusion(
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self,
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occlusion_percent: int,
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should_detect: bool,
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) -> 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: 实现遮挡测试
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# detector = LogoDetector()
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# image_path = f"tests/fixtures/images/logo_occluded_{occlusion_percent}pct.jpg"
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# result = detector.detect(image_path)
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#
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# if should_detect:
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# assert len(result.detections) > 0, \
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# f"{occlusion_percent}% 遮挡应能检测到 Logo"
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# # 置信度可能较低
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# assert result.detections[0].confidence >= 0.5
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pytest.skip(f"待实现：{occlusion_percent}% 遮挡 Logo 检测")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_partial_logo_detection(self) -> None:
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"""测试部分可见 Logo 检测"""
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# TODO: 实现部分可见测试
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/logo_partial.jpg")
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#
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# # 部分可见的 Logo 应标记 partial=True
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# if len(result.detections) > 0:
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# assert result.detections[0].is_partial
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pytest.skip("待实现：部分可见 Logo 检测")
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class TestLogoDynamicUpdate:
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"""Logo 动态更新测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_add_new_logo_instant_effect(self) -> None:
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"""
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测试新 Logo 上传即刻生效
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验收标准：新增竞品 Logo 应立即可检测
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"""
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# TODO: 实现动态添加测试
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# detector = LogoDetector()
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#
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# # 检测前应无法识别
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# result_before = detector.detect("tests/fixtures/images/with_new_logo.jpg")
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# assert not any(d.brand_name == "NewBrand" for d in result_before.detections)
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#
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# # 添加新 Logo
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# detector.add_logo(
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# logo_image="tests/fixtures/logos/new_brand_logo.png",
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# brand_name="NewBrand"
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# )
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#
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# # 检测后应能识别
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# result_after = detector.detect("tests/fixtures/images/with_new_logo.jpg")
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# assert any(d.brand_name == "NewBrand" for d in result_after.detections)
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pytest.skip("待实现：Logo 动态添加")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_remove_logo(self) -> None:
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"""测试移除 Logo"""
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# TODO: 实现 Logo 移除
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# detector = LogoDetector()
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#
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# # 移除前可检测
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# result_before = detector.detect("tests/fixtures/images/with_existing_logo.jpg")
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# assert any(d.brand_name == "ExistingBrand" for d in result_before.detections)
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#
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# # 移除 Logo
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# detector.remove_logo(brand_name="ExistingBrand")
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#
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# # 移除后不再检测
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# result_after = detector.detect("tests/fixtures/images/with_existing_logo.jpg")
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# assert not any(d.brand_name == "ExistingBrand" for d in result_after.detections)
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pytest.skip("待实现：Logo 移除")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_update_logo_variants(self) -> None:
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"""测试更新 Logo 变体"""
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# TODO: 实现 Logo 变体更新
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# detector = LogoDetector()
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#
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# # 添加多个变体
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# detector.add_logo_variant(
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# brand_name="Brand",
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# variant_image="tests/fixtures/logos/brand_variant_dark.png",
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# variant_type="dark_mode"
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# )
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#
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# # 应能检测新变体
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# result = detector.detect("tests/fixtures/images/with_dark_logo.jpg")
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# assert len(result.detections) > 0
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pytest.skip("待实现：Logo 变体更新")
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class TestLogoVideoProcessing:
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"""视频 Logo 检测测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_detect_logo_in_video_frames(self) -> None:
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"""测试视频帧中的 Logo 检测"""
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# TODO: 实现视频帧检测
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# detector = LogoDetector()
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# frame_paths = [
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# f"tests/fixtures/images/video_frame_{i}.jpg"
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# for i in range(30)
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# ]
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#
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# results = detector.batch_detect(frame_paths)
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#
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# assert len(results) == 30
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# # 至少部分帧应检测到 Logo
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# frames_with_logo = sum(1 for r in results if len(r.detections) > 0)
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# assert frames_with_logo > 0
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pytest.skip("待实现：视频帧 Logo 检测")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_logo_tracking_across_frames(self) -> None:
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"""测试跨帧 Logo 跟踪"""
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# TODO: 实现跨帧跟踪
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# detector = LogoDetector()
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#
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# # 检测连续帧
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# frame_results = []
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# for i in range(10):
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# result = detector.detect(f"tests/fixtures/images/tracking_frame_{i}.jpg")
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# frame_results.append(result)
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#
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# # 跟踪应返回相同的 track_id
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# track_ids = [
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# r.detections[0].track_id
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# for r in frame_results
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# if len(r.detections) > 0
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# ]
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# assert len(set(track_ids)) == 1 # 同一个 Logo
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pytest.skip("待实现：跨帧 Logo 跟踪")
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class TestLogoSpecialCases:
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"""Logo 检测特殊情况测试"""
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@pytest.mark.ai
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@pytest.mark.unit
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def test_no_logo_image(self) -> None:
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"""测试无 Logo 图片"""
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# TODO: 实现无 Logo 测试
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/no_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：无 Logo 图片处理")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_multiple_logos_detection(self) -> None:
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"""测试多 Logo 检测"""
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# TODO: 实现多 Logo 测试
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/multiple_logos.jpg")
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#
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# assert len(result.detections) >= 2
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# # 每个检测应有唯一 ID
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# logo_ids = [d.logo_id for d in result.detections]
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# assert len(logo_ids) == len(set(logo_ids))
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pytest.skip("待实现：多 Logo 检测")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_similar_logo_distinction(self) -> None:
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"""测试相似 Logo 区分"""
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# TODO: 实现相似 Logo 区分
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# detector = LogoDetector()
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# result = detector.detect("tests/fixtures/images/similar_logos.jpg")
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#
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# # 应能区分相似但不同的 Logo
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# brand_names = [d.brand_name for d in result.detections]
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# assert "BrandA" in brand_names
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# assert "BrandB" in brand_names # 相似但不同
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pytest.skip("待实现：相似 Logo 区分")
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@pytest.mark.ai
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@pytest.mark.unit
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def test_distorted_logo_detection(self) -> None:
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"""测试变形 Logo 检测"""
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# TODO: 实现变形 Logo 测试
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# detector = LogoDetector()
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#
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# # 测试不同变形
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# test_cases = [
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# "logo_stretched.jpg",
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# "logo_rotated.jpg",
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# "logo_skewed.jpg",
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# ]
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#
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# for image_name in test_cases:
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# result = detector.detect(f"tests/fixtures/images/{image_name}")
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# assert len(result.detections) > 0, f"变形 Logo {image_name} 应被检测"
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pytest.skip("待实现：变形 Logo 检测")
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class TestLogoPerformance:
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"""Logo 检测性能测试"""
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@pytest.mark.ai
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@pytest.mark.performance
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def test_detection_speed(self) -> None:
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"""测试检测速度"""
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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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# detector = LogoDetector()
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#
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# start_time = time.time()
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# result = detector.detect("tests/fixtures/images/1080p_sample.jpg")
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# processing_time = time.time() - start_time
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#
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# # 单张图片应 < 200ms
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# assert processing_time < 0.2
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pytest.skip("待实现：Logo 检测速度测试")
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@pytest.mark.ai
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@pytest.mark.performance
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def test_batch_detection_speed(self) -> None:
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"""测试批量检测速度"""
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# TODO: 实现批量性能测试
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# import time
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#
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# detector = LogoDetector()
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# frame_paths = [
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# f"tests/fixtures/images/frame_{i}.jpg"
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# for i in range(30)
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# ]
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#
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# start_time = time.time()
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# results = detector.batch_detect(frame_paths)
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# processing_time = time.time() - start_time
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#
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# # 30 帧应在 2 秒内完成
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# assert processing_time < 2.0
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pytest.skip("待实现：批量 Logo 检测速度测试")
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