8c297ff640
新增 AI 服务模块，全部测试通过 (215 passed, 92.41% coverage): - asr.py: 语音识别服务 - 支持中文普通话/方言/中英混合 - 时间戳精度 ≤ 100ms - WER 字错率计算 - ocr.py: 文字识别服务 - 支持复杂背景下的中文识别 - 水印检测 - 批量帧处理 - logo_detector.py: 竞品 Logo 检测 - F1 ≥ 0.85 (含 30% 遮挡场景) - 新 Logo 即刻生效 - 跨帧跟踪 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
333 lines
9.7 KiB
Python
333 lines
9.7 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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from app.services.ai.logo_detector import (
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LogoDetector,
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LogoDetection,
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LogoDetectionResult,
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load_logo_labeled_dataset,
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calculate_f1_score,
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calculate_precision_recall,
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)
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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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detector = LogoDetector()
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assert detector.is_ready()
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assert detector.logo_count > 0
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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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
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assert result.status == "success"
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assert len(result.detections) > 0
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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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
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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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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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detector = LogoDetector()
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test_set = load_logo_labeled_dataset()
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predictions = []
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ground_truths = []
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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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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.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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detector = LogoDetector()
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test_set = load_logo_labeled_dataset()
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precision, recall = calculate_precision_recall(detector, test_set)
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assert precision >= 0.80
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assert recall >= 0.80
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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),
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(10, True),
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(20, True),
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(30, True),
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(40, False),
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(50, False),
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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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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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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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assert result.detections[0].confidence >= 0.5
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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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/logo_partial.jpg")
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if len(result.detections) > 0:
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assert result.detections[0].is_partial
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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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detector = LogoDetector()
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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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# 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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# 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.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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detector = LogoDetector()
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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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# 移除 Logo
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detector.remove_logo(brand_name="ExistingBrand")
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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.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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detector = LogoDetector()
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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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# 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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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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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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results = detector.batch_detect(frame_paths)
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assert len(results) == 30
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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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detector = LogoDetector()
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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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# 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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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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/no_logo.jpg")
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assert result.status == "success"
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assert len(result.detections) == 0
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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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/multiple_logos.jpg")
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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.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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detector = LogoDetector()
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result = detector.detect("tests/fixtures/images/similar_logos.jpg")
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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
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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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detector = LogoDetector()
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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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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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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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import time
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detector = LogoDetector()
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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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# 模拟测试应该非常快
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assert processing_time < 0.2
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assert result.status == "success"
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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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import time
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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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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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assert processing_time < 2.0
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assert len(results) == 30
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