"""asset_quality_scoring 纯逻辑单测 — 第89波. 测试评分纯函数，不依赖 FFmpeg/视频文件。 覆盖：分辨率评分、帧率评分、码率评分、清晰度评分、稳定性评分、 质量总评分、9分类评分、分类结果计算。 """ import numpy as np import pytest from apps.worker.worker_app.tasks.asset_quality_scoring import ( AudioAnalysis, ClassificationResult, ColorAnalysis, MotionAnalysis, QualityScore, VideoInfo, calculate_category_scores, calculate_quality_score, classify_from_analysis, score_bitrate, score_clarity, score_framerate, score_resolution, score_stability, ) from packages.domain.classification import AssetClassification # ── 分辨率评分 ──────────────────────────────────────────────────────── class TestScoreResolution: """分辨率评分边界测试.""" def test_4k_full_score(self): """4K 及以上满分 25.""" assert score_resolution(3840, 2160) == 25.0 assert score_resolution(4096, 2160) == 25.0 assert score_resolution(7680, 4320) == 25.0 # 8K def test_2k_score(self): """2K 档 22 分.""" assert score_resolution(2560, 1440) == 22.0 assert score_resolution(3000, 1600) == 22.0 # 刚好低于 4K assert score_resolution(3839, 2159) == 22.0 def test_1080p_score(self): """1080p 档 20 分.""" assert score_resolution(1920, 1080) == 20.0 assert score_resolution(2000, 1080) == 20.0 # 刚好低于 2K assert score_resolution(2559, 1439) == 20.0 def test_720p_score(self): """720p 档 15 分.""" assert score_resolution(1280, 720) == 15.0 assert score_resolution(1280, 720) == 15.0 # 刚好低于 1080p assert score_resolution(1919, 1079) == 15.0 # 1080x720 像素数 < 1280x720，掉到 480p 档 assert score_resolution(1080, 720) == 8.0 def test_480p_score(self): """480p 档 8 分.""" assert score_resolution(854, 480) == 8.0 assert score_resolution(854, 480) == 8.0 # 刚好低于 720p assert score_resolution(1279, 719) == 8.0 # 720x480 像素数 < 854x480，掉到最低档 assert score_resolution(720, 480) == 3.0 def test_low_resolution_score(self): """低于 480p 给 3 分.""" assert score_resolution(640, 360) == 3.0 assert score_resolution(320, 240) == 3.0 assert score_resolution(0, 0) == 3.0 def test_non_standard_aspect_ratio(self): """非标准宽高比按像素总数计算.""" # 竖屏 1080x1920 像素数 = 1080p assert score_resolution(1080, 1920) == 20.0 # 超宽屏 assert score_resolution(2560, 1080) == 20.0 # 像素≈2.7M < 2K(3.6M) # 1x1 极低分辨率 assert score_resolution(1, 1) == 3.0 def test_negative_values(self): """负尺寸：负负得正按像素数算，一正一负 = 负数像素 = 最低档.""" # 一正一负 → 负像素总数 → < 480p → 3分 assert score_resolution(1920, -1080) == 3.0 assert score_resolution(-1920, 1080) == 3.0 # 都是 0 → 3分 assert score_resolution(0, 0) == 3.0 # ── 帧率评分 ────────────────────────────────────────────────────────── class TestScoreFramerate: """帧率评分边界测试.""" def test_60fps_full_score(self): """60fps 及以上满分 20.""" assert score_framerate(60) == 20.0 assert score_framerate(120) == 20.0 assert score_framerate(240) == 20.0 def test_30fps_score(self): """30-59fps 给 15 分.""" assert score_framerate(30) == 15.0 assert score_framerate(59.9) == 15.0 assert score_framerate(59) == 15.0 def test_24fps_score(self): """24-29fps 给 10 分.""" assert score_framerate(24) == 10.0 assert score_framerate(29.97) == 10.0 assert score_framerate(25) == 10.0 def test_15fps_score(self): """15-23fps 给 7 分.""" assert score_framerate(15) == 7.0 assert score_framerate(23.9) == 7.0 assert score_framerate(20) == 7.0 def test_low_fps_score(self): """低于 15fps 给 5 分.""" assert score_framerate(10) == 5.0 assert score_framerate(1) == 5.0 assert score_framerate(0) == 5.0 def test_negative_fps(self): """负帧率按最低档.""" assert score_framerate(-30) == 5.0 def test_float_fps(self): """浮点帧率正确判断边界.""" # 29.97 (NTSC) < 30 → 24fps 档 assert score_framerate(29.97) == 10.0 assert score_framerate(23.976) == 7.0 assert score_framerate(59.94) == 15.0 # 59.94 < 60 → 30fps 档 assert score_framerate(30.0) == 15.0 # ── 码率评分 ────────────────────────────────────────────────────────── class TestScoreBitrate: """码率评分边界测试.""" def test_high_bitrate_full_score(self): """10Mbps 以上满分 20.""" assert score_bitrate(10_000_001) == 20.0 assert score_bitrate(50_000_000) == 20.0 assert score_bitrate(100_000_000) == 20.0 def test_5mbps_score(self): """5-10Mbps 给 15 分.""" assert score_bitrate(5_000_000) == 15.0 assert score_bitrate(8_000_000) == 15.0 assert score_bitrate(10_000_000) == 15.0 # 刚好 10Mbps = 不 > 10 def test_2mbps_score(self): """2-5Mbps 给 10 分.""" assert score_bitrate(2_000_000) == 10.0 assert score_bitrate(3_000_000) == 10.0 assert score_bitrate(4_999_999) == 10.0 def test_05mbps_score(self): """0.5-2Mbps 给 5 分.""" assert score_bitrate(500_000) == 5.0 assert score_bitrate(1_000_000) == 5.0 assert score_bitrate(1_999_999) == 5.0 def test_low_bitrate_score(self): """低于 0.5Mbps 给 3 分.""" assert score_bitrate(499_999) == 3.0 assert score_bitrate(100_000) == 3.0 assert score_bitrate(0) == 3.0 def test_negative_bitrate(self): """负码率按最低档.""" assert score_bitrate(-5_000_000) == 3.0 def test_zero_bitrate(self): """0 码率 = 最低档.""" assert score_bitrate(0) == 3.0 # ── 清晰度评分 ──────────────────────────────────────────────────────── class TestScoreClarity: """清晰度评分测试.""" def test_empty_frames_default_score(self): """空帧列表给默认 10 分.""" assert score_clarity([]) == 10.0 def test_constant_image_low_clarity(self): """纯色图像比高细节图像清晰度低很多.""" # 纯灰色图像 gray_frame = np.full((100, 100), 128, dtype=np.uint8) score_constant = score_clarity([gray_frame]) # 高细节随机图像 detail_frame = np.random.randint(0, 256, (100, 100), dtype=np.uint8) score_detail = score_clarity([detail_frame]) # 纯色图应该显著低于高细节图 assert score_constant < score_detail assert 0.0 <= score_constant <= 20.0 def test_edge_rich_image_high_clarity(self): """高频边缘图像有较高清晰度得分.""" # 棋盘格图案，边缘丰富 frame = np.zeros((100, 100), dtype=np.uint8) for i in range(0, 100, 10): for j in range(0, 100, 10): if (i // 10 + j // 10) % 2 == 0: frame[i : i + 10, j : j + 10] = 255 score = score_clarity([frame]) assert score > 1.0 # 应有一定清晰度 assert 0.0 <= score <= 20.0 def test_rgb_frame_converts_to_gray(self): """RGB 帧会被转灰度后计算.""" rgb_frame = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8) score_rgb = score_clarity([rgb_frame]) # 对应灰度图 gray = np.dot(rgb_frame[..., :3], [0.299, 0.587, 0.114]).astype(np.uint8) score_gray = score_clarity([gray]) # 两者应近似相等 assert abs(score_rgb - score_gray) < 0.01 def test_only_first_five_frames_analyzed(self): """只分析前 5 帧.""" # 10 帧：前 5 帧纯色，后 5 帧高对比度 frames = [] for _ in range(5): frames.append(np.full((50, 50), 128, dtype=np.uint8)) for _ in range(5): high_freq = np.random.randint(0, 256, (50, 50), dtype=np.uint8) frames.append(high_freq) score_10 = score_clarity(frames) score_5 = score_clarity(frames[:5]) # 前 5 帧相同，得分应相同 assert abs(score_10 - score_5) < 0.01 def test_score_within_bounds(self): """得分始终在 0-20 范围内.""" for _ in range(10): frame = np.random.randint(0, 256, (30, 30, 3), dtype=np.uint8) score = score_clarity([frame]) assert 0.0 <= score <= 20.0 def test_multiple_frames_averaged(self): """多帧取平均方差.""" # 第 1 帧低细节，第 2 帧高细节 low_detail = np.full((50, 50), 100, dtype=np.uint8) high_detail = np.random.randint(0, 256, (50, 50), dtype=np.uint8) score_low = score_clarity([low_detail]) score_high = score_clarity([high_detail]) score_both = score_clarity([low_detail, high_detail]) # 混合得分应在两者之间 assert score_low <= score_both <= score_high # ── 稳定性评分 ──────────────────────────────────────────────────────── class TestScoreStability: """稳定性评分测试.""" def test_single_frame_default_score(self): """不足 2 帧给默认 10 分.""" assert score_stability([]) == 10.0 assert score_stability([np.zeros((10, 10, 3), dtype=np.uint8)]) == 10.0 def test_identical_frames_max_stability(self): """完全相同的帧 = 高稳定性.""" frame = np.random.randint(50, 200, (100, 100, 3), dtype=np.uint8) score = score_stability([frame, frame.copy()]) assert score > 10.0 # 应该接近满分 15 def test_very_different_frames_low_stability(self): """位移方差大的多帧序列 = 低稳定性.""" # 构造 4 帧：帧间位移差异大（有的帧相似、有的帧完全不同） # 位移方差大 → 不稳定 → 低分 base = np.random.randint(100, 150, (100, 100, 3), dtype=np.uint8) frames = [ base, # 帧0 base, # 帧1 (完全相同 → 位移0) np.full_like(base, 255), # 帧2 (纯白 → 位移大) base, # 帧3 (回到基准 → 位移又大) ] score = score_stability(frames) # 位移差异大 → 方差大 → 稳定性低 assert score < 10.0 assert 0.0 <= score <= 15.0 def test_score_within_bounds(self): """得分始终在 0-15 范围内.""" for _ in range(10): f1 = np.random.randint(0, 256, (40, 40, 3), dtype=np.uint8) f2 = np.random.randint(0, 256, (40, 40, 3), dtype=np.uint8) score = score_stability([f1, f2]) assert 0.0 <= score <= 15.0 def test_gray_frames_also_work(self): """灰度帧也能计算.""" f1 = np.random.randint(0, 256, (50, 50), dtype=np.uint8) f2 = np.random.randint(0, 256, (50, 50), dtype=np.uint8) score = score_stability([f1, f2]) assert 0.0 <= score <= 15.0 def test_multiple_frame_pairs(self): """多对帧取方差.""" base = np.random.randint(100, 150, (60, 60, 3), dtype=np.uint8) # 5 帧相似的 frames = [] for _ in range(5): f = base.copy() # 轻微变化 f = np.clip(f.astype(int) + np.random.randint(-5, 6, f.shape), 0, 255).astype(np.uint8) frames.append(f) score = score_stability(frames) assert score > 5.0 # 相似帧应该有一定稳定性 # ── 质量总评分 ──────────────────────────────────────────────────────── class TestCalculateQualityScore: """质量综合评分测试.""" def test_perfect_video_near_100(self): """完美参数的视频接近 100 分.""" info = VideoInfo( width=3840, height=2160, fps=60, bitrate=20_000_000, ) # 用高细节帧提升清晰度分 frame = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) result = calculate_quality_score(info, [frame]) assert isinstance(result, QualityScore) assert result.resolution_score == 25.0 assert result.fps_score == 20.0 assert result.bitrate_score == 20.0 assert 50.0 <= result.total <= 100.0 def test_low_quality_video(self): """低质量视频得分低.""" info = VideoInfo( width=320, height=240, fps=10, bitrate=100_000, ) result = calculate_quality_score(info, []) assert isinstance(result, QualityScore) assert result.resolution_score == 3.0 assert result.fps_score == 5.0 assert result.bitrate_score == 3.0 # 无帧时清晰度和稳定性各给 10 分默认 assert result.clarity_score == 10.0 assert result.stability_score == 10.0 assert result.total == 31.0 # 3+5+3+10+10 def test_no_frames_uses_defaults(self): """不传 frames 时清晰度/稳定性给默认分.""" info = VideoInfo(width=1920, height=1080, fps=30, bitrate=5_000_000) result = calculate_quality_score(info) assert result.clarity_score == 10.0 assert result.stability_score == 10.0 assert result.resolution_score == 20.0 assert result.fps_score == 15.0 assert result.bitrate_score == 15.0 assert result.total == 70.0 def test_total_capped_at_100(self): """总分不超过 100.""" info = VideoInfo( width=7680, height=4320, fps=240, bitrate=100_000_000, ) # 即使所有维度都满，总分不超 100 result = calculate_quality_score(info, []) assert result.total <= 100.0 def test_total_minimum_zero(self): """总分不低于 0.""" info = VideoInfo(width=0, height=0, fps=0, bitrate=0) result = calculate_quality_score(info, []) assert result.total >= 0.0 def test_total_is_rounded(self): """总分保留 1 位小数.""" info = VideoInfo(width=1920, height=1080, fps=30, bitrate=5_000_000) result = calculate_quality_score(info, []) # 检查是 1 位小数 assert round(result.total, 1) == result.total # ── 分类评分 ────────────────────────────────────────────────────────── class TestCalculateCategoryScores: """分类评分计算测试.""" def test_scenic_high_green_and_motion(self): """绿色+适度运动+自然音 → 风景高分.""" color = ColorAnalysis( green_ratio=0.5, avg_saturation=0.5, avg_brightness=0.6, warm_ratio=0.2, cool_ratio=0.3, ) motion = MotionAnalysis(motion_score=0.3, scene_changes=1) audio = AudioAnalysis(has_audio=True, ambient_ratio=0.7) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.SCENIC.value] > 0.5 assert scores[AssetClassification.SCENIC.value] <= 1.0 def test_product_low_motion_cool_tone(self): """低运动+冷色调 → 产品高分.""" color = ColorAnalysis( avg_brightness=0.5, avg_saturation=0.3, cool_ratio=0.5, green_ratio=0.1, warm_ratio=0.2, ) motion = MotionAnalysis(motion_score=0.1, scene_changes=0) audio = AudioAnalysis(has_audio=False) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.PRODUCT.value] > 0.5 def test_person_with_speech(self): """有人声+适度运动 → 人物高分.""" color = ColorAnalysis(avg_brightness=0.5) motion = MotionAnalysis(motion_score=0.25, scene_changes=1) audio = AudioAnalysis(has_audio=True, speech_ratio=0.6) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.PERSON.value] > 0.5 def test_animal_high_motion(self): """高运动+多场景切换 → 动物高分.""" color = ColorAnalysis() motion = MotionAnalysis(motion_score=0.6, scene_changes=5) audio = AudioAnalysis(has_audio=True, ambient_ratio=0.5) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.ANIMAL.value] > 0.5 def test_food_warm_saturated(self): """暖色调+高饱和 → 美食高分.""" color = ColorAnalysis( warm_ratio=0.6, avg_saturation=0.7, avg_brightness=0.6, green_ratio=0.1, cool_ratio=0.2, ) motion = MotionAnalysis(motion_score=0.1, scene_changes=0) audio = AudioAnalysis(has_audio=False) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.FOOD.value] > 0.5 def test_tech_cool_low_saturation(self): """冷色调+低饱和+低运动 → 科技高分.""" color = ColorAnalysis( cool_ratio=0.6, avg_saturation=0.3, avg_brightness=0.5, green_ratio=0.1, warm_ratio=0.2, ) motion = MotionAnalysis(motion_score=0.1, scene_changes=0) audio = AudioAnalysis(has_audio=False) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.TECH.value] > 0.5 def test_sport_high_motion(self): """高运动+多场景 → 运动高分.""" color = ColorAnalysis(avg_brightness=0.6) motion = MotionAnalysis(motion_score=0.7, scene_changes=5) audio = AudioAnalysis(has_audio=False) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.SPORT.value] > 0.5 def test_music_high_music_ratio(self): """高音乐比例 → 音乐高分.""" color = ColorAnalysis(avg_saturation=0.6, green_ratio=0.1) motion = MotionAnalysis(motion_score=0.2) audio = AudioAnalysis(has_audio=True, music_ratio=0.7) scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.MUSIC.value] > 0.5 def test_other_has_base_score(self): """其他分类有基础分 0.1.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() scores = calculate_category_scores(color, motion, audio) assert scores[AssetClassification.OTHER.value] == 0.1 def test_all_scores_within_bounds(self): """所有分类得分都在 0-1 范围内.""" color = ColorAnalysis( green_ratio=0.9, warm_ratio=0.9, cool_ratio=0.9, avg_saturation=0.99, avg_brightness=0.99, ) motion = MotionAnalysis(motion_score=0.9, scene_changes=100) audio = AudioAnalysis( has_audio=True, speech_ratio=0.99, music_ratio=0.99, ambient_ratio=0.99, ) scores = calculate_category_scores(color, motion, audio) for cat, score in scores.items(): assert 0.0 <= score <= 1.0, f"{cat} score {score} out of bounds" def test_all_nine_categories_present(self): """返回 9 个分类的得分.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() scores = calculate_category_scores(color, motion, audio) assert len(scores) == 9 # ── 分类结果计算 ────────────────────────────────────────────────────── class TestClassifyFromAnalysis: """分类结果计算测试.""" def test_returns_classification_result(self): """返回 ClassificationResult 对象.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() result = classify_from_analysis(color, motion, audio) assert isinstance(result, ClassificationResult) assert isinstance(result.category, AssetClassification) assert isinstance(result.confidence, float) assert isinstance(result.scores, dict) def test_highest_score_wins(self): """得分最高的分类胜出.""" # 构造明显偏向风景的特征 color = ColorAnalysis( green_ratio=0.8, avg_saturation=0.6, avg_brightness=0.7, ) motion = MotionAnalysis(motion_score=0.3) audio = AudioAnalysis(has_audio=True, ambient_ratio=0.8) result = classify_from_analysis(color, motion, audio) assert result.category == AssetClassification.SCENIC def test_confidence_within_bounds(self): """置信度在 0.3-0.95 范围内.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() result = classify_from_analysis(color, motion, audio) assert 0.3 <= result.confidence <= 0.95 def test_confidence_capped_at_095(self): """极高得分也被限制在 0.95.""" color = ColorAnalysis( green_ratio=0.9, avg_saturation=0.9, avg_brightness=0.9, warm_ratio=0.9, ) motion = MotionAnalysis(motion_score=0.9, scene_changes=10) audio = AudioAnalysis( has_audio=True, speech_ratio=0.9, music_ratio=0.9, ambient_ratio=0.9, ) result = classify_from_analysis(color, motion, audio) assert result.confidence <= 0.95 def test_confidence_floored_at_03(self): """极低得分也有 0.3 最低置信度.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() result = classify_from_analysis(color, motion, audio) assert result.confidence >= 0.3 def test_scores_dict_included(self): """结果中包含完整分数字典.""" color = ColorAnalysis() motion = MotionAnalysis() audio = AudioAnalysis() result = classify_from_analysis(color, motion, audio) assert len(result.scores) == 9 assert AssetClassification.OTHER.value in result.scores def test_food_category_wins_on_warm_colors(self): """暖色调+高饱和 → 美食分类胜出.""" color = ColorAnalysis( warm_ratio=0.7, avg_saturation=0.8, avg_brightness=0.6, green_ratio=0.05, ) motion = MotionAnalysis(motion_score=0.05) audio = AudioAnalysis(has_audio=False) result = classify_from_analysis(color, motion, audio) assert result.category == AssetClassification.FOOD # ── 数据类默认值 ────────────────────────────────────────────────────── class TestDataclassDefaults: """数据类默认值测试.""" def test_video_info_defaults(self): info = VideoInfo() assert info.width == 0 assert info.height == 0 assert info.fps == 0.0 assert info.bitrate == 0 assert info.has_audio is False def test_color_analysis_defaults(self): color = ColorAnalysis() assert color.green_ratio == 0.0 assert color.avg_brightness == 0.0 assert color.dominant_hue == 0.0 def test_motion_analysis_defaults(self): motion = MotionAnalysis() assert motion.motion_score == 0.0 assert motion.scene_changes == 0 def test_audio_analysis_defaults(self): audio = AudioAnalysis() assert audio.has_audio is False assert audio.speech_ratio == 0.0 assert audio.music_ratio == 0.0 def test_quality_score_requires_total(self): with pytest.raises(TypeError): QualityScore() qs = QualityScore(total=50.0) assert qs.total == 50.0 assert qs.resolution_score == 0.0