"""
竞品 Logo 检测服务单元测试

TDD 测试用例 - 基于 FeatureSummary.md F-12 的验收标准

验收标准：
- F1 ≥ 0.85（含遮挡 30% 场景）
- 新 Logo 上传即刻生效
"""

import pytest
from typing import Any

# 导入待实现的模块（TDD 红灯阶段）
# from app.services.ai.logo_detector import LogoDetector, LogoDetection


class TestLogoDetector:
    """Logo 检测器测试"""

    @pytest.mark.ai
    @pytest.mark.unit
    def test_logo_detector_initialization(self) -> None:
        """测试 Logo 检测器初始化"""
        # TODO: 实现 Logo 检测器
        # detector = LogoDetector()
        # assert detector.is_ready()
        # assert detector.logo_count > 0  # 预加载的 Logo 数量
        pytest.skip("待实现：Logo 检测器初始化")

    @pytest.mark.ai
    @pytest.mark.unit
    def test_detect_logo_in_image(self) -> None:
        """测试图片中的 Logo 检测"""
        # TODO: 实现 Logo 检测
        # detector = LogoDetector()
        # result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
        #
        # assert result.status == "success"
        # assert len(result.detections) > 0
        pytest.skip("待实现：Logo 检测")

    @pytest.mark.ai
    @pytest.mark.unit
    def test_logo_detection_output_format(self) -> None:
        """测试 Logo 检测输出格式"""
        # TODO: 实现 Logo 检测
        # detector = LogoDetector()
        # result = detector.detect("tests/fixtures/images/with_competitor_logo.jpg")
        #
        # # 验证输出结构
        # assert hasattr(result, "detections")
        # for detection in result.detections:
        #     assert hasattr(detection, "logo_id")
        #     assert hasattr(detection, "brand_name")
        #     assert hasattr(detection, "confidence")
        #     assert hasattr(detection, "bbox")
        #     assert 0 <= detection.confidence <= 1
        #     assert len(detection.bbox) == 4
        pytest.skip("待实现：Logo 检测输出格式")


class TestLogoDetectionAccuracy:
    """Logo 检测准确率测试"""

    @pytest.mark.ai
    @pytest.mark.unit
    def test_f1_score_threshold(self) -> None:
        """
        测试 Logo 检测 F1 值

        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")