# video_face_recognition.py
import cv2
import numpy as np
import time
from insightface.app import FaceAnalysis
from typing import List, Dict, Tuple
import os


class VideoFaceRecognition:
    """
    视频人脸识别系统
    支持实时视频流和视频文件处理
    """

    def __init__(self, model_name: str = 'buffalo_l', use_gpu: bool = True):
        # 初始化人脸识别模型
        self.app = FaceAnalysis(name=model_name)
        self.app.prepare(
            ctx_id=0 if use_gpu else -1,
            det_thresh=0.2,
            det_size=(640, 640)
        )

        self.target_embedding = None
        self.target_id = None
        self.similarity_threshold = 0.3

        # 质量阈值设置
        self.clarity_threshold = 50.0  # 清晰度阈值，低于此值认为人脸模糊
        self.min_face_size = 40  # 最小人脸像素尺寸

        # 性能统计
        self.frame_count = 0
        self.processing_times = []

        print(f"✅ 视频人脸识别系统初始化完成 - GPU: {use_gpu}")

    def set_target_face(self, image_path: str, person_id: str = "target") -> bool:
        """设置目标人脸"""
        img = cv2.imread(image_path)
        if img is None:
            print(f"❌ 无法读取目标图像: {image_path}")
            return False

        faces = self.app.get(img)
        if not faces:
            print(f"❌ 目标图像中未检测到人脸: {image_path}")
            return False

        self.target_embedding = faces[0].embedding
        self.target_id = person_id
        print(f"✅ 目标人脸设置: {person_id}")
        return True

    def calculate_clarity(self, face_region: np.ndarray) -> float:
        """
        计算人脸区域的清晰度/模糊度
        使用拉普拉斯方差方法：值越高表示图像越清晰
        """
        if len(face_region.shape) == 3:
            gray = cv2.cvtColor(face_region, cv2.COLOR_BGR2GRAY)
        else:
            gray = face_region

        # 计算拉普拉斯算子的方差
        laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
        return laplacian_var

    def is_face_quality_acceptable(self, face, frame: np.ndarray) -> Tuple[bool, Dict]:
        """
        综合判断人脸质量是否可接受
        返回: (是否可接受, 质量指标字典)
        """
        quality_metrics = {}

        # 1. 检测置信度
        quality_metrics['det_score'] = float(face.det_score)

        # 2. 人脸姿态角度
        if hasattr(face, 'pose') and face.pose is not None:
            pitch, yaw, roll = face.pose
            quality_metrics['pitch'] = float(pitch)
            quality_metrics['yaw'] = float(yaw)
            quality_metrics['roll'] = float(roll)
        else:
            quality_metrics['pitch'] = 0.0
            quality_metrics['yaw'] = 0.0
            quality_metrics['roll'] = 0.0

        # 3. 人脸边界框信息
        bbox = face.bbox
        x1, y1, x2, y2 = bbox.astype(int)
        width = x2 - x1
        height = y2 - y1
        quality_metrics['bbox_area'] = width * height
        quality_metrics['aspect_ratio'] = width / height if height > 0 else 0

        # 4. 图像清晰度检测
        # 提取人脸区域
        h, w = frame.shape[:2]
        x1_clip = max(0, x1)
        y1_clip = max(0, y1)
        x2_clip = min(w, x2)
        y2_clip = min(h, y2)

        if x2_clip > x1_clip and y2_clip > y1_clip:
            face_region = frame[y1_clip:y2_clip, x1_clip:x2_clip]
            clarity_score = self.calculate_clarity(face_region)
            quality_metrics['clarity_score'] = clarity_score
        else:
            quality_metrics['clarity_score'] = 0.0

        # 5. 关键点质量评估
        if hasattr(face, 'kps') and face.kps is not None:
            kps = face.kps
            if len(kps) >= 5:
                distances = []
                for i in range(len(kps)):
                    for j in range(i + 1, len(kps)):
                        dist = np.linalg.norm(kps[i] - kps[j])
                        distances.append(dist)
                if distances:
                    quality_metrics['kps_variance'] = float(np.var(distances))
                else:
                    quality_metrics['kps_variance'] = 0.0
            else:
                quality_metrics['kps_variance'] = 0.0
        else:
            quality_metrics['kps_variance'] = 0.0

        # 6. 综合质量评分
        base_score = quality_metrics['det_score']

        # 清晰度惩罚
        clarity_penalty = 0.0
        if quality_metrics['clarity_score'] < self.clarity_threshold:
            clarity_penalty = 0.3  # 清晰度不足严重惩罚

        # 姿态惩罚
        pose_penalty = 0.0
        if abs(quality_metrics['yaw']) > 30:
            pose_penalty += 0.2
        if abs(quality_metrics['pitch']) > 20:
            pose_penalty += 0.2

        # 尺寸惩罚
        size_penalty = 0.0
        if quality_metrics['bbox_area'] < (self.min_face_size ** 2):
            size_penalty = 0.2

        quality_metrics['quality_score'] = max(0.1, base_score - clarity_penalty - pose_penalty - size_penalty)

        # 判断是否可接受
        is_acceptable = (
                quality_metrics['det_score'] > 0.5 and  # 基础检测置信度
                quality_metrics['clarity_score'] >= self.clarity_threshold and  # 清晰度要求
                quality_metrics['bbox_area'] >= (self.min_face_size ** 2) and  # 最小尺寸要求
                abs(quality_metrics['yaw']) < 60 and  # 偏航角限制
                abs(quality_metrics['pitch']) < 45  # 俯仰角限制
        )

        return is_acceptable, quality_metrics

    def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict]]:
        """
        处理单帧图像
        返回: (处理后的帧, 识别结果列表)
        """
        start_time = time.time()

        # 人脸检测和识别
        faces = self.app.get(frame)

        results = []
        for face in faces:
            # 检查人脸质量是否可接受
            is_acceptable, quality_metrics = self.is_face_quality_acceptable(face, frame)

            similarity = 0.0
            # 只有在人脸质量可接受时才计算相似度
            if is_acceptable and self.target_embedding is not None:
                emb1 = face.embedding / np.linalg.norm(face.embedding)
                emb2 = self.target_embedding / np.linalg.norm(self.target_embedding)
                similarity = float(np.dot(emb1, emb2))

            result = {
                'bbox': face.bbox.astype(int).tolist(),
                'similarity': similarity,
                'is_match': similarity >= self.similarity_threshold,
                'gender': 'Male' if face.gender == 1 else 'Female',
                'age': int(face.age),
                'det_score': float(face.det_score),
                'quality_metrics': quality_metrics,
                'is_acceptable': is_acceptable  # 新增：是否可接受标志
            }
            results.append(result)

            # 在帧上绘制结果
            frame = self._draw_detection(frame, result)

        # 性能统计
        processing_time = (time.time() - start_time) * 1000
        self.processing_times.append(processing_time)
        self.frame_count += 1

        return frame, results

    def _draw_detection(self, frame: np.ndarray, result: Dict) -> np.ndarray:
        """在帧上绘制检测结果和质量信息"""
        bbox = result['bbox']
        similarity = result['similarity']
        is_match = result['is_match']
        is_acceptable = result['is_acceptable']
        quality_metrics = result['quality_metrics']

        # 选择颜色
        if not is_acceptable:
            color = (128, 128, 128)  # 灰色 - 质量不可接受
        elif is_match:
            color = (0, 255, 0)  # 绿色 - 匹配
        else:
            color = (0, 0, 255)  # 红色 - 不匹配

        # 绘制人脸框
        x1, y1, x2, y2 = bbox
        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)

        # 准备显示文本
        text_lines = []

        # 第一行：质量状态和匹配状态
        if not is_acceptable:
            text_lines.append("LOW QUALITY")
        elif self.target_id:
            status = f"MATCH: {similarity:.3f}" if is_match else f"NO MATCH: {similarity:.3f}"
            text_lines.append(status)
        else:
            text_lines.append(f"Similarity: {similarity:.3f}")

        # 第二行：基础信息
        text_lines.append(f"{result['gender']}/{result['age']}")

        # 第三行：质量得分
        text_lines.append(f"Quality: {quality_metrics['quality_score']:.3f}")
        text_lines.append(f"DetScore: {quality_metrics['det_score']:.3f}")

        # 第四行：清晰度（关键指标）
        clarity_status = "CLEAR" if quality_metrics['clarity_score'] >= self.clarity_threshold else "BLUR"
        text_lines.append(f"Clarity: {clarity_status} ({quality_metrics['clarity_score']:.1f})")

        # 第五行：姿态角度
        text_lines.append(f"Pitch: {quality_metrics['pitch']:.1f}°")
        text_lines.append(f"Yaw: {quality_metrics['yaw']:.1f}°")
        text_lines.append(f"Roll: {quality_metrics['roll']:.1f}°")

        # 第六行：其他质量指标
        text_lines.append(f"Area: {quality_metrics['bbox_area']:.0f}")

        # 计算文本区域大小
        max_text_width = 0
        total_text_height = 0
        line_heights = []

        for line in text_lines:
            (text_width, text_height), baseline = cv2.getTextSize(line, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)
            max_text_width = max(max_text_width, text_width)
            line_heights.append(text_height + baseline)
            total_text_height += text_height + baseline + 2

        # 绘制文本背景
        bg_x1 = x1
        bg_y1 = y1 - total_text_height - 10
        bg_x2 = x1 + max_text_width + 10
        bg_y2 = y1

        # 如果背景超出图像顶部，调整到框下方
        if bg_y1 < 0:
            bg_y1 = y2
            bg_y2 = y2 + total_text_height + 10

        # 绘制半透明背景
        overlay = frame.copy()
        cv2.rectangle(overlay, (bg_x1, bg_y1), (bg_x2, bg_y2), (0, 0, 0), -1)
        alpha = 0.6
        cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)

        # 绘制文本
        current_y = bg_y1 + 15
        for i, line in enumerate(text_lines):
            # 根据内容选择颜色
            if i == 0:  # 状态行
                if not is_acceptable:
                    text_color = (128, 128, 128)  # 灰色 - 质量差
                elif is_match:
                    text_color = (0, 255, 0)  # 绿色 - 匹配
                else:
                    text_color = (0, 0, 255)  # 红色 - 不匹配
            elif i == 3:  # 清晰度行
                if quality_metrics['clarity_score'] >= self.clarity_threshold:
                    text_color = (0, 255, 0)  # 绿色 - 清晰
                else:
                    text_color = (0, 0, 255)  # 红色 - 模糊
            elif i in [4, 5, 6]:  # 姿态角度行
                if abs(quality_metrics['yaw']) > 45 or abs(quality_metrics['pitch']) > 30:
                    text_color = (0, 0, 255)  # 红色 - 角度过大
                elif abs(quality_metrics['yaw']) > 30 or abs(quality_metrics['pitch']) > 20:
                    text_color = (0, 255, 255)  # 黄色 - 角度偏大
                else:
                    text_color = (0, 255, 0)  # 绿色 - 角度良好
            else:
                text_color = (255, 255, 255)  # 白色

            cv2.putText(frame, line, (x1 + 5, current_y),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.4, text_color, 1)
            current_y += line_heights[i]

        return frame

    def set_quality_thresholds(self, clarity_threshold: float = None, min_face_size: int = None):
        """设置质量阈值"""
        if clarity_threshold is not None:
            self.clarity_threshold = clarity_threshold
        if min_face_size is not None:
            self.min_face_size = min_face_size
        print(f"✅ 质量阈值更新 - 清晰度: {self.clarity_threshold}, 最小尺寸: {self.min_face_size}")

    # 原有的 process_video_file 和 process_webcam 方法保持不变
    def process_video_file(self, video_path: str, output_path: str = None,
                           skip_frames: int = 0, show_preview: bool = True):
        """
        处理视频文件

        Args:
            video_path: 输入视频路径
            output_path: 输出视频路径
            skip_frames: 跳帧数，用于提高处理速度
            show_preview: 是否显示实时预览
        """
        if not os.path.exists(video_path):
            print(f"❌ 视频文件不存在: {video_path}")
            return

        # 打开视频文件
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            print(f"❌ 无法打开视频文件: {video_path}")
            return

        # 获取视频信息
        fps = cap.get(cv2.CAP_PROP_FPS)
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

        print(f"📹 视频信息: {width}x{height}, {fps:.1f}FPS, 总帧数: {total_frames}")

        # 设置输出视频
        if output_path:
            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
            out = cv2.VideoWriter(output_path, fourcc, fps / (skip_frames + 1), (width, height))
        else:
            out = None

        # 处理视频帧
        frame_index = 0
        processed_frames = 0
        start_time = time.time()

        print("🚀 开始处理视频...")

        while True:
            ret, frame = cap.read()
            if not ret:
                break

            # 跳帧处理
            if skip_frames > 0 and frame_index % (skip_frames + 1) != 0:
                frame_index += 1
                continue

            # 处理当前帧
            processed_frame, results = self.process_frame(frame)

            # 写入输出视频
            if out:
                out.write(processed_frame)

            # 显示预览
            if show_preview:
                # 添加性能信息
                fps_text = f"Frame: {frame_index}/{total_frames} | Faces: {len(results)}"
                cv2.putText(processed_frame, fps_text, (10, 30),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)

                cv2.imshow('Video Face Recognition', processed_frame)
                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break

            frame_index += 1
            processed_frames += 1

            # 进度显示
            if frame_index % 30 == 0:
                progress = (frame_index / total_frames) * 100
                print(f"📊 处理进度: {progress:.1f}% ({frame_index}/{total_frames})")

        # 清理资源
        cap.release()
        if out:
            out.release()
        if show_preview:
            cv2.destroyAllWindows()

        # 性能统计
        total_time = time.time() - start_time
        avg_processing_time = np.mean(self.processing_times) if self.processing_times else 0

        print(f"\n🎉 视频处理完成!")
        print(f"📊 性能统计:")
        print(f"   总处理帧数: {processed_frames}")
        print(f"   总耗时: {total_time:.1f}秒")
        print(f"   平均每帧: {avg_processing_time:.1f}ms")
        print(f"   实际FPS: {processed_frames / total_time:.1f}")
        if output_path:
            print(f"   输出视频: {output_path}")

    def process_webcam(self, camera_id: int = 0, output_path: str = None):
        """
        处理摄像头实时视频流
        """
        cap = cv2.VideoCapture(camera_id)
        if not cap.isOpened():
            print(f"❌ 无法打开摄像头 {camera_id}")
            return

        # 设置摄像头分辨率（可选）
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

        # 设置输出视频
        if output_path:
            fps = cap.get(cv2.CAP_PROP_FPS)
            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        else:
            out = None

        print("🎥 开始摄像头实时识别 (按 'q' 退出)...")

        while True:
            ret, frame = cap.read()
            if not ret:
                print("❌ 无法读取摄像头帧")
                break

            # 处理当前帧
            processed_frame, results = self.process_frame(frame)

            # 添加实时信息
            current_fps = 1000 / self.processing_times[-1] if self.processing_times else 0
            info_text = f"FPS: {current_fps:.1f} | Faces: {len(results)}"
            cv2.putText(processed_frame, info_text, (10, 30),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)

            # 写入输出
            if out:
                out.write(processed_frame)

            # 显示预览
            cv2.imshow('Real-time Face Recognition', processed_frame)

            # 按'q'退出
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        # 清理资源
        cap.release()
        if out:
            out.release()
        cv2.destroyAllWindows()

        print("✅ 摄像头处理结束")


# 使用示例
def main():
    # 创建视频识别系统
    video_system = VideoFaceRecognition(use_gpu=True)

    # 设置质量阈值（可根据实际情况调整）
    video_system.set_quality_thresholds(
        clarity_threshold=50.0,  # 清晰度阈值，可能需要根据你的视频调整
        min_face_size=40
    )

    # 设置目标人脸（可选）
    target_image = "test_data/register/sy.jpg"

    if os.path.exists(target_image):
        video_system.set_target_face(target_image, "目标人物")

    # 选择处理模式
    print("请选择处理模式:")
    print("1. 处理视频文件")
    print("2. 实时摄像头")

    choice = input("请输入选择 (1 或 2): ").strip()

    if choice == "1":
        # 处理视频文件
        video_path = "test_data/video/video_1.mp4"
        output_path = "test_data/output_video/video_5_quality.mp4"

        # 性能优化：跳帧处理
        skip_frames = 1

        video_system.process_video_file(
            video_path=video_path,
            output_path=output_path,
            skip_frames=skip_frames,
            show_preview=True
        )

    elif choice == "2":
        # 实时摄像头
        output_path = "webcam_recording.mp4"

        video_system.process_webcam(
            camera_id=0,
            output_path=output_path
        )

    else:
        print("❌ 无效选择")


if __name__ == "__main__":
    main()