CardVideoSummary/app/services/video_service.py

import cv2
import os
import uuid
import math
from app.core.config import settings
from app.core.logger import get_logger
from app.schemas.models import CardInfoInput, CardInfoOutput

logger = get_logger("VideoService")


class VideoService:
    def __init__(self):
        # 高斯函数中的 sigma (标准差) 决定了时间权重的下降速度。
        self.weight_sigma = 10.0

    def time_str_to_ms(self, time_str: str) -> int:
        try:
            parts = list(map(int, time_str.split(':')))
            if len(parts) == 3:
                h, m, s = parts
                return (h * 3600 + m * 60 + s) * 1000
            elif len(parts) == 2:
                m, s = parts
                return (m * 60 + s) * 1000
            return 0
        except ValueError:
            return 0

    def get_laplacian_sharpness(self, frame) -> float:
        """
        计算图像的拉普拉斯方差。
        方差越大，代表图像包含的高频边缘信息越多，也就意味着对焦越准、越清晰。
        """
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        return cv2.Laplacian(gray, cv2.CV_64F).var()

    def calculate_weight(self, current_time_ms: int, target_time_ms: int) -> float:
        """
        计算时间权重：使用高斯衰减函数。距离目标时间越近，权重越高。
        """
        diff_seconds = abs(current_time_ms - target_time_ms) / 1000.0
        return math.exp(- (diff_seconds ** 2) / (2 * self.weight_sigma ** 2))

    def capture_frames(self, video_path: str, cards: list[CardInfoInput]) -> list[CardInfoOutput]:
        if not os.path.exists(video_path):
            logger.error(f"❌ 找不到视频文件: {video_path}")
            raise FileNotFoundError(f"Video file not found: {video_path}")

        logger.info(f"🎬 打开视频文件: {video_path}")
        logger.info(f"📋 待处理卡片数量: {len(cards)}")

        cap = cv2.VideoCapture(video_path)
        # 获取视频帧率，用于计算安全边界
        fps = cap.get(cv2.CAP_PROP_FPS)
        if fps <= 0:
            fps = 30.0

        output_list = []
        success_count = 0

        for idx, card_input in enumerate(cards):
            card_output = CardInfoOutput(**card_input.dict())
            target_time_ms = self.time_str_to_ms(card_output.time)

            # 设定搜索窗口区间： [目标时间 - 1秒, 目标时间 + 4秒]
            start_time_ms = max(0, target_time_ms - 1000)
            end_time_ms = target_time_ms + 4000

            logger.info(
                f"📸[{idx + 1}/{len(cards)}] 智能截取 {card_output.time} ({target_time_ms}ms) - {card_output.card_name_cn or '未知卡名'}")
            logger.info(f"   => 搜索区间: [{start_time_ms}ms ~ {end_time_ms}ms]")

            # 定位到窗口开始时间
            cap.set(cv2.CAP_PROP_POS_MSEC, start_time_ms)

            best_frame = None
            best_score = -1.0
            best_time_ms = start_time_ms
            best_sharpness = 0.0

            # 保护机制：最多读取这么多次，防止由于视频末尾造成的无限死循环
            max_reads = int((end_time_ms - start_time_ms) / 1000.0 * fps) + 30
            read_count = 0

            while read_count < max_reads:
                current_pos_ms = cap.get(cv2.CAP_PROP_POS_MSEC)

                # 超出窗口最大时间，停止当前卡片的搜索
                if current_pos_ms > end_time_ms:
                    break

                ret, frame = cap.read()
                if not ret:
                    break  # 视频结束

                # 计算原图清晰度
                sharpness = self.get_laplacian_sharpness(frame)
                # 计算时间偏移带来的衰减权重
                weight = self.calculate_weight(current_pos_ms, target_time_ms)

                # 综合评分 = 清晰度 * 时间权重
                score = sharpness * weight

                # 更新最佳候选帧
                if score > best_score:
                    best_score = score
                    best_frame = frame
                    best_time_ms = current_pos_ms
                    best_sharpness = sharpness

                read_count += 1

            # 保存最清晰的一张
            if best_frame is not None:
                filename = f"{uuid.uuid4()}_{int(best_time_ms)}.jpg"
                save_path = os.path.join(settings.FRAMES_DIR, filename)

                try:
                    cv2.imwrite(save_path, best_frame)

                    image_url = f"{settings.BASE_URL}/static/frames/{filename}"
                    card_output.frame_image_path = image_url
                    success_count += 1

                    time_diff = (best_time_ms - target_time_ms) / 1000.0
                    logger.info(
                        f"   ✅ 保存成功: {filename} (偏移: {time_diff:+.2f}s, 清晰度: {best_sharpness:.1f}, 综合分: {best_score:.1f})")
                except Exception as e:
                    logger.error(f"   ❌ 保存图片失败: {e}")
            else:
                logger.warning(f"   ⚠️ 无法在窗口内读取到视频帧 (可能时间戳超出视频长度)")

            output_list.append(card_output)

        cap.release()
        logger.info(f"🏁 截取任务结束. 成功: {success_count}, 总数: {len(cards)}")
        return output_list