forked from 77media/video-flow
477 lines
14 KiB
TypeScript
477 lines
14 KiB
TypeScript
import { VideoFlowProjectResponse } from "@/api/allMovieType";
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import { task_item, VideoSegmentEntityAdapter } from "../adapter/oldErrAdapter";
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import { VideoSegmentEntity } from "../domain/Entities";
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import { LensType } from "../domain/valueObject";
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import {
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getShotList,
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regenerateShot,
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optimizeShotContent,
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updateShotPrompt,
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detailScriptEpisodeNew,
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faceRecognition,
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batchUpdateVideoSegments,
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} from "@/api/video_flow";
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/**
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* 视频片段编辑用例
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* 负责视频片段内容的初始化、修改和优化
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*/
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export class VideoSegmentEditUseCase {
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private loading: boolean = false;
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/**
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* @description 获取视频片段列表
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* @param projectId 项目ID
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* @returns Promise<VideoSegmentEntity[]> 视频片段列表
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*/
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async getVideoSegmentList(projectId: string): Promise<VideoSegmentEntity[]> {
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try {
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this.loading = true;
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const response = await getShotList({ project_id: projectId });
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if (!response.successful) {
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throw new Error(response.message || "获取视频片段列表失败");
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}
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const Segments = VideoSegmentEntityAdapter.toVideoSegmentEntity(response.data) || [];
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const detail = await detailScriptEpisodeNew({ project_id: projectId });
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if (!detail.successful || !detail.data) {
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throw new Error(detail.message || "获取视频片段列表失败");
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}
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// 匹配视频片段ID
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return this.matchVideoSegmentsWithIds(Segments, detail.data);
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} catch (error) {
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console.error("获取视频片段列表失败:", error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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/**
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* @description 为视频片段匹配对应的video_id
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* @param segments 视频片段列表
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* @param detail 项目详情数据
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* @returns VideoSegmentEntity[] 匹配后的视频片段列表
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* @throws Error 当有视频片段未匹配到video_id时
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*/
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/**
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* @description 为视频片段匹配对应的video_id，并输出调试信息
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* @param segments 视频片段列表
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* @param detail 项目详情数据
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* @returns VideoSegmentEntity[] 匹配后的视频片段列表
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* @throws Error 当有视频片段未匹配到video_id时
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*/
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private matchVideoSegmentsWithIds(
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segments: VideoSegmentEntity[],
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detail: VideoFlowProjectResponse
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): VideoSegmentEntity[] {
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console.log('[matchVideoSegmentsWithIds] 输入segments:', segments);
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console.log('[matchVideoSegmentsWithIds] 输入detail:', detail);
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const videoData = detail?.data?.video?.data;
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if (!videoData || !Array.isArray(videoData)) {
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console.log('[matchVideoSegmentsWithIds] videoData无效，直接返回原segments');
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return segments;
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}
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// 建立URL到VideoItem的映射表，提高查找效率
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const urlToVideoMap = new Map<string, any>();
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videoData.forEach(videoItem => {
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if (videoItem.urls && Array.isArray(videoItem.urls)) {
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videoItem.urls.forEach((url: string) => {
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urlToVideoMap.set(url, videoItem);
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});
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}
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});
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console.log('[matchVideoSegmentsWithIds] urlToVideoMap keys:', Array.from(urlToVideoMap.keys()));
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// 为每个视频片段匹配video_id并重新创建实体
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const updatedSegments: VideoSegmentEntity[] = [];
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segments.forEach(segment => {
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if (segment.videoUrl && Array.isArray(segment.videoUrl)) {
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// 查找匹配的视频项
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const matchedVideo = segment.videoUrl.find(url => urlToVideoMap.has(url));
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const videoItem = matchedVideo ? urlToVideoMap.get(matchedVideo) : null;
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if (videoItem) {
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// 创建新的实体实例，设置正确的id
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const updatedSegment: VideoSegmentEntity = {
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...segment,
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id: videoItem.video_id
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};
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updatedSegments.push(updatedSegment);
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console.log(`[matchVideoSegmentsWithIds] segment "${segment.name}" 匹配到 video_id:`, videoItem.video_id);
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} else {
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// 如果没有匹配到，保持原样
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updatedSegments.push(segment);
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console.log(`[matchVideoSegmentsWithIds] segment "${segment.name}" 未匹配到video_id, 保持原样`);
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}
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} else {
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updatedSegments.push(segment);
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console.log(`[matchVideoSegmentsWithIds] segment "${segment.name}" videoUrl无效, 保持原样`);
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}
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});
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// 检查是否所有视频片段都匹配上了id
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const unmatchedSegments = updatedSegments.filter(segment =>
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segment.id.startsWith('video_mock_') || !segment.id
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);
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if (unmatchedSegments.length > 0) {
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console.warn('[matchVideoSegmentsWithIds] 以下视频片段未匹配到video_id:', unmatchedSegments.map(s => ({ name: s.name, videoUrl: s.videoUrl })));
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// throw new Error(`有 ${unmatchedSegments.length} 个视频片段未匹配到对应的video_id`);
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}
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console.log('[matchVideoSegmentsWithIds] 匹配后segments:', updatedSegments);
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return updatedSegments;
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}
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/**
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* @description 保存分镜提示词数据
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* @param project_id 项目ID
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* @param shot_id 分镜ID
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* @param shot_descriptions 镜头描述数据
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* @returns Promise<any> 保存结果
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*/
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async saveShotPrompt(
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project_id: string,
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shot_id: string,
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shot_descriptions: task_item
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): Promise<any> {
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try {
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this.loading = true;
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const response = await updateShotPrompt({
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project_id,
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shot_id,
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shot_descriptions,
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});
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if (!response.successful) {
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throw new Error(response.message || "保存分镜提示词数据失败");
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}
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return response.data;
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} catch (error) {
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console.error("保存分镜提示词数据失败:", error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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/**
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* @description 通过视频镜头描述数据重新生成视频
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* @param project_id 项目ID
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* @param shot_Lens 镜头描述数据
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* @param shot_id 视频片段ID（可选，如果重新生成现有片段）
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* @returns Promise<VideoSegmentEntity> 重新生成的视频片段
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*/
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async regenerateVideoSegment(
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project_id: string,
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shot_Lens: LensType[],
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shot_id?: string,
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// roleReplaceParams?: { oldId: string; newId: string }[],
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// sceneReplaceParams?: { oldId: string; newId: string }[]
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): Promise<VideoSegmentEntity> {
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try {
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this.loading = true;
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const shot_descriptions = VideoSegmentEntityAdapter.lensTypeToTaskItem(shot_Lens);
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// 如果有shot_id，先保存分镜数据
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if (shot_id) {
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await this.saveShotPrompt(project_id, shot_id, shot_descriptions);
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}
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const response = await regenerateShot({
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project_id,
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shot_id,
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shot_descriptions,
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// roleReplaceParams,
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// sceneReplaceParams,
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});
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if (!response.successful) {
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throw new Error(response.message || "重新生成视频片段失败");
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}
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return response.data;
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} catch (error) {
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console.error("重新生成视频片段失败:", error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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/**
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* @description 通过AI优化镜头数据（包含对话内容）
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* @param shotId 视频片段ID
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* @param userRequirement 用户优化需求
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* @param lensData 镜头数据数组
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* @returns Promise<LensType[]> 优化后的镜头数据
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*/
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async optimizeVideoContent(
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shotId: string,
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userRequirement: string,
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lensData: LensType[]
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): Promise<LensType[]> {
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try {
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this.loading = true;
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// 调用AI优化接口
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const response = await optimizeShotContent({
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shotId,
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userRequirement,
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lensData,
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});
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if (!response.successful) {
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throw new Error(response.message || "AI优化视频内容失败");
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}
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return response.data;
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} catch (error) {
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console.error("AI优化视频内容失败:", error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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/**
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* @description 批量更新除当前选中片段外的所有视频片段
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* @param projectId 项目ID
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* @param currentSegmentId 当前选中的视频片段ID
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* @param optimizedDescription 优化后的描述文本
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* @param keywords 关键词列表
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* @returns Promise<boolean> 更新是否成功
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*/
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async batchUpdateOtherVideoSegments(
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projectId: string,
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currentSegmentId: string,
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optimizedDescription: string,
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keywords: string[]
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): Promise<boolean> {
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try {
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this.loading = true;
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// 获取当前项目的视频片段列表
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const segments = await this.getVideoSegmentList(projectId);
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// 过滤出除当前选中片段外的所有片段
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const otherSegments = segments.filter(segment => segment.id !== currentSegmentId);
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if (otherSegments.length === 0) {
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console.log('没有其他视频片段需要更新');
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return true;
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}
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// 准备更新数据
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const updateData = otherSegments.map(segment => ({
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shot_id: segment.id,
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video_urls: segment.videoUrl || [],
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status: segment.status,
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optimized_description: optimizedDescription,
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keywords: keywords
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}));
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// 调用批量更新API
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const response = await batchUpdateVideoSegments({
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project_id: projectId,
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segments: updateData
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});
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if (!response.successful) {
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throw new Error(response.message || '批量更新视频片段失败');
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}
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console.log(`成功更新 ${response.data.success_count} 个视频片段，失败 ${response.data.failed_count} 个`);
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return response.data.failed_count === 0;
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} catch (error) {
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console.error('批量更新视频片段失败:', error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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/**
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* @description 获取加载状态
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* @returns boolean 是否正在加载
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*/
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isLoading(): boolean {
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return this.loading;
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}
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/**
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* @description 识别视频帧中的角色
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* @param projectId 项目ID
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* @param videoId 视频ID
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* @param targetImageUrl 目标图片URL
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* @returns Promise<any> 识别结果
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*/
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async recognizeRoleFromImage(
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projectId: string,
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videoId: string,
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targetImageUrl: string
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): Promise<any> {
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try {
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this.loading = true;
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const response = await faceRecognition({
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project_id: projectId,
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video_id: videoId,
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target_image_url: targetImageUrl,
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});
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if (!response.successful) {
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throw new Error(response.message || "人脸识别失败");
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}
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// 打印返回结果
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console.log('人脸识别接口返回结果:', response);
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return response.data;
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} catch (error) {
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console.error("人脸识别失败:", error);
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throw error;
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} finally {
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this.loading = false;
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}
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}
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}
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/**
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* 边界框坐标信息
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* @description 描述检测到的人物在图片中的位置和尺寸
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*/
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export interface BoundingBox {
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/** X坐标（像素） */
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x: number;
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/** Y坐标（像素） */
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y: number;
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/** 宽度（像素） */
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width: number;
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/** 高度（像素） */
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height: number;
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}
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/**
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* 匹配的人物信息
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* @description 从图片中识别出的与已知角色匹配的人物信息
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*/
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export interface MatchedPerson {
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/** 人物ID */
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person_id: string;
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/** 边界框信息 */
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bbox: BoundingBox;
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/** 匹配置信度（0-1之间） */
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confidence: number;
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}
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/**
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* 角色识别结果数据
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* @description AI识别图片中人物后的详细结果
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*/
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export interface RecognitionResultData {
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/** 目标图片URL */
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target_image_url: string;
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/** 检测到的总人数 */
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total_persons_detected: number;
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/** 匹配的人物列表 */
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matched_persons: MatchedPerson[];
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}
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/**
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* 角色识别响应结果
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* @description API返回的角色识别完整响应
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*/
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export interface RecognitionResult {
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/** 响应状态码 */
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code: number;
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/** 响应消息 */
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message: string;
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/** 识别结果数据 */
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data: RecognitionResultData;
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}
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/**
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* 使用的角色信息
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* @description 在识别过程中使用的已知角色信息
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*/
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export interface CharacterUsed {
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/** 角色名称 */
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character_name: string;
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/** 角色C-ID */
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c_id: string;
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/** 角色图片路径 */
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image_path: string;
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/** 角色头像 */
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avatar: string;
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}
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/**
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* 角色识别API响应类型
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* @description 完整的角色识别API响应结构
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*/
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export interface RoleRecognitionResponse {
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/** 项目ID */
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project_id: string;
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/** 视频ID */
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video_id: string;
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/** 目标图片URL */
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target_image_url: string;
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/** 已知人物数量 */
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known_persons_count: number;
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/** 识别结果 */
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recognition_result: RecognitionResult;
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/** 使用的角色列表 */
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characters_used: CharacterUsed[];
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}
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const roleRecognitionResponse:RoleRecognitionResponse = {
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"project_id": "d0df7120-e27b-4f84-875c-e532f1bd318c",
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"video_id": "984f3347-c81c-4af8-9145-49ead82becde",
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"target_image_url": "https://cdn.qikongjian.com/videos/1754970412744_kqxplx.png",
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"known_persons_count": 1,
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"recognition_result": {
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"code": 200,
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"message": "识别完成",
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"data":
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{
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"target_image_url": "https://cdn.qikongjian.com/videos/1754970412744_kqxplx.png",
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"total_persons_detected": 1,
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"matched_persons": [
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{
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"person_id": "CH-01",
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"bbox": {
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"x": 269,
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"y": 23,
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"width": 585,
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"height": 685
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},
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"confidence": 0.36905956268310547
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}
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]
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}
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},
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"characters_used": [
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{
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"character_name": "CH-01",
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"c_id": "无C-ID",
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"image_path": "无image_path",
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"avatar": "https://cdn.huiying.video/template/Whisk_9afb196368.jpg"
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}
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]
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}
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