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134
README.md
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@ -1,102 +1,102 @@
# video-product-snapshot — 视频商品以图搜图
# video-product-snapshot
从视频中提取最佳商品帧，以图搜图在 1688 找同款。
Detect ecommerce products in video frames using Claude Vision, extract the best product snapshot, and optionally search for matching products via image-search API.
## 工作原理
## How it works
1. `ffmpeg` 按 0.5s 间隔抽帧（最多 60 帧）
2. 视觉质量预过滤（亮度/方差剔除模糊帧）
3. 容器/架子类产品检测 → 自动选择空载帧
4. 视觉模型多帧对比排序，选出最佳商品帧
5. 裁剪商品区域 → 上传 → 1688 图搜
6. 后置过滤（视觉模型判断结果是否同款）→ rerank 排序
1. Extracts frames from the video at a configurable interval using `ffmpeg`
2. Sends each frame to a vision model to detect whether a product is visible and rate confidence
3. Picks the highest-confidence frame as the best snapshot
4. Optionally calls an image-search API with the snapshot to find matching products
## 安装
## Install
```bash
./install.sh # 安装 auth-rt + 依赖
bun install
bun run build # 输出到 dist/run.js
bun run build # outputs dist/run.js
```
## 使用方法
## Usage
```bash
bun dist/run.js <command> [options]
```
### 命令
### Commands
| 命令 | 说明 |
|------|------|
| `detect-best-and-search <video>` | **推荐。** 最佳帧 → 图搜 → rerank |
| `detect-best <video>` | 只提取最佳商品帧，不搜图 |
| `detect-and-search <video>` | 两阶段过滤后图搜（较慢） |
| `detect <video>` | 抽帧并逐帧检测商品 |
| `search <image>` | 用已有图片搜同款 |
| `rerank` | 关键词对图搜结果交叉过滤 |
| `session` | 获取当前认证会话 token |
| Command | Description |
|---------|-------------|
| `detect <video>` | Extract frames and detect product snapshots |
| `search <image>` | Search products by image via API |
| `detect-and-search <video>` | Full pipeline: detect best snapshot then search |
| `session` | Print current auth session token |
### 选项（`detect-best` / `detect-best-and-search`）
### Options (`detect` / `detect-and-search`)
| 参数 | 默认值 | 说明 |
|------|--------|------|
| `--interval=<秒>` | `0.5` | 帧采样间隔 |
| `--max-frames=<n>` | `60` | 最大分析帧数 |
| `--output-dir=<目录>` | 视频同目录 | 截图保存目录 |
| `--session-id=<id>` | 自动生成 | Langfuse session ID |
| `--dry-run` | — | 解析参数，不实际执行 |
| Flag | Default | Description |
|------|---------|-------------|
| `--interval=<sec>` | `1` | Seconds between sampled frames |
| `--max-frames=<n>` | `60` | Max frames to analyze |
| `--output-dir=<dir>` | next to video | Directory to save extracted frames |
| `--min-confidence=<0-1>` | `0.7` | Minimum confidence to include a frame |
| `--dry-run` | — | Parse args and print config without running |
## 输出
### Examples
所有命令输出 JSON 到 stdout，包含 `sessionId` 字段用于 Langfuse 追踪。
```bash
# Detect products, sample every 3 seconds
bun dist/run.js detect ./demo.mp4 --interval=3
# Full pipeline with higher confidence threshold
bun dist/run.js detect-and-search ./demo.mp4 --interval=5 --min-confidence=0.85
# Search using an existing snapshot image
bun dist/run.js search ./snapshot.jpg
```
## Output
All commands return JSON to stdout.
```json
{
"sessionId": "skill-20260426-184345-lb06",
"status": "success",
"command": "detect-best-and-search",
"bestSnapshot": {
"frameIndex": 7,
"timestampSeconds": 3,
"imagePath": "/path/to/frame_0007.jpg",
"croppedImagePath": "/path/to/frame_0007_cropped.jpg",
"description": "黑色金属床底鞋架 可折叠移动"
"frameIndex": 4,
"timestampSeconds": 9,
"imagePath": "/path/to/frame_0004.jpg",
"confidence": 0.92,
"description": "White sneaker with blue logo, left side view",
"boundingHint": "centered"
},
"rerank": {
"keyword": "床底鞋架",
"results": [
{ "num_iid": 123, "title": "...", "price": "44.00", "sales": 87, "detail_url": "..." }
]
}
"productFrames": [...],
"searchBody": { ... }
}
```
## 鉴权架构
- `productFrames` — all detected frames sorted by confidence (highest first)
- `bestSnapshot` — the top-ranked frame
- `searchBody` — image-search API response (only for `search` / `detect-and-search`)
## Environment variables
The only required configuration is `CLIENT_KEY` in `~/.openclaw/.env`:
```
~/.openclaw/.env
CLIENT_KEY ──→ auth-rt ──→ 业务系统
├── /session → access_token
└── /client-config → provider.api_key
provider.base_url
provider.model
CLIENT_KEY=sk_xxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxxx
```
仅需配置 `CLIENT_KEY`，LLM 凭据和端点均由业务系统下发。
All credentials and endpoints are fetched automatically from the client config via `auth-rt`. No per-skill env vars needed.
## 环境变量
### Optional overrides
| 变量 | 说明 |
|------|------|
| `CLIENT_KEY` | **必需。** 在 `~/.openclaw/.env` 中配置 |
| `VISION_MODEL` | 覆盖模型名称（默认来自 client config） |
| `SKILL_SESSION_ID` | Langfuse session ID（自动生成，格式 `skill-YYYYMMDD-HHMMSS-xxxx`） |
| `AUTH_RT_BIN` | 覆盖 `auth-rt` 二进制路径 |
| `TELEMETRY_ENDPOINT` | 遥测上报接口 |
| Variable | Description |
|----------|-------------|
| `VISION_MODEL` | Override model name (default: `aliyun-cp-multimodal`) |
| `AUTH_RT_BIN` | Override path to the `auth-rt` binary |
| `TELEMETRY_ENDPOINT` | POST execution results to a telemetry endpoint |
## 前置依赖
## Prerequisites
- [Bun](https://bun.sh) 运行时
- 系统 PATH 中包含 `ffmpeg` / `ffprobe`（帧提取）
- `auth-rt` CLI（鉴权/API 调用，`install.sh` 自动安装）
- [Bun](https://bun.sh) runtime
- `ffmpeg` and `ffprobe` in PATH
- `auth-rt` CLI in PATH (required for `search` / `detect-and-search`)

142
SKILL.md
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@ -1,94 +1,126 @@
---
name: video-product-snapshot
description: "Extract product snapshot from video and search 1688 by image. / 从视频中提取最佳商品帧，以图搜图在1688找同款。当用户提供视频想找商品时使用。"
description: "Detect ecommerce products in video frames using Claude Vision, extract the best product snapshot, and optionally search via image-search API. Use when the user provides a video and wants to find/identify products shown in it."
---
# Video Product Snapshot — 视频商品以图搜图
# Video Product Snapshot
从视频中截取最清晰的商品帧（容器类产品自动选空载帧），上传图片在 1688 以图搜图找同款。
Extract ecommerce product snapshots from video using Claude Vision, then optionally search for matching products via image-search API.
## 运行
## Run
```bash
bun dist/run.js <command> [args] [--dry-run]
```
## 命令列表
## Commands
| 命令 | 使用场景 |
|------|---------|
| `detect-best-and-search <video>` | **推荐。** 提取最佳商品帧 → 图搜 → rerank 返回结果。 |
| `detect-best <video>` | 只提取最佳商品帧，不搜图。 |
| `detect-and-search <video>` | 两阶段过滤后图搜（比 detect-best 慢）。 |
| `search <image-path>` | 已有商品图，直接图搜。 |
| `rerank` | 用关键词对图搜结果交叉过滤。 |
| `session` | 获取当前认证会话 token。 |
| Command | Description |
|---------|-------------|
| `detect <video-path> [options]` | Extract frames, detect product snapshots |
| `search <image-path>` | Search products by image via API |
| `detect-and-search <video-path> [options]` | Detect best snapshot then run image search |
| `session` | Get auth session token |
## 主命令：`detect-best-and-search`
流程：
1. ffmpeg 按 0.5s 间隔提取帧（最多 60 帧）
2. 视觉模型检测是否为容器/架子类产品
3. 容器类：只从前 40% 帧（空载阶段）中选最佳帧
4. 非容器类：全帧中选最清晰帧
5. 裁剪商品区域
6. 上传裁剪图 → 1688 图搜
7. rerank：图搜结果与关键词搜索结果交叉过滤
## Options for `detect-best` / `detect-best-and-search`
## Options for `detect` / `detect-and-search`
| Flag | Default | Description |
|------|---------|-------------|
| `--interval=<sec>` | `0.5` | 帧采样间隔（秒） |
| `--max-frames=<n>` | `60` | 最大分析帧数 |
| `--output-dir=<dir>` | 视频同目录 | 截图保存目录 |
| `--interval=<sec>` | `1` | Seconds between sampled frames |
| `--max-frames=<n>` | `60` | Max frames to analyze |
| `--output-dir=<dir>` | next to video | Directory to save snapshot images |
| `--min-confidence=<0-1>` | `0.7` | Minimum detection confidence threshold |
## 输出格式
## Examples
### `detect-best-and-search`
```bash
# Detect product frames in a video
bun dist/run.js detect ./product-demo.mp4
# Sample every 5 seconds, higher confidence threshold
bun dist/run.js detect ./product-demo.mp4 --interval=5 --min-confidence=0.85
# Search for products using an existing image
bun dist/run.js search ./snapshot.jpg
# Full pipeline: detect best product frame then search
bun dist/run.js detect-and-search ./product-demo.mp4 --interval=3 --max-frames=20
```
## Output
Returns JSON with:
- `productFrames[]`: all detected product frames sorted by confidence (highest first)
- `bestSnapshot`: the highest-confidence product frame
- `searchBody`: image search API response (for `detect-and-search` and `search`)
Each `ProductFrame` contains:
```json
{
"bestSnapshot": {
"frameIndex": 7,
"timestampSeconds": 3,
"imagePath": "/path/to/frame_0007.jpg",
"croppedImagePath": "/path/to/frame_0007_cropped.jpg",
"description": "黑色金属床底鞋架 可折叠移动"
},
"rerank": {
"keyword": "床底鞋架",
"results": [
{ "num_iid": 123, "title": "...", "price": "44.00", "sales": 87, "detail_url": "..." }
]
}
"frameIndex": 4,
"timestampSeconds": 9,
"imagePath": "/path/to/snapshot/frame_0004.jpg",
"confidence": 0.92,
"description": "White sneaker with blue logo, left side view",
"boundingHint": "centered"
}
```
## 结果展示格式
## Prerequisites
将 `rerank.results`（优先）或 `searchBody.data.items.item` 格式化为 markdown 表格，**最多 5 条**：
- `ffmpeg` and `ffprobe` in PATH
- `VISION_API_KEY` — API key for the vision endpoint
- `VISION_API_BASE` — (optional) OpenAI-compatible base URL; omit to use OpenAI default
- `VISION_MODEL` — (optional) model name, default `gpt-4o-mini`
- `auth-rt` in PATH (for `search` / `detect-and-search` API calls)
### Example provider configs
```bash
# OpenAI (default)
VISION_API_KEY=sk-...
# Any OpenAI-compatible endpoint (local Ollama, Together, Groq, etc.)
VISION_API_KEY=...
VISION_API_BASE=http://localhost:11434/v1
VISION_MODEL=llava:13b
```
## Result formatting
After the CLI completes, format `rerank.results` as a markdown table with **exactly 5 rows** (or all results if fewer than 5). Do NOT split into "最佳匹配" / "其他热门选项" — show everything in one flat table.
| # | 商品名称 | 价格 | 销量 | 链接 |
|---|----------|------|------|------|
| 1 | {title} | ¥{promotion_price \|\| price} | {sales ?? —}件 | [查看]({detail_url}) |
| 1 | {title} | ¥{promotion_price \|\| price} | {sales ?? —}件 | [查看](https://detail.1688.com/offer/{num_iid}.html) |
- 有 `promotion_price` 用促销价，否则用原价
- `sales` 缺失或为零时显示 `—`
- 始终用 markdown 表格展示，不要用列表
- Use `promotion_price` when present, otherwise `price`
- If `sales` is missing or zero, show `—`
- Always render as a markdown table, never as bullet points
## 执行规则
## Execution rules
1. **不要重试。** 命令失败就直接报错。
2. **信任工具输出。** CLI 内部已处理 session 管理和错误格式化。
3. **慢命令用 sub-agent**（detect-best-and-search / detect-and-search），直接运行会超时。
### For `detect` and `detect-and-search` (slow — use sub-agent)
### sub-agent 执行模板
Spawn a sub-agent via `sessions_spawn`. Do **not** run the command directly.
```
sessions_spawn(
task: "cd /path/to/skill && Run this command and return the raw JSON output:\n\nbun dist/run.js detect-best-and-search <video-path>\n\nCopy the entire JSON output as your reply.",
task: "Run this command and return the raw JSON output:\n\nbun dist/run.js <full command here>\n\nCopy the entire JSON output as your reply.",
label: "video-product-snapshot",
runTimeoutSeconds: 300,
)
```
- Announce immediately that processing has started and share the `runId`.
- Wait for the sub-agent announcement, then parse and format the result for the user.
### For `search` and `session` (fast — run directly)
Run the CLI command inline, no sub-agent needed.
### General rules
1. **No fallback strategies.** Report errors as-is; do NOT try alternative approaches.
2. **No retry loops.** If detection or search fails, report the failure.
3. **Trust the tool's output.** The CLI handles session management and error formatting internally.

50
scripts/run.ts
View File

@ -22,43 +22,31 @@ function loadDotenv(path: string): void {
}
function printUsage(): void {
console.error(`用法:
console.error(`Usage:
bun scripts/run.ts [--api-base=<url>] <command> [args...] [--dry-run]
命令:
Commands:
session
获取认证 session token
Get auth session token
detect <video-path> [options]
从视频抽帧并检测商品画面
选项:
--interval=<秒> 抽帧间隔（默认: 1）
--max-frames=<数量> 最多分析帧数（默认: 60）
--output-dir=<目录> 截图保存目录（默认: 视频所在目录）
--min-confidence=<0-1> 最低检测置信度（默认: 0.7）
Extract frames and detect ecommerce product snapshots
Options:
--interval=<seconds> Frame sampling interval (default: 1)
--max-frames=<n> Max frames to analyze (default: 60)
--output-dir=<dir> Where to save snapshots (default: next to video)
--min-confidence=<0-1> Minimum detection confidence (default: 0.7)
search <image-path>
用图片搜索商品（调用 ecom image-search API）
Search for products using an image via the ecom image-search API
detect-and-search <video-path> [options]
检测最佳商品画面 → 图片搜索 → 关键词重排序
detect-best <video-path> [options]
从视频抽帧并选择最佳商品画面（更快更稳定）
detect-best-and-search <video-path> [options]
最佳画面 → 图片搜索 → 关键词重排序
detect-video <video-path>
识别商品描述和搜索关键词（当前实现：从视频抽帧选最佳帧）
detect-video-and-search <video-path>
识别商品 → 图片搜索 → 1688 关键词重排序（当前实现：从视频抽帧选最佳帧）
Detect best product snapshot from video then run image search + rerank
rerank --image-results=<json> [--description=<text>] [--keyword=<text>] [--top=<n>]
通过关键词交并集过滤搜索结果
Filter image search results using keyword intersection
配置文件: ~/.openclaw/.env (CLIENT_KEY), skill 目录 .env (VISION_API_KEY)
Config: ~/.openclaw/.env (CLIENT_KEY), skill .env (VISION_API_KEY)
`);
}
@ -81,8 +69,6 @@ async function main(): Promise<void> {
dryRun = true;
} else if (arg.startsWith('--api-base=')) {
process.env.API_BASE = arg.slice('--api-base='.length).trim();
} else if (arg.startsWith('--session-id=')) {
process.env.SKILL_SESSION_ID = arg.slice('--session-id='.length).trim();
} else if (arg === '-h' || arg === '--help') {
printUsage(); process.exit(0);
} else {
@ -93,7 +79,6 @@ async function main(): Promise<void> {
if (positionals.length < 1) { printUsage(); process.exit(1); }
const command = positionals[0] as Command;
const sessionId = process.env.SKILL_SESSION_ID!; // set by auth-cli.ts at module load
const startMs = Date.now();
let result: Awaited<ReturnType<typeof run>>;
@ -101,14 +86,13 @@ async function main(): Promise<void> {
result = await run(command, positionals.slice(1), dryRun);
} catch (err) {
const error = err instanceof Error ? err.message : String(err);
console.log(JSON.stringify({ status: 'failed', command, dryRun, sessionId, error }, null, 2));
if (!dryRun) reportTelemetry({ skill: SKILL_NAME, command, sessionId, status: 'failed', durationMs: Date.now() - startMs, error });
console.log(JSON.stringify({ status: 'failed', command, dryRun, error }, null, 2));
if (!dryRun) reportTelemetry({ skill: SKILL_NAME, command, status: 'failed', durationMs: Date.now() - startMs, error });
process.exit(1);
}
const output = { ...result, sessionId } as Record<string, unknown>;
console.log(JSON.stringify(output, null, 2));
if (!dryRun) reportTelemetry({ skill: SKILL_NAME, command, sessionId, status: result.status, durationMs: Date.now() - startMs, error: (result as any).error });
console.log(JSON.stringify(result, null, 2));
if (!dryRun) reportTelemetry({ skill: SKILL_NAME, command, status: result.status, durationMs: Date.now() - startMs, error: (result as any).error });
}
main().catch((err) => {

12
src/auth-cli.ts
View File

@ -20,18 +20,6 @@ import * as path from 'path';
import * as os from 'os';
const home = process.env.HOME || os.homedir();
// ── session ID (Langfuse tracing) ──
// Priority: SKILL_SESSION_ID env > auto-generate
const SESSION_ID = process.env.SKILL_SESSION_ID || (() => {
const ts = new Date();
const pad = (n: number) => String(n).padStart(2, '0');
const tsPart = `${ts.getFullYear()}${pad(ts.getMonth()+1)}${pad(ts.getDate())}-${pad(ts.getHours())}${pad(ts.getMinutes())}${pad(ts.getSeconds())}`;
const rand = Math.random().toString(36).slice(2, 6);
return `skill-${tsPart}-${rand}`;
})();
process.env.SKILL_SESSION_ID = SESSION_ID;
const AUTH_RT_BIN = process.env.AUTH_RT_BIN
|| (() => {
// Check if auth-rt is in PATH

270
src/index.ts
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@ -1,10 +1,10 @@
import * as fs from 'fs';
import * as path from 'path';
import type { Command, DetectOptions, DetectResult, SearchResult, OutputResult, SearchItem, DetectVideoResult, DetectVideoAndSearchResult } from './types.ts';
import type { Command, DetectOptions, DetectResult, SearchResult, OutputResult, SearchItem } from './types.ts';
import { createSkillClient } from './auth-cli.ts';
import { extractFrames } from './frame-extractor.ts';
import { detectProductFrames, detectBestFrame } from './product-detector.ts';
import { postFilterByImage } from './post-filter.ts';
import { detectProductFrames } from './product-detector.ts';
import { imageToBase64 } from './frame-extractor.ts';
import { generateText } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';
@ -12,7 +12,6 @@ export interface VisionConfig {
apiKey: string;
baseURL?: string;
model: string;
sessionId?: string;
}
async function loadVisionConfig(client: ReturnType<typeof createSkillClient>): Promise<VisionConfig> {
@ -23,7 +22,6 @@ async function loadVisionConfig(client: ReturnType<typeof createSkillClient>): P
apiKey,
baseURL: cfg.metadata?.provider?.base_url,
model: process.env.VISION_MODEL ?? cfg.metadata?.provider?.model ?? 'aliyun-cp-multimodal',
sessionId: process.env.SKILL_SESSION_ID || `skill_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`,
};
}
@ -41,14 +39,6 @@ export async function run(
return runSearch(args, dryRun);
case 'detect-and-search':
return runDetectAndSearch(args, dryRun);
case 'detect-best':
return runDetectBest(args, dryRun);
case 'detect-best-and-search':
return runDetectBestAndSearch(args, dryRun);
case 'detect-video':
return runDetectVideo(args, dryRun);
case 'detect-video-and-search':
return runDetectVideoAndSearch(args, dryRun);
case 'rerank':
return runRerank(args, dryRun);
default:
@ -135,192 +125,6 @@ async function runSearch(args: string[], dryRun: boolean): Promise<SearchResult>
return { status: 'success', command: 'search', dryRun, imagePath, searchHttpStatus, searchBody: body };
}
async function runDetectBest(args: string[], dryRun: boolean): Promise<DetectResult> {
const videoPath = args[0];
if (!videoPath) return { status: 'failed', command: 'detect-best', dryRun, error: 'detect-best requires <video-path>' };
if (!fs.existsSync(videoPath)) return { status: 'failed', command: 'detect-best', dryRun, error: `video not found: ${videoPath}` };
const outputDir = getFlag(args, '--output-dir') || path.join(
path.dirname(videoPath),
`snapshots_${path.basename(videoPath, path.extname(videoPath))}_${Date.now()}`,
);
const intervalSeconds = parseFloat(getFlag(args, '--interval') || '0.5');
const maxFrames = parseInt(getFlag(args, '--max-frames') || '60', 10);
if (dryRun) {
return { status: 'success', command: 'detect-best', dryRun, videoPath, totalFramesExtracted: 0, productFrames: [], bestSnapshot: undefined };
}
const client = createSkillClient();
const visionConfig = await loadVisionConfig(client);
const frames = extractFrames(videoPath, outputDir, intervalSeconds, maxFrames);
if (frames.length === 0) {
return { status: 'failed', command: 'detect-best', dryRun, videoPath, error: 'no frames extracted from video' };
}
const best = await detectBestFrame(frames, visionConfig, 20);
return {
status: 'success',
command: 'detect-best',
dryRun,
videoPath,
totalFramesExtracted: frames.length,
productFrames: best ? [best] : [],
bestSnapshot: best ?? undefined,
};
}
async function runDetectBestAndSearch(args: string[], dryRun: boolean): Promise<OutputResult> {
const detectResult = await runDetectBest(args, dryRun) as DetectResult;
if (detectResult.status === 'failed') return detectResult;
if (!detectResult.bestSnapshot) {
if (dryRun) return { ...detectResult, command: 'detect-best-and-search' };
return { ...detectResult, status: 'failed', error: 'no frame could be extracted from video' };
}
const best = detectResult.bestSnapshot;
const imageForSearch = best.croppedImagePath || best.imagePath;
const searchResult = await runSearch([imageForSearch], dryRun) as SearchResult;
// Post-filter: drop results whose pic_url isn't the same product type as our snapshot
let postFilter: any = undefined;
if (!dryRun && searchResult.status === 'success' && searchResult.searchBody) {
const items: SearchItem[] = (searchResult.searchBody as any)?.data?.items?.item ?? [];
if (items.length > 0) {
try {
const client = createSkillClient();
const visionConfig = await loadVisionConfig(client);
const result = await postFilterByImage(imageForSearch, items, visionConfig, { description: best.description });
(searchResult.searchBody as any).data.items.item = result.kept;
postFilter = {
totalChecked: result.totalChecked,
keptCount: result.kept.length,
rejectedCount: result.rejected.length,
failed: result.failed,
};
} catch (e: any) {
postFilter = { error: e.message };
}
}
}
let rerankResult: any = undefined;
// If post-filter produced focused results, sort them directly by sales — they're already the best matches.
// Otherwise fall back to the keyword-intersection rerank.
if (!dryRun && postFilter && !postFilter.error && postFilter.keptCount > 0) {
const items: SearchItem[] = (searchResult.searchBody as any)?.data?.items?.item ?? [];
const sorted = [...items].sort((a, b) => (b.sales ?? 0) - (a.sales ?? 0)).slice(0, 5);
rerankResult = {
source: 'post-filter',
results: sorted,
count: sorted.length,
};
} else if (!dryRun && searchResult.status === 'success' && searchResult.searchBody) {
const tmpFile = path.join(path.dirname(imageForSearch), `search_body_${Date.now()}.json`);
try {
fs.writeFileSync(tmpFile, JSON.stringify(searchResult.searchBody));
rerankResult = await runRerank([
`--image-results=${tmpFile}`,
`--description=${best.description}`,
'--top=5',
], dryRun);
} catch (e: any) {
rerankResult = { error: e.message };
} finally {
try { fs.unlinkSync(tmpFile); } catch {}
}
}
return {
...detectResult,
command: 'detect-best-and-search',
searchHttpStatus: searchResult.searchHttpStatus,
searchBody: searchResult.searchBody,
searchError: searchResult.error,
postFilter,
rerank: rerankResult,
} as any;
}
async function runDetectVideo(args: string[], dryRun: boolean): Promise<DetectVideoResult> {
const videoPath = args[0];
if (!videoPath) return { status: 'failed', command: 'detect-video', dryRun, error: 'detect-video requires <video-path>' };
if (!fs.existsSync(videoPath)) return { status: 'failed', command: 'detect-video', dryRun, error: `video not found: ${videoPath}` };
const detectResult = await runDetectBest(args, dryRun) as DetectResult;
if (detectResult.status === 'failed') {
return { status: 'failed', command: 'detect-video', dryRun, videoPath, error: detectResult.error || 'failed to detect best frame' };
}
const description = detectResult.bestSnapshot?.description?.trim();
const snapshotImagePath = detectResult.bestSnapshot?.croppedImagePath || detectResult.bestSnapshot?.imagePath;
if (!description) {
return { status: 'failed', command: 'detect-video', dryRun, videoPath, error: 'no product description detected from video' };
}
if (dryRun) {
return { status: 'success', command: 'detect-video', dryRun, videoPath, videoUrl: null, description, keyword: '<dry-run-keyword>', snapshotImagePath };
}
const client = createSkillClient();
const visionConfig = await loadVisionConfig(client);
const keyword = await generateChineseKeyword(description, visionConfig);
return { status: 'success', command: 'detect-video', dryRun, videoPath, videoUrl: null, description, keyword, snapshotImagePath };
}
async function runDetectVideoAndSearch(args: string[], dryRun: boolean): Promise<DetectVideoAndSearchResult> {
const videoPath = args[0];
if (!videoPath) return { status: 'failed', command: 'detect-video-and-search', dryRun, error: 'detect-video-and-search requires <video-path>' };
if (!fs.existsSync(videoPath)) return { status: 'failed', command: 'detect-video-and-search', dryRun, error: `video not found: ${videoPath}` };
if (dryRun) {
return { status: 'success', command: 'detect-video-and-search', dryRun, videoPath, videoUrl: null, description: '<dry-run>', keyword: '<dry-run>', searchResults: [] };
}
// Reuse existing pipeline: best snapshot → image search → keyword rerank
const detectAndSearch = await runDetectBestAndSearch(args, dryRun) as any;
if (detectAndSearch.status === 'failed') {
return { status: 'failed', command: 'detect-video-and-search', dryRun, videoPath, error: detectAndSearch.error || 'detect-best-and-search failed' };
}
const description = String(detectAndSearch.bestSnapshot?.description || '').trim();
const rerank = detectAndSearch.rerank;
const keyword = String(rerank?.keyword || '').trim();
const searchResults = (rerank?.results || []) as SearchItem[];
// Fallback: if rerank didn't produce anything, do keyword search directly.
if (!searchResults.length) {
const client = createSkillClient();
const visionConfig = await loadVisionConfig(client);
const fallbackKeyword = keyword || (description ? await generateChineseKeyword(description, visionConfig) : '');
const items = fallbackKeyword ? await keywordSearch(client, fallbackKeyword, 1) : [];
return {
status: 'success',
command: 'detect-video-and-search',
dryRun,
videoPath,
videoUrl: null,
description,
keyword: fallbackKeyword,
searchResults: items,
};
}
return {
status: 'success',
command: 'detect-video-and-search',
dryRun,
videoPath,
videoUrl: null,
description,
keyword,
searchResults,
};
}
async function runDetectAndSearch(args: string[], dryRun: boolean): Promise<OutputResult> {
const detectResult = await runDetect(args, dryRun) as DetectResult;
if (detectResult.status === 'failed') return detectResult;
@ -333,47 +137,15 @@ async function runDetectAndSearch(args: string[], dryRun: boolean): Promise<Outp
const imageForSearch = best.croppedImagePath || best.imagePath;
const searchResult = await runSearch([imageForSearch], dryRun) as SearchResult;
// Post-filter: drop results whose pic_url isn't the same product type as our snapshot
let postFilter: any = undefined;
if (!dryRun && searchResult.status === 'success' && searchResult.searchBody) {
const items: SearchItem[] = (searchResult.searchBody as any)?.data?.items?.item ?? [];
if (items.length > 0) {
try {
const client = createSkillClient();
const visionConfig = await loadVisionConfig(client);
const result = await postFilterByImage(imageForSearch, items, visionConfig, { description: best.description });
(searchResult.searchBody as any).data.items.item = result.kept;
postFilter = {
totalChecked: result.totalChecked,
keptCount: result.kept.length,
rejectedCount: result.rejected.length,
failed: result.failed,
};
} catch (e: any) {
postFilter = { error: e.message };
}
}
}
let rerankResult: any = undefined;
// If post-filter produced focused results, sort them directly by sales — they're already the best matches.
// Otherwise fall back to the keyword-intersection rerank.
if (!dryRun && postFilter && !postFilter.error && postFilter.keptCount > 0) {
const items: SearchItem[] = (searchResult.searchBody as any)?.data?.items?.item ?? [];
const sorted = [...items].sort((a, b) => (b.sales ?? 0) - (a.sales ?? 0)).slice(0, 5);
rerankResult = {
source: 'post-filter',
results: sorted,
count: sorted.length,
};
} else if (!dryRun && searchResult.status === 'success' && searchResult.searchBody) {
if (!dryRun && searchResult.status === 'success' && searchResult.searchBody) {
const tmpFile = path.join(path.dirname(imageForSearch), `search_body_${Date.now()}.json`);
try {
fs.writeFileSync(tmpFile, JSON.stringify(searchResult.searchBody));
rerankResult = await runRerank([
`--image-results=${tmpFile}`,
`--description=${best.description}`,
'--top=5',
'--top=10',
], dryRun);
} catch (e: any) {
rerankResult = { error: e.message };
@ -388,7 +160,6 @@ async function runDetectAndSearch(args: string[], dryRun: boolean): Promise<Outp
searchHttpStatus: searchResult.searchHttpStatus,
searchBody: searchResult.searchBody,
searchError: searchResult.error,
postFilter,
rerank: rerankResult,
} as any;
}
@ -417,25 +188,7 @@ function getFlag(args: string[], flag: string): string | undefined {
}
function createVisionModel(config: VisionConfig) {
const sessionId = config.sessionId || '';
const originFetch = globalThis.fetch;
// Inject metadata.session_id into request body so LiteLLM → Langfuse creates sessions
const wrapped = async (input: RequestInfo | URL, init?: RequestInit) => {
if (init?.body && typeof init.body === 'string') {
try {
const body = JSON.parse(init.body);
if (!body.metadata) body.metadata = {};
if (!body.metadata.session_id) body.metadata.session_id = sessionId;
body.metadata.tags = ['skill:video-product-snapshot'];
init = { ...init, body: JSON.stringify(body) };
} catch {}
}
return originFetch(input, init);
};
const openai = createOpenAI({
apiKey: config.apiKey, baseURL: config.baseURL,
fetch: wrapped as typeof globalThis.fetch,
});
const openai = createOpenAI({ apiKey: config.apiKey, baseURL: config.baseURL });
return openai(config.model);
}
@ -490,10 +243,9 @@ function extractKeywordsFromTitles(items: SearchItem[], topN = 5): string {
async function runRerank(args: string[], dryRun: boolean): Promise<OutputResult> {
// --image-results=<path> --keyword=<text> --top=<n>
const positionals = args.filter((a) => !a.startsWith('--'));
const imageResultsArg = getFlag(args, '--image-results') || positionals[0];
const keywordArg = getFlag(args, '--keyword') || positionals[1];
const topN = parseInt(getFlag(args, '--top') || '5', 10);
const imageResultsArg = getFlag(args, '--image-results') || args[0];
const keywordArg = getFlag(args, '--keyword') || args[1];
const topN = parseInt(getFlag(args, '--top') || '10', 10);
const description = getFlag(args, '--description') || '';
@ -568,3 +320,7 @@ async function runRerank(args: string[], dryRun: boolean): Promise<OutputResult>
results: sorted,
} as any;
}
function parseJsonSafe(text: string): unknown {
try { return JSON.parse(text); } catch { return text; }
}

123
src/post-filter.ts
View File

@ -1,123 +0,0 @@
import { generateText } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';
import type { SearchItem } from './types.ts';
import type { VisionConfig } from './index.ts';
import { imageToBase64 } from './frame-extractor.ts';
export interface PostFilterResult {
kept: SearchItem[];
rejected: SearchItem[];
totalChecked: number;
failed: boolean;
}
const FILTER_PROMPT = (count: number, description?: string) => {
const productLine = description
? `查询商品是：${description}`
: '第1张图是查询商品。';
return `${productLine}
后面的 ${count} 张图是搜索结果。
任务：判断每张候选图是否与查询商品是**完全相同的具体产品类型**。
- 必须是同一个具体产品（例如：查询是"鞋架"，候选必须也是鞋架；不是其他类型的架子如纸巾架、首饰架、收纳盒）
- 颜色、材质、款式、尺寸不同但同一具体类型 → 算同类
- 用途不同就不算同类（例如：查询是鞋架 vs 候选是纸巾架 → 不算；查询是鞋架 vs 候选是床下收纳箱 → 不算，除非明确是鞋类收纳）
- 关键判断：候选商品的主要用途是否与查询商品一致
按候选图顺序输出每一张的判断，每行一个，格式严格遵守：
1: YES
2: NO
3: YES
...
只输出 ${count} 行结果，不要解释，不要前后空行。`;
};
function createModel(config: VisionConfig) {
const sessionId = config.sessionId || '';
const originFetch = globalThis.fetch;
const wrapped = async (input: RequestInfo | URL, init?: RequestInit) => {
if (init?.body && typeof init.body === 'string') {
try {
const body = JSON.parse(init.body);
if (!body.metadata) body.metadata = {};
if (!body.metadata.session_id) body.metadata.session_id = sessionId;
body.metadata.tags = ['skill:video-product-snapshot'];
init = { ...init, body: JSON.stringify(body) };
} catch {}
}
return originFetch(input, init);
};
const provider = createOpenAI({
apiKey: config.apiKey, baseURL: config.baseURL,
fetch: wrapped as typeof globalThis.fetch,
});
return provider(config.model);
}
async function classifyBatch(
model: ReturnType<ReturnType<typeof createOpenAI>>,
queryImageDataUrl: string,
batch: SearchItem[],
description?: string,
): Promise<boolean[]> {
const content: any[] = [{ type: 'image', image: queryImageDataUrl }];
for (const item of batch) {
content.push({ type: 'image', image: item.pic_url });
}
content.push({ type: 'text', text: FILTER_PROMPT(batch.length, description) });
const { text } = await generateText({
model,
messages: [{ role: 'user', content }],
maxTokens: 200,
});
const flags = batch.map(() => false);
for (const line of text.split('\n')) {
const m = line.match(/^\s*(\d+)\s*[:：]\s*(YES|NO|是|否)/i);
if (!m) continue;
const idx = parseInt(m[1], 10) - 1;
const yes = /YES|是/i.test(m[2]);
if (idx >= 0 && idx < flags.length) flags[idx] = yes;
}
return flags;
}
export async function postFilterByImage(
queryImagePath: string,
items: SearchItem[],
visionConfig: VisionConfig,
options: { description?: string; batchSize?: number } = {},
): Promise<PostFilterResult> {
if (items.length === 0) {
return { kept: [], rejected: [], totalChecked: 0, failed: false };
}
const batchSize = options.batchSize ?? 10;
const description = options.description;
const model = createModel(visionConfig);
const queryDataUrl = `data:image/jpeg;base64,${imageToBase64(queryImagePath)}`;
const kept: SearchItem[] = [];
const rejected: SearchItem[] = [];
let anyFailed = false;
for (let i = 0; i < items.length; i += batchSize) {
const batch = items.slice(i, i + batchSize);
try {
const flags = await classifyBatch(model, queryDataUrl, batch, description);
batch.forEach((item, idx) => {
if (flags[idx]) kept.push(item);
else rejected.push(item);
});
} catch {
// On batch failure, keep items (don't lose them) but flag the run as partial
anyFailed = true;
kept.push(...batch);
}
}
return { kept, rejected, totalChecked: items.length, failed: anyFailed };
}

307
src/product-detector.ts
View File

@ -1,4 +1,4 @@
import { generateObject, generateText } from 'ai';
import { generateObject } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';
import { z } from 'zod';
import type { ExtractedFrame } from './frame-extractor.ts';
@ -28,74 +28,24 @@ Discard (keep=false) if: only hands/texture/contents visible, motion blur, black
reason options: product_visible | content_only | hands_only | blur | transition | background_only`;
const CONTAINER_CHECK_PROMPT = `Is the main product in this image a CONTAINER, RACK, or HOLDER (something designed to store/hold other items)?
Examples YES: shoe rack, shelf, storage box, organizer, basket, drawer, wardrobe, trolley, bin, tray, cabinet.
Examples NO: shoes, clothing, electronics, food, toys, cosmetics, tools.
Reply with only one word: YES or NO.`;
const RANKING_PROMPT = (count: number) => `You are selecting the single best product image from ${count} video frames for ecommerce image search.
const RANKING_PROMPT_CONTAINER = (count: number) => `You are selecting ONE frame from ${count} video frames to use as the query image for an ecommerce reverse-image search.
The frames are numbered 0 to ${count - 1} in the order shown.
The hero product is a CONTAINER / RACK / HOLDER / ORGANIZER.
CRITICAL CONSTRAINT — read this first:
Image search engines identify objects by visual appearance. If the container holds items (shoes, clothes, etc.), the search engine will match those ITEMS, not the container — returning completely wrong products.
YOUR ONLY JOB: find the frame where the container structure itself is most visible with the FEWEST or NO items inside.
ABSOLUTE PRIORITY ORDER (do not deviate):
1. Frame with container completely EMPTY — highest priority regardless of angle or assembly state
2. Frame with container partially assembled or partially visible but EMPTY — still better than any loaded frame
3. Frame with fewest items inside (1-2 items, mostly empty)
4. Frame with moderate load — only if no emptier option exists
5. Frame fully loaded — last resort only if no other frames exist
A frame showing the rack mid-assembly with zero items is ALWAYS better than a perfectly-lit fully-assembled rack filled with shoes.
Frames are numbered 0 to ${count - 1} in order shown. You MUST pick ONE.
Pick the ONE frame where the HERO PRODUCT is:
1. Cleanest — fewest distractions, no hands blocking it, no clutter in foreground
2. Most complete — full product silhouette visible, no edges cropped
3. Most isolated — product stands out from background clearly
4. Empty/minimal load preferred — a product without contents (e.g. an empty rack) beats one stuffed with items if both show the full structure equally
Return:
- bestFrameIndex: 0-based index of the emptiest container frame
- description: concise Chinese search query ≤12 words (container type + material + color + key feature)
- reasoning: describe how many items are visible inside the chosen frame and why it's the emptiest option
- boundingBox: tight box of the PRODUCT STRUCTURE ONLY as [x1, y1, x2, y2] normalized 0.0–1.0. Exclude any items stored inside.`;
const RANKING_PROMPT_GENERAL = (count: number) => `You are selecting the single best product frame from ${count} video frames for ecommerce search.
Frames are numbered 0 to ${count - 1} in order shown.
IMPORTANT: You MUST pick ONE frame — even if product visibility is imperfect or no frame looks ideal. Always make your best guess.
Pick the frame where the MAIN SELLING PRODUCT is:
1. Most recognizable — clearest view of the item being sold
2. Most complete — full product silhouette visible, not cropped at edges
3. Cleanest — minimal obstruction (hands, clutter, motion blur, labels)
4. Best lit and in focus
Return:
- bestFrameIndex: 0-based index
- description: concise search query under 12 words (product type + material + color + key features), in Chinese
- reasoning: one sentence explaining choice
- boundingBox: tight box of the PRODUCT ONLY as [x1, y1, x2, y2] normalized 0.0–1.0, top-left origin. Exclude hands, background, and unrelated objects. The product is near the center of the frame.`;
- bestFrameIndex: 0-based index of chosen frame
- description: concise search query under 12 words (product type + material + color + key feature)
- reasoning: one sentence explaining why this frame was chosen
- boundingBox: tight bounding box of the HERO PRODUCT ONLY in the chosen frame as [x1, y1, x2, y2] normalized 0.0–1.0 (top-left origin). Exclude hands, background, and unrelated objects. The product is assumed to be near the center.`;
function createVisionModel(config: VisionConfig) {
const sessionId = config.sessionId || '';
const originFetch = globalThis.fetch;
const wrapped = async (input: RequestInfo | URL, init?: RequestInit) => {
if (init?.body && typeof init.body === 'string') {
try {
const body = JSON.parse(init.body);
if (!body.metadata) body.metadata = {};
if (!body.metadata.session_id) body.metadata.session_id = sessionId;
body.metadata.tags = ['skill:video-product-snapshot'];
init = { ...init, body: JSON.stringify(body) };
} catch {}
}
return originFetch(input, init);
};
const provider = createOpenAI({
apiKey: config.apiKey, baseURL: config.baseURL,
fetch: wrapped as typeof globalThis.fetch,
});
const provider = createOpenAI({ apiKey: config.apiKey, baseURL: config.baseURL });
return provider(config.model);
}
@ -120,52 +70,15 @@ async function filterFrame(
return object.keep;
}
async function isContainerProduct(
firstFrame: ExtractedFrame,
model: ReturnType<ReturnType<typeof createOpenAI>>,
): Promise<boolean> {
try {
const { text } = await generateText({
model,
messages: [{
role: 'user',
content: [
{ type: 'image', image: `data:image/jpeg;base64,${imageToBase64(firstFrame.imagePath)}` },
{ type: 'text', text: CONTAINER_CHECK_PROMPT },
],
}],
maxTokens: 5,
});
return text.trim().toUpperCase().startsWith('Y');
} catch {
return false;
}
}
function takeEarliestFrames(candidates: ExtractedFrame[], fraction: number = 0.4): ExtractedFrame[] {
// Ecommerce videos show the container empty/unboxing early, then full.
// Taking the first 40% of frames reliably captures empty states.
const sorted = [...candidates].sort((a, b) => a.frameIndex - b.frameIndex);
const cutoff = Math.max(1, Math.ceil(sorted.length * fraction));
return sorted.slice(0, cutoff);
}
async function rankCandidates(
candidates: ExtractedFrame[],
model: ReturnType<ReturnType<typeof createOpenAI>>,
isContainer: boolean,
): Promise<{ bestFrame: ExtractedFrame; description: string; reasoning: string; boundingBox: [number, number, number, number] }> {
const imageContent = candidates.map((f) => ({
type: 'image' as const,
image: `data:image/jpeg;base64,${imageToBase64(f.imagePath)}`,
}));
const prompt = isContainer
? RANKING_PROMPT_CONTAINER(candidates.length)
: RANKING_PROMPT_GENERAL(candidates.length);
const { object } = await generateObject({
model,
schema: RankingSchema,
@ -174,7 +87,7 @@ async function rankCandidates(
role: 'user',
content: [
...imageContent,
{ type: 'text', text: prompt },
{ type: 'text', text: RANKING_PROMPT(candidates.length) },
],
}],
});
@ -201,17 +114,7 @@ export async function cropProduct(
let [x1, y1, x2, y2] = boundingBox;
// Normalize coords: ensure x1<x2 and y1<y2
if (x1 > x2) [x1, x2] = [x2, x1];
if (y1 > y2) [y1, y2] = [y2, y1];
// Clamp to [0, 1]
x1 = Math.max(0, Math.min(1, x1));
y1 = Math.max(0, Math.min(1, y1));
x2 = Math.max(0, Math.min(1, x2));
y2 = Math.max(0, Math.min(1, y2));
// Add padding
// add padding
const pw = (x2 - x1) * paddingFactor;
const ph = (y2 - y1) * paddingFactor;
x1 = Math.max(0, x1 - pw);
@ -219,11 +122,6 @@ export async function cropProduct(
x2 = Math.min(1, x2 + pw);
y2 = Math.min(1, y2 + ph);
// Validate minimum area
if (x2 - x1 < 0.005 || y2 - y1 < 0.005) {
throw new Error('bounding box too small after normalization');
}
const left = Math.round(x1 * W);
const top = Math.round(y1 * H);
const width = Math.round((x2 - x1) * W);
@ -253,142 +151,6 @@ async function withConcurrency<T>(
return results;
}
// ── Frame quality pre-filtering ──────────────────────────────────────
interface FrameQuality {
valid: boolean;
meanBrightness: number;
variance: number;
}
async function assessFrameQuality(imagePath: string): Promise<FrameQuality> {
const sharp = (await import('sharp')).default;
const { data, info } = await sharp(imagePath)
.grayscale()
.raw()
.toBuffer({ resolveWithObject: true });
const pixels = new Uint8Array(data);
let sum = 0;
let sumSq = 0;
for (let i = 0; i < pixels.length; i++) {
sum += pixels[i];
sumSq += pixels[i] * pixels[i];
}
const mean = sum / pixels.length;
const variance = sumSq / pixels.length - mean * mean;
// Skip near-black, near-white, or very low variance (blurry/blank/transition)
const valid = mean > 15 && mean < 240 && variance > 50;
return { valid, meanBrightness: mean, variance };
}
async function filterQualityFrames(frames: ExtractedFrame[]): Promise<ExtractedFrame[]> {
const results = await Promise.all(
frames.map(async (frame) => {
try {
const q = await assessFrameQuality(frame.imagePath);
return { frame, valid: q.valid };
} catch {
return { frame, valid: true };
}
}),
);
const valid = results.filter(r => r.valid).map(r => r.frame);
return valid.length > 0 ? valid : frames;
}
function isValidBoundingBox(bbox: [number, number, number, number]): boolean {
const [x1, y1, x2, y2] = bbox;
return (
x1 >= 0 && x1 <= 1 &&
y1 >= 0 && y1 <= 1 &&
x2 >= 0 && x2 <= 1 &&
y2 >= 0 && y2 <= 1 &&
x1 < x2 &&
y1 < y2 &&
(x2 - x1) * (y2 - y1) > 0.005
);
}
// Skips Pass 1 filter entirely — ranks all frames and always returns the best one.
// Evenly samples down to maxCandidates when there are too many frames.
export async function detectBestFrame(
frames: ExtractedFrame[],
visionConfig: VisionConfig,
maxCandidates: number = 20,
): Promise<ProductFrame | null> {
if (frames.length === 0) return null;
// 1. Filter out obviously bad frames (black, white, blurry)
let candidates = await filterQualityFrames(frames);
// 2. Sample if too many
if (candidates.length > maxCandidates) {
const step = candidates.length / maxCandidates;
candidates = Array.from({ length: maxCandidates }, (_, i) => candidates[Math.floor(i * step)]);
}
const model = createVisionModel(visionConfig);
// 3. Check if product is a container/rack type (use first candidate frame)
const container = await isContainerProduct(candidates[0], model);
// 4. For containers: restrict ranking to earliest frames (empty/unboxing phase)
if (container) {
const early = takeEarliestFrames(candidates);
if (early.length > 0) candidates = early;
}
// 5. Try Vision ranking with error isolation
try {
const { bestFrame, description, reasoning, boundingBox } = await rankCandidates(candidates, model, container);
if (isValidBoundingBox(boundingBox)) {
const croppedPath = bestFrame.imagePath.replace(/\.jpg$/, '_cropped.jpg');
try {
await cropProduct(bestFrame.imagePath, boundingBox, croppedPath);
} catch {
// cropping is optional — keep original frame
}
return {
frameIndex: bestFrame.frameIndex,
timestampSeconds: bestFrame.timestampSeconds,
imagePath: bestFrame.imagePath,
...(croppedPath ? { croppedImagePath: croppedPath } : {}),
confidence: 0.95,
description,
boundingHint: reasoning,
};
}
} catch {
// Vision ranking failed — fall through to fallback
}
// 4. Fallback: rank by frame quality (variance) and return the sharpest
const withQuality = await Promise.all(
candidates.map(async (f) => {
try {
const q = await assessFrameQuality(f.imagePath);
return { frame: f, score: q.variance };
} catch {
return { frame: f, score: 0 };
}
}),
);
withQuality.sort((a, b) => b.score - a.score);
const best = withQuality[0].frame;
return {
frameIndex: best.frameIndex,
timestampSeconds: best.timestampSeconds,
imagePath: best.imagePath,
confidence: 0.5,
description: 'product frame (auto-selected)',
boundingHint: 'picked by frame quality analysis (Vision ranking failed)',
};
}
export async function detectProductFrames(
frames: ExtractedFrame[],
minConfidence: number,
@ -407,33 +169,18 @@ export async function detectProductFrames(
if (candidates.length === 0) return [];
// Pass 2: single comparative call — model sees all candidates at once
const container = await isContainerProduct(candidates[0], model);
let bestSnapshot: ProductFrame | undefined;
try {
const { bestFrame, description, reasoning, boundingBox } = await rankCandidates(candidates, model, container);
const { bestFrame, description, reasoning, boundingBox } = await rankCandidates(candidates, model);
if (isValidBoundingBox(boundingBox)) {
const croppedPath = bestFrame.imagePath.replace(/\.jpg$/, '_cropped.jpg');
try {
await cropProduct(bestFrame.imagePath, boundingBox, croppedPath);
} catch {}
bestSnapshot = {
frameIndex: bestFrame.frameIndex,
timestampSeconds: bestFrame.timestampSeconds,
imagePath: bestFrame.imagePath,
...(croppedPath ? { croppedImagePath: croppedPath } : {}),
confidence: 0.95,
description,
boundingHint: reasoning,
};
}
} catch {
// ranking failed
}
const croppedPath = bestFrame.imagePath.replace(/\.jpg$/, '_cropped.jpg');
await cropProduct(bestFrame.imagePath, boundingBox, croppedPath);
if (!bestSnapshot) {
return [];
}
return [bestSnapshot];
return [{
frameIndex: bestFrame.frameIndex,
timestampSeconds: bestFrame.timestampSeconds,
imagePath: bestFrame.imagePath,
croppedImagePath: croppedPath,
confidence: 0.95,
description,
boundingHint: reasoning,
}];
}

37
src/types.ts
View File

@ -1,13 +1,4 @@
export type Command =
| 'detect'
| 'search'
| 'detect-and-search'
| 'detect-best'
| 'detect-best-and-search'
| 'detect-video'
| 'detect-video-and-search'
| 'rerank'
| 'session';
export type Command = 'detect' | 'search' | 'detect-and-search' | 'rerank' | 'session';
export interface SearchItem {
num_iid: number;
@ -20,30 +11,6 @@ export interface SearchItem {
detail_url: string;
}
export interface DetectVideoResult {
status: 'success' | 'failed';
command: 'detect-video';
dryRun: boolean;
videoPath?: string;
videoUrl?: string | null;
description?: string;
keyword?: string;
snapshotImagePath?: string;
error?: string;
}
export interface DetectVideoAndSearchResult {
status: 'success' | 'failed';
command: 'detect-video-and-search';
dryRun: boolean;
videoPath?: string;
videoUrl?: string | null;
description?: string;
keyword?: string;
searchResults?: SearchItem[];
error?: string;
}
export interface DetectOptions {
videoPath: string;
intervalSeconds: number;
@ -84,4 +51,4 @@ export interface SearchResult {
error?: string;
}
export type OutputResult = DetectResult | SearchResult | DetectVideoResult | DetectVideoAndSearchResult;
export type OutputResult = DetectResult | SearchResult;