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make-and-model-recognition品牌和型号识别

Agent Skill

make-and-model-recognition 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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4,727

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下载量

1,544
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:make-and-model-recognition(品牌和型号识别)
来源仓库:https://github.com/eyedea-ai/make-and-model-recognition
安装命令:
openclaw skills install make-and-model-recognition
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install make-and-model-recognition

简介

从图像中检测车辆并识别其品牌与型号,同时提取车牌信息。

  • 适用于交通监控、停车场管理或保险定损等视觉分析场景。
  • 基于 TrafficEye 模型输出结构化数据,便于后续业务逻辑处理。
  • 依赖图像清晰度与光照条件,遮挡严重时识别准确率可能下降。
  • make-and-model-recognition 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
make-and-model-recogntion
description
Detect the largest vehicle from an image using TrafficEye car-box detection, run make and model recognition for that vehicle, and return all license plates attached to the same road-user payload. Use when the user wants the dominant vehicle, vehicle classification, car box detection, make and model recognition, or the plates associated with the main vehicle in a local image file. You can obtain API key and tokens from https://trafficeye.ai.
metadata
openclaw
requires
env
anyBins
primaryEnv
TRAFFICEYE_API_KEY
homepage
https://trafficeye.ai
os

TrafficEye Largest Road User Reader

Use this skill when the user wants the largest detected vehicle from an image, along with its make and model classification and every detected license plate belonging to that same road user.

What This Skill Does

  1. Accepts a local image path.
  2. Uploads the image to the TrafficEye recognition API.
  3. Sends a recognition request that asks for detection, OCR, and MMR with box preference by default.
  4. Parses the API response, including responses wrapped as { "status": ..., "data": ... }.
  5. Picks the largest detected road user by box.position area.
  6. Returns a wrapper object containing roadUser, box, plates, area, and source, preserving the full selected road-user payload.

Expected Input

  • A local image file path.
  • If the user supplied an attachment instead of a path, first resolve it to a local file path and then run the helper.

Default Runtime Assumptions

  • The API endpoint defaults to https://trafficeye.ai/recognition.
  • The default request payload is {"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}.
  • The default API-key transport matches the TrafficEye public API example: header mode with header name apikey.
  • Auth and request fields remain configurable in case your deployment differs.

Environment Variables

  • TRAFFICEYE_API_KEY: required unless passed explicitly to the helper.
  • TRAFFICEYE_API_URL: optional, defaults to https://trafficeye.ai/recognition.
  • TRAFFICEYE_API_KEY_MODE: one of header, bearer, form, query. Default: header.
  • TRAFFICEYE_API_KEY_NAME: key name for header, form, or query mode. Default: apikey.
  • TRAFFICEYE_FILE_FIELD: multipart field for the image. Default: file.
  • TRAFFICEYE_REQUEST_FIELD: multipart field for the JSON request. Default: request.
  • TRAFFICEYE_REQUEST_JSON: JSON string to include as the request field. By default this is {"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}.
  • TRAFFICEYE_TIMEOUT_S: optional timeout in seconds. Default: 30.

Only TRAFFICEYE_API_KEY is required for the default live API flow. The other variables are optional overrides.

How To Run

Setup your API key:

export TRAFFICEYE_API_KEY='YOUR_REAL_KEY'

Use the road-user helper:

python3 recognize_road_user.py /absolute/path/to/image.jpg

For structured output:

python3 recognize_road_user.py /absolute/path/to/image.jpg --format json

If the deployment expects Bearer auth:

TRAFFICEYE_API_KEY_MODE=bearer python3 recognize_road_user.py /absolute/path/to/image.jpg

If the deployment needs an explicit request payload:

TRAFFICEYE_REQUEST_JSON='{"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}' python3 recognize_road_user.py /absolute/path/to/image.jpg --format json

Equivalent to the documented public API example:

curl -X POST \
  -H "Content-Type: multipart/form-data" \
  -H "apikey: YOUR_API_KEY_HERE" \
  -F "file=@image.jpg" \
  -F 'request={"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}' \
  https://trafficeye.ai/recognition

Agent Workflow

  1. Verify that the image path exists.
  2. Run python3 recognize_road_user.py <image-path> --format json.
  3. Present the full selected road-user payload to the user, especially box, mmr, and the complete plates array.
  4. If the selected road user has no plates, explain that the largest vehicle was found but no plates were attached to that road user.
  5. If authentication fails, ask the user which auth mode their deployment expects and retry with the matching environment variables.

Offline Validation

You can validate the selection logic without calling the API:

python3 recognize_road_user.py --response-json-file examples/sample_response.json --format json

Output Shape

The helper prints JSON with this top-level structure:

{
  "roadUser": {"box": {}, "plates": [], "mmr": {}},
  "box": {},
  "plates": [],
  "area": 0,
  "source": {
    "combinationIndex": 0,
    "roadUserIndex": 0,
    "path": "combinations[0].roadUsers[0]"
  }
}
  • roadUser is the original selected road-user payload from TrafficEye.
  • box repeats roadUser.box for convenience.
  • plates repeats roadUser.plates for convenience and may be empty.
  • area is the computed rectangle area used for winner selection.
  • source identifies where the selected road user came from in the API response.

Notes

  • The helper intentionally chooses the largest boxed vehicle by geometric area, not by detection confidence.
  • The response parser first checks data.combinations[].roadUsers[], then combinations[].roadUsers[], then roadUsers[], and finally nested road-user payloads discovered recursively.
  • The default request and auth header mirror the public example at https://www.trafficeye.ai/api.
  • The selected result now includes the original road-user payload from the API so mmr, box, all plates, and their scores are preserved.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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98.42%
按下载量换算1,520

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敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

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来源信息

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