/install make-and-model-recognition
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
- Accepts a local image path.
- Uploads the image to the TrafficEye recognition API.
- Sends a recognition request that asks for detection, OCR, and MMR with box preference by default.
- Parses the API response, including responses wrapped as
{ "status": ..., "data": ... }. - Picks the largest detected road user by
box.positionarea. - Returns a wrapper object containing
roadUser,box,plates,area, andsource, 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 tohttps://trafficeye.ai/recognition.TRAFFICEYE_API_KEY_MODE: one ofheader,bearer,form,query. Default:header.TRAFFICEYE_API_KEY_NAME: key name forheader,form, orquerymode. 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 "[email protected]" \
-F 'request={"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}' \
https://trafficeye.ai/recognition
Agent Workflow
- Verify that the image path exists.
- Run
python3 recognize_road_user.py \x3Cimage-path> --format json. - Present the full selected road-user payload to the user, especially
box,mmr, and the completeplatesarray. - If the selected road user has no plates, explain that the largest vehicle was found but no plates were attached to that road user.
- 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]"
}
}
roadUseris the original selected road-user payload from TrafficEye.boxrepeatsroadUser.boxfor convenience.platesrepeatsroadUser.platesfor convenience and may be empty.areais the computed rectangle area used for winner selection.sourceidentifies 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[], thencombinations[].roadUsers[], thenroadUsers[], 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, allplates, and their scores are preserved.
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install make-and-model-recognition - After installation, invoke the skill by name or use
/make-and-model-recognition - Provide required inputs per the skill's parameter spec and get structured output
What is Make and Model Recognition?
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 a... It is an AI Agent Skill for Claude Code / OpenClaw, with 157 downloads so far.
How do I install Make and Model Recognition?
Run "/install make-and-model-recognition" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Make and Model Recognition free?
Yes, Make and Model Recognition is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Make and Model Recognition support?
Make and Model Recognition is cross-platform and runs anywhere OpenClaw / Claude Code is available (linux, macos, windows).
Who created Make and Model Recognition?
It is built and maintained by eyedea-ai (@eyedea-ai); the current version is v1.0.1.