@pipedream/platerecognizer
Version:
Pipedream Plate Recognizer Components
72 lines (67 loc) • 2.12 kB
JavaScript
import { getFileStream } from "@pipedream/platform";
import platerecognizer from "../../platerecognizer.app.mjs";
export default {
key: "platerecognizer-run-recognition",
name: "Run Recognition",
description: "Triggers a recognition process using the Plate Recognizer SDK.",
version: "0.1.1",
type: "action",
props: {
platerecognizer,
imageFileOrUrl: {
type: "string",
label: "Image Path or URL",
description: "The image to be recognized. Provide either a file URL or a path to a file in the `/tmp` directory (for example, `/tmp/myImage.jpg`)",
},
regions: {
type: "string[]",
label: "Regions",
description: "Regions to select specific license plate patterns. [See further details here](https://guides.platerecognizer.com/docs/other/country-codes/#country-codes)",
optional: true,
},
cameraId: {
type: "string",
label: "Camera ID",
description: "The ID of the camera that took the image.",
optional: true,
},
mmc: {
type: "boolean",
label: "MMC",
description: "Whether to detect vehicle make, model, and color.",
optional: true,
},
config: {
type: "object",
label: "Config",
description: "Additional configuration. [See further details here](https://guides.platerecognizer.com/docs/snapshot/api-reference/#engine-configuration)",
optional: true,
},
syncDir: {
type: "dir",
accessMode: "read",
sync: true,
optional: true,
},
},
async run({ $ }) {
const stream = await getFileStream(this.imageFileOrUrl);
const chunks = [];
for await (const chunk of stream) {
chunks.push(chunk);
}
const buffer = Buffer.concat(chunks);
const response = await this.platerecognizer.runRecognition({
$,
data: {
upload: buffer.toString("base64"),
regions: this.regions,
camera_id: this.cameraId,
mmc: this.mmc,
config: this.config,
},
});
$.export("$summary", "Recognition process triggered successfully");
return response;
},
};