Perform Face Detection and Analysis with Zoho Catalyst API

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Perform Face Detection and Analysis with the Zoho Catalyst API

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Getting Started#

Create a workflow to Perform Face Detection and Analysis with the Zoho Catalyst API. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Configure the Perform Face Detection and Analysis action
1. Connect your Zoho Catalyst account
2. Select a Project ID
3. Configure Image Path
4. Optional- Select a Mode
5. Optional- Configure Emotion
6. Optional- Configure Age
7. Optional- Configure Gender
Select a trigger to run your workflow on HTTP requests, schedules or app events
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Integrations#

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Pipedream + Zoho Catalyst

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Details#

This is a pre-built, source-available component from Pipedream's GitHub repo. The component is developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Perform Face Detection and Analysis on Zoho Catalyst

Description:Perform face detection and analysis on an image. [See the documentation](https://catalyst.zoho.com/help/api/zia/face-analytics.html)

Version:0.0.1

Key:zoho_catalyst-perform-face-detection-and-analysis

View on GitHub

Code#

import { defineAction } from "@pipedream/types";
import app from "../../app/zoho_catalyst.app.mjs";
import { getImageFormData } from "../../common/methods.mjs";
import { FACE_ANALYTICS_MODE_OPTIONS } from "../../common/constants.mjs";
export default defineAction({
    key: "zoho_catalyst-perform-face-detection-and-analysis",
    name: "Perform Face Detection and Analysis",
    description: "Perform face detection and analysis on an image. [See the documentation](https://catalyst.zoho.com/help/api/zia/face-analytics.html)",
    version: "0.0.1",
    type: "action",
    props: {
        app,
        projectId: {
            propDefinition: [
                app,
                "projectId",
            ],
        },
        imagePath: {
            propDefinition: [
                app,
                "imagePath",
            ],
        },
        mode: {
            propDefinition: [
                app,
                "mode",
            ],
            description: "Denotes the number of facial landmarks to be detected in a face.",
            options: FACE_ANALYTICS_MODE_OPTIONS,
        },
        emotion: {
            type: "boolean",
            label: "Emotion",
            description: "Whether to detect emotion.",
            optional: true,
            default: true,
        },
        age: {
            type: "boolean",
            label: "Age",
            description: "Whether to detect age.",
            optional: true,
            default: true,
        },
        gender: {
            type: "boolean",
            label: "Gender",
            description: "Whether to detect gender.",
            optional: true,
            default: true,
        },
    },
    async run({ $ }) {
        const { imagePath, projectId, } = this;
        const data = getImageFormData(imagePath);
        for (const prop of [
            "mode",
            "emotion",
            "age",
            "gender",
        ]) {
            if (this[prop]) {
                data.append(prop, this[prop].toString());
            }
        }
        const response = await this.app.performImageFaceDetection({
            $,
            projectId,
            headers: {
                ...data.getHeaders(),
            },
            data,
        });
        $.export("$summary", "Successfully performed image face detection");
        return response;
    },
});

Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Zoho Catalyst	`app`	`app`	This component uses the Zoho Catalyst app.
Project ID	`projectId`	`integer`	Select a value from the drop down menu.
Image Path	`imagePath`	`string`	A file path in the `/tmp` directory. See the documentation on working with files.
Mode	`mode`	`string`	Select a value from the drop down menu:`{ "label": "0-point landmark detector that detects the boundary of the face alone", "value": "basic" }{ "label": "5-point landmark detector that detects- Eyes: The center of both eyes, Nose: Nose tip, Lips: The center of both lips", "value": "moderate" }{ "label": "68-point landmark detector that detects- Jawline: Face boundary, Eyebrows: Left and right eyebrow, Eyes: Left and right eye, Nose bridge, Nostril line, Upper lips: Upper and lower edge, Lower lips: Upper and lower edge", "value": "advanced" }`
Emotion	`emotion`	`boolean`	Whether to detect emotion.
Age	`age`	`boolean`	Whether to detect age.
Gender	`gender`	`boolean`	Whether to detect gender.

Authentication#

Zoho Catalyst uses OAuth authentication. When you connect your Zoho Catalyst account, Pipedream will open a popup window where you can sign into Zoho Catalyst and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Zoho Catalyst API.

Pipedream requests the following authorization scopes when you connect your account:

ZohoCatalyst.projects.READZohoCatalyst.projects.CREATEZohoCatalyst.projects.UPDATEZohoCatalyst.projects.users.READZohoCatalyst.projects.users.CREATEZohoCatalyst.tables.READZohoCatalyst.tables.rows.READZohoCatalyst.tables.rows.CREATEZohoCatalyst.tables.rows.UPDATEZohoCatalyst.tables.columns.READZohoCatalyst.folders.READZohoCatalyst.folders.CREATEZohoCatalyst.folders.UPDATEZohoCatalyst.files.READZohoCatalyst.files.CREATEZohoCatalyst.mlkit.READ

About Zoho Catalyst#

Catalyst by Zoho is a highly scalable serverless platform that lets developers build and deploy world-class solutions without managing servers. Even better, you pay nothing till you deploy the project to production. Get a free, full-featured sandbox and up to 125 million free invocations