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Process Batch of Images with Imagga API

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Process Batch of Images with the Imagga API
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Getting Started

Create a workflow to Process Batch of Images with the Imagga API. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. Configure the Process Batch of Images action
    1. Connect your Imagga account
    2. Configure File Paths or URLs
    3. Optional- Configure Callback URL
    4. Select a Image Process Type
  2. Select a trigger to run your workflow on HTTP requests, schedules or app events
  3. Deploy the workflow
  4. Send a test event to validate your setup
  5. Turn on the trigger

Integrations

Process Batch of Images with Imagga API on New Requests (Payload Only) from HTTP / Webhook API
HTTP / Webhook + Imagga
 
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Process Batch of Images with Imagga API on New Submission from Typeform API
Typeform + Imagga
 
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Process Batch of Images with Imagga API on Custom Events from Zoom API
Zoom + Imagga
 
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Process Batch of Images with Imagga API on New Submission (Instant) from Jotform API
Jotform + Imagga
 
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Process Batch of Images with Imagga API on New Scheduled Tasks from Pipedream API
Pipedream + Imagga
 
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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.

Process Batch of Images on Imagga
Description:Analyzes a batch of images for categorization, tagging, or color extraction. [See the documentation](https://docs.imagga.com/)
Version:1.0.0
Key:imagga-process-batch

Code

import { prepareAdditionalProps } from "../../common/constants.mjs";
import {
  delay,
  prepareBatchFile,
} from "../../common/utils.mjs";
import imagga from "../../imagga.app.mjs";

export default {
  key: "imagga-process-batch",
  name: "Process Batch of Images",
  description: "Analyzes a batch of images for categorization, tagging, or color extraction. [See the documentation](https://docs.imagga.com/)",
  version: "1.0.0",
  type: "action",
  props: {
    imagga,
    imageFile: {
      label: "File Paths or URLs",
      description:
        "The image files to process. For each entry, provide either a file URL or a path to a file in the `/tmp` directory (for example, `/tmp/myFile.jpg`)",
      type: "string[]",
    },
    callbackUrl: {
      propDefinition: [
        imagga,
        "callbackUrl",
      ],
      optional: true,
    },
    imageProcessType: {
      propDefinition: [
        imagga,
        "imageProcessType",
      ],
      reloadProps: true,
    },
  },
  async additionalProps() {
    return prepareAdditionalProps({
      props: imagga.propDefinitions,
      type: this.imageProcessType,
      batch: true,
    });
  },
  async run({ $ }) {
    const {
      imagga,
      imageFile,
      language = [],
      saveIndex,
      saveId,
      extractOverallColors,
      extractObjectColors,
      deterministic,
      verbose,
      threshold,
      decreaseParents,
      callbackUrl,
      categorizerId,
      overallCount,
      separatedCount,
      imageProcessType,
      ...params
    } = this;

    const file = await prepareBatchFile({
      imageFile,
      imagga,
    });

    await delay(2000);

    const response = await this.imagga.analyzeBatch({
      data: {
        callback_url: callbackUrl,
        [`/${imageProcessType}`]: file.map((item) => {
          if (typeof verbose === "boolean") item.params.verbose = +verbose;
          if (typeof decreaseParents === "boolean") item.params["decrease_parents"] = +decreaseParents;
          if (threshold) item.params.threshold = parseFloat(threshold);
          if (typeof extractOverallColors === "boolean") item.params.extract_overall_colors = +extractOverallColors;
          if (typeof extractObjectColors === "boolean") item.params.extract_object_colors = +extractObjectColors;
          if (typeof deterministic === "boolean") item.params.deterministic = +deterministic;

          item.params = {
            ...params,
            ...item.params,
            categorizer_id: categorizerId,
            language: language.toString(),
            overall_count: overallCount,
            separated_count: separatedCount,
            save_index: saveIndex,
            save_id: saveId,
          };
          return item;
        }),
      },
    });
    $.export("$summary", `Successfully submitted batch for ${imageProcessType} analysis`);
    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.
LabelPropTypeDescription
ImaggaimaggaappThis component uses the Imagga app.
File Paths or URLsimageFilestring[]

The image files to process. For each entry, provide either a file URL or a path to a file in the /tmp directory (for example, /tmp/myFile.jpg)

Callback URLcallbackUrlstring

The callback URL to be invoked after batch processing

Image Process TypeimageProcessTypestringSelect a value from the drop down menu:{ "label": "Tags", "value": "tags" }{ "label": "Categories", "value": "categories" }{ "label": "Colors", "value": "colors" }

Authentication

Imagga uses API keys for authentication. When you connect your Imagga account, Pipedream securely stores the keys so you can easily authenticate to Imagga APIs in both code and no-code steps.

Navigate to your dashboard, and copy your API Key and API Secret below.

About Imagga

Imagga Image Recognition API provides solutions for image tagging & categorization, visual search, content moderation.

More Ways to Use Imagga

Triggers

New Batch Processed from the Imagga API

Emit new event when a batch of images has been processed for categorization, tagging, or color extraction.

 
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Actions

Analyze Image with the Imagga API

Assign a category to a single image based on its visual content. See the documentation

 
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