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:camera: Sample Node.js Application for the IBM Watson Visual Recognition Service

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Main language CSS
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Last commit over 1 year ago
Repo Created almost 5 years ago
Repo Last Updated over 1 year ago
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Homepage https://visual-re...
Organization / Authorwatson-developer-cloud
Latest Releasev3.0.0
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Visual Recognition Demo

Build Status

The Visual Recognition Service uses deep learning algorithms to analyze images for scenes, objects, faces, text, and other subjects that can give you insights into your visual content. You can organize image libraries, understand an individual image, and create custom classifiers for specific results that are tailored to your needs.

Give it a try! Click the button below to fork into IBM DevOps Services and deploy your own copy of this application on the IBM Cloud.

Deploy to IBM Cloud

Getting Started

  1. You need a IBM Cloud account. If you don't have one, sign up. Experimental Watson Services are free to use.

  2. Download and install the Cloud-foundry CLI tool if you haven't already.

  3. Edit the manifest.yml file and change <application-name> to something unique. The name you use determines the URL of your application. For example, <application-name>

      label: watson_vision_combined
      plan: free
    - name: <application-name>
    path: .
    command: npm start
    memory: 512M
    - visual-recognition-service
      NODE_ENV: production
  4. Connect to the IBM Cloud with the command line tool.

  cf api
  cf login
  1. Create the Visual Recognition service in the IBM Cloud.

    cf create-service watson_vision_combined free visual-recognition-service
    cf create-service-key visual-recognition-service myKey
    cf service-key visual-recognition-service myKey
  2. Create a .env file in the root directory by copying the sample .env.example file using the following command:

  cp .env.example .env

You will update the .env with the information you retrieved in steps 5 and 6

The .env file will look something like the following:

  1. Install the dependencies you application need:
  npm install
  1. Start the application locally:
  npm start
  1. Point your browser to http://localhost:3000.

  2. Optional: Push the application to the IBM Cloud:

  cf push

After completing the steps above, you are ready to test your application. Start a browser and enter the URL of your application.

        <your application name>

For more details about developing applications that use Watson Developer Cloud services in the IBM Cloud, see Getting started with Watson Developer Cloud and the IBM Cloud.

Environment Variables

  • VISUAL_RECOGNITION_API_KEY : This is the API key for the vision service, used if you don't have one in your IBM Cloud account.
  • PRESERVE_CLASSIFIERS : Set if you don't want classifiers to be deleted after one hour. (optional)
  • PORT : The port the server should run on. (optional, defaults to 3000)
  • OVERRIDE_CLASSIFIER_ID : Set to a classifer ID if you want to always use a custom classifier. This classifier will be used instead of training a new one. (optional)

Changing the Included Images

Sample Images

The sample images are the first 7 images when the site loads. They are called from a Jade mixin found in views/mixins/sampleImages.jade. If you just want to replace those images with different images, you can replace them in public/images/samples and they are numbered 1 - 7 and are jpg formatted.

Custom Classifier Bundles

Adding new/different custom classifer bundles is much more invovled. You can follow the template of the existing bundles found in views/includes/train.jade.

Or, you can train a custom classifier using the api or the form and then use the classifier ID.

Getting the Classifier ID

When you train a custom classifier, the name of the classifier is displayed in the test form.

Classifier ID Tooltip

If you hover your mouse over the classifier name, the classifier ID will be shown in the tooltip. You can also click on the name, and it will toggle between the classifier name and the classifier ID.

You can then use this custom classifier id by placing it after the hash in the request URL. For example, lets say you are running the system locally, so the base URL is http://localhost:3000 and then you train a classifier. This newly trained classifier might have an id like SatelliteImagery_859438478. If you wanted to use this classifier instead of training a new one, you can navigate to http://localhost:3000/train#SatelliteImagery_859438478 and use the training form with your existing classifier.


This sample code is licensed under Apache 2.0. Full license text is available in LICENSE.



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visual-recognition-nodejs open issues Ask a question     (View All Issues)
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visual-recognition-nodejs open pull requests (View All Pulls)
  • watson-developer-cloud@1.2.3 untested ⚠️
  • watson-developer-cloud@1.2.2 untested ⚠️
  • body-parser@1.15.0 untested ⚠️
  • watson-developer-cloud@1.2.5 untested ⚠️
  • Update validator to version 5.1.0 ?
  • watson-developer-cloud@1.2.4 untested ⚠️
  • Update validator to version 5.0.0 ?
  • validator@4.9.0 untested ⚠️
  • Added simple tests
  • UI is now more complete
  • asset only: added json image link image
  • Adds raw json links to results
  • Fixes bug 254
  • Adding support for custom food classifier
  • Adding support for custom food classifier
  • use published npm package
visual-recognition-nodejs list of languages used
visual-recognition-nodejs latest release notes
v3.0.0 v3

Allow training with custom images

v2-beta v2-beta

Visual Recognition


v2.0.0 v2.0.0


  • Improve loading page
  • Switch to 3 pages instead of a single page application
  • Using LocalStore and Cookies
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