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Visual-recognition – API Reference, IBM Watson Developer Cloud

Watson

Introduction

The IBM Watson™ Visual Recognition service uses deep learning algorithms to identify scenes, objects, and celebrity faces in pictures you upload to the service. You can create and train a custom-built classifier to identify subjects that suit your needs.

Descriptions of Knot classes referred to in this reference are available in the Knot documentation for the Watson Developer Cloud Knot.js SDK.

Descriptions of Java classes referred to in this reference are available in the Javadoc for the Watson Developer Cloud Java SDK.

Descriptions of Python classes referred to in this reference are available in the Python documentation for the Watson Developer Cloud Python SDK.

Significant: If you have Bluemix Dedicated, this may not be your service endpoint. Dual check your endpoint URL on the Service Credentials page in your example of the Visual Recognition Service on Bluemix.

API explorer

To interact with this API, use the Visual Recognition Service API explorer. Use the explorer to test your calls to the API, and to view live responses from the server.

Authentication

You authenticate to the Visual Recognition API by providing the API key that is provided in the service credentials for the service example that you want to use.

After you create an example of the Visual Recognition service, you can view the API key by selecting Service Credentials from the left pane of the service dashboard.

Substitute with your API key.

Methods

Classify an pic

Upload pictures or URLs to identify classes by default. Photos must be in .jpeg, or .png format. Use pics with a minimum of two hundred twenty four x two hundred twenty four pixels for best quality results. To identify custom-built classifiers, include the classifier_ids or owners parameters.

GET Request

Use the GET method to classify URLs against classifiers as a query parameter.

The 2-letter primary language code as assigned in ISO standard 639. Supported languages are en (English), ar (Arabic), de (German), es (Spanish), it (Italian), ja (Japanese), and ko (Korean).

The response might not be in the specified language in these conditions:

  • English is returned when the requested language is not supported (see the parameter description for details).
  • Classes are not returned when there is no translation for them.
  • Custom-made classifiers returned with this method comeback tags in the language of the custom-made classifier.

Example GET request

To attempt out the API quickly, paste the GET request into your browser. Substitute with your API key from Bluemix.

POST Request

Use the POST method to upload a single picture, URL, or a compressed (.zip) file of numerous pictures. You can analyze pics against classifiers or against an array of classifier IDs you upload in a JSON file.

The 2-letter primary language code as assigned in ISO standard 639. Supported languages are en (English), ar (Arabic), de (German), es (Spanish), it (Italian), ja (Japanese), and ko (Korean).

The response might not be in the specified language in these conditions:

  • English is returned when the requested language is not supported (see the parameter description for details).
  • Classes are not returned when there is no translation for them.
  • Custom-built classifiers returned with this method comeback tags in the language of the custom-made classifier.

Example POST request

Example POST request with a parameters JSON

Example JSON file

Response

Detect faces

Analyze faces in photos and get data about them, such as estimated age, gender, plus names of celebrities. Photos must be in .jpeg, or .png format. This functionality is not trainable, and does not support general biometric facial recognition.

For each photo, the response includes face location, a minimum and maximum estimated age, a gender, and confidence scores. Scores range from zero – one with a higher score indicating greater correlation.

GET Request

Use the GET method to detect faces in a single URL.

Example GET request

To attempt out the API quickly, paste the GET request into your browser. Substitute with your API key from Bluemix.

POST Request

Use the POST method to upload a single picture, URL, or a compressed (.zip) file of numerous pics.

Example POST request

Example parameters JSON file

Response

Custom-built classifiers

Create a classifier

Train a fresh multi-faceted classifier on the uploaded picture data. A fresh custom-built classifier can be trained by several compressed (.zip) files, including files containing positive or negative pictures (.jpg, or .png). You must supply at least two compressed files, either two positive example files or one positive and one negative example file.

Compressed files containing positive examples are used to create “classes” that define what the fresh classifier is. The prefix that you specify for each positive example parameter is used as the class name within the fresh classifier. The “_positive_examples” suffix is required. There is no limit on the number of positive example files you can upload in a single call.

The compressed file containing negative examples is not used to create a class within the created classifier, but does define what the fresh classifier is not. Negative example files should contain pics that do not depict the subject of any of the positive examples. You can only specify one negative example file in a single call. For more information, see Structure of the training data, and Guidelines for good training.

Request

Depending on this size of the training files, this call can take several minutes to accomplish.

Response

Retrieve a list of custom-built classifiers

Retrieve a list of user-created classifiers .

Request

Response

Retrieve classifier details

Retrieve information about a specific classifier.

Request

Response

Update a classifier

Update an existing classifier by adding fresh classes, or by adding fresh photos to existing classes. You cannot update a custom-built classifier with a free API Key.

To update the existing classifier, use several compressed (.zip) files, including files containing positive or negative pics (.jpg, or .png). You must supply at least one compressed file, with extra positive or negative examples.

Compressed files containing positive examples are used to create and update “classes” to influence all of the classes in that classifier. The prefix that you specify for each positive example parameter is used as the class name within the fresh classifier. The “_positive_examples” suffix is required. There is no limit on the number of positive example files you can upload in a single call.

The compressed file containing negative examples is not used to create a class within the created classifier, but does define what the updated classifier is not. Negative example files should contain photos that do not depict the subject of any of the positive examples. You can only specify one negative example file in a single call. For more information, see Updating custom-made classifiers. If you submit retraining requests in parallel, the last request overwrites all previous requests.

Request

Depending on this size of the training files, this call can take several minutes to accomplish.

Substitute with the ID of the classifier that you want to update.

Substitute with your API key.

Response

Delete a classifier

Delete a custom-built classifier with the specified classifier ID.

Request

Substitute with the ID of the classifier that you want to update.

Substitute with your API key.

Response

Collections – BETA

Beta. Create a fresh collection, add pictures to that collection, and then use Similarity Search to search the collection for similar pictures.

Create a collection

Beta. Create a fresh collection of pictures to search. You can create a maximum of five collections.

Request

Response

List collections

Beta. List all custom-made collections.

Request

Response

Retrieve collection details

Beta. Retrieve information about a specific collection.

Request

Response

Delete a collection

Beta. Delete a user created collection.

Request

Response

Add photos to a collection

Beta. Add pics to a collection. Each collection can contain one million pictures. It takes one 2nd to upload one pictures, so uploading one million pictures takes eleven days.

Request

Example JSON metadata file

Response

List pictures in a collection

Beta. List one hundred photos in a collection. This comes back an arbitrary selection of one hundred pictures. Each collection can contain one million pictures.

Request

Response

List pic details

Beta. List details about a specific photo in a collection.

Request

Response

Delete an picture

Beta. Delete an pic from a collection.

Request

Response

Add or update metadata

Beta. Add metadata to a specific pic in a collection. Use metadata for your own reference to identify pictures. You cannot filter the find_similar method by metadata.

Request

Example JSON metadata file

Response

List metadata

Beta. View the metadata for a specific photo in a collection.

Request

Response

Delete metadata

Beta. Delete all metadata associated with an picture.

Request

Response

Find similar photos

Beta. Upload an pic to find similar pics in your custom-made collection.

Request

Response

Data collection

By default, all Watson services log requests and their results. Logging is done only to improve the services for future users. The logged data is not collective or made public. To prevent IBM from accessing your data for general service improvements, set the X-Watson-Learning-Opt-Out header parameter to true for all requests. (Any value other than false or zero disables request logging for that call.) You must set the header on each request that you do not want IBM to access for general service improvements. set the X-Watson-Learning-Opt-Out header parameter to true when you create the service example. (Any value other than false or zero disables request logging for that call.) You must set the header when you create the service for any any call that you do not want IBM to access for general service improvements. set the x-watson-learning-opt-out header parameter to true when you create the service example. (Any value other than false or zero disables request logging for that call.) You must set the header when you create the service for any any call that you do not want IBM to access for general service improvements.

Error treating

The Visual Recognition service uses standard HTTP response codes to display whether a method finished successfully. A two hundred response always indicates success. A four hundred type response is some sort of failure, and a five hundred type response usually indicates an internal system error.

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