/**
* Copyright 2014 IBM Corp. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
var extend = require('extend');
var pick = require('object.pick');
var isStream = require('isstream');
var requestFactory = require('../lib/requestwrapper');
var util = require('util');
var BaseService = require('../lib/base_service');
var NEGATIVE_EXAMPLES = 'negative_examples';
/**
* JS-style logical XOR - works on objects, booleans, strings, etc following normal js truthy/falsy conventions
* @private
* @param {*} a
* @param {*} b
* @returns {boolean}
* @constructor
*/
function xor(a, b) {
return ( a || b ) && !( a && b );
}
/**
* Determine content-type header for .zip, .png, and .jpg files
* (The only formats currently supported by the service)
*
* based on https://github.com/watson-developer-cloud/node-sdk/issues/333
*
* @param {Buffer} buffer
* @returns {String|undefined}
*/
function detectContentType(buffer) {
var signature = buffer.readUInt32BE();
switch(signature) { // eslint-disable-line default-case
case 0x504B0304:
case 0x504B0506:
case 0x504B0708:
return 'application/zip';
case 0x89504E47:
return 'image/png';
case 0xFFD8FFE0:
case 0xFFD8FFE1:
case 0xFFD8FFE8:
return 'image/jpeg';
// default is `return undefined`
}
}
/**
* Verifies that a stream images_file or a string url is included
* also gracefully handles cases of image_file instead of images_file
*
* @private
*/
function fixupImageParam(params) {
if (params && params.image_file && !params.images_file) {
params.images_file = params.image_file;
}
if (!params || !xor(params.images_file, params.url)) {
throw new Error('Watson VisualRecognition.classify() requires either an images_file or a url parameter');
}
if (Buffer.isBuffer(params.images_file)) {
params.images_file = {
value: params.images_file,
options: {
contentType: detectContentType(params.images_file)
}
}
}
}
/**
* Formats error
*
* @private
*/
function errorFormatter(cb) {
var callback = typeof cb === 'function' ? cb : function() { /* no op */};
return function(err, result) {
if (err) {
callback(err, result);
} else {
if (result.status === 'ERROR') {
if (result.statusInfo === 'invalid-api-key') {
callback({
error: result.statusInfo,
code: result.statusInfo === 'invalid-api-key' ? 401 : 400
}, null);
}
} else {
callback(err, result);
}
}
};
}
/**
*
* @param options
* @constructor
*/
function VisualRecognitionV3(options) {
BaseService.call(this, options);
// Check if 'version_date' was provided
if (typeof this._options.version_date === 'undefined') {
throw new Error('Argument error: version_date was not specified, use 2016-05-20');
}
this._options.qs.version = this._options.version_date; // todo: confirm service expects version not version_date
}
util.inherits(VisualRecognitionV3, BaseService);
VisualRecognitionV3.prototype.name = 'visual_recognition';
VisualRecognitionV3.prototype.version = 'v3';
VisualRecognitionV3.URL = 'https://gateway-a.watsonplatform.net/visual-recognition/api';
VisualRecognitionV3.prototype.serviceDefaults = {
alchemy: true
};
/**
* Grab the api key
*
* @param options
* @private
*/
VisualRecognitionV3.prototype.initCredentials = function(options) {
options.api_key = options.api_key || options.apikey;
options = extend(
{},
this.getCredentialsFromBluemix(this.name), // todo: test if this works
this.getCredentialsFromEnvironment(this.name),
options
);
if (!options.use_unauthenticated) {
if (!options.api_key) {
throw new Error('Argument error: api_key was not specified');
}
// Per documentation, Alchemy* services use `apikey`, but Visual Recognition uses (`api_key`)
// (Either will work in most cases, but the VR Collections & Similarity Search beta only supports `api_key`)
options.qs = extend({ api_key : options.api_key }, options.qs);
}
return options
};
/**
* Pulls api_key from SERVICE_NAME_API_KEY env property
*
* @param {String} name
* @returns {{api_key: String|undefined}}
*/
VisualRecognitionV3.prototype.getCredentialsFromEnvironment = function(name) {
return {
api_key: process.env[name.toUpperCase() + '_API_KEY']
}
};
/**
* Bluemix uses a different naming convention for VR v3 than for other services
* @returns {*}
*/
VisualRecognitionV3.prototype.getCredentialsFromBluemix = function() {
return BaseService.prototype.getCredentialsFromBluemix.call(this, 'watson_vision_combined');
};
/**
* Accepts either a url, a single image file, or a zip file with multiple
* images (.jpeg, .png, .gif) and scores every available classifier
* on each image. It then applies a threshold and returns the list
* of relevant classifier scores for each image.
*
* @example
* * {
* "images": [{
* "classifiers": [{
* "classes": [{
* "class": "animal",
* "score": 0.998771,
* "type_hierarchy": "/animals"
* }, {
* "class": "mammal",
* "score": 0.998499,
* "type_hierarchy": "/animals/mammal"
* }, {
* "class": "dog",
* "score": 0.900249,
* "type_hierarchy": "/animals/pets/dog"
* }, {
* "class": "puppy",
* "score": 0.5,
* "type_hierarchy": "/animals/pets/puppy"
* }],
* "classifier_id": "default",
* "name": "default"
* }],
* "image": "dog.jpg"
* }],
* "images_processed": 1
* }
*
* @param {Object} params
* @param {ReadStream|Buffer|Object} [params.images_file] The image file (.jpg, .png, .gif) or compressed (.zip) file of images to classify. The total number of images is limited to 20. Either images_file or url must be specified. The SDK attempts to determine content-type automatically, but this may be overridden by providing an object: {value: Buffer|Stream, options: {filename: 'file.ext', contentType: 'image/type'}}
* @param {String} [params.url] The URL of an image (.jpg, .png, .gif). Redirects are followed, so you can use shortened URLs. The resolved URL is returned in the response. Either images_file or url must be specified.
* @param {Array} [params.classifier_ids=['default']] An array of classifier IDs to classify the images against.
* @param {Array} [params.owners=['me','IBM']] An array with the value(s) "IBM" and/or "me" to specify which classifiers to run.
* @param {Number} [params.threshold] A floating point value that specifies the minimum score a class must have to be displayed in the response.
* @param {Function} callback
*
* @returns {ReadableStream|undefined}
*
*/
VisualRecognitionV3.prototype.classify = function(params, callback) {
try {
fixupImageParam(params);
} catch (e) {
callback(e);
return;
}
params = extend({
classifier_ids: ['default'],
owners: ['me','IBM']
}, params);
var parameters;
if(params.images_file) {
parameters = {
options: {
url: '/v3/classify',
method: 'POST',
formData: {
images_file: params.images_file,
parameters: {
value: JSON.stringify(pick(params, ['classifier_ids', 'owners', 'threshold'])),
options: {
contentType: 'application/json'
}
}
},
headers: pick(params, 'Accept-Language')
},
defaultOptions: this._options
};
} else {
parameters = {
options: {
url: '/v3/classify',
method: 'GET',
json: true,
qs: pick(params, ['url', 'classifier_ids', 'owners', 'threshold']),
headers: pick(params, 'Accept-Language')
},
defaultOptions: this._options
};
}
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Accepts either a url, a single image file, or a zip file with multiple
* images (.jpeg, .png, .gif) and attempts to extract faces and
* identities. It then applies a threshold
* and returns the list of relevant identities, locations, and metadata
* for found faces for each image.
*
* @example
* {
* "images": [{
* "faces": [{
* "age": {
* "max": 54,
* "min": 45,
* "score": 0.40459
* },
* "face_location": {
* "height": 131,
* "left": 80,
* "top": 68,
* "width": 123
* },
* "gender": {
* "gender": "MALE",
* "score": 0.993307
* },
* "identity": {
* "name": "Barack Obama",
* "score": 0.970688,
* "type_hierarchy": "/people/politicians/democrats/barack obama"
* }
* }],
* "image": "obama.jpg"
* }],
* "images_processed": 1
* }
*
* @param {Object} params
* @param {ReadStream} [params.images_file] The image file (.jpg, .png, .gif) or compressed (.zip) file of images to classify. The total number of images is limited to 15. Either images_file or url must be specified.
* @param {String} [params.url] The URL of an image (.jpg, .png, .gif). Redirects are followed, so you can use shortened URLs. The resolved URL is returned in the response. Either images_file or url must be specified.
* @param {Function} callback
*
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.detectFaces = function(params, callback) {
try {
fixupImageParam(params);
} catch (e) {
callback(e);
return;
}
var parameters;
if(params.images_file) {
parameters = {
options: {
url: '/v3/detect_faces',
method: 'POST',
json: true,
formData: pick(params, ['images_file'])
},
defaultOptions: this._options
};
} else {
parameters = {
options: {
url: '/v3/detect_faces',
method: 'GET',
json: true,
qs: pick(params, ['url'])
},
defaultOptions: this._options
};
}
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Accepts either a url, single image file, or a zip file with multiple
* images (.jpeg, .png, .gif) and attempts to recognize text
* found in the image. It then applies a threshold
* and returns the list of relevant locations, strings, and metadata
* for discovered text in each image.
*
* @example
* {
* "images": [{
* "image": "car.png",
* "text": "3 jag [brio]",
* "words": [{
* "line_number": 0,
* "location": {
* "height": 53,
* "left": 204,
* "top": 294,
* "width": 27
* },
* "score": 0.50612,
* "word": "3"
* }, {
* "line_number": 0,
* "location": {
* "height": 32,
* "left": 264,
* "top": 288,
* "width": 56
* },
* "score": 0.958628,
* "word": "jag"
* }, {
* "line_number": 0,
* "location": {
* "height": 40,
* "left": 324,
* "top": 288,
* "width": 92
* },
* "score": 0.00165806,
* "word": "brio"
* }]
* }],
* "images_processed": 1
* }
*
* @param {Object} params
* @param {ReadStream} [params.images_file] The image file (.jpg, .png, .gif) or compressed (.zip) file of images to classify. The total number of images is limited to 10. Either images_file or url must be specified.
* @param {String} [params.url] The URL of an image (.jpg, .png, .gif). Redirects are followed, so you can use shortened URLs. The resolved URL is returned in the response. Either images_file or url must be specified.
* @param {Function} callback
*
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.recognizeText = function(params, callback) {
try {
fixupImageParam(params);
} catch (e) {
callback(e);
return;
}
var parameters;
if(params.images_file) {
parameters = {
options: {
url: '/v3/recognize_text',
method: 'POST',
json: true,
formData: pick(params, ['images_file'])
},
defaultOptions: this._options
};
} else {
parameters = {
options: {
url: '/v3/recognize_text',
method: 'GET',
json: true,
qs: pick(params, ['url'])
},
defaultOptions: this._options
};
}
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Train a new classifier from example images which are uploaded.
* This call returns before training has completed. You'll need to use the
* getClassifer method to make sure the classifier has completed training and
* was successful before you can classify any images with the newly created
* classifier.
*
* @example
* {
* foo_positive_examples: fs.createReadStream('./foo-pics.zip'),
* negative_examples: fs.createReadStream('./not-foo-pics.zip'),
* name: 'to-foo-or-not'
* }
* @example
* {
* foo_positive_examples: fs.createReadStream('./foo-pics.zip'),
* bar_positive_examples: fs.createReadStream('./bar-pics.zip'),
* name: 'foo-vs-bar'
* }
* @example
* {
* foo_positive_examples: fs.createReadStream('./foo-pics.zip'),
* bar_positive_examples: fs.createReadStream('./bar-pics.zip'),
* negative_examples: fs.createReadStream('./not-foo-pics.zip'),
* name: 'foo-bar-not'
* }
*
* @example
* {
* "classifier_id": "fruit_679357912",
* "name": "fruit",
* "owner": "a3a48ea7-492b-448b-87d7-9dade8bde5a9",
* "status": "training",
* "created": "2016-05-23T21:50:41.680Z",
* "classes": [{
* "class": "banana"
* }, {
* "class": "apple"
* }]
* }
*
* @param {Object} params
* @param {String} params.name The desired short name of the new classifier.
* @param {ReadStream} params.classname_positive_examples <your_class_name>_positive_examples One or more compressed (.zip) files of images that depict the visual subject for a class within the new classifier. Must contain a minimum of 10 images. You may supply multiple files with different class names in the key.
* @param {ReadStream} [params.negative_examples] A compressed (.zip) file of images that do not depict the visual subject of any of the classes of the new classifier. Must contain a minimum of 10 images. Required if only one positive set is provided.
*
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.createClassifier = function(params, callback) {
params = params || {};
var example_keys = Object.keys(params).filter(function(key) {
return key === NEGATIVE_EXAMPLES || key.match(/^.+_positive_examples$/);
});
if (example_keys.length < 2) {
callback(new Error('Missing required parameters: either two *_positive_examples or one *_positive_examples and one negative_examples must be provided.'));
return;
}
// todo: validate that all *_examples are streams or else objects with buffers and content-types
var allowed_keys = ['name', NEGATIVE_EXAMPLES].concat(example_keys);
var parameters = {
options: {
url: '/v3/classifiers',
method: 'POST',
json: true,
formData: pick(params, allowed_keys)
},
requiredParams: ['name'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Retrain a existing classifier from example images which are uploaded.
* This call returns before retraining has completed. You'll need to use the
* getClassifer method to make sure the classifier has completed retraining and
* was successful before you can classify any images with the retrained
* classifier.
*
* @example
* {
* "classifier_id": "fruit_679357912",
* "name": "fruit",
* "owner": "a3a48ea7-492b-448b-87d7-9dade8bde5a9",
* "status": "training",
* "created": "2016-05-23T21:50:41.680Z",
* "classes": [{
* "class": "banana"
* }, {
* "class": "apple"
* }]
* }
*
* @param {Object} params
* @param {ReadStream} params.classname_positive_examples <your_class_name>_positive_examples One or more compressed (.zip) files of images that depict the visual subject for a class within the classifier. You may supply multiple files with different class names in the key.
* @param {ReadStream} [params.negative_examples] A compressed (.zip) file of images that do not depict the visual subject of any of the classes of the classifier.
*
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.retrainClassifier = function(params, callback) {
params = params || {};
var allowed_keys = Object.keys(params).filter(function(key) {
return key === NEGATIVE_EXAMPLES || key.match(/^.+_positive_examples$/);
});
var parameters = {
options: {
url: '/v3/classifiers/' + params.classifier_id,
method: 'POST',
json: true,
formData: pick(params, allowed_keys)
},
requiredParams: [],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Retrieve a list of all classifiers, including built-in and
* user-created classifiers.
*
* @example
* {
* "classifiers": [{
* "classifier_id": "fruit_679357912",
* "name": "fruit",
* "status": "ready"
* }, {
* "classifier_id": "Dogs_2017013066",
* "name": "Dogs",
* "status": "ready"
* }]
* }
*
* @param {Object} params
* @param {Boolean} [params.verbose=false]
* @param {Function} callback
* @return {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.listClassifiers = function(params, callback) {
var parameters = {
options: {
method: 'GET',
url: '/v3/classifiers',
qs: pick(params, ['verbose']),
json: true,
},
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Retrieves information about a specific classifier.
*
* @example
* {
* "classifier_id": "fruit_679357912",
* "name": "fruit",
* "owner": "a3a42ea7-492b-448b-87d7-9dfde8bde519 ",
* "status": "ready",
* "created": "2016-05-23T21:50:41.680Z",
* "classes": [{
* "class": "banana"
* }, {
* "class": "apple"
* }]
* }
*
* @param {Object} params
* @param {Boolean} params.classifier_id The classifier id
* @param {Function} callback
* @return {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.getClassifier = function(params, callback) {
var parameters = {
options: {
method: 'GET',
url: '/v3/classifiers/{classifier_id}',
path: params,
json: true
},
requiredParams: ['classifier_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Deletes a custom classifier with the specified classifier id.
*
* @param {Object} params
* @param {String} params.classifier_id The classifier id
* @param {Function} callback
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.deleteClassifier = function(params, callback) {
var parameters = {
options: {
method: 'DELETE',
url: '/v3/classifiers/{classifier_id}',
path: params,
json: true,
},
requiredParams: ['classifier_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
// collections & similarity search
/**
* Create a collection
* Beta. Create a new collection of images to search. You can create a maximum of 5 collections.
*
* Example response:
{ collection_id: 'integration_test_1474313373701_d9665f',
name: 'integration_test_1474313373701',
status: 'available',
created: '2016-09-19T19:29:34.019Z',
images: 0,
capacity: 1000000 }
* @param {Object} params
* @param {String} params.name The name of the new collection. The name can be a maximum of 128 UTF8 characters, with no spaces.
* @param {Function} callback
*/
VisualRecognitionV3.prototype.createCollection = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections',
method: 'POST',
json: true,
formData: pick(params, ['name'])
},
requiredParams: ['name'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Retrieve collection details
* Beta. Retrieve information about a specific collection.
*
* Example response:
{ collection_id: 'integration_test_1474313373701_d9665f',
name: 'integration_test_1474313373701',
status: 'available',
created: '2016-09-19T19:29:34.019Z',
images: 0,
capacity: 1000000 }
* @param {Object} params
* @param {String} params.collection_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.getCollection = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}',
method: 'GET',
json: true,
path: params
},
requiredParams: ['collection_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* List collections
* Beta. List all custom collections.
*
* Example response:
{ collections:
[ { collection_id: 'integration_test_1474313967414_0e320b',
name: 'integration_test_1474313967414',
status: 'available',
created: '2016-09-19T19:39:27.811Z',
images: 0,
capacity: 1000000 } ] }
* @param {Object} [params]
* @param {Function} callback
*/
VisualRecognitionV3.prototype.listCollections = function(params, callback) {
if (typeof params === 'function' && !callback) {
callback = params;
}
var parameters = {
options: {
url: '/v3/collections',
method: 'GET',
json: true
},
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Delete a collection
* Beta. Delete a user created collection.
*
* @param {Object} params
* @param {String} params.collection_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.deleteCollection = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}',
method: 'DELETE',
json: true,
path: params
},
requiredParams: ['collection_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Add an image to a collection
* Beta. Add images to a collection. Each collection can contain 1000000 images.
*
* Example Response:
{
"images": [
{
"image_id": "9725bc",
"image_file": "obama.jpg",
"created": "2016-09-20T14:41:49.927Z",
"metadata": {
"foo": "bar"
}
}
],
"images_processed": 1
}
* @param {Object} params
* @param {String} params.collection_id
* @param {ReadableStream} params.image_file The image file (.jpg or .png) of the image to add to the collection. Maximum file size of 2 MB.
* @param {Object} [params.metadata] optional arbitrary metadata. This can be anything that can be specified in a JSON object. For example, key-value pairs. Maximum 2 KB of metadata for each image.
* @param {Function} callback
*/
VisualRecognitionV3.prototype.addImage = function(params, callback) {
params = params || {};
if (!params.image_file || !isStream(params.image_file)) {
throw new Error('image_file param must be a standard Node.js Stream');
}
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images',
method: 'POST',
json: true,
path: params,
formData: {
image_file: params.image_file,
metadata: {
value: JSON.stringify(params.metadata || {}),
options: {
contentType: 'application/json',
filename: 'metadata.json' // it doesn't matter what the filename is, but the service requires that *some* filename be set or else it ignores the metadata
}
}
}
},
requiredParams: ['collection_id', 'image_file'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* List images in a collection
* Beta. List the first 100 images in a collection. Each collection can contain 1000000 images.
*
* Example Response:
{ images:
[ { image_id: '83f3ff',
image_file: 'obama.jpg',
created: '2016-09-19T21:07:15.141Z' } ] }
*
* @param {Object} params
* @param {String} params.collection_id
* @param {ReadableStream} params.image_file The image file (.jpg or .png) of the image to add to the collection. Maximum file size of 2 MB.
* @param {Object} [params.metadata] optional arbitrary metadata. This can be anything that can be specified in a JSON object. For example, key-value pairs. Maximum 2 KB of metadata for each image.
* @param {Function} callback
*/
VisualRecognitionV3.prototype.listImages = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images',
method: 'GET',
json: true,
path: params
},
requiredParams: ['collection_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Get image details
* Beta. List details about a specific image in a collection.
*
* Example Response:
{ image_id: '83f3ff',
image_file: 'obama.jpg',
created: '2016-09-19T21:07:15.141Z'
* @param {Object} params
* @param {String} params.collection_id
* @param {String} params.image_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.getImage = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images/{image_id}',
method: 'GET',
json: true,
path: params
},
requiredParams: ['collection_id', 'image_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Delete an image
* Beta. Delete an image from a collection.
*
* @param {Object} params
* @param {String} params.collection_id
* @param {String} params.image_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.deleteImage = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images/{image_id}',
method: 'DELETE',
json: true,
path: params
},
requiredParams: ['collection_id', 'image_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Add or update metadata
* Beta. Add metadata to a specific image in a collection.
*
* @param {Object} params
* @param {String} params.collection_id
* @param {String} params.image_id
* @param {Object} params.metadata Can be anything that can be specified in a JSON object. For example, key-value pairs. Maximum 2 KB of metadata for each image.
* @param {Function} callback
*/
VisualRecognitionV3.prototype.setImageMetadata = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images/{image_id}/metadata',
method: 'PUT',
json: true,
path: params,
headers: {"Content-Type": "multipart/form-data"},
// todo: manually create a body string that looks like a POST form data body even though it's a PUT
formData: {
metadata: {
value: JSON.stringify(params.metadata || {}),
options: {
contentType: 'application/json',
filename: 'metadata.json' // it doesn't matter what the filename is, but the service requires that *some* filename be set or else it gives a confusing "Missing multipart/form-data" error
}
}
}
},
requiredParams: ['collection_id', 'image_id', 'metadata'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Get image metadata
* Beta. View the metadata for a specific image in a collection.
*
* Example Response:
{"foo": "bar"}
*
* @param {Object} params
* @param {String} params.collection_id
* @param {ReadableStream} params.image_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.getImageMetadata = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images/{image_id}/metadata',
method: 'GET',
json: true,
path: params
},
requiredParams: ['collection_id', 'image_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Delete image metadata
* Beta. Delete all metadata associated with an image.
*
* @param {Object} params
* @param {String} params.collection_id
* @param {ReadableStream} params.image_id
* @param {Function} callback
*/
VisualRecognitionV3.prototype.deleteImageMetadata = function(params, callback) {
params = params || {};
var parameters = {
options: {
url: '/v3/collections/{collection_id}/images/{image_id}/metadata',
method: 'DELETE',
json: true,
path: params
},
requiredParams: ['collection_id', 'image_id'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
/**
* Find similar images
* Beta. Upload an image to find similar images in your custom collection.
*
* Example response:
{
"similar_images":[
{
"image_id":"dresses_1257263",
"created":"2016-09-04T21:49:16.908Z",
"metadata":{
"weight":10,
"cut":"a line",
"color":"red"
},
"score":"0.79"
}
],
"image_file":"red_dress.jpg",
"images_processed": 1
}
* @param {Object} params
* @param {String} params.collection_id The collection id
* @param {ReadableStream} params.image_file The image file (.jpg or .png) of the image to search against the collection.
* @param {Number} [params.limit=10] limit The number of similar results you want returned. Default limit is 10 results, you can specify a maximum limit of 100 results.
* @param {Function} callback
* @returns {ReadableStream|undefined}
*/
VisualRecognitionV3.prototype.findSimilar = function(params, callback) {
params = params || {};
if (!params.image_file || !isStream(params.image_file)) {
throw new Error('image_file param must be a standard Node.js Stream');
}
var parameters = {
options: {
url: '/v3/collections/{collection_id}/find_similar',
method: 'POST',
json: true,
qs: pick(params, ['limit']),
formData: pick(params, ['image_file']),
path: pick(params, ['collection_id'])
},
requiredParams: ['collection_id', 'image_file'],
defaultOptions: this._options
};
return requestFactory(parameters, errorFormatter(callback));
};
module.exports = VisualRecognitionV3;