* Detect objects that are in video, returns bounding box, label, and confidence scores * @param {HTMLVideoElement|HTMLImageElement|HTMLCanvasElemen

* Detects multiple internal function * @param {HTMLImageElement || HTMLVideoElement} imgToClassify * @param {Object} faceApiOptions

* Detect objects that are in video, returns bounding box, label, and confidence scores * @param {HTMLVideoElement|HTMLImageElement|HTMLCanvasElemen

* Detect objects that are in video, returns bounding box, label, and confidence scores * @param {HTMLVideoElement|HTMLImageElement|HTMLCanvasElemen

* Detects only a single feature * @param {HTMLImageElement || HTMLVideoElement} imgToClassify * @param {Object} faceApiOptions

* Extend cluster by running algorithm on neighbours and detect neighbours that are core points as well * @param {number} pointIndex * @param {nu

* Fetch data for a single HTML/JS file, or return * an url to the file's CDN location

* Run DBSCAN algorithm.

* fatten a nested array

* STEP 4 * Take a parent directory and prepare it for injestion! * @param {*} sketch * @param {*} user

* Takes the tensor from compute() and returns an object of the generate image data * @param {object} latentVector - an array containing the laten

* STEP 1: Get the top level cateogories

* STEP 2: Get the examples for each category * e.g. Posenet: * - /posenet_image_single * - /posenet_part_selection

* Return last generated cluster id

* getDTypesFromData * gets the data types of the data we're using * important for handling oneHot

* getDataOneHot * creates onehot encodings for the input and outputs * and adds them to the meta info * @param {*} dataRaw

* get stats about the data * @param {*} dataRaw

* get the data units, inputshape and output units * @param {*} dataRaw

* @public * Get the property names for each variable in the manifest. * * @return {Promise<string[]>}

* Fetch a list of all projects from the API