codePrincess

import Foundation
import UIKit
import PlaygroundSupport

var myCanvas = MyDoodleCanvas()
myCanvas.backgroundColor = .white
myCanvas.isUserInteractionEnabled = true

var canvasView = UIView(frame: CGRect(x: 0, y: 0, width: 450, height: 630))
myCanvas.frame = canvasView.frame

codePrincess

module.exports = function(context, req) {
    //context.log('Node.js HTTP trigger function processed a request. RequestUri=%s', req.originalUrl);
    context.log('push data:', req.body);
    var str = req.body;
    var results = str.split("&");

    var slackText = getParam(results, "text").replace(/\+/g, " ").replace(/mytodo/g, "");
    var userName = getParam(results, "user_name").replace(/\+/g, " ");

    context.log(getParam(results, "token"));

codePrincess

#r "Newtonsoft.Json"

using System;
using System.Net;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Linq;
using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

codePrincess

from coremltools.converters import sklearn as sklearn_to_ml
from sklearn.externals import joblib

model = joblib.load('mymodel.pkl')

print('Converting model')
coreml_model = sklearn_to_ml.convert(model)

print('Saving CoreML model')
coreml_model.save('mycoremlmodel.mlmodel')

codePrincess

#!/usr/bin/env python
#print(__doc__)

# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# License: BSD 3 clause

# Standard scientific Python imports
import matplotlib.pyplot as plt
import pickle

codePrincess

func drawStroke(context: CGContext?, touch: UITouch) {
    let location = touch.location(in: self)
    if touch.type == .stylus {
        minX = min(minX, Int(location.x))
        minY = min(minY, Int(location.y))
        maxX = max(maxX, Int(location.x))
        maxY = max(maxY, Int(location.y))
        //....
    }
    //....

codePrincess

func generateMultiArrayFrom(image: UIImage) -> MLMultiArray? {
  guard let data = try? MLMultiArray(shape: [8,8], dataType: .double) else {
    return nil
  }
        
  let hTileWidth = image.size.width / 8
  let vTileWidth = image.size.height / 8
        
  var xPos : CGFloat = 0
  var yPos : CGFloat = 0

codePrincess

func recognizeWithLocalModel(_ image: UIImage) {
    if let data = generateMultiArrayFrom(image: image) {
        guard let modelOutput = try? singleNumberModel?.prediction(input: data) else {
            return
        }
            
        if let result = modelOutput?.classLabel {
            self.addLabelForOCR(text: "\(result)")
        } else {
            print("no result available")

codePrincess

let model = try VNCoreMLModel(for: catmoodprediction().model)
let request = VNCoreMLRequest(model: model, completionHandler: self.handleClassification)
var classificationRequest = [ request ]

codePrincess

let image = UIImage(named:"catinmood")
do {
  let pixelBuffer = image.pixelBuffer(width: 227, height:227)
  let classifierRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer!, options: [:])
  try classifierRequestHandler.perform(classificationRequest!)
} catch {
  print("something went terribly wrong during classification")
}