ozgurshn

    @objc protocol VideoRangeSliderDelegate
    {
        func videoRange (videoRange:VideoRangeSlider, didChangeLeftPosition:CGFloat, rightPosition:CGFloat)
        optional func videoRange(videoRange:VideoRangeSlider, didGestureStateEndedLeftPosition:CGFloat, rightPosition:CGFloat)
    }

    // Usage ensuring call if method is implemented
    delegate?.videoRange?(self, didGestureStateEndedLeftPosition: self.leftPosition, rightPosition: self.rightPosition)

ozgurshn

import UIKit
@IBDesignable
class CustomView: UIView {

@IBInspectable var gradient:Bool = false {
     
        didSet {
            if gradient
            {
                let topColor = UIColor(red: 3/256, green: 51/256, blue: 75/256, alpha: 1.0)

ozgurshn

import UIKit
@IBDesignable
class CustomView: UIView {

    @IBInspectable var cornerRadius: CGFloat = 0 {
        didSet {
            layer.cornerRadius = cornerRadius
            layer.masksToBounds = cornerRadius > 0
        }

ozgurshn

import UIKit
@IBDesignable
class CustomView: UIView {

    var fromColor: UIColor?
    var toColor: UIColor?
    var gradientLayer: CAGradientLayer?
    
   
    

ozgurshn

func drawRoutes(sourceLocation:CLLocationCoordinate2D , destinationLocation:CLLocationCoordinate2D)
    {

        let sourcePlacemark = MKPlacemark(coordinate: sourceLocation, addressDictionary: nil)
        let destinationPlacemark = MKPlacemark(coordinate: destinationLocation, addressDictionary: nil)
        
        
        let sourceMapItem = MKMapItem(placemark: sourcePlacemark)
        let destinationMapItem = MKMapItem(placemark: destinationPlacemark)
        

ozgurshn

@objc protocol LocationSelectorDelegate: class {
    func showMap(tag: Int)
}

@IBDesignable
class LocationSelectorView: UIView {

     @IBOutlet weak var delegate: LocationSelectorDelegate?
  

ozgurshn

def get_model():
  
    input_tensor = Input(shape=(224, 224, 3))  # this assumes K.image_data_format() == 'channels_last'

    # create the base pre-trained model
    base_model = ResNet50(input_tensor=input_tensor,weights='imagenet',include_top=False)

    for layer in base_model.layers:
        layer.trainable=False

ozgurshn

def compile_model(compiledModel):

    compiledModel.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=Adadelta(),
                  metrics=['accuracy'])

ozgurshn

def modelFitGenerator(fitModel):

    num_train_samples = sum([len(files) for r, d, files in os.walk(train_data_dir)])
    num_valid_samples = sum([len(files) for r, d, files in os.walk(validation_data_dir)])

    num_train_steps = math.floor(num_train_samples/batch_size)
    num_valid_steps = math.floor(num_valid_samples/batch_size)
    
    train_datagen = ImageDataGenerator(  
      rotation_range=90,      

ozgurshn

def saveCoreMLModel(kerasModel):
    coreml_model = coremltools.converters.keras.convert(kerasModel,
                                                    input_names=['input'],
                                                    output_names=['probs'],
                                                    image_input_names='input',
                                                    predicted_feature_name='predictedMoney',
                                                    class_labels = 'drive/Resnet/labels.txt')
    coreml_model.save('resnet50custom.mlmodel') 
    print('CoreML model saved')