alexiscreuzot

#!/bin/sh

# Some things taken from here
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx

# Set the colours you can use
black='\033[0;30m'
white='\033[0;37m'
red='\033[0;31m'
green='\033[0;32m'

alexiscreuzot

public enum Endpoints {
    case Colors(String)
    case AddColor(Color)
}

alexiscreuzot

public var method: Alamofire.Method {
    switch self {
    case .Colors:
        return Alamofire.Method.GET
    case .AddColor:
        return Alamofire.Method.POST
    }
}

public var path: String {

alexiscreuzot

public static func request(
    endpoint: API.Endpoints,
    completionHandler: Response<AnyObject, NSError> -> Void)
    -> Request {
        let request =  Manager.sharedInstance.request(
            endpoint.method,
            endpoint.path,
            parameters: endpoint.parameters,
            encoding: .URL,
            headers: nil

alexiscreuzot

API.request(.Colors("Sunny")) { response in
    if (response.result.error) != nil {
        // ERROR HANDLING
    } else {
        // PARSING...
    }
}

alexiscreuzot

python neural_style/neural_style.py train \
--dataset ~/Documents/dataset \
--style-image ~/Documents/images/styles/starry_night.jpg \
--style-size 512 \
--batch-size 2 \
--style-weight 1e10 \
--content-weight 1e5 \
--checkpoint-model-dir ~/Documents/checkpoints \
--save-model-dir ~/Documents/models \
--log-interval 10 \

alexiscreuzot

python neural_style/neural_style.py eval \
--model ~/Documents/checkpoint_5000.pth \
--content-image ~/Documents/images/original.jpg \
--output-image ~/Documents/images/stylized-test.jpg \
--cuda 0

alexiscreuzot

python ./neural_style/neural_style.py eval  \
--content-image ~/Documents/data/images/original.jpg \
--output-image ~/Documents/data/images/stylized-test.jpg \
--model ~/Documents/checkpoints/checkpoint_5000.pth \
--cuda 0 \
--export_onnx ~/Documents/models/pytorch_model.onnx \
&& \
python ./onnx_to_coreml.py \
~/Documents/models/pytorch_model.onnx  \
~/Documents/models/final_model.mlmodel

alexiscreuzot

func process(input: UIImage, completion: @escaping FilteringCompletion) {
        
        // Initialize the NST model
        let model = StarryNight()
        
        // Next steps are pretty heavy, better process them on a background thread
        DispatchQueue.global().async {
            
            // 1 - Transform our UIImage to a PixelBuffer of appropriate size
            guard let cvBufferInput = input.pixelBuffer(width: 720, height: 720) else {

alexiscreuzot

import sys
from onnx import onnx_pb
from onnx_coreml import convert

model_in = sys.argv[1]
model_out = sys.argv[2]

model_file = open(model_in, 'rb')
model_proto = onnx_pb.ModelProto()
model_proto.ParseFromString(model_file.read())