はじめに

前回紹介した簡単に写真の中のオブジェクトを検出できるCustom Visionは学習データのフォークやはさみの位置をドラッグ＆ドロップでポチポチ１つずつ登録していました。
もし、他の学習モデルで同じ画像を使いたいとなったときは不便ですよね・・・。
そこで、ここを参考にプロジェクトの作成とプログラムから一括で学習データの登録、学習をしていきます。

Custom Visionでの情報の入手

Custom VisionのProjectsページで右上の歯車マークを押すと、以下のようにTraining Keyなどを取得することができます。

今回必要な情報は以下です。

Project Id
Training Key
Training Endpoint(先頭から.microsoft.com/まで)
Prediction Resource Id

プログラムの入手

ここから以下のコマンドでコードを入手します。

git clone https://github.com/Azure-Samples/cognitive-services-dotnet-sdk-samples.git

クローンが終わったら、cognitive-services-dotnet-sdk-samples＞CustomVisionにあるCustomVision.slnをVisual Studioで開きます。

プログラムの改変

今回はプロジェクトの作成とプログラムから一括で学習データの登録、学習のみしたいので、ObjectDetectionのProgram.csを以下のように書き換えます。

using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Prediction;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training;
using Microsoft.Azure.CognitiveServices.Vision.CustomVision.Training.Models;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading;

namespace ObjectDetection
{
    class Program
    {
        private const string JapanEastEndpoint = "https://japaneast.api.cognitive.microsoft.com";

        static void Main(string[] args)
        {
            // Add your training & prediction key from the settings page of the portal
            string trainingKey = "<your training key here>";
            string predictionKey = "<your prediction key here>";

            // Create the Api, passing in the training key
            CustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient()
            {
                ApiKey = trainingKey,
                Endpoint = JapanEastEndpoint
            };

            // Find the object detection domain
            var domains = trainingApi.GetDomains();
            var objDetectionDomain = domains.FirstOrDefault(d => d.Type == "ObjectDetection");

            // Create a new project
            Console.WriteLine("Creating new project:");
            var project = trainingApi.CreateProject("My New Project", null, objDetectionDomain.Id);

            // Make two tags in the new project
            var forkTag = trainingApi.CreateTag(project.Id, "fork");
            var scissorsTag = trainingApi.CreateTag(project.Id, "scissors");

            Dictionary<string, double[]> fileToRegionMap = new Dictionary<string, double[]>()
            {
                // FileName, Left, Top, Width, Height
                {"scissors_1", new double[] { 0.4007353, 0.194068655, 0.259803921, 0.6617647 } },
                {"scissors_2", new double[] { 0.426470578, 0.185898721, 0.172794119, 0.5539216 } },
                {"scissors_3", new double[] { 0.289215684, 0.259428144, 0.403186262, 0.421568632 } },
                {"scissors_4", new double[] { 0.343137264, 0.105833367, 0.332107842, 0.8055556 } },
                {"scissors_5", new double[] { 0.3125, 0.09766343, 0.435049027, 0.71405226 } },
                {"scissors_6", new double[] { 0.379901975, 0.24308826, 0.32107842, 0.5718954 } },
                {"scissors_7", new double[] { 0.341911763, 0.20714055, 0.3137255, 0.6356209 } },
                {"scissors_8", new double[] { 0.231617644, 0.08459154, 0.504901946, 0.8480392 } },
                {"scissors_9", new double[] { 0.170343131, 0.332957536, 0.767156839, 0.403594762 } },
                {"scissors_10", new double[] { 0.204656869, 0.120539248, 0.5245098, 0.743464053 } },
                {"scissors_11", new double[] { 0.05514706, 0.159754932, 0.799019635, 0.730392158 } },
                {"scissors_12", new double[] { 0.265931368, 0.169558853, 0.5061275, 0.606209159 } },
                {"scissors_13", new double[] { 0.241421565, 0.184264734, 0.448529422, 0.6830065 } },
                {"scissors_14", new double[] { 0.05759804, 0.05027781, 0.75, 0.882352948 } },
                {"scissors_15", new double[] { 0.191176474, 0.169558853, 0.6936275, 0.6748366 } },
                {"scissors_16", new double[] { 0.1004902, 0.279036, 0.6911765, 0.477124184 } },
                {"scissors_17", new double[] { 0.2720588, 0.131977156, 0.4987745, 0.6911765 } },
                {"scissors_18", new double[] { 0.180147052, 0.112369314, 0.6262255, 0.6666667 } },
                {"scissors_19", new double[] { 0.333333343, 0.0274019931, 0.443627447, 0.852941155 } },
                {"scissors_20", new double[] { 0.158088237, 0.04047389, 0.6691176, 0.843137264 } },
                {"fork_1", new double[] { 0.145833328, 0.3509314, 0.5894608, 0.238562092 } },
                {"fork_2", new double[] { 0.294117659, 0.216944471, 0.534313738, 0.5980392 } },
                {"fork_3", new double[] { 0.09191177, 0.0682516545, 0.757352948, 0.6143791 } },
                {"fork_4", new double[] { 0.254901975, 0.185898721, 0.5232843, 0.594771266 } },
                {"fork_5", new double[] { 0.2365196, 0.128709182, 0.5845588, 0.71405226 } },
                {"fork_6", new double[] { 0.115196079, 0.133611143, 0.676470637, 0.6993464 } },
                {"fork_7", new double[] { 0.164215669, 0.31008172, 0.767156839, 0.410130739 } },
                {"fork_8", new double[] { 0.118872553, 0.318251669, 0.817401946, 0.225490168 } },
                {"fork_9", new double[] { 0.18259804, 0.2136765, 0.6335784, 0.643790841 } },
                {"fork_10", new double[] { 0.05269608, 0.282303959, 0.8088235, 0.452614367 } },
                {"fork_11", new double[] { 0.05759804, 0.0894935, 0.9007353, 0.3251634 } },
                {"fork_12", new double[] { 0.3345588, 0.07315363, 0.375, 0.9150327 } },
                {"fork_13", new double[] { 0.269607842, 0.194068655, 0.4093137, 0.6732026 } },
                {"fork_14", new double[] { 0.143382356, 0.218578458, 0.7977941, 0.295751631 } },
                {"fork_15", new double[] { 0.19240196, 0.0633497, 0.5710784, 0.8398692 } },
                {"fork_16", new double[] { 0.140931368, 0.480016381, 0.6838235, 0.240196079 } },
                {"fork_17", new double[] { 0.305147052, 0.2512582, 0.4791667, 0.5408496 } },
                {"fork_18", new double[] { 0.234068632, 0.445702642, 0.6127451, 0.344771236 } },
                {"fork_19", new double[] { 0.219362751, 0.141781077, 0.5919118, 0.6683006 } },
                {"fork_20", new double[] { 0.180147052, 0.239820287, 0.6887255, 0.235294119 } }
            };

            // Add all images for fork
            var imagePath = Path.Combine("Images", "fork");
            var imageFileEntries = new List<ImageFileCreateEntry>();
            foreach (var fileName in Directory.EnumerateFiles(imagePath))
            {
                var region = fileToRegionMap[Path.GetFileNameWithoutExtension(fileName)];
                imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), null, new List<Region>(new Region[] { new Region(forkTag.Id, region[0], region[1], region[2], region[3]) })));
            }
            trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));

            // Add all images for scissors
            imagePath = Path.Combine("Images", "scissors");
            imageFileEntries = new List<ImageFileCreateEntry>();
            foreach (var fileName in Directory.EnumerateFiles(imagePath))
            {
                var region = fileToRegionMap[Path.GetFileNameWithoutExtension(fileName)];
                imageFileEntries.Add(new ImageFileCreateEntry(fileName, File.ReadAllBytes(fileName), null, new List<Region>(new Region[] { new Region(scissorsTag.Id, region[0], region[1], region[2], region[3]) })));
            }
            trainingApi.CreateImagesFromFiles(project.Id, new ImageFileCreateBatch(imageFileEntries));

            // Now there are images with tags start training the project
            Console.WriteLine("\tTraining");
            var iteration = trainingApi.TrainProject(project.Id);

            // The returned iteration will be in progress, and can be queried periodically to see when it has completed
            while (iteration.Status == "Training")
            {
                Thread.Sleep(1000);

                // Re-query the iteration to get its updated status
                iteration = trainingApi.GetIteration(project.Id, iteration.Id);
            }

            // The iteration is now trained. Publish it to the prediction end point.
            var publishedModelName = "toolModel";
            var predictionResourceId = "<target prediction resource ID>";
            trainingApi.PublishIteration(project.Id, iteration.Id, publishedModelName, predictionResourceId);
            Console.WriteLine("Done!\n");

            
        }
    }
}