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Object Detection Example ReadMe (#148)
* Added shell scripts to run the object detection example on Windows and Mac OS simply. Scripts download trained model, .pbtxt file and run example against the image under the test_images folder. * Added README file with a description of the example
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‎Examples/ExampleCommon/CatalogUtil.cs

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@@ -1,12 +1,17 @@
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using System.Collections.Generic;
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using System;
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using System.Collections.Generic;
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using System.Globalization;
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using System.IO;
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using System.Text.RegularExpressions;
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namespace ExampleCommon
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{
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public static class CatalogUtil
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{
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private static string CATALOG_ITEM_PATTERN = @"item {\r\n name: ""(?<name>.*)""\r\n id: (?<id>\d+)\r\n display_name: ""(?<displayName>.*)""\r\n}";
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// regexes with different new line symbols
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private static string CATALOG_ITEM_PATTERN = @"item {{{0} name: ""(?<name>.*)""{0} id: (?<id>\d+){0} display_name: ""(?<displayName>.*)""{0}}}";
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private static string CATALOG_ITEM_PATTERN_ENV = string.Format(CultureInfo.InvariantCulture, CATALOG_ITEM_PATTERN, Environment.NewLine);
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private static string CATALOG_ITEM_PATTERN_UNIX = string.Format (CultureInfo.InvariantCulture, CATALOG_ITEM_PATTERN, "\n");
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/// <summary>
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/// Reads catalog of well-known objects from text file.
@@ -22,8 +27,13 @@ public static IEnumerable<CatalogItem> ReadCatalogItems (string file)
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yield break;
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}
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Regex regex = new Regex (CATALOG_ITEM_PATTERN);
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Regex regex = new Regex (CATALOG_ITEM_PATTERN_ENV);
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var matches = regex.Matches (text);
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if(matches.Count == 0) {
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regex = new Regex (CATALOG_ITEM_PATTERN_UNIX);
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matches = regex.Matches (text);
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}
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foreach (Match match in matches) {
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var name = match.Groups [1].Value;
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var id = int.Parse (match.Groups [2].Value);

‎Examples/ExampleObjectDetection/ExampleObjectDetection.csproj

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<WarningLevel>4</WarningLevel>
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</PropertyGroup>
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<ItemGroup>
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<Reference Include="ICSharpCode.SharpZipLib, Version=0.86.0.518, Culture=neutral, PublicKeyToken=1b03e6acf1164f73, processorArchitecture=MSIL">
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<HintPath>..\..\packages\SharpZipLib.0.86.0\lib\20\ICSharpCode.SharpZipLib.dll</HintPath>
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</Reference>
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<Reference Include="Mono.Options, Version=5.0.0.0, Culture=neutral, processorArchitecture=MSIL">
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<HintPath>..\..\packages\Mono.Options.5.3.0.1\lib\net4-client\Mono.Options.dll</HintPath>
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</Reference>
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<Reference Include="System" />
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<Reference Include="System.Core" />
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<Reference Include="System.Drawing" />
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<Reference Include="System.IO.Compression" />
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<Reference Include="System.IO.Compression.FileSystem" />
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<Reference Include="System.ValueTuple, Version=4.0.2.0, Culture=neutral, PublicKeyToken=cc7b13ffcd2ddd51, processorArchitecture=MSIL">
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<HintPath>..\..\packages\System.ValueTuple.4.4.0\lib\net461\System.ValueTuple.dll</HintPath>
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</Reference>
@@ -68,5 +73,10 @@
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<Name>ExampleCommon</Name>
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</ProjectReference>
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</ItemGroup>
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<ItemGroup>
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<Content Include="test_images\input.jpg">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</Content>
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</ItemGroup>
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<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
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</Project>

‎Examples/ExampleObjectDetection/ImageEditor.cs

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@@ -57,7 +57,9 @@ public void AddBox (float xmin, float xmax, float ymin, float ymax, string text
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Pen pen = new Pen (brush);
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_graphics.DrawRectangle (pen, left, top, right - left, bottom - top);
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_graphics.DrawString (text, new Font (_fontFamily, _fontSize), brush, new PointF (left, top));
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var font = new Font (_fontFamily, _fontSize);
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SizeF size = _graphics.MeasureString (text, font);
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_graphics.DrawString (text, font, brush, new PointF (left, top - size.Height));
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}
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public void Dispose ()

‎Examples/ExampleObjectDetection/Program.cs

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@@ -5,14 +5,21 @@
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using TensorFlow;
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using ExampleCommon;
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using Mono.Options;
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using System.IO.Compression;
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using System.Reflection;
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using System.Net;
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using ICSharpCode.SharpZipLib.Zip;
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using ICSharpCode.SharpZipLib.Tar;
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using ICSharpCode.SharpZipLib.GZip;
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namespace ExampleObjectDetection
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{
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class Program
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{
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private static IEnumerable<CatalogItem> _catalog;
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private static string _input;
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private static string _output;
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private static string _currentDir = Path.GetDirectoryName (Assembly.GetExecutingAssembly ().Location);
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private static string _input = Path.Combine (_currentDir, "test_images/input.jpg");
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private static string _output = Path.Combine (_currentDir, "test_images/output.jpg");
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private static string _catalogPath;
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private static string _modelPath;
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@@ -28,20 +35,12 @@ class Program
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};
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/// <summary>
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/// The utility processes the image and produces output image highlighting detected objects on it.
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/// You need to proceed following steps to get the example working:
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/// 1. Download and unzip one of trained models from
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/// https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md
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///
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/// for instance 'faster_rcnn_inception_resnet_v2_atrous_coco'
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/// 2. Download mscoco_label_map.pbtxt from
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/// https://github.com/tensorflow/models/blob/master/object_detection/data/mscoco_label_map.pbtxt
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///
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/// 3. Run the ExampleObjectDetection util from command line specifying input_image, output_image, catalog and model options
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/// where input_image - the path to the image for processing
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/// output_image - the path where the image with detected objects will be saved
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/// catalog - the path to the 'mscoco_label_map.pbtxt' file (see 2)
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/// model - the path to the 'frozen_inference_graph.pb' file (see 1)
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/// Run the ExampleObjectDetection util from command line. Following options are available:
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/// input_image - optional, the path to the image for processing (the default is 'test_images/input.jpg')
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/// output_image - optional, the path where the image with detected objects will be saved (the default is 'test_images/output.jpg')
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/// catalog - optional, the path to the '*.pbtxt' file (by default, 'mscoco_label_map.pbtxt' been loaded)
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/// model - optional, the path to the '*.pb' file (by default, 'frozen_inference_graph.pb' model been used, but you can download any other from here
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/// https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md or train your own)
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///
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/// for instance,
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/// ExampleObjectDetection --input_image="/demo/input.jpg" --output_image="/demo/output.jpg" --catalog="/demo/mscoco_label_map.pbtxt" --model="/demo/frozen_inference_graph.pb"
@@ -51,20 +50,12 @@ static void Main (string [] args)
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{
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options.Parse (args);
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if(_input == null) {
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throw new ArgumentException ("Missing required option --input_image=");
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}
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if (_output == null) {
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throw new ArgumentException ("Missing required option --output_image=");
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}
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if (_catalogPath == null) {
63-
throw new ArgumentException ("Missing required option --catalog=");
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_catalogPath = DownloadDefaultTexts (_currentDir);
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}
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if (_modelPath == null) {
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throw new ArgumentException ("Missing required option --model=");
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_modelPath = DownloadDefaultModel (_currentDir);
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}
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_catalog = CatalogUtil.ReadCatalogItems (_catalogPath);
@@ -100,7 +91,47 @@ static void Main (string [] args)
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}
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}
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}
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private static string DownloadDefaultModel (string dir)
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{
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const string defaultModelUrl = "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017.tar.gz";
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var modelFile = Path.Combine (dir, "faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017/frozen_inference_graph.pb");
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var zipfile = Path.Combine (dir, "faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017.tar.gz");
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if (File.Exists (modelFile))
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return modelFile;
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105+
if (!File.Exists (zipfile)) {
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var wc = new WebClient ();
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wc.DownloadFile (defaultModelUrl, zipfile);
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}
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ExtractToDirectory (zipfile, dir);
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File.Delete (zipfile);
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return modelFile;
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}
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private static void ExtractToDirectory (string file, string targetDir)
117+
{
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using (Stream inStream = File.OpenRead (file))
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using (Stream gzipStream = new GZipInputStream (inStream)) {
120+
TarArchive tarArchive = TarArchive.CreateInputTarArchive (gzipStream);
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tarArchive.ExtractContents (targetDir);
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}
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}
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private static string DownloadDefaultTexts (string dir)
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{
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const string defaultTextsUrl = "https://raw.githubusercontent.com/tensorflow/models/master/object_detection/data/mscoco_label_map.pbtxt";
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var textsFile = Path.Combine (dir, "mscoco_label_map.pbtxt");
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var wc = new WebClient ();
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wc.DownloadFile (defaultTextsUrl, textsFile);
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return textsFile;
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}
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private static void DrawBoxes (float [,,] boxes, float [,] scores, float [,] classes, string inputFile, string outputFile, double minScore)
105136
{
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var x = boxes.GetLength (0);
@@ -117,18 +148,18 @@ private static void DrawBoxes (float [,,] boxes, float [,] scores, float [,] cla
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for (int k = 0; k < z; k++) {
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var box = boxes [i, j, k];
119150
switch (k) {
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case 0:
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ymin = box;
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break;
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case 1:
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xmin = box;
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break;
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case 2:
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ymax = box;
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break;
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case 3:
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xmax = box;
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break;
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case 0:
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ymin = box;
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break;
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case 1:
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xmin = box;
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break;
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case 2:
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ymax = box;
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break;
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case 3:
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xmax = box;
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break;
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}
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}

‎Examples/ExampleObjectDetection/README.md

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# Object Detection Example #
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This example uses tensorflow [object detection model API](https://github.com/tensorflow/models/tree/master/object_detection) and TensorFlowSharp library to identify multiple objects in a single image using .NET programming languages like C# and F#.
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![alt tag](demo-picture.jpg)
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## Run example ##
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1. ``` git clone https://github.com/migueldeicaza/TensorFlowSharp ```
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2. build _TensorFlowSharp.sln_
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3. copy _'libtensorflow.dylib'_ (Mac OS) or _'libtensorflow.dll'_ (Windows) to the project output path (see where you can get the library under [Working on TensorFlowSharp](https://github.com/migueldeicaza/TensorFlowSharp#working-on-tensorflowsharp) section)
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4. Run the ExampleObjectDetection util from command line:
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```
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ExampleObjectDetection
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```
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By default, the example downloads a pretrained model, but you can specify your own using the following options:
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_input_image_ - optional, the path to the image for processing (the default is 'test_images/input.jpg')
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_output_image_ - optional, the path where the image with detected objects will be saved (the default is 'test_images/output.jpg')
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_catalog_ - optional, the path to the '*.pbtxt' file (by default, 'mscoco_label_map.pbtxt' been loaded)
21+
_model_ - optional, the path to the '*.pb' file (by default, 'frozen_inference_graph.pb' model been used, but you can download any other from here https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md or train your own):
22+
```
23+
ExampleObjectDetection --input_image="/demo/input.jpg" --output_image="/demo/output.jpg" --catalog="/demo/mscoco_label_map.pbtxt" --model="/demo/frozen_inference_graph.pb"
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```
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## I found an issue in the example ##
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If you want to address a bug or a question related to the object detection example - just create a new issue on github starting with [Object Detection Example] tag.

‎Examples/ExampleObjectDetection/demo-picture.jpg

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‎Examples/ExampleObjectDetection/packages.config

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<?xml version="1.0" encoding="utf-8"?>
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<packages>
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<package id="Mono.Options" version="5.3.0.1" targetFramework="net461" />
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<package id="SharpZipLib" version="0.86.0" targetFramework="net461" />
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<package id="System.ValueTuple" version="4.4.0" targetFramework="net461" />
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</packages>

‎Examples/ExampleObjectDetection/test_images/input.jpg

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‎tests/TensorFlowSharp.Tests.CSharp/packages.config

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<?xml version="1.0" encoding="utf-8"?>
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<packages>
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<package id="Microsoft.DotNet.InternalAbstractions" version="1.0.500-preview2-1-003177" targetFramework="net461" />
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<package id="Microsoft.TestPlatform.TestHost" version="15.3.0" targetFramework="net461" />
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<package id="System.ValueTuple" version="4.3.1" targetFramework="net461" />
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<package id="xunit" version="2.2.0" targetFramework="net452" />
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<package id="xunit.abstractions" version="2.0.1" targetFramework="net452" />
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<package id="xunit.assert" version="2.2.0" targetFramework="net452" />
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<package id="xunit.core" version="2.2.0" targetFramework="net452" />
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<package id="xunit.extensibility.core" version="2.2.0" targetFramework="net452" />
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<package id="xunit.extensibility.execution" version="2.2.0" targetFramework="net452" />
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<package id="xunit.runner.console" version="2.2.0" targetFramework="net452" developmentDependency="true" />
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<package id="xunit" version="2.2.0" targetFramework="net461" />
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<package id="xunit.abstractions" version="2.0.1" targetFramework="net461" />
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<package id="xunit.assert" version="2.2.0" targetFramework="net461" />
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<package id="xunit.core" version="2.2.0" targetFramework="net461" />
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<package id="xunit.extensibility.core" version="2.2.0" targetFramework="net461" />
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<package id="xunit.extensibility.execution" version="2.2.0" targetFramework="net461" />
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<package id="xunit.runner.console" version="2.2.0" targetFramework="net461" developmentDependency="true" />
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<package id="xunit.runner.visualstudio" version="2.3.0-beta3-build3705" targetFramework="net461" developmentDependency="true" />
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</packages>

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