Implemented new TCC algorithm.

28 files changed, 1369 insertions, 578 deletions

diff --git a/Software/Visual_Studio/Build/Shortcuts/Machine Emulator.lnk b/Software/Visual_Studio/Build/Shortcuts/Machine Emulator.lnk
index 09f542836..407543254 100644
--- a/Software/Visual_Studio/Build/Shortcuts/Machine Emulator.lnk
+++ b/Software/Visual_Studio/Build/Shortcuts/Machine Emulator.lnk
diff --git a/Software/Visual_Studio/Build/Shortcuts/Machine Studio.lnk b/Software/Visual_Studio/Build/Shortcuts/Machine Studio.lnk
index 34c82a731..a09171af6 100644
--- a/Software/Visual_Studio/Build/Shortcuts/Machine Studio.lnk
+++ b/Software/Visual_Studio/Build/Shortcuts/Machine Studio.lnk
diff --git a/Software/Visual_Studio/Build/Shortcuts/Proto Compiler GUI.lnk b/Software/Visual_Studio/Build/Shortcuts/Proto Compiler GUI.lnk
index 86ac8562d..02d854013 100644
--- a/Software/Visual_Studio/Build/Shortcuts/Proto Compiler GUI.lnk
+++ b/Software/Visual_Studio/Build/Shortcuts/Proto Compiler GUI.lnk
diff --git a/Software/Visual_Studio/Build/Shortcuts/Stubs Execution GUI.lnk b/Software/Visual_Studio/Build/Shortcuts/Stubs Execution GUI.lnk
index b0950b3d7..bb5ebf9bf 100644
--- a/Software/Visual_Studio/Build/Shortcuts/Stubs Execution GUI.lnk
+++ b/Software/Visual_Studio/Build/Shortcuts/Stubs Execution GUI.lnk
diff --git a/Software/Visual_Studio/Build/Shortcuts/Transport Router.lnk b/Software/Visual_Studio/Build/Shortcuts/Transport Router.lnk
index 5285bbf10..533808c11 100644
--- a/Software/Visual_Studio/Build/Shortcuts/Transport Router.lnk
+++ b/Software/Visual_Studio/Build/Shortcuts/Transport Router.lnk
diff --git a/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.Common/Tango.MachineStudio.Common.csproj b/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.Common/Tango.MachineStudio.Common.csproj
index 4cf1855ca..3c272892b 100644
--- a/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.Common/Tango.MachineStudio.Common.csproj
+++ b/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.Common/Tango.MachineStudio.Common.csproj
@@ -55,7 +55,9 @@
     <Reference Include="MaterialDesignThemes.Wpf, Version=2.3.1.953, Culture=neutral, processorArchitecture=MSIL">
       <HintPath>..\..\packages\MaterialDesignThemes.2.3.1.953\lib\net45\MaterialDesignThemes.Wpf.dll</HintPath>
     </Reference>
-    <Reference Include="Microsoft.SqlServer.AzureStorageEnum, Version=14.100.0.0, Culture=neutral, PublicKeyToken=89845dcd8080cc91, processorArchitecture=MSIL" />
+    <Reference Include="Microsoft.SqlServer.AzureStorageEnum">
+      <HintPath>..\..\Referenced Assemblies\SMO\Microsoft.SqlServer.AzureStorageEnum.dll</HintPath>
+    </Reference>
     <Reference Include="Microsoft.WindowsAzure.Storage, Version=4.3.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35, processorArchitecture=MSIL" />
     <Reference Include="System" />
     <Reference Include="System.ComponentModel.DataAnnotations" />
@@ -386,7 +388,7 @@
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
   <ProjectExtensions>
     <VisualStudio>
-      <UserProperties BuildVersion_StartDate="2000/1/1" BuildVersion_UseGlobalSettings="False" BuildVersion_BuildVersioningStyle="None.None.Increment.TimeStamp" BuildVersion_UpdateAssemblyVersion="True" BuildVersion_AssemblyInfoFilename="Properties\AssemblyInfo.cs" />
+      <UserProperties BuildVersion_AssemblyInfoFilename="Properties\AssemblyInfo.cs" BuildVersion_UpdateAssemblyVersion="True" BuildVersion_BuildVersioningStyle="None.None.Increment.TimeStamp" BuildVersion_UseGlobalSettings="False" BuildVersion_StartDate="2000/1/1" />
     </VisualStudio>
   </ProjectExtensions>
 </Project>
\ No newline at end of file
diff --git a/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.UI/Tango.MachineStudio.UI.csproj b/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.UI/Tango.MachineStudio.UI.csproj
index 8d9255926..c94102df5 100644
--- a/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.UI/Tango.MachineStudio.UI.csproj
+++ b/Software/Visual_Studio/MachineStudio/Tango.MachineStudio.UI/Tango.MachineStudio.UI.csproj
@@ -420,6 +420,10 @@
       <Project>{5001990f-977b-48ff-b217-0236a5022ad8}</Project>
       <Name>Tango.Web</Name>
     </ProjectReference>
+    <ProjectReference Include="..\..\TCC\Tango.TCC.BL\Tango.TCC.BL.csproj">
+      <Project>{f209fae8-73f9-441b-97f4-0844a0279390}</Project>
+      <Name>Tango.TCC.BL</Name>
+    </ProjectReference>
     <ProjectReference Include="..\Modules\MachineStudio.Dispensers\Tango.MachineStudio.Dispensers.csproj">
       <Project>{f69da3a8-f823-461e-87cf-a9275abc0b15}</Project>
       <Name>Tango.MachineStudio.Dispensers</Name>
@@ -520,6 +524,14 @@
     <Resource Include="Images\settings.png" />
   </ItemGroup>
   <ItemGroup>
+    <Content Include="..\..\Build\TCC\Debug\Tango.TCC.ColorDetector.dll">
+      <Link>Tango.TCC.ColorDetector.dll</Link>
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </Content>
+    <Content Include="..\..\Build\TCC\Debug\Tango.TCC.ColorDetector.pdb">
+      <Link>Tango.TCC.ColorDetector.pdb</Link>
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </Content>
     <Resource Include="Images\firmware_upgrade.png" />
     <Resource Include="Images\external-bridge-emulator.png" />
     <EmbeddedResource Include="..\..\Versioning\ChangeLog.txt">
diff --git a/Software/Visual_Studio/PPC/Tango.PPC.UI/app.manifest b/Software/Visual_Studio/PPC/Tango.PPC.UI/app.manifest
index d72e75011..efc5f8179 100644
--- a/Software/Visual_Studio/PPC/Tango.PPC.UI/app.manifest
+++ b/Software/Visual_Studio/PPC/Tango.PPC.UI/app.manifest
@@ -16,7 +16,7 @@
             Remove this element if your application requires this virtualization for backwards
             compatibility.
         -->
-        <requestedExecutionLevel level="requireAdministrator" uiAccess="false" />
+        <!--<requestedExecutionLevel level="requireAdministrator" uiAccess="false" />-->
       </requestedPrivileges>
     </security>
   </trustInfo>
diff --git a/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.dll b/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.dll
new file mode 100644
index 000000000..3ae79ac4e
--- /dev/null
+++ b/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.dll
diff --git a/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.xml b/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.xml
new file mode 100644
index 000000000..ba89bf93d
--- /dev/null
+++ b/Software/Visual_Studio/Referenced Assemblies/SMO/Microsoft.SqlServer.AzureStorageEnum.xml
@@ -0,0 +1,238 @@
+<?xml version="1.0"?>
+<doc>
+    <assembly>
+        <name>Microsoft.SqlServer.AzureStorageEnum</name>
+    </assembly>
+    <members>
+        <member name="T:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorage">
+            <summary>
+            This class implements the AzureStore level of the enumerator(root level). This level 
+            represents the Azure Storage server. Urns are this level are of the form: AzureStorage[@Name='accountname'] 
+            The following properties are exposed.
+            Name: The account name associated with this server.
+            Urn: The Urn of this enumerator object.
+            Uri: The base URI of the storage account.
+            </summary>
+        </member>
+        <member name="T:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject">
+            <summary>
+            <para> 
+            This is the base class of all enumerator objects in the Azure Storage hierarchy.
+            This abstract class provides implementations of the abstract entities in EnumObject, 
+            and defines a number of additional abstract entities for subclasses to implement. 
+            In short, subclasses have two responsibilities: contributing to a filter (via 
+            AddFilter) or fulfilling a request for data (via GetDataImpl). Only the final 
+            level of a request will be asked to fetch data. All intermediate levels will, at most, 
+            contribute to a filter. We support filtering on only two dimensions: blobName name and containerName name.
+            </para>
+            <para> 
+            For example, suppose we're given the urn: AzureStorage/Container[@Name='mycontainer']/Blob[@Name='myblob']
+            The will result in three calls to the GetData method in EnumObject, once for each level of the urn
+            (AzureStorage, Container, Blob, in that order). The first call will no nothing, since no filter is
+            given on the first level. The second call will add 'mycontainer' to the containerName filter.
+            The third call will add 'myblob' to the blobName filter, then will issue a request to the storage 
+            server for the attributes of the blobName 'myblob' in containerName 'mycontainer'.
+            </para>
+            <para> 
+            This class also provides some convenience methods and properties.
+            </para>
+            </summary>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.#ctor">
+            <summary>
+            Initializes a new instance of the AzureStorageEnumObject class, and adds properties 
+            common to all storage enumerator objects.
+            </summary>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.GetData(Microsoft.SqlServer.Management.Sdk.Sfc.EnumResult)">
+            <summary>
+            Implementation of the GetData method from EnumObject. The algorithm here is:
+            If we're at the first level of the urn, we construct a new AzureStorageEnumResult object.
+            This object will be passed to all subsequent calls. Next, if we're not at the final level 
+            of the urn, and our level has a filter, we add that filter to the AzureStorageEnumResult
+            object. Lastly, if we're at the final level of the enumerator, we fetch data from the 
+            storage server and package it into a DataTable.
+            </summary>
+            <param name="parent">The result from the parent request.</param>
+            <returns>An EnumResult for the current level. If we're on the final level, the result will contain a DataTable.</returns>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.AddFilter(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult)">
+            <summary>
+            Adds a filter to the given AzureStorageEnumResult.  This is called from GetData when 
+            a level specifies a filter.
+            </summary>
+            <param name="er">The current enumerator result.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.PopulateDataTable(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult,System.Data.DataTable)">
+            <summary>
+            Populates a data table with a response to the current request, based on the 
+            given AzureStorageEnumResult (which keeps track of all the filters added 
+            across all levels).
+            </summary>
+            <param name="er">The current enumerator result.</param>
+            <param name="table">The table to populate.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.AddStringProperty(System.String,Microsoft.SqlServer.Management.Sdk.Sfc.ObjectPropertyUsages)">
+            <summary>
+            Adds a string property with the given usages to this enumerator object. This property
+            will be nonexpensive.
+            </summary>
+            <param name="name">The name of the property.</param>
+            <param name="usages">The usages of the property.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.AddProperty(System.String,System.String,Microsoft.SqlServer.Management.Sdk.Sfc.ObjectPropertyUsages)">
+            <summary>
+            Adds a property with the given type and usages to this enumerator object. This property will 
+            be nonexpensive.
+            </summary>
+            <param name="name">The name of the property.</param>
+            <param name="type">The name of the type of the property (e.g. "System.String")</param>
+            <param name="usages">The usages of the property.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.AddProperty(System.String,System.String,Microsoft.SqlServer.Management.Sdk.Sfc.ObjectPropertyUsages,System.Boolean)">
+            <summary>
+            Adds a property with the given type and usages to this enumerator object.
+            If the expensive parameter is set to true, this property will be marked as expensive.
+            </summary>
+            <param name="name">The name of the property.</param>
+            <param name="type">The name of the type of the property (e.g. "System.String")</param>
+            <param name="usages">The usages of the property.</param>
+            <param name="expensive">Determines wheter this property is marked as expensive or not.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.PrepareDataTable(Microsoft.SqlServer.Management.Sdk.Sfc.Request)">
+            <summary>
+            Creates a data table with columns determined by the fields in the given Request object.
+            </summary>
+            <param name="req">A request to the enumerator.</param>
+            <returns>An data table with zero rows, and a column for each requested field in the given Request object.</returns>
+        </member>
+        <member name="P:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.ResultTypes">
+            <summary>
+            Returns the result types supported by this 
+            </summary>
+        </member>
+        <member name="P:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.StorageConnectionInfo">
+            <summary>
+            Gets the storage connection info passed to this enumerator.
+            </summary>
+        </member>
+        <member name="P:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumObject.BlobClient">
+            <summary>
+            Gets the blobName client associated with the connection info passed to this enumerator.
+            </summary>
+        </member>
+        <member name="F:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorage.UrnSkeleton">
+            <summary>
+            The parameterized Urn for objects at this level.
+            </summary>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorage.AddFilter(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult)">
+            <summary>
+            There is no filter at this level, so this is a NOOP.
+            </summary>
+            <param name="result">The AzureStorageEnumResult object for this enumerator query.</param>
+        </member>
+        <member name="T:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult">
+            <summary>
+            The class is used to build up filters as we travel down the enumerator query.
+            At the last node, we apply any filters in this object to the query we send 
+            to the azure storage server.
+            </summary>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult.#ctor">
+            <summary>
+            Initializes a new instance of the AzureStorageEnumResult class, with empty filters.
+            </summary>
+        </member>
+        <member name="P:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult.ContainerFilters">
+            <summary>
+            Gets a list of container names we want to get data for.
+            </summary>
+        </member>
+        <member name="P:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult.BlobFilters">
+            <summary>
+            Gets a list of blob names we want to get data for.
+            </summary>
+        </member>
+        <member name="T:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Blob">
+            <summary>
+            This class implements the blobName level of the Azure Storage enumerator. This is the 
+            third level of the hierarchy. Urns are this level are of the form: 
+            AzureStorage[@Name='accountname']/Container[@Name='mycontainer']/Blob[@Name='myblob']
+            The following properties are exposed.
+            Name: The containerName name.
+            Urn: The Urn of this enumerator object.
+            Uri: The base URI of the containerName.
+            LastModifiedUtc: The base URI of the storage account.
+            Size: The size of the containerName.
+            </summary>
+        </member>
+        <member name="F:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Blob.UrnSkeleton">
+            <summary>
+            The parameterized Urn for objects at this level.
+            </summary>   
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Blob.AddFilter(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult)">
+            <summary>
+            At this level, we add any blob Name filters to the given AzureStorageEnumResult.
+            </summary>
+            <param name="er">The AzureStorageEnumResult object to which we add the filter.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Blob.PopulateDataTable(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult,System.Data.DataTable)">
+            <summary>
+            At this level, we are fetching blob data, honoring any blob filters and container filters that are present.
+            </summary>
+            <param name="er">The AzureStorageEnumResult for this query.</param>
+            <param name="table">The data table to populate.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Blob.AddRow(System.Data.DataTable,Microsoft.SqlServer.Management.Sdk.Sfc.Request,Microsoft.WindowsAzure.StorageClient.CloudBlob)">
+            <summary>
+            Add a row to the given data table representing the given blob. We only 
+            add the properties specified in the given Request.
+            </summary>
+            <param name="table">The data table to add a row to.</param>
+            <param name="req">A request to this enumerator.</param>
+            <param name="blob">The blob to add to the given table.</param>
+        </member>
+        <member name="T:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Container">
+            <summary>
+            This class implements the containerName level of the Azure Storage enumerator. This is the 
+            second level of the hierarchy. Urns are this level are of the form: AzureStorage[@Name='accountname']/Container[@Name='mycontainer']
+            The following properties are exposed.
+            Name: The containerName name.
+            Urn: The Urn of this enumerator object.
+            Uri: The base URI of the containerName.
+            LastModifiedUtc: The base URI of the storage account.
+            </summary>
+        </member>
+        <member name="F:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Container.UrnSkeleton">
+            <summary>
+            The parameterized Urn for objects at this level.
+            </summary>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Container.AddFilter(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult)">
+            <summary>
+            At this level, we add any container Name filters present to the given AzureStorageEnumResult.
+            </summary>
+            <param name="er">The AzureStorageEnumResult for this query.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Container.PopulateDataTable(Microsoft.SqlServer.Management.Smo.AzureStorageEnum.AzureStorageEnumResult,System.Data.DataTable)">
+            <summary>
+            Retrieve data for this request. At this level, we are fetching containerName data.
+            We must honor any containerName filters present. If there is no filter, we fetch 
+            data for all containers.
+            </summary>
+            <param name="er">The AzureStorageEnumResult for this query.</param>
+            <param name="table">The data table to populate.</param>
+        </member>
+        <member name="M:Microsoft.SqlServer.Management.Smo.AzureStorageEnum.Container.AddRow(System.Data.DataTable,Microsoft.SqlServer.Management.Sdk.Sfc.Request,Microsoft.WindowsAzure.StorageClient.CloudBlobContainer)">
+            <summary>
+            Add a row to the given data table representing the given container. We only 
+            add the properties specified in the given Request.
+            </summary>
+            <param name="table">The data table to add a row to.</param>
+            <param name="req">A request to this enumerator.</param>
+            <param name="container">The container to add to the given table.</param>
+        </member>
+    </members>
+</doc>
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.BL/Tango.TCC.BL.csproj b/Software/Visual_Studio/TCC/Tango.TCC.BL/Tango.TCC.BL.csproj
index 7d677edc3..6b973e4f8 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.BL/Tango.TCC.BL.csproj
+++ b/Software/Visual_Studio/TCC/Tango.TCC.BL/Tango.TCC.BL.csproj
@@ -75,6 +75,10 @@
     <Compile Include="Web\LoginResponse.cs" />
   </ItemGroup>
   <ItemGroup>
+    <Content Include="..\..\Build\TCC\Debug\Tango.TCC.ColorDetector.pdb">
+      <Link>Tango.TCC.ColorDetector.pdb</Link>
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </Content>
     <Content Include="..\Benchmarks\benchmarks_rgb_lab.csv">
       <Link>TCC\benchmarks_rgb_lab.csv</Link>
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
@@ -115,7 +119,7 @@
   <ItemGroup>
     <Content Include="..\..\Build\TCC\Debug\Tango.TCC.ColorDetector.dll">
       <Link>Tango.TCC.ColorDetector.dll</Link>
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
     </Content>
     <Content Include="..\..\Build\TCC\Debug\Tango.TCC.LoadTestLib.dll">
       <Link>Tango.TCC.LoadTestLib.dll</Link>
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.cpp
index 2f162087a..c5830dbd7 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.cpp
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.cpp
@@ -53,6 +53,13 @@ ColorConvert::ColorConvert(Illum D, C_RGB_XYZ_Lab SourceWhite)
 	m_SourceWhite = SourceWhite;
 	CAT_BradfordMat();
 }
+ColorConvert::ColorConvert(C_RGB_XYZ_Lab DestWhite, C_RGB_XYZ_Lab SourceWhite)
+{
+	m_ReferenceWhite.Set(DestWhite.Get_x(), DestWhite.Get_y(), DestWhite.Get_z());
+	m_InvReferenceWhite.Set((1.0 / m_ReferenceWhite.Get_x()), (1.0 / m_ReferenceWhite.Get_y()), (1.0 / m_ReferenceWhite.Get_z()));
+	m_SourceWhite = SourceWhite;
+	CAT_BradfordMat();
+}
 
 ColorConvert::ColorConvert(Illum DDest, Illum DSource)
 {
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.h
index 1529982ec..dae9c09a3 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.h
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorConvert.h
@@ -18,6 +18,7 @@ class ColorConvert
 
 		ColorConvert (void);
 		ColorConvert (Illum D, C_RGB_XYZ_Lab SourceWhite);
+		ColorConvert(C_RGB_XYZ_Lab DestWhite, C_RGB_XYZ_Lab SourceWhite);
 		ColorConvert(Illum DDest, Illum DSource);
 		ColorConvert (const ColorConvert &rhs);
 		ColorConvert &operator = (const ColorConvert &rhs);
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.cpp
index fe92c0f4c..1df7a14df 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.cpp
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.cpp
@@ -9,74 +9,12 @@
 #include "bitmap_image.hpp"
 #include "Utils.h"
 #include  <stdio.h>
-
-//-----------------------------FILE READING--------------------------------------------------------------
-///FOR FILE READING
 #include <fstream>
 #include <iostream>
 #include <sstream>
 #include <string>
 #include <vector>
-using namespace std;
-using namespace cv;
-
-typedef vector <double> record_t;
-typedef vector <record_t> data_t;
-//-----------------------------------------------------------------------------
-// Let's overload the stream input operator to read a list of CSV fields (which a CSV record).
-// Remember, a record is a list of doubles separated by commas ','.
-istream& operator >> (istream& ins, record_t& record)
-{
-	// make sure that the returned record contains only the stuff we read now
-	record.clear();
-
-	// read the entire line into a string (a CSV record is terminated by a newline)
-	string line;
-	getline(ins, line);
-
-	// now we'll use a stringstream to separate the fields out of the line
-	stringstream ss(line);
-	string field;
-	while (getline(ss, field, ','))
-	{
-		// for each field we wish to convert it to a double
-		// (since we require that the CSV contains nothing but floating-point values)
-		stringstream fs(field);
-		double f = 0.0;  // (default value is 0.0)
-		fs >> f;
-
-		// add the newly-converted field to the end of the record
-		record.push_back(f);
-	}
-
-	// Now we have read a single line, converted into a list of fields, converted the fields
-	// from strings to doubles, and stored the results in the argument record, so
-	// we just return the argument stream as required for this kind of input overload function.
-	return ins;
-}
-
-//-----------------------------------------------------------------------------
-// Let's likewise overload the stream input operator to read a list of CSV records.
-// This time it is a little easier, just because we only need to worry about reading
-// records, and not fields.
-istream& operator >> (istream& ins, data_t& data)
-{
-	// make sure that the returned data only contains the CSV data we read here
-	data.clear();
-
-	// For every record we can read from the file, append it to our resulting data
-	record_t record;
-	while (ins >> record)
-	{
-		data.push_back(record);
-	}
-
-	// Again, return the argument stream as required for this kind of input stream overload.
-	return ins;
-}
-
-
-//-----------------------------FILE READING--------------------------------------------------------------
+#include "ColorDetectionMirta.h"
 
 ColorDetection::ColorDetection()
 {
@@ -87,284 +25,8 @@ ColorDetection::~ColorDetection()
 
 }
 
-#define RAND_BYTE (rand() % 256)
-
-
-bool ColorDetection::DetectColor(
-	int caliberationTargetVersion, //Version of the sample target version (i.e the colored box)
-	int appVersion, // cellular version 
-	const std::vector<COLORREF>& caliberationColors,
-	COLORREF sampleColor,
-	int& outAlgoVersion,
-	COLORREF& outDetectedColor,
-	std::string& outErrorString)
-{
-	data_t data;                //Data that we receive from the mobile phone (caliberationColors) is a list of colors (9X9 i.e. 81 points)
-	data_t benchmarkdata;       //this is the benchmark data set in other words the comparing table or LUT, this is a single non changing color matrix
-	data_t benchmarkdataLab;       //Lab data of benchmark data based on measurements of the printed color chart
-
-	//BENCHMARK DATASET--------------------------------------------------------------------------
-	//Read the color benchmark it into data - this data should be exactly the data on the color chart
-	//this should be a one time operation, 
-	//taking raw data in RGB from the image - this is a single non changing data 
-
-	outErrorString = "Started Detection...";
-
-	ifstream Benchmarkfile("D:\\home\\site\\wwwroot\\bin\\BENCHMARK.csv");
-	Benchmarkfile >> benchmarkdata;
-
-	if (!Benchmarkfile.eof()) {
-		cout << "Benchmark CSV file not found!\n";
-		outErrorString = "Benchmark CSV file not found!\n";
-		outDetectedColor = RGB(0, 0, 0);
-		return false;
-	}
-	Benchmarkfile.close();
-	//Benchmark date is completely filled
-	//--------------------------------------------------------------------------
-	int numberofrecords = benchmarkdata.size(); //i.e. 80 records in our case
-
-	//Read BenchMarkDataLab
-	ifstream BenchmarkfileLab("D:\\home\\site\\wwwroot\\bin\\BenchMarkLab.csv");
-	BenchmarkfileLab >> benchmarkdataLab;
-
-
-	if (!BenchmarkfileLab.eof())
-	{
-		cout << "Benchmark data Lab CSV file not found!\n";
-		outErrorString = "Benchmark data Lab CSV file not found!\n";
-		outDetectedColor = RGB(2, 2, 2);
-		return false;
-	}
-	BenchmarkfileLab.close();
-	//BenchmarkfileLab is completely filled
-	//--------------------------------------------------------------------------
-
-	unsigned columnsbenchmark = 0;
-	unsigned columnsdata = 0;
-	for (unsigned n = 0; n < numberofrecords; n++)
-		if (columnsbenchmark < benchmarkdata[n].size())
-			columnsbenchmark = benchmarkdata[n].size();
-	//read sampled data
-	for (unsigned n = 0; n < numberofrecords; n++) //110 records
-	{
-		record_t record;
-		record.push_back(GetBValue(caliberationColors[n]));
-		record.push_back(GetGValue(caliberationColors[n]));
-		record.push_back(GetRValue(caliberationColors[n]));
-		data.push_back(record);
-	}
-
-	for (unsigned n = 0; n < numberofrecords; n++) //RGB of what is read
-		if (columnsdata < data[n].size())
-			columnsdata = data[n].size();
-
-
-	outErrorString = "Passed read sampled data...";
-
-	//--------------------------------------------------------------------------
-	//We have now both the data and the benchmark and benchmarkdataLab in their appropriate formats
-
-	//Eliminate fiducials from list,
-	//fiducials are placed at 0, 8, 72
-	//Sampled data is placed at 80
-	//int fid_index[4] = { 0, 8, 72, 80 };
-	int fid_index[5] = { 0, 10, 99,101, 109 };
-	int i_nonpInd = sizeof(fid_index) / sizeof(int);
-	//calculate distance to gray values matrix
-	int BMcols = 11;
-	int BMrows = 10;
-	if (numberofrecords != BMcols * BMrows)
-		return(false);
-	int m_interpPoints = 10;
-	MatrixXi m_dist2grayInd(numberofrecords - i_nonpInd, m_interpPoints);
-	VectorXi m_dist2GrayIndSample(m_interpPoints);
-
-	DistanceMatrix(BMrows, BMcols, fid_index, i_nonpInd, m_dist2grayInd, m_dist2GrayIndSample);
-	//
-	/*FILE *fp;
-	fopen_s(&fp, "C:\\Mirta\\DistMat.txt", "w");
-	for (int i = 0; i < m_dist2grayInd.rows(); ++i)
-	{
-		fprintf(fp, "%d\t", i);
-		for (int j = 0; j < m_dist2grayInd.cols(); ++j)
-			fprintf(fp, "%d\t", m_dist2grayInd(i, j));
-		fprintf(fp, "\n");
-	}
-	fprintf(fp, "s\t");
-	for (int j = 0; j < m_dist2grayInd.cols(); ++j)
-		fprintf(fp, "%d\t", m_dist2GrayIndSample(j));
-	fprintf(fp, "\n");
-	fclose(fp);  */
-	//
-	MatrixXd GroundTruthData(numberofrecords - i_nonpInd, columnsbenchmark);
-	MatrixXd GroundTruthDataLab(numberofrecords - i_nonpInd, columnsbenchmark);
-	MatrixXd CamData(numberofrecords - i_nonpInd, columnsbenchmark);
-	VectorXd SampleColor(columnsbenchmark);
-	int ind = -1;
-	for (int i = 0; i < numberofrecords; ++i)
-	{
-		if (i != fid_index[0] && i != fid_index[1] && i != fid_index[2] && i != fid_index[3] && i != fid_index[4])
-		{
-			ind++;
-			for (int j = 0; j < (int)columnsbenchmark; ++j)
-			{
-				//distance Matrix is already cleaned of fiducials and Sample
-				GroundTruthData(ind, j) = benchmarkdata[i][j];
-				GroundTruthDataLab(ind, j) = benchmarkdataLab[i][j];
-				CamData(ind, j) = data[i][j];
-			}
-		}
-	}
-	//
-/*	FILE *fp1;
-	fopen_s(&fp1, "C:\\Mirta\\GTData.txt", "w");
-	FILE *fp2;
-	fopen_s(&fp2, "C:\\Mirta\\GTLabData.txt", "w");
-	FILE *fp3;
-	fopen_s(&fp3, "C:\\Mirta\\CamData.txt", "w");
-	for (int i = 0; i < GroundTruthData.rows(); ++i)
-	{
-		fprintf(fp1, "%d\t", i);
-		fprintf(fp2, "%d\t", i);
-		fprintf(fp3, "%d\t", i);
-		for (int j = 0; j < GroundTruthData.cols(); ++j)
-		{
-			fprintf(fp1, "%f\t", GroundTruthData(i, j));
-			fprintf(fp2, "%f\t", GroundTruthDataLab(i, j));
-			fprintf(fp3, "%f\t", CamData(i, j));
-		}
-		fprintf(fp1, "\n");
-		fprintf(fp2, "\n");
-		fprintf(fp3, "\n");
-	} */
-	//	
-	for (int i = 0; i < (int)columnsbenchmark; ++i)
-		SampleColor(i) = data[fid_index[3]][i];
-	//
-	/*	fprintf(fp3, "s\t");
-		for (int j = 0; j < GroundTruthData.cols(); ++j)
-			fprintf(fp3, "%f\t", SampleColor(j));
-		fprintf(fp3, "\n");
-		fclose(fp1);
-		fclose(fp2);
-		fclose(fp3); */
-		//
-	Detect DetectSampledColor;
-	char *outErrorMsg = new char[1024];
-	int retVal = DetectSampledColor.Init(GroundTruthData, GroundTruthDataLab, CamData, SampleColor,
-		m_dist2grayInd, m_dist2GrayIndSample, outErrorMsg);
-
-	if (retVal < 0)
-	{
-		printf("Failed to initialize data\n");
-		outDetectedColor = RGB(3, 3, 3);
-		return false;
-	}
-	VectorXd outResult(3);
-	VectorXd outResultLab(3);
-
-	int ret = DetectSampledColor.MatchSampleColor(outResult, outResultLab, outErrorMsg);
-
-	outDetectedColor = RGB(BYTE(round(outResult(0))), BYTE(round(outResult(1))), BYTE(round(outResult(2))));
-	//	outDetectedColor = RGB(4, 4, 4);
-		//Calculate rSquare and RMSE
-	double **RGBEst = new double*[numberofrecords - i_nonpInd];
-	double **LabEst = new double*[numberofrecords - i_nonpInd];
-	double **RespVec = new double*[numberofrecords - i_nonpInd];
-	double **RespVecLab = new double*[numberofrecords - i_nonpInd];
-	double SumME[3] = { 0.0, 0.0, 0.0 };
-	double MeanBM[3] = { 0.0, 0.0, 0.0 };
-	double SumSQBM[3] = { 0.0, 0.0, 0.0 };
-	double SumMELab[3] = { 0.0, 0.0, 0.0 };
-	double MeanBMLab[3] = { 0.0, 0.0, 0.0 };
-	double SumSQBMLab[3] = { 0.0, 0.0, 0.0 };
-	//FILE *fp;
-	//fp = fopen("C:\\Mirta\\Rsq.txt", "w");
-	//fopen_s(&fp, "C:\\Mirta\\Rsq.txt", "w");
-	MatrixXd RespVecLabM(numberofrecords, 3);
-	RespVecLabM = DetectSampledColor.GetBenchMarkDataLabRel();
-	int ndist = m_dist2GrayIndSample.size();
-	for (int i = 0; i < numberofrecords - i_nonpInd; ++i)
-	{
-		RGBEst[i] = new double[3];
-		LabEst[i] = new double[3];
-		RespVec[i] = new double[3];
-		RespVecLab[i] = new double[3];
-		for (int j = 0; j < (int)columnsbenchmark; ++j)
-		{
-			SampleColor(j) = CamData(i, j);
-			RespVec[i][j] = GroundTruthData(i, j);
-			RespVecLab[i][j] = RespVecLabM(i, j);
-		}
-		for (int j = 0; j < ndist; ++j)
-			m_dist2GrayIndSample(j) = m_dist2grayInd(i, j);
-
-		retVal = DetectSampledColor.Init(GroundTruthData, GroundTruthDataLab, CamData, SampleColor,
-			m_dist2grayInd, m_dist2GrayIndSample, outErrorMsg);
-		ret = DetectSampledColor.MatchSampleColor(outResult, outResultLab, outErrorMsg);
-		for (int j = 0; j < 3; ++j)
-		{
-			RGBEst[i][j] = outResult(j);
-			LabEst[i][j] = outResultLab(j);
-			SumME[j] += (RGBEst[i][j] - RespVec[i][j])*(RGBEst[i][j] - RespVec[i][j]);
-			SumMELab[j] += (LabEst[i][j] - RespVecLab[i][j])*(LabEst[i][j] - RespVecLab[i][j]);
-			MeanBM[j] += RespVec[i][j];
-			MeanBMLab[j] += RespVecLab[i][j];
-			SumSQBM[j] += RespVec[i][j] * RespVec[i][j];
-			SumSQBMLab[j] += RespVecLab[i][j] * RespVecLab[i][j];
-		}
-		//fprintf(fp, "%f\t%f\t%f\t", RGBEst[i][0], RGBEst[i][1], RGBEst[i][2]);
-		//fprintf(fp, "%f\t%f\t%f\t", LabEst[i][0], LabEst[i][1], LabEst[i][2]);
-		//fprintf(fp, "%f\t%f\t%f\t", RespVec[i][0], RespVec[i][1], RespVec[i][2]);
-		//fprintf(fp, "%f\t%f\t%f\n", RespVecLab[i][0], RespVecLab[i][1], RespVecLab[i][2]);
-	}
-	for (int j = 0; j < 3; ++j)
-	{
-		MeanBM[j] /= (numberofrecords - i_nonpInd);
-		MeanBMLab[j] /= (numberofrecords - i_nonpInd);
-	}
-
-	double RSquare = 0.0;
-	double RSquareLab = 0.0;
-	double SumTop = SumME[0] + SumME[1] + SumME[2];
-	double SumTopLab = SumMELab[0] + SumMELab[1] + SumMELab[2];
-	double SumBot1 = SumSQBM[0] + SumSQBM[1] + SumSQBM[2];
-	double SumBot1Lab = SumSQBMLab[0] + SumSQBMLab[1] + SumSQBMLab[2];
-	double SumBot2 = (numberofrecords - i_nonpInd)*(MeanBM[0] * MeanBM[0] + MeanBM[1] * MeanBM[1] + MeanBM[2] * MeanBM[2]);
-	double SumBot2Lab = (numberofrecords - i_nonpInd)*(MeanBMLab[0] * MeanBMLab[0] + MeanBMLab[1] * MeanBMLab[1] + MeanBMLab[2] * MeanBMLab[2]);
-	RSquare = 1 - (SumTop / (SumBot1 - SumBot2));
-	RSquareLab = 1 - (SumTopLab / (SumBot1Lab - SumBot2Lab));
-	if (RSquare < 0)
-		RSquare = 0.0;
-	if (RSquare > 1)
-		RSquare = 1.0;
-	double rmse = sqrt(SumTop / (numberofrecords - i_nonpInd));
-	double rmseLab = sqrt(SumTopLab / (numberofrecords - i_nonpInd));
-	//fprintf(fp, "%f\t%f\t%f\t%f\n", RSquare, RSquareLab, rmse, rmseLab);
-	for (int i = 0; i < (numberofrecords - i_nonpInd); ++i)
-	{
-		delete RGBEst[i];
-		delete LabEst[i];
-		delete RespVec[i];
-		delete RespVecLab[i];
-	}
-
-	delete[] RGBEst;
-	delete[] LabEst;
-	delete[] RespVec;
-	delete[] RespVecLab;
-	delete[] outErrorMsg;
-	//fclose(fp);
-
-	outAlgoVersion = 4; //Jan 9, 2018
-
-	outErrorString = "Done";
-
-	return true;
-}
-
 size_t ColorDetection::DetectColorNew(uint8_t * input_buffer, size_t input_buffer_size, uint8_t *& output_buffer)
+//includes  CallDettectColor (old version)
 {
 	try
 	{
@@ -464,14 +126,19 @@ size_t ColorDetection::DetectColorNew(uint8_t * input_buffer, size_t input_buffe
 		}
 		 
 		//Put original target color.
-		detectionOutput->rawcolor = initDetectionColor(target_mean.val[2], target_mean.val[1], target_mean.val[0]);
+		detectionOutput->rawcolor = initDetectionColor((int)target_mean.val[2], (int)target_mean.val[1], (int)target_mean.val[0]);
 
 		//Get benchmarks...
 		detectionInput->benchmarks[0]->red;
 		detectionInput->benchmarks[0]->l;
 
 		//Put processed target color.
-		detectionOutput->processedcolor = initDetectionColor(target_mean.val[2] + 10, target_mean.val[1] + 10, target_mean.val[0] + 10);
+
+		ColorDetectionMirta mirtaDetection;
+		//ColorDetectionMirta was ColorDetection
+		// Detect was DetectColor
+		DetectionColor* result = mirtaDetection.CDetect(detectionInput->benchmarks, means, detectionInput->columns, detectionInput->rows, detectionInput->targetindex);
+		detectionOutput->processedcolor = result;
 
 		detectionOutput->has_number = true;
 		detectionOutput->number = detectionInput->number + 10;
@@ -549,118 +216,3 @@ DetectionColor* ColorDetection::initDetectionColor(int r, int g, int b)
 	return color;
 }
 
-void ColorDetection::DistanceMatrix(int rows, int cols, int *nonpInd, int i_nonpInd, MatrixXi &m_dist2grayInd, VectorXi &m_dist2GrayIndSample)
-{
-	int i, j, k, l;
-	int indk1, indk2;
-	int totSize = cols * rows;
-	MatrixXi aa(rows, cols);
-	for (int i = 0; i < rows; i++)
-		for (j = 0; j < cols; j++)
-			aa(i, j) = i * cols + j;
-
-	//	int i_nonpInd = sizeof(nonpInd);
-	MatrixXd dist2gray(totSize, totSize);
-	dist2gray.setOnes();
-	dist2gray = -dist2gray;
-
-	//grays are positioned at card borders
-	for (i = 0; i < rows; i++)
-		for (j = 0; j < cols; j++)
-		{
-			for (k = 0; k < rows; k++)
-			{
-				indk1 = aa(k, 0);
-				indk2 = aa(k, cols - 1);
-				dist2gray(aa(i, j), indk1) = sqrt((i - k)*(i - k) + (j*j));
-				dist2gray(aa(i, j), indk2) = sqrt((i - k)*(i - k) + (j - (cols - 1))*(j - (cols - 1)));
-			}
-			for (l = 0; l < cols; l++)
-			{
-				indk1 = aa(0, l);
-				indk2 = aa(rows - 1, l);
-				dist2gray(aa(i, j), indk1) = sqrt((i*i) + (j - l)*(j - l));
-				dist2gray(aa(i, j), indk2) = sqrt((i - (rows - 1))*(i - (rows - 1)) + (j - l)*(j - l));
-			}
-		}
-
-	//Eliminate corners and Target index
-	VectorXd atmp(totSize);
-	VectorXd btmp(totSize);
-
-	VectorXi atmpInd(totSize);
-	VectorXi btmpInd(totSize);
-	VectorXi ctmpInd(totSize);
-	VectorXi indb(totSize);
-	int tmpInd = 0;
-	int nElimFid = 0;
-	VectorXi PtType(totSize);
-	PtType.setZero();
-	for (i = 0; i < i_nonpInd; ++i)
-		PtType(nonpInd[i]) = 1;
-	PtType(nonpInd[3]) = 2;
-	int PtInd = -1;
-	std::vector<VectorPair> IndVal;
-	for (i = 0; i < totSize; ++i)
-	{
-		if (PtType(i) == 0 || PtType(i) == 2)
-		{
-			if (PtType(i) == 0)
-				PtInd += 1;
-			tmpInd = -1;
-			for (j = 0; j < totSize; ++j)
-			{
-				if (dist2gray(i, j) > -1)
-				{
-					tmpInd++;
-					atmp(tmpInd) = dist2gray(i, j);
-					atmpInd(tmpInd) = j;
-				}
-			}
-			nElimFid = -1;
-			//eliminate fiducials and target, store in btmp
-			for (j = 0; j < tmpInd + 1; ++j)
-			{
-				if (atmpInd(j) >= 0 && PtType(atmpInd(j)) == 0)
-				{
-					nElimFid++;
-					btmp(nElimFid) = atmp(j);
-					btmpInd(nElimFid) = atmpInd(j);
-				}
-			}
-
-			if (nElimFid > -1)
-			{
-				// shift indices to compensate for corners and target
-				IndVal.resize(nElimFid + 1);
-				for (int j = 0; j < nElimFid + 1; ++j)
-				{
-					IndVal[j].m_ID = j;
-					IndVal[j].m_val = btmp(j);
-				}
-				std::stable_sort(IndVal.begin(), IndVal.end(), ByDouble());
-				//keep the first 10 indices
-				for (j = 0; j < 10; ++j)
-				{
-					ctmpInd(j) = btmpInd(IndVal[j].m_ID);
-					if (ctmpInd(j) >= nonpInd[0] && ctmpInd(j) < nonpInd[1])
-						ctmpInd(j) = ctmpInd[j] - 1;
-					else if (ctmpInd(j) >= nonpInd[1] && ctmpInd(j) < nonpInd[2])
-						ctmpInd(j) = ctmpInd(j) - 2;
-					else if (ctmpInd(j) >= nonpInd[2] && ctmpInd(j) < nonpInd[3])
-						ctmpInd[j] = ctmpInd[j] - 3;
-					else if (ctmpInd(j) >= nonpInd[3] && ctmpInd(j) < nonpInd[4])
-						ctmpInd(j) = ctmpInd(j) - 4;
-					else
-						ctmpInd(j) = ctmpInd(j) - 5;
-					if (PtType(i) == 0)
-						m_dist2grayInd(PtInd, j) = ctmpInd(j);
-					else
-						m_dist2GrayIndSample(j) = ctmpInd(j);
-				}
-			}
-
-		}
-	}
-}
-
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.h
index e9d4c2da7..56b008a32 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.h
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetection.h
@@ -1,26 +1,12 @@
 #pragma once
 
-#include "Windows.h"
 #include "DetectionColor.pb-c.h"
 #include <string>
 #include <vector>
-#include <Eigen/Dense>
-using Eigen::MatrixXd;
-using Eigen::VectorXd;
-using Eigen::MatrixXi;
-using Eigen::VectorXi;
-#include "ConsolidateData.h"
 
-#ifdef __ColorDetectionDll__
-#define ColorDetectionApi __declspec(dllexport)
-#else
-#define ColorDetectionApi __declspec(dllimport)
-#endif
-
-class ColorDetectionApi ColorDetection
+class ColorDetection
 {
 private:
-	void DistanceMatrix(int rows, int cols, int *nonpInd, int i_nonpInd, MatrixXi &dist2grayInd, VectorXi &dist2GrayIndSample);
 	bool isRectFiducial(int index, int columns, int rows);
 	DetectionColor* initDetectionColor(int r, int g, int b);
 	int m_totSize;
@@ -28,15 +14,6 @@ private:
 public:
 	ColorDetection();
 	~ColorDetection();
-	bool DetectColor(
-		int caliberationTargetVersion,
-		int appVersion,
-		const std::vector<COLORREF>& caliberationColors,
-		COLORREF sampleColor,
-		int& outAlgoVersion,
-		COLORREF& outDetectedColor,
-		std::string& outErrorString);
-
 	size_t DetectColorNew(uint8_t * input_buffer, size_t input_buffer_size, uint8_t *& output_buffer);
 };
 
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionAlgorithm.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionAlgorithm.cpp
deleted file mode 100644
index 2729cd910..000000000
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionAlgorithm.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// ColorDetectionAlgorithm.cpp : Defines the entry point for the console application.
-//
-
-#include "stdafx.h"
-#include "ColorDetection.h"
-
-
-extern "C" bool __declspec(dllexport) __cdecl CallDetectColor(int targetVersion, int appVersion,
-	int calibrationCount, int calibration[], int sampleColor,
-	int& algoVersion, int& detectedColorI, int errorMessageSize, wchar_t* errorMessage)
-{
-	std::vector<COLORREF> caliberationSamples;
-	caliberationSamples.resize(calibrationCount);
-	for (int i = 0; i < calibrationCount; ++i) caliberationSamples[i] = calibration[i];
-	COLORREF detectedColor = 0;
-	std::string errorString;
-
-	ColorDetection detectionAlgorithm;
-	auto result = detectionAlgorithm.DetectColor(targetVersion, appVersion, caliberationSamples, sampleColor, algoVersion, detectedColor, errorString);
-	
-	detectedColorI = detectedColor;
-	ZeroMemory(errorMessage, errorMessageSize*sizeof(wchar_t));
-	MultiByteToWideChar(CP_UTF8, 0, errorString.c_str(), -1, errorMessage, errorMessageSize-1);
-	return result;
-}
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.cpp
new file mode 100644
index 000000000..159935276
--- /dev/null
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.cpp
@@ -0,0 +1,334 @@
+#include "ColorDetectionMirta.h"
+#include "ColorDetection.h"
+#include "Detect.h"
+
+ColorDetectionMirta::ColorDetectionMirta()
+{
+}
+
+
+ColorDetectionMirta::~ColorDetectionMirta()
+{
+}
+
+DetectionColor* ColorDetectionMirta::CDetect(DetectionBenchmark** benchmarks, std::vector<cv::Scalar> means, int nCols, int nRows, int targetIndex)
+{
+	//insert Data
+	MatrixXd CalibrationSamples(nCols*nRows, 3);
+	MatrixXi  Benchmark_RGB(nCols*nRows, 3);
+	MatrixXd  Benchmark_Lab(nCols*nRows, 3);
+	int nTot = nCols * nRows;
+	//means are BGR, have to be inverted
+	for (int i = 0; i < nTot; ++i)
+	{
+		for (int j = 0; j < 3; ++j)
+			CalibrationSamples(i, j) = means[i][2-j];
+		Benchmark_RGB(i, 0) = benchmarks[i]->red;
+		Benchmark_RGB(i, 1) = benchmarks[i]->green;
+		Benchmark_RGB(i, 2) = benchmarks[i]->blue;
+		Benchmark_Lab(i, 0) = benchmarks[i]->l;
+		Benchmark_Lab(i, 1) = benchmarks[i]->a;
+		Benchmark_Lab(i, 2) = benchmarks[i]->b;
+	}
+	//VectorXd SampleColor(3);
+	VectorXd DetectedColor(3);
+	VectorXd SampleColor(3);
+	for (int j = 0; j < 3; ++j)
+		SampleColor(j) = CalibrationSamples(targetIndex, j);
+	//processing goes here
+
+	//	calculate distance to gray values matrix
+
+	m_interpPoints = 7;
+	MatrixXi m_dist2grayInd(nTot - 5, m_interpPoints);
+	VectorXi m_dist2GrayIndSample(m_interpPoints);
+	//find gray elements' placement on both cards, includes fiducials position
+	int nGrays = 2 * ((nCols - 2) + nRows);
+	VectorXi grayInd(nGrays);
+	int indG = -1;
+	for (int i = 0; i < nTot; ++i)
+	{
+		if ((Benchmark_RGB(i, 0) == Benchmark_RGB(i, 1)) & (Benchmark_RGB(i, 1) == Benchmark_RGB(i, 2)))
+		{
+			indG++;
+			grayInd(indG) = i;
+		}
+	}
+
+	DistanceMatrix(nRows, nCols, m_dist2grayInd, m_dist2GrayIndSample, grayInd, targetIndex);
+
+	int BMCols = Benchmark_RGB.cols();
+	MatrixXd GroundTruthData(nTot - 5, BMCols);
+	MatrixXd GroundTruthDataLab(nTot - 5, BMCols);
+	MatrixXd CamData(nTot - 5, BMCols);
+
+	int ind = -1;
+	int fidpos[] = { 0, nCols - 1, (nRows - 1)*nCols, nRows*nCols - 1 };
+	for (int i = 0; i < nTot; ++i)
+	{
+		if (i != fidpos[0] && i != fidpos[1] && i != fidpos[2] && i != fidpos[3] && i != targetIndex)
+		{
+			ind++;
+			for (int j = 0; j < (int)BMCols; ++j)
+			{
+				//distance Matrix is already cleaned of fiducials and Sample
+				GroundTruthData(ind, j) = Benchmark_RGB(i, j);
+				GroundTruthDataLab(ind, j) = Benchmark_Lab(i, j);
+				CamData(ind, j) = CalibrationSamples(i, j);
+			}
+		}
+	}
+
+	for (int i = 0; i < (int)BMCols; ++i)
+		SampleColor(i) = CalibrationSamples(targetIndex, i);
+
+	Detect DetectSampledColor;
+	char *outErrorMsg = new char[1024];
+	int retVal = DetectSampledColor.Init(GroundTruthData, GroundTruthDataLab, CamData, SampleColor,
+		m_dist2grayInd, m_dist2GrayIndSample, outErrorMsg);
+	DetectionColor *retColor;
+	if (retVal < 0)
+	{
+		printf("Failed to initialize data\n");
+		retColor = initDetectionColor(3, 3, 3);
+		return false;
+	}
+	VectorXd outResult(3);
+	VectorXd outResultLab(3);
+
+	int ret = DetectSampledColor.MatchSampleColor(outResult, outResultLab, outErrorMsg);
+	//This is the output
+	retColor = initDetectionColor(BYTE(round(outResult(0))), BYTE(round(outResult(1))), BYTE(round(outResult(2))));
+
+	//Calculate rSquare and RMSE - while debugging only
+#ifdef DEBUG
+	double **RGBEst = new double*[nTot -5];
+	double **LabEst = new double*[nTot - 5];
+	double **RespVec = new double*[nTot -5];
+	double **RespVecLab = new double*[nTot - 5];
+	double SumME[3] = { 0.0, 0.0, 0.0 };
+	double MeanBM[3] = { 0.0, 0.0, 0.0 };
+	double SumSQBM[3] = { 0.0, 0.0, 0.0 };
+	double SumMELab[3] = { 0.0, 0.0, 0.0 };
+	double MeanBMLab[3] = { 0.0, 0.0, 0.0 };
+	double SumSQBMLab[3] = { 0.0, 0.0, 0.0 };
+	FILE *fp;
+	fopen_s(&fp, "C:\\Mirta\\Rsq.txt", "w");
+	MatrixXd RespVecLabM(nTot, 3);
+	RespVecLabM = DetectSampledColor.GetBenchMarkDataLabRel();
+	int ndist = m_dist2GrayIndSample.size();
+	for (int i = 0; i < nTot - 5; ++i)
+	{
+		RGBEst[i] = new double[3];
+		LabEst[i] = new double[3];
+		RespVec[i] = new double[3];
+		RespVecLab[i] = new double[3];
+		for (int j = 0; j < (int)3; ++j)
+		{
+			SampleColor(j) = CamData(i, j);
+			RespVec[i][j] = GroundTruthData(i, j);
+			RespVecLab[i][j] = RespVecLabM(i, j);
+		}
+		for (int j = 0; j < ndist; ++j)
+			m_dist2GrayIndSample(j) = m_dist2grayInd(i, j);
+
+		retVal = DetectSampledColor.Init(GroundTruthData, GroundTruthDataLab, CamData, SampleColor,
+			m_dist2grayInd, m_dist2GrayIndSample, outErrorMsg);
+		ret = DetectSampledColor.MatchSampleColor(outResult, outResultLab, outErrorMsg);
+		for (int j = 0; j < 3; ++j)
+		{
+			RGBEst[i][j] = outResult(j);
+			LabEst[i][j] = outResultLab(j);
+			SumME[j] += (RGBEst[i][j] - RespVec[i][j])*(RGBEst[i][j] - RespVec[i][j]);
+			SumMELab[j] += (LabEst[i][j] - RespVecLab[i][j])*(LabEst[i][j] - RespVecLab[i][j]);
+			MeanBM[j] += RespVec[i][j];
+			MeanBMLab[j] += RespVecLab[i][j];
+			SumSQBM[j] += RespVec[i][j] * RespVec[i][j];
+			SumSQBMLab[j] += RespVecLab[i][j] * RespVecLab[i][j];
+		}
+		fprintf(fp, "%f\t%f\t%f\t", RGBEst[i][0], RGBEst[i][1], RGBEst[i][2]);
+		fprintf(fp, "%f\t%f\t%f\t", LabEst[i][0], LabEst[i][1], LabEst[i][2]);
+		fprintf(fp, "%f\t%f\t%f\t", RespVec[i][0], RespVec[i][1], RespVec[i][2]);
+		fprintf(fp, "%f\t%f\t%f\n", RespVecLab[i][0], RespVecLab[i][1], RespVecLab[i][2]);
+	}
+	for (int j = 0; j < 3; ++j)
+	{
+		MeanBM[j] /= (numberofrecords - i_nonpInd);
+		MeanBMLab[j] /= (numberofrecords - i_nonpInd);
+	}
+
+	double RSquare = 0.0;
+	double RSquareLab = 0.0;
+	double SumTop = SumME[0] + SumME[1] + SumME[2];
+	double SumTopLab = SumMELab[0] + SumMELab[1] + SumMELab[2];
+	double SumBot1 = SumSQBM[0] + SumSQBM[1] + SumSQBM[2];
+	double SumBot1Lab = SumSQBMLab[0] + SumSQBMLab[1] + SumSQBMLab[2];
+	double SumBot2 = (nTot - 5)*(MeanBM[0] * MeanBM[0] + MeanBM[1] * MeanBM[1] + MeanBM[2] * MeanBM[2]);
+	double SumBot2Lab = (nTot - 5)*(MeanBMLab[0] * MeanBMLab[0] + MeanBMLab[1] * MeanBMLab[1] + MeanBMLab[2] * MeanBMLab[2]);
+	RSquare = 1 - (SumTop / (SumBot1 - SumBot2));
+	RSquareLab = 1 - (SumTopLab / (SumBot1Lab - SumBot2Lab));
+	if (RSquare < 0)
+		RSquare = 0.0;
+	if (RSquare > 1)
+		RSquare = 1.0;
+	double rmse = sqrt(SumTop / (nTot - 5));
+	double rmseLab = sqrt(SumTopLab / (nTot - 5);
+	fprintf(fp, "%f\t%f\t%f\t%f\n", RSquare, RSquareLab, rmse, rmseLab);
+	for (int i = 0; i < (nTot - 5); ++i)
+	{
+		delete RGBEst[i];
+		delete LabEst[i];
+		delete RespVec[i];
+		delete RespVecLab[i];
+	}
+
+	delete[] RGBEst;
+	delete[] LabEst;
+	delete[] RespVec;
+	delete[] RespVecLab;
+	delete[] outErrorMsg;
+	fclose(fp);
+	#endif
+	return retColor;
+}
+
+DetectionColor* ColorDetectionMirta::initDetectionColor(int r, int g, int b)
+{
+	DetectionColor* color = (DetectionColor*)malloc(sizeof(DetectionColor));
+	detection_color__init(color);
+	color->has_r = true;
+	color->has_g = true;
+	color->has_b = true;
+	color->r = r;
+	color->g = g;
+	color->b = b;
+	return color;
+}
+
+void ColorDetectionMirta::DistanceMatrix(int nRows, int nCols, MatrixXi &m_dist2grayInd, VectorXi &m_dist2GrayIndSample, VectorXi GrayInd, int TargetInd)
+{
+	int i, j, k;
+	int totSize = nCols * nRows;
+	MatrixXi aa(nRows, nCols);
+	for (i = 0; i < nRows; i++)
+		for (j = 0; j < nCols; j++)
+			aa(i, j) =i * nCols + j;
+
+	//	int i_nonpInd = sizeof(nonpInd);
+	MatrixXd dist2gray(totSize, totSize);
+	dist2gray.setOnes();
+	dist2gray = -dist2gray;
+	int nGray = GrayInd.size();
+	int iG, jG, iB, jB;
+
+	//grays are positioned at GrayInd
+	for (i = 0; i < totSize; i++)
+	{
+		iB = (int)floor(i / nCols);
+		jB = (int)i - iB * nCols;
+		for (k = 0; k < nGray; k++)
+		{
+			iG = (int)floor(GrayInd(k) / nCols);
+			jG = GrayInd(k) - iG * nCols;
+			dist2gray(i, GrayInd(k)) = sqrt((iB - iG)*(iB - iG) + (jB - jG)*(jB - jG));
+		}
+	}
+
+	//Eliminate corners and Target index
+	//make sure fiducial indices and target are sorted
+	int fidpos[] = { 0, nCols - 1, (nRows - 1)*nCols, nRows*nCols - 1,  TargetInd };
+	std::vector<VectorPair> fidVal;
+	int nfid = sizeof(fidpos) / sizeof(int);
+	fidVal.resize(nfid);
+	for (int j = 0; j < nfid; ++j)
+	{
+		fidVal[j].m_ID = j;
+		fidVal[j].m_val = (double)fidpos[j];
+	}
+	std::stable_sort(fidVal.begin(), fidVal.end(), ByDouble());
+	for (int j = 0; j < nfid; ++j)
+	{
+		fidpos[j] = (int)fidVal[j].m_val;
+	}
+
+	VectorXd atmp(totSize);
+	VectorXd btmp(totSize);
+
+	VectorXi atmpInd(totSize);
+	VectorXi btmpInd(totSize);
+	VectorXi ctmpInd(totSize);
+	VectorXi indb(totSize);
+	int tmpInd = 0;
+	int nElimFid = 0;
+	VectorXi PtType(totSize);
+	PtType.setZero();
+	for (i = 0; i < nfid; ++i)
+	{
+		PtType(fidpos[i]) = 1;
+		if (fidpos[i] == TargetInd)
+			PtType(fidpos[i]) = 2;
+	}
+
+	int PtInd = -1;
+	std::vector<VectorPair> IndVal;
+	for (i = 0; i < totSize; ++i)
+	{
+		if (PtType(i) == 0 || PtType(i) == 2)
+		{
+			if (PtType(i) == 0)
+				PtInd += 1;
+			tmpInd = -1;
+			for (j = 0; j < totSize; ++j)
+			{
+				if (dist2gray(i, j) > -1)
+				{
+					tmpInd++;
+					atmp(tmpInd) = dist2gray(i,j);
+					atmpInd(tmpInd) = j;
+				}
+			}
+			nElimFid = -1;
+			//eliminate fiducials and target, store in btmp
+			for (j = 0; j < tmpInd + 1; ++j)
+			{
+				if (atmpInd(j) >= 0 && PtType(atmpInd(j)) == 0)
+				{
+					nElimFid++;
+					btmp(nElimFid) = atmp(j);
+					btmpInd(nElimFid) = atmpInd(j);
+				}
+			}
+
+			if (nElimFid > -1)
+			{
+				// shift indices to compensate for corners and target
+				IndVal.resize(nElimFid + 1);
+				for (int j = 0; j < nElimFid + 1; ++j)
+				{
+					IndVal[j].m_ID = j;
+					IndVal[j].m_val = btmp(j);
+				}
+				std::stable_sort(IndVal.begin(), IndVal.end(), ByDouble());
+					//keep the first 10 indices
+				for (j = 0; j < m_interpPoints; ++j)
+				{
+					ctmpInd(j) = btmpInd(IndVal[j].m_ID);
+					if (ctmpInd(j) >= fidpos[0] && ctmpInd(j) < fidpos[1])
+						ctmpInd(j) = ctmpInd[j] - 1;
+					else if (ctmpInd(j) >= fidpos[1] && ctmpInd(j) < fidpos[2])
+						ctmpInd(j) = ctmpInd(j) - 2;
+					else if (ctmpInd(j) >= fidpos[2] && ctmpInd(j) < fidpos[3])
+						ctmpInd[j] = ctmpInd[j] - 3;
+					else if (ctmpInd(j) >= fidpos[3] && ctmpInd(j) < fidpos[4])
+						ctmpInd(j) = ctmpInd(j) - 4;
+					else
+						ctmpInd(j) = ctmpInd(j) - 5;
+					if (PtType(i) == 0)
+						m_dist2grayInd(PtInd, j) = ctmpInd(j);
+					else
+						m_dist2GrayIndSample(j) = ctmpInd(j);
+				}
+			}
+		}
+	}
+}
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.h
new file mode 100644
index 000000000..dce77f687
--- /dev/null
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ColorDetectionMirta.h
@@ -0,0 +1,27 @@
+#include "Windows.h"
+#include "DetectionColor.pb-c.h"
+#include "DetectionBenchmark.pb-c.h"
+#include <string>
+#include <vector>
+#include <Eigen/Dense>
+#include "ConsolidateData.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+using Eigen::MatrixXd;
+using Eigen::VectorXd;
+using Eigen::MatrixXi;
+using Eigen::VectorXi;
+
+class ColorDetectionMirta
+{
+private:
+	DetectionColor* initDetectionColor(int r, int g, int b);
+	void DistanceMatrix(int rows, int cols, MatrixXi &m_dist2grayInd, VectorXi &m_dist2GrayIndSample,VectorXi GrayInd, int TargetInd);
+	int m_interpPoints;
+public:
+	ColorDetectionMirta();
+	~ColorDetectionMirta();
+	DetectionColor* CDetect(DetectionBenchmark** benchmarks, std::vector<cv::Scalar> means, int column, int rows, int targetIndex);
+};
+
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.cpp
index c2c3e2c27..9e2e1b9f9 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.cpp
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.cpp
@@ -19,7 +19,11 @@ ConsolidateData::ConsolidateData() :
 	m_nSep(0),
 	m_SortIndex(NULL),
 	m_nMeasurements(0),
-	m_diffTol(0.0)
+	m_diffTol(0.0),
+	m_Values1(0,0), m_Values2(0,0),
+	m_SortedValues1(0,0), m_SortedValues2(0,0),
+	m_breaks(0), m_coeffs(0,0), m_pchipSlopes(0),
+	m_intervals(0)
 {
 }
 
@@ -27,7 +31,11 @@ ConsolidateData::ConsolidateData() :
 ConsolidateData::ConsolidateData(double diffTol):
 	m_nSep(0),
 	m_SortIndex(NULL),
-	m_nMeasurements(0)
+	m_nMeasurements(0),
+	m_Values1(0,0), m_Values2(0,0),
+	m_SortedValues1(0,0), m_SortedValues2(0,0),
+	m_breaks(0), m_coeffs(0,0), m_pchipSlopes(0),
+	m_intervals(0)
 {
 	m_diffTol = diffTol;
 }
@@ -352,15 +360,7 @@ int ConsolidateData::LinInterp(VectorXd xIn, VectorXd yIn, double xVal, double &
 		yOut = min(max(yOut, 0.0), 1.0);
 		return(0);
 	}
-	if (xVal > xIn(lx - 1))
-	{
-		w0 = (xIn(lx - 1) - xVal) / (xIn(lx - 1) - xIn(lx - 2));
-		w1 = 1 - w0;
-		for (int j = 0; j < n_ycols; ++j)
-			yOut = yIn(lx - 2)* w0 + w1*yIn(lx - 1);
-		yOut = min(max(yOut, 0.0), 1.0);
-		return(0);
-	}
+
 	for (int i = 0; i < lx - 1; ++i)
 	{
 		if ((xVal <= xIn(i + 1)) & (xVal >= xIn(i)))
@@ -370,9 +370,18 @@ int ConsolidateData::LinInterp(VectorXd xIn, VectorXd yIn, double xVal, double &
 			for (int j = 0; j < n_ycols; ++j)
 				yOut = yIn(i)* w0 + w1*yIn(i + 1);
 			yOut = min(max(yOut, 0.0), 1.0);
-			break;
+			return(0);
 		}
 	}
+	if (xVal > xIn(lx - 1))
+	{
+		w0 = (xIn(lx - 1) - xVal) / (xIn(lx - 1) - xIn(lx - 2));
+		w1 = 1 - w0;
+		for (int j = 0; j < n_ycols; ++j)
+			yOut = yIn(lx - 2)* w0 + w1 * yIn(lx - 1);
+		yOut = min(max(yOut, 0.0), 1.0);
+		return(0);
+	}
 	return(0);
 }
 
@@ -397,6 +406,203 @@ int ConsolidateData::LinInterp(VectorXd xIn, MatrixXd yIn, VectorXd  xVal, Matri
 	return(0);
 }
 
+int ConsolidateData::PCHIP(VectorXd xIn, VectorXd yIn, double xVal, double &yOut)
+{
+	int RetValue = CheckData(xIn, yIn);
+	if (RetValue < 0)
+		return(RetValue);
+
+	//Comput Slopes
+	int n = xIn.size();
+	VectorXd diffX(n - 1);
+	VectorXd diffY(n - 1);
+	VectorXd del(n - 1);
+	
+	for (int i = 0; i < n - 1; ++i)
+	{
+		diffX(i) = xIn(i + 1) - xIn(i);
+		diffY(i) = yIn(i + 1) - yIn(i);
+		del(i) = diffY(i) / diffX(i);
+	}
+	m_coeffs.resize(n - 1, 4);
+	GetPchipSlopes(xIn, yIn,  del);
+	GetPiecewiseCubicElements(xIn, yIn, diffX, del);
+
+	return(0);
+}
+	
+void ConsolidateData::PCHIPInterp(VectorXd xIn, VectorXd yIn, double xVal, double &yOut)
+{
+	double w0, w1;
+	int lx = xIn.size();
+	int n_ycols = yIn.cols();
+	if (xVal < xIn(0))
+	{
+		w0 = (xIn(1) - xVal) / (xIn(1) - xIn(0));
+		w1 = 1 - w0;
+		yOut = yIn(0)* w0 + w1 * yIn(1);
+		yOut = min(max(yOut, 0.0), 1.0);
+		return;
+	}
+	else if (xVal >= xIn(lx-1))
+	{
+		w0 = (xIn(lx - 1) - xVal) / (xIn(lx - 1) - xIn(lx - 2));
+		w1 = 1 - w0;
+		yOut = yIn(lx - 2)* w0 + w1 * yIn(lx - 1);
+		yOut = min(max(yOut, 0.0), 1.0);
+		return;
+	}
+	else
+	{
+		for (int i = 0; i < lx - 1; ++i)
+			//find interval
+			if ((xVal <= xIn(i + 1)) & (xVal >= xIn(i)))
+			{
+				//local coordinates
+				xVal = xVal - m_breaks(i);
+				yOut = m_coeffs(i, 0);
+				for (int j = 1; j < 4; ++j)
+					yOut = xVal * yOut + m_coeffs(i, j);
+				break;
+			}
+	}
+	return;
+}
+
+void ConsolidateData::GetPiecewiseCubicElements(VectorXd xIn, VectorXd yIn, VectorXd diffX,  VectorXd del)
+{
+	int n = xIn.size();
+	VectorXd dzzdx(n - 1);
+	VectorXd dzdxdx(n - 1);
+	for (int i = 0; i < n - 1; ++i)
+	{
+		dzzdx(i) = (del(i) - m_pchipSlopes(i)) / diffX(i);
+		dzdxdx(i) = (m_pchipSlopes(i + 1) - del(i)) / diffX(i);
+	}
+	m_breaks = xIn;
+	for (int i = 0; i <n-1; ++i)
+	{
+		m_coeffs(i, 0) = (dzdxdx(i) - dzzdx(i)) / diffX(i);
+		m_coeffs(i, 1) = 2 * dzzdx(i) - dzdxdx(i);
+		m_coeffs(i, 2) = m_pchipSlopes(i);
+		m_coeffs(i, 3) = yIn(i);
+	}
+}
+
+void ConsolidateData::GetPchipSlopes(VectorXd xIn, VectorXd yIn, VectorXd del)
+{
+	//Compute the first derivatives of the Piecewise Cubic Hermite Interpolating Polynomial
+	int n = xIn.size();
+	m_pchipSlopes.resize(n);
+	if (n == 2)
+	{
+		for (int i = 0; i < n; ++i)
+		{
+			m_pchipSlopes(i) = del(0);
+		}
+		return;
+	}
+	//  Slopes at interior points.
+	//d(k) = weighted average of del(k - 1) and del(k) when they have the same sign.
+	//d(k) = 0 when del(k - 1) and del(k) have opposites signs or either is zero.
+	VectorXd hdiff(n - 1);
+	VectorXd hsdiff(n - 2);
+	VectorXd w1(n - 2);
+	VectorXd w2(n - 2);
+	VectorXd dmax(n - 2);
+	VectorXd dmin(n - 2);
+	VectorXi kInd(n - 2);
+	for (int i = 0; i < n - 2; ++i)
+	{
+		if (del(i)*del(i + 1) > 0)
+			kInd(i) = i;
+		else
+			kInd(i) = -1;
+	}
+	//Difference
+	for (int i = 0; i < n - 1; ++i)
+		hdiff(i) = xIn(i + 1) - xIn(i);
+	for (int i = 0; i < n - 2; ++i)
+	{
+		if (kInd(i) > -1)
+			hsdiff(i) = hdiff(i) + hdiff(i + 1);
+		else
+			hsdiff(i) = 0;
+	}
+	for (int i = 0; i < n - 2; ++i)
+	{
+		if (kInd(i) > -1)
+		{
+			w1(i) = (hdiff(i) + hsdiff(i)) / (3 * hsdiff(i));
+			w2(i) = (hsdiff(i) + hdiff(i + 1)) / (3 * hsdiff(i));
+			dmax(i) = max(abs(del(i)), abs(del(i + 1)));
+			dmin(i) = min(abs(del(i)), abs(del(i + 1)));
+			m_pchipSlopes(i + 1) = dmin(i) / (w1(i)*(del(i) / dmax(i)) + w2(i)*(del(i + 1) / dmax(i)));
+		}
+		else
+		{
+			m_pchipSlopes(i + 1) = 0;
+		}
+	}
+
+		//Calculate Slopes at endpoints
+		m_pchipSlopes(0) = ((2 * hdiff(0) + hdiff(1))*del(0) - hdiff(0)*del(1)) / (hdiff(0) + hdiff(1));
+		if (m_pchipSlopes(0)*del(0)<0)
+			m_pchipSlopes(0) = 0;
+		else if (((del(0) * del(1)) < 0) & (std::abs(m_pchipSlopes(0)) > std::abs(3 * del(0))))
+			m_pchipSlopes(0) = 3 * del(0);
+
+		m_pchipSlopes(n - 1) = ((2 * hdiff(n - 2) + hdiff(n - 3))*del(n - 2) - hdiff(n - 2)*del(n - 3)) / (hdiff(n - 2) + hdiff(n - 3));
+		if ((m_pchipSlopes(n - 1)) *(del(n - 2)) < 0)
+			m_pchipSlopes(n - 1) = 0;
+		else if (((del(n - 2)*del(n - 3)) < 0) & (std::abs(m_pchipSlopes(n - 1)) > (std::abs(del(n - 2)))))
+			m_pchipSlopes(n - 1) = 3 * del(n - 2);
+
+	return;
+}
+
+
+	int ConsolidateData::CheckData(VectorXd xIn, VectorXd yIn)
+	{
+		if (xIn.size() != yIn.size())  //vectors have to be same length
+			return(-1);
+
+		int n = xIn.size();
+		if (n < 2)
+			return(-2);
+		//check for strict monotonicity
+		double diff = -1;
+		double mindiff = 1e+05;
+		for (int i = 1; i < n; ++i)
+		{
+			diff = xIn(i) - xIn(i - 1);
+			mindiff = min(mindiff, diff);
+		}
+		if (mindiff < 0) // Data is not strictly monotonic
+		{
+			//sort x and y
+			std::vector<std::pair<double, double> >V;
+			for (int i = 0; i < n; i++)
+			{
+				std::pair<double, double>P = std::make_pair(xIn(i), yIn(i));
+				V.push_back(P);
+			}
+			std::sort(V.begin(), V.end());
+			xIn(0) = V[0].first;
+			yIn(0) = V[0].second;
+			for (int i = 1; i < n; i++)
+			{
+				xIn(i) = V[i].first;
+				yIn(i) = V[i].second;
+				diff = xIn(i) - xIn(i - 1);
+				if (diff <= 0)
+					return(-3);
+			}
+		}
+		return(0);
+	}
+
+
 
 
 bool ConsolidateData::CountExpandReplicates(int *SortIndex, int *ExpCount)
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.h
index 5e6ecca4c..4041e137b 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.h
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/ConsolidateData.h
@@ -7,6 +7,8 @@
 #include <functional>
 #include "Eigen/Dense"
 #include "Detect.h"
+#include <algorithm>
+#include <utility>
 using Eigen::MatrixXd;
 using Eigen::VectorXd;
 
@@ -16,10 +18,14 @@ struct VectorPair
 	int m_ID;
 	double m_val;
 };
-
-struct ByInt : public std::binary_function<VectorPair, VectorPair, bool>
+struct VectorPairInt
 {
-	bool operator()(const VectorPair& lhs, const VectorPair& rhs) const
+	int m_ID;
+	int m_val;
+};
+struct ByInt : public std::binary_function<VectorPairInt, VectorPairInt, bool>
+{
+	bool operator()(const VectorPairInt& lhs, const VectorPairInt& rhs) const
 	{
 		return lhs.m_ID< rhs.m_ID;
 	}
@@ -50,6 +56,10 @@ public:
 	int LinInterp(VectorXd xIn, MatrixXd yIn, double xVal, VectorXd yOut);
 	int LinInterp(VectorXd xIn, MatrixXd yIn, VectorXd  xVal, MatrixXd yOut);
 	int LinInterp(VectorXd xIn, VectorXd yIn, double xVal, double &yOut);
+	int PCHIP(VectorXd xIn, MatrixXd yIn, double xVal, VectorXd yOut);
+	int PCHIP(VectorXd xIn, MatrixXd yIn, VectorXd  xVal, MatrixXd yOut);
+	int PCHIP(VectorXd xIn, VectorXd yIn, double xVal, double &yOut);
+	void PCHIPInterp(VectorXd xIn, VectorXd yIn, double xVal, double &yOut);
 private:
 	MatrixXd m_Values1;
 	MatrixXd m_Values2;
@@ -60,6 +70,11 @@ private:
 
 	int *m_CountDuplicates;
 	int *m_MarkValues;
+	VectorXd m_breaks;
+	MatrixXd m_coeffs;
+	VectorXd m_pchipSlopes;
+	int m_polOrder;
+	int m_intervals;
 
 	bool m_bLab;
 	int m_nSep;
@@ -77,6 +92,10 @@ private:
 
 	bool SortValues(int *SortIndex);
 	bool CountExpandReplicates(int *SortIndex, int *ExpCount);
+	void GetPchipSlopes(VectorXd x, VectorXd y, VectorXd del);
+	void GetPiecewiseCubicElements(VectorXd x, VectorXd y, VectorXd diffX, VectorXd del);
+	int CheckData(VectorXd xIn, VectorXd yIn);
+	
 };
 
 
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.cpp
index e9b933cd6..520627829 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.cpp
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.cpp
@@ -160,7 +160,7 @@ int Detect::MatchSampleColor(VectorXd &outResult, VectorXd &outResultLab, char *
 		outErrorMsg = msg;
 		return(ret);
 	}
-
+	//VectorXd outLabVec = Solve.eval(m_sampleColorLab );
 	VectorXd outLabVec = Solve.eval(m_sampleColorLab - m_SampledDataLabWP);
 	for (j = 0; j < nDim; ++j)
 		outLabVec(j) += m_SampledDataLabWP(j);
@@ -206,7 +206,7 @@ int Detect::PrepareData()
 	ColorConvert CConvertD65(D65, D65);
 	C_RGB_XYZ_Lab BenchMarkDataWPXYZ = CConvertD65.LabToXYZ(BenchMarkDataWPLab);
 	
-	ColorConvert CConvert(D65, BenchMarkDataWPXYZ);
+	ColorConvert CConvert(BenchMarkDataWPXYZ, BenchMarkDataWPXYZ);
 	C_RGB_XYZ_Lab LowLab(0.0, -128.0, -128.0);
 	C_RGB_XYZ_Lab HighLab(100.0, 127.0, 127.0);
 	int j = 0;
@@ -224,11 +224,11 @@ int Detect::PrepareData()
 		SampledLabData(i, 1) = DataLab.Get_y();
 		SampledLabData(i, 2) = DataLab.Get_z();
 
-		//Convert Benchmark Data Lab Relative Colorimetric
+		//Convert Benchmark Data Lab Relative Colorimetric, use White pount of BenchMark Data
 		DataLab.Set(m_BenchMarkDataLab(i, 0), m_BenchMarkDataLab(i, 1), m_BenchMarkDataLab(i, 2));
 		DataXYZ = CConvertD65.LabToXYZ(DataLab);
-		DataXYZ_Norm = CConvert.ApplyBradfordMat(DataXYZ);
-		DataLab = CConvertD65.XYZToLab(DataXYZ_Norm);
+		//DataXYZ_Norm = CConvert.ApplyBradfordMat(DataXYZ);
+		DataLab = CConvert.XYZToLab(DataXYZ);
 		DataLab.Clamp(LowLab, HighLab);
 		BenchMarkLabDataRel(i, 0) = DataLab.Get_x();
 		BenchMarkLabDataRel(i, 1) = DataLab.Get_y();
@@ -460,6 +460,9 @@ void Detect::LocalGrayCorrection()
 	MatrixXd SDGray(ndistcorr, ncols);
 	VectorXd LocBDGray(1);
 	VectorXd LocSDGray(1);
+	VectorXd VecBDGray(1);
+	VectorXd tmpLocBDGray(1);
+	VectorXd tmpLocSDGray(1);
 	MatrixXd tmpBDGray(ndistcorr, ncols);
 	MatrixXd tmpSDGray(ndistcorr, ncols);
 	
@@ -468,6 +471,7 @@ void Detect::LocalGrayCorrection()
 	ConsolidateData Consolidate;
 	m_SampledDataCorr.resize(nrows, ncols);
 	int i, j, k;
+	int tmpN;
 	for (i = 0; i < nrows; ++i)
 	{
 		for (j = 0; j < ndistcorr; ++j)
@@ -478,44 +482,31 @@ void Detect::LocalGrayCorrection()
 				SDGray(j,k) = m_SampledData(m_dist2grayInd(i, j), k);
 			}
 		}
-//		fprintf(fp1, "%d\n",i);
-/*		for (j = 0; j < ndistcorr; ++j)
-		{
-			for (k = 0; k < ncols; ++k)
-				fprintf(fp1, "%f\t", BDGray(j, k));
-			fprintf(fp1, "\n");
-			for (k = 0; k < ncols; ++k)
-				fprintf(fp1, "%f\t", SDGray(j, k));
-			fprintf(fp1, "\n");
-		}
-		*/
+
 		Consolidate.Init(BDGray, SDGray);
+		// values are sorted in MergeValues
 		Consolidate.MergeValues();
 		tmpBDGray = Consolidate.getOutValues1();
 		tmpSDGray = Consolidate.getOutValues2();
-		int tmpN = tmpBDGray.rows();
+		tmpN = tmpBDGray.rows();
+		VecBDGray.resize(tmpN);
+		for (k = 0; k < tmpN; ++k)
+			VecBDGray(k) = tmpBDGray(k, 1);
+		Consolidate.CheckMonotonicity(VecBDGray, tmpSDGray);
+		int kstart = 0;
 		LocBDGray.resize(tmpN);
 		LocSDGray.resize(tmpN);
-		for (k = 0; k < tmpN; ++k)
-			LocBDGray(k) = tmpBDGray(k, 1);		
-		Consolidate.CheckMonotonicity(LocBDGray, tmpSDGray);
 		for (j=0; j<ncols; ++j)
 		{ 
 			for (k=0; k<tmpN; ++k)
 			{ 
-				LocBDGray(k) = tmpBDGray(k, j);
+				LocBDGray(k) = VecBDGray(k);
 				LocSDGray(k) = tmpSDGray(k, j);
-			}
+			}			
 			Consolidate.CheckUniqueness(LocBDGray, LocSDGray);
+//			Consolidate.PCHIP(LocSDGray, LocBDGray, m_SampledData(i, j), m_SampledDataCorr(i, j));
+//			Consolidate.PCHIPInterp(LocSDGray, LocBDGray, m_SampledData(i, j), m_SampledDataCorr(i, j));
 			Consolidate.LinInterp(LocSDGray, LocBDGray, m_SampledData(i, j), m_SampledDataCorr(i, j));
-
-/*				for (k = 0; k < tmpN; ++k)
-					fprintf(fp1, "%f\t", LocBDGray(k));
-				fprintf(fp1, "\n");
-				for (k = 0; k < tmpN; ++k)
-					fprintf(fp1, "%f\t", LocSDGray(k));
-				fprintf(fp1, "\n");
-				fprintf(fp1, "%f\t%f\n", m_SampledData(i, j), m_SampledDataCorr(i, j));			*/
 		}
 	}
 
@@ -528,36 +519,28 @@ void Detect::LocalGrayCorrection()
 			SDGray(j, k) = m_SampledData(m_dist2GrayIndSample(j), k);
 		}
 	}
-/*	fprintf(fp1, "s\n");
-	for (j = 0; j < ndistcorr; ++j)
-	{
-		for (k = 0; k < ncols; ++k)
-			fprintf(fp1, "%f\t", BDGray(j, k));
-		fprintf(fp1, "\n");
-		for (k = 0; k < ncols; ++k)
-			fprintf(fp1, "%f\t", SDGray(j, k));
-		fprintf(fp1, "\n");
-	}
-	*/
+
 	Consolidate.Init(BDGray, SDGray);
 	m_SampleColorCorr.resize(ncols);
 	Consolidate.MergeValues();
 	tmpBDGray = Consolidate.getOutValues1();
 	tmpSDGray = Consolidate.getOutValues2();
-	int tmpN = tmpBDGray.rows();
+	tmpN = tmpBDGray.rows();
 	LocBDGray.resize(tmpN);
 	LocSDGray.resize(tmpN);
 	for (k = 0; k < tmpN; ++k)
-		LocBDGray(k) = tmpBDGray(k, 1);
-	Consolidate.CheckMonotonicity(LocBDGray, tmpSDGray);
-	for (j = 0; j<ncols; ++j)
+		VecBDGray(k) = tmpBDGray(k, 1);
+	Consolidate.CheckMonotonicity(VecBDGray, tmpSDGray);
+	int kstart1 =0;
+	for (j = 0; j < ncols; ++j)
 	{
-		for (k = 0; k<tmpN; ++k)
+		for (k = 0; k < tmpN; ++k)
 		{
-			LocBDGray(k) = tmpBDGray(k, j);
+			LocBDGray(k) = VecBDGray(k);
 			LocSDGray(k) = tmpSDGray(k, j);
 		}
 		Consolidate.CheckUniqueness(LocBDGray, LocSDGray);
+	//	Consolidate.PCHIP(LocSDGray, LocBDGray, m_sampleColor(j), m_SampleColorCorr(j));
 		Consolidate.LinInterp(LocSDGray, LocBDGray, m_sampleColor(j), m_SampleColorCorr(j));
 
 /*		for (k = 0; k < tmpN; ++k)
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.h
index 0cf65ccbf..daf09c243 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.h
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Detect.h
@@ -6,7 +6,7 @@
 #include <Eigen/Dense>
 #include "CRGB_XYZ_Lab.h"
 #include "ConsolidateData.h"
-
+#include <Eigen/Dense>
 using Eigen::MatrixXd;
 using Eigen::VectorXd;
 using Eigen::MatrixXi;
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.cpp
new file mode 100644
index 000000000..3230793e8
--- /dev/null
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.cpp
@@ -0,0 +1,341 @@
+#include "stdafx.h"
+#include "Interp.h"
+
+#define sgn(x)  ((x) > 0) ? (1) : (((x) < (0)) ? (-1) : (0))
+#include <algorithm>
+#include <utility>
+#include <Eigen/Dense>
+
+
+//default Constructor
+Interp::Interp() :
+m_xIn(1),
+m_yIn(1),
+m_breaks(1),
+m_coeffs(1,1),
+m_pchipSlopes(1),
+m_polOrder(0),
+m_intervals(0)
+{
+}
+
+Interp::Interp(VectorXd xIn, VectorXd yIn):
+m_breaks(1),
+m_coeffs(1, 1),
+m_polOrder(3),
+m_intervals(0),
+m_pchipSlopes(1)
+{
+	m_xIn = xIn;
+	m_yIn = yIn;
+}
+
+Interp::~Interp()
+{
+}
+
+ void Interp::SetCoeffs(MatrixXd  coeffs)
+ {
+	 int nxsize = m_xIn.size();
+	 for (int i = 1; i < nxsize; ++i)
+		 for (int j = 0; j <= m_polOrder; ++j)
+			 m_coeffs(i, j) = coeffs(i, j);
+ }
+
+ int Interp::LinInterp( MatrixXd yIn, double xVal, VectorXd &yOut)
+ {
+	 //check sizes
+	 int lx = m_xIn.size();
+	 if (lx != yIn.rows())
+		 return (-1);
+	 double w0, w1;
+	 int n_ycols = yIn.cols();
+
+	 //find interval
+	 if (xVal < m_xIn(0))
+	 {
+		 w0 = (m_xIn(1) - xVal) / (m_xIn(1) - m_xIn(0));
+		 w1 = 1 - w0;
+		 for (int j = 0; j < n_ycols; ++j)
+			 yOut(j) = yIn(0, j)* w0 + w1 * yIn(1, j);
+		 return(0);
+	 }
+	 if (xVal > m_xIn(lx - 1))
+	 {
+		 w0 = (m_xIn(lx - 1) - xVal) / (m_xIn(lx - 1) - m_xIn(lx - 2));
+		 w1 = 1 - w0;
+		 for (int j = 0; j < n_ycols; ++j)
+			 yOut(j) = yIn(lx - 2, j)* w0 + w1 * yIn(lx - 1, j);
+		 return(0);
+	 }
+	 for (int i = 0; i < lx - 1; ++i)
+	 {
+		 if ((xVal <= m_xIn(i + 1)) & (xVal >= m_xIn(i)))
+		 {
+			 w0 = (m_xIn(i + 1) - xVal) / (m_xIn(i + 1) - m_xIn(i));
+			 w1 = 1 - w0;
+			 for (int j = 0; j < n_ycols; ++j)
+				 yOut(j) = yIn(i, j)* w0 + w1 * yIn(i + 1, j);
+		 }
+	 }
+	 return(0);
+ }
+
+ int Interp::LinInterp(double xVal, double &yOut)
+ {
+	 //check sizes
+	 int lx = m_xIn.size();
+	 if (lx != m_yIn.rows())
+		 return (-1);
+	 double w0, w1;
+	 int n_ycols = m_yIn.cols();
+
+	 //find interval
+	 if (xVal < m_xIn(0))
+	 {
+		 w0 = (m_xIn(1) - xVal) / (m_xIn(1) - m_xIn(0));
+		 w1 = 1 - w0;
+		 for (int j = 0; j < n_ycols; ++j)
+			 yOut = m_yIn(0)* w0 + w1 * m_yIn(1);
+		 yOut = min(max(yOut, 0.0), 1.0);
+		 return(0);
+	 }
+	 if (xVal > m_xIn(lx - 1))
+	 {
+		 w0 = (m_xIn(lx - 1) - xVal) / (m_xIn(lx - 1) - m_xIn(lx - 2));
+		 w1 = 1 - w0;
+		 for (int j = 0; j < n_ycols; ++j)
+			 yOut = m_yIn(lx - 2)* w0 + w1 * m_yIn(lx - 1);
+		 yOut = min(max(yOut, 0.0), 1.0);
+		 return(0);
+	 }
+	 for (int i = 0; i < lx - 1; ++i)
+	 {
+		 if ((xVal <= m_xIn(i + 1)) & (xVal >= m_xIn(i)))
+		 {
+			 w0 = (m_xIn(i + 1) - xVal) / (m_xIn(i + 1) - m_xIn(i));
+			 w1 = 1 - w0;
+			 for (int j = 0; j < n_ycols; ++j)
+				 yOut = m_yIn(i)* w0 + w1 * m_yIn(i + 1);
+			 yOut = min(max(yOut, 0.0), 1.0);
+			 break;
+		 }
+	 }
+	 return(0);
+ }
+
+ int Interp::LinInterp(MatrixXd yIn, VectorXd  xVal, MatrixXd &yOut)
+ {
+	 //check sizes
+	 int lx = m_xIn.size();
+	 if (lx != yIn.rows())
+		 return (-1);
+	 int n_ycols = yIn.cols();
+	 int n_ValSize = xVal.size();
+	 VectorXd VyOut(n_ycols);
+
+	 for (int i = 0; i<n_ValSize; ++i)
+	 {
+		 LinInterp( yIn, xVal(i), VyOut);
+		 for (int j = 0; j < n_ycols; ++j)
+		 {
+			 yOut(i, j) = VyOut(j);
+		 }
+	 }
+	 return(0);
+ }
+
+ int Interp::PCHIP( double xVal, double &yOut)
+ {
+	// int RetValue = CheckData();
+	// if (RetValue < 0)
+	//	 return(RetValue);
+
+	 //Compute Slopes
+	 int n = m_xIn.size();
+	 VectorXd diffX(n - 1);
+	 VectorXd diffY(n - 1);
+	 VectorXd del(n - 1);
+	 for (int i = 0; i < n - 1; ++i)
+	 {
+		 diffX(i) = m_xIn(i + 1) - m_xIn(i);
+		 diffY(i) = m_yIn(i + 1) - m_yIn(i);
+		 del(i) = diffY(i) / diffX(i);
+	 }
+	 
+	 GetPchipSlopes( del);
+	 GetPiecewiseCubicElements( diffX,  del);
+
+	 return(0);
+ }
+
+ int Interp::PCHIPInterp(double xVal, double &yOut)
+ {
+	 double w0, w1;
+	 int lx = m_xIn.size();
+	 int n_ycols = m_yIn.cols();
+	 if(xVal<m_xIn(0))
+	 {
+		 w0 = (m_xIn(1) - xVal) / (m_xIn(1) - m_xIn(0));
+		 w1 = 1 - w0;
+		 yOut = m_yIn(0)* w0 + w1 * m_yIn(1);
+		 yOut = min(max(yOut, 0.0), 1.0);
+		 return(0);
+	 }
+	 else if (xVal >= m_xIn(lx))
+	 {
+		 w0 = (m_xIn(lx - 1) - xVal) / (m_xIn(lx - 1) - m_xIn(lx - 2));
+		 w1 = 1 - w0;
+		 yOut = m_yIn(lx - 2)* w0 + w1 * m_yIn(lx - 1);
+		 yOut = min(max(yOut, 0.0), 1.0);
+		 return(0);
+	 }
+	 else
+	 {
+		 for (int i=0; i<lx-1; ++i)
+		 //find interval
+		 if ((xVal <= m_xIn(i + 1)) & (xVal >= m_xIn(i)))
+		 {
+			 //local coordinates
+			 xVal = xVal - m_breaks(i);
+			 yOut = m_coeffs(i, 0);
+			 for (int j = 1; j < 4; ++j)
+				 yOut = xVal * yOut + m_coeffs(i, j);
+			 break;
+		 }
+	 }
+	 return(0);
+ }
+
+ void Interp::GetPiecewiseCubicElements(VectorXd diffX, VectorXd del)
+ {
+	 int n = m_xIn.size();
+	 
+	 VectorXd dzzdx(n - 1);
+	 VectorXd dzdxdx(n - 1);
+	 for (int i = 0; i < n - 1; ++i)
+	 {
+		 dzzdx(i) = (del(i) - m_pchipSlopes(i)) / diffX(i);
+		 dzdxdx(i) = (m_pchipSlopes(i+1) - del(i)) / diffX(i);
+	 }
+	 m_breaks = m_xIn;
+	 for (int i = 0; i <= n; ++i)
+	 {
+		 m_coeffs(i, 0) = (dzdxdx(i) - dzzdx(i)) / diffX(i);
+		 m_coeffs(i, 1) = 2 * dzzdx(i) - dzdxdx(i);
+		 m_coeffs(i, 2) = m_pchipSlopes(i);
+		 m_coeffs(i, 3) = m_yIn(i);
+	 }
+}
+
+
+
+ void Interp::GetPchipSlopes( VectorXd del)
+ {
+	 //Compute the first derivatives of the Piecewise Cubic Hermite Interpolating Polynomial
+	 int n = m_xIn.size();
+	 m_pchipSlopes.resize(n);
+	 if (n == 2)
+	 {
+		 for (int i = 0; i < n; ++i)
+		 {
+			 m_pchipSlopes(i) = del(0);
+		 }
+		 return;
+	 }
+	 //  Slopes at interior points.
+	 //d(k) = weighted average of del(k - 1) and del(k) when they have the same sign.
+	 //d(k) = 0 when del(k - 1) and del(k) have opposites signs or either is zero.
+	 VectorXd hdiff(n - 1);
+	 VectorXd hsdiff(n - 2);
+	 VectorXd w1(n - 2);
+	 VectorXd w2(n - 2);
+	 VectorXd dmax(n - 2);
+	 VectorXd dmin(n - 2);
+	 VectorXi kInd(n - 2);
+	 for (int i = 0; i < n - 2; ++i)
+	 {
+		 if (del(i)*del(i + 1) > 0)
+			 kInd(i) = i;
+		 else
+			 kInd(i) = 0;
+	 }
+	 //Difference
+	 for (int i = 0; i < n - 1; ++i)
+		 hdiff(i) = m_xIn(i + 1) - m_xIn(i);
+	 for (int i = 0; i < n - 2; ++i)
+	 {
+		 if (kInd(i) > 0)
+			 hsdiff(i) = hdiff(i) + hdiff(i + 1);
+		 else
+			 hsdiff(i) =  0;
+	 }
+	 for (int i = 0; i < n - 2; ++i)
+	 {
+		 if (kInd(i) > 0)
+		 {
+			 w1(i) = (hdiff(i) + hsdiff(i)) / (3 * hsdiff(i));
+			 w2(i) = (hsdiff(i) + hdiff(i + 1)) / (3 * hsdiff(i));
+			 dmax(i) = max(abs(del(i)), abs(del(i + 1)));
+			 dmin(i) = min(abs(del(i)), abs(del(i + 1)));
+			 m_pchipSlopes(i + 1) = dmin(i) / (w1(i)*(del(i) / dmax(i)) + w2(i)*(del(i + 1) / dmax(i)));
+		 }
+		 else
+			 m_pchipSlopes(i + 1) = 0;
+	 }
+
+	 //Calculate Slopes at endpoints
+	 m_pchipSlopes(0) = ((2 * hdiff(0) + hdiff(1))*del(0) - hdiff(0)*del(1)) / (hdiff(0) + hdiff(1));
+	 if (sgn(m_pchipSlopes(0)) != sgn(del(0)))
+		 m_pchipSlopes(0) = 0;
+	 else if ((sgn(del(0)) != sgn(del(1))) & (abs(m_pchipSlopes(0)) > abs(3 *del(0))))
+		 m_pchipSlopes(0) = 3 * del(0);
+
+	 m_pchipSlopes(n - 1) = ((2 * hdiff(n - 2) + hdiff(n - 3))*del(n - 2) - hdiff(n - 2)*del(n - 3)) / (hdiff(n - 2) + hdiff(n - 3));
+	 if (sgn(m_pchipSlopes(n - 1)) != sgn(del(n - 2)))
+		 m_pchipSlopes(n - 1) = 0;
+	 else if ((sgn(del(n - 2)) != sgn(del(n - 3))) & (abs(m_pchipSlopes(n - 1)) > abs(3 *del(n - 2))))
+		 m_pchipSlopes(n - 1) = 3 * del(n - 2);
+
+	 return;
+ }
+
+ int Interp::CheckData()
+ {
+	 if (m_xIn.size() != m_yIn.size())  //vectors have to be same length
+		 return(-1);
+	
+	 int n = m_xIn.size();
+	 if (n < 2)
+		 return(-2);
+	 //check for strict monotonicity
+	 double diff = -1;
+	 double mindiff = 1e+05;
+	 for (int i = 1; i < n; ++i)
+	 {
+		 diff = m_xIn(i) - m_xIn(i - 1);
+		 mindiff = min(mindiff, diff);
+	 }
+	 if (mindiff < 0) // Data is not strictly monotonic
+	 {
+		 //sort x and y
+		 std::vector<std::pair<double, double> >V;
+		 for (int i = 0; i < n; i++)
+		 {
+			 std::pair<double, double>P = std::make_pair(m_xIn(i), m_yIn(i));
+			 V.push_back(P);
+		 }
+		 std::sort(V.begin(), V.end());
+		 m_xIn(0) = V[0].first;
+		 m_yIn(0) = V[0].second;
+		 for (int i = 1; i < n; i++)
+		 {
+			 m_xIn(i) = V[i].first;
+			 m_yIn(i) = V[i].second;
+			 diff = m_xIn(i) - m_xIn(i - 1);
+			 if (diff <= 0)
+				 return(-3);
+		 }
+	 }
+	 return(0);
+ }
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.h b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.h
new file mode 100644
index 000000000..2086a94d5
--- /dev/null
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Interp.h
@@ -0,0 +1,39 @@
+#ifndef __INTERP_H__
+#define __INTERP_H__
+
+#include "Eigen/Dense"
+#include "Detect.h"
+
+using Eigen::MatrixXd;
+using Eigen::VectorXd;
+
+class Interp
+{
+public:
+	Interp();
+	Interp(VectorXd xIn, VectorXd yIn);
+	~Interp();
+	int LinInterp( MatrixXd yIn, double xVal, VectorXd &yOut);
+	int LinInterp( MatrixXd yIn, VectorXd  xVal, MatrixXd &yOut);
+	int LinInterp( double xVal, double &yOut);
+//	int PCHIP(VectorXd xIn, MatrixXd yIn, double xVal, VectorXd yOut);
+//	int PCHIP(VectorXd xIn, MatrixXd yIn, VectorXd  xVal, MatrixXd yOut);
+	int PCHIP( double xVal, double &yOut);
+	int PCHIPInterp(double xVal, double &yOut);
+private:
+	int CheckData();
+	void GetPchipSlopes(VectorXd del);
+	void GetPiecewiseCubicElements( VectorXd diffX, VectorXd del);
+	VectorXd m_xIn;
+	VectorXd m_yIn;
+	VectorXd m_breaks;
+	MatrixXd m_coeffs;
+	VectorXd m_pchipSlopes;
+	int m_polOrder;
+	int m_intervals;
+	void SetCoeffs(MatrixXd  coeffs);
+	void SetIntervalBreaks(VectorXd breaks) { m_breaks = breaks; };
+	void SetPolynomialOrder(int polOrder) { m_polOrder = polOrder; };
+	void SetNumberOfIntervals(int intervals) {m_intervals = intervals;};
+};
+#endif
\ No newline at end of file
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/LLS_Solver.cpp b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/LLS_Solver.cpp
index e62181007..27e7b25fe 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/LLS_Solver.cpp
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/LLS_Solver.cpp
@@ -146,5 +146,5 @@ VectorXd LLS_Solver::eval(VectorXd SampleRGB)
 	}
 //	std::cout << "The Lab solution is:\n"
 //		<< resultData << std::endl;
-	return(resultData);
+return(resultData);
 }
\ No newline at end of file
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj
index daffed975..f2e62b41e 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj
@@ -185,10 +185,12 @@
     <ClInclude Include="bitmap_image.hpp" />
     <ClInclude Include="ColorConvert.h" />
     <ClInclude Include="ColorDetection.h" />
+    <ClInclude Include="ColorDetectionMirta.h" />
     <ClInclude Include="ConsolidateData.h" />
     <ClInclude Include="CRGB_XYZ_Lab.h" />
     <ClInclude Include="Detect.h" />
     <ClInclude Include="Exports.h" />
+    <ClInclude Include="Interp.h" />
     <ClInclude Include="LLS_Solver.h" />
     <ClInclude Include="PMR\TCC\DetectionBenchmark.pb-c.h" />
     <ClInclude Include="PMR\TCC\DetectionColor.pb-c.h" />
@@ -201,11 +203,12 @@
   <ItemGroup>
     <ClCompile Include="ColorConvert.cpp" />
     <ClCompile Include="ColorDetection.cpp" />
-    <ClCompile Include="ColorDetectionAlgorithm.cpp" />
+    <ClCompile Include="ColorDetectionMirta.cpp" />
     <ClCompile Include="ConsolidateData.cpp" />
     <ClCompile Include="C_RGB_XYZ_Lab.cpp" />
     <ClCompile Include="Detect.cpp" />
     <ClCompile Include="Exports.cpp" />
+    <ClCompile Include="Interp.cpp" />
     <ClCompile Include="LLS_Solver.cpp" />
     <ClCompile Include="PMR\TCC\DetectionBenchmark.pb-c.c" />
     <ClCompile Include="PMR\TCC\DetectionColor.pb-c.c" />
diff --git a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj.filters b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj.filters
index e3af22d85..61beb5852 100644
--- a/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj.filters
+++ b/Software/Visual_Studio/TCC/Tango.TCC.ColorDetector/Tango.TCC.ColorDetector.vcxproj.filters
@@ -69,14 +69,17 @@
     <ClInclude Include="PMR\TCC\DetectionBenchmark.pb-c.h">
       <Filter>PMR</Filter>
     </ClInclude>
+    <ClInclude Include="ColorDetectionMirta.h">
+      <Filter>Header Files</Filter>
+    </ClInclude>
+    <ClInclude Include="Interp.h">
+      <Filter>Header Files</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="stdafx.cpp">
       <Filter>Source Files</Filter>
     </ClCompile>
-    <ClCompile Include="ColorDetectionAlgorithm.cpp">
-      <Filter>Source Files</Filter>
-    </ClCompile>
     <ClCompile Include="ColorDetection.cpp">
       <Filter>Source Files</Filter>
     </ClCompile>
@@ -113,5 +116,11 @@
     <ClCompile Include="PMR\TCC\DetectionBenchmark.pb-c.c">
       <Filter>PMR</Filter>
     </ClCompile>
+    <ClCompile Include="ColorDetectionMirta.cpp">
+      <Filter>Source Files</Filter>
+    </ClCompile>
+    <ClCompile Include="Interp.cpp">
+      <Filter>Source Files</Filter>
+    </ClCompile>
   </ItemGroup>
 </Project>
\ No newline at end of file
diff --git a/Software/Visual_Studio/Tango_TCC.sln b/Software/Visual_Studio/Tango_TCC.sln
new file mode 100644
index 000000000..5656e6679
--- /dev/null
+++ b/Software/Visual_Studio/Tango_TCC.sln
@@ -0,0 +1,62 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 15
+VisualStudioVersion = 15.0.28307.539
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Tango.TCC.BL", "TCC\Tango.TCC.BL\Tango.TCC.BL.csproj", "{F209FAE8-73F9-441B-97F4-0844A0279390}"
+EndProject
+Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "Tango.TCC.ColorDetector", "TCC\Tango.TCC.ColorDetector\Tango.TCC.ColorDetector.vcxproj", "{862328A4-8632-43AF-B466-55825C7962FE}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Tango.MachineStudio.UI", "MachineStudio\Tango.MachineStudio.UI\Tango.MachineStudio.UI.csproj", "{116DFDB0-7681-46FE-8BAD-FE8AE09BB076}"
+EndProject
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+		Release|x86 = Release|x86
+	EndGlobalSection
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