Matlab binaries from new cmake build

src/MATLAB/+HIP/@Cuda/CheckConfig.m

+% CheckConfig - Get Hydra library configuration information.
+%    [hydraConfig] = HIP.Cuda.CheckConfig()
+%    Returns hydraConfig structure with configuration information.
+%    
+function [hydraConfig] = CheckConfig()
+    [hydraConfig] = HIP.Cuda.HIP('CheckConfig');
+end

src/MATLAB/+HIP/@Cuda/Closure.m

@@ -18,5 +18,5 @@
 %    
 %    	imageOut = This will be an array of the same type and shape as the input array.
 function [imageOut] = Closure(imageIn,kernel,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('Closure',imageIn,kernel,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('Closure',imageIn,kernel,numIterations,device);
 end

src/MATLAB/+HIP/@Cuda/Cuda.m

 classdef (Abstract,Sealed) Cuda
 methods (Static)
+    [imageOut] = Closure(imageIn,kernel,numIterations,device)
+    [imageOut] = HighPassFilter(imageIn,sigmas,device)
+    [numCudaDevices,memStats] = DeviceCount()
     Help(command)
+    [hydraConfig] = CheckConfig()
+    [imageOut] = IdentityFilter(imageIn,device)
     [cmdInfo] = Info()
-    [numCudaDevices,memStats] = DeviceCount()
+    [imageOut] = Gaussian(imageIn,sigmas,numIterations,device)
+    [imageOut] = MeanFilter(imageIn,kernel,numIterations,device)
     [deviceStatsArray] = DeviceStats()
     [imageOut] = ElementWiseDifference(image1In,image2In,device)
+    [imageOut] = MultiplySum(imageIn,kernel,numIterations,device)
     [imageOut] = EntropyFilter(imageIn,kernel,device)
-    [imageOut] = HighPassFilter(imageIn,sigmas,device)
-    [imageOut] = Gaussian(imageIn,sigmas,numIterations,device)
-    [imageOut] = Closure(imageIn,kernel,numIterations,device)
+    [imageOut] = StdFilter(imageIn,kernel,numIterations,device)
     [minVal,maxVal] = GetMinMax(imageIn,device)
-    [imageOut] = WienerFilter(imageIn,kernel,noiseVariance,device)
-    [imageOut] = Opener(imageIn,kernel,numIterations,device)
-    [imageOut] = IdentityFilter(imageIn,device)
-    [imageOut] = MultiplySum(imageIn,kernel,numIterations,device)
     [imageOut] = LoG(imageIn,sigmas,device)
-    [imageOut] = StdFilter(imageIn,kernel,numIterations,device)
-    [imageOut] = MeanFilter(imageIn,kernel,numIterations,device)
     [imageOut] = MedianFilter(imageIn,kernel,numIterations,device)
     [imageOut] = MinFilter(imageIn,kernel,numIterations,device)
     [imageOut] = MaxFilter(imageIn,kernel,numIterations,device)
     [imageOut] = NLMeans(imageIn,h,searchWindowRadius,nhoodRadius,device)
+    [imageOut] = Opener(imageIn,kernel,numIterations,device)
     [imageOut] = Sum(imageIn,device)
     [imageOut] = VarFilter(imageIn,kernel,numIterations,device)
+    [imageOut] = WienerFilter(imageIn,kernel,noiseVariance,device)
 end
 methods (Static, Access = private)
-    varargout = Mex(command, varargin)
+    varargout = HIP(command, varargin)
 end
 end

src/MATLAB/+HIP/@Cuda/DeviceCount.m

@@ -5,5 +5,5 @@
 %    		The memory structure contains the total memory on the device and the memory available for a Cuda call.
 %    
 function [numCudaDevices,memStats] = DeviceCount()
-    [numCudaDevices,memStats] = HIP.Cuda.Mex('DeviceCount');
+    [numCudaDevices,memStats] = HIP.Cuda.HIP('DeviceCount');
 end

src/MATLAB/+HIP/@Cuda/DeviceStats.m

@@ -3,5 +3,5 @@
 %    	DeviceStatsArray -- this is an array of structs, one struct per device.
 %    		The struct has these fields: name, major, minor, constMem, sharedMem, totalMem, tccDriver, mpCount, threadsPerMP, warpSize, maxThreads.
 function [deviceStatsArray] = DeviceStats()
-    [deviceStatsArray] = HIP.Cuda.Mex('DeviceStats');
+    [deviceStatsArray] = HIP.Cuda.HIP('DeviceStats');
 end

src/MATLAB/+HIP/@Cuda/ElementWiseDifference.m

@@ -14,5 +14,5 @@
 %    
 %    	imageOut = This will be an array of the same type and shape as the input array.
 function [imageOut] = ElementWiseDifference(image1In,image2In,device)
-    [imageOut] = HIP.Cuda.Mex('ElementWiseDifference',image1In,image2In,device);
+    [imageOut] = HIP.Cuda.HIP('ElementWiseDifference',image1In,image2In,device);
 end

src/MATLAB/+HIP/@Cuda/EntropyFilter.m

@@ -15,5 +15,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = EntropyFilter(imageIn,kernel,device)
-    [imageOut] = HIP.Cuda.Mex('EntropyFilter',imageIn,kernel,device);
+    [imageOut] = HIP.Cuda.HIP('EntropyFilter',imageIn,kernel,device);
 end

src/MATLAB/+HIP/@Cuda/Gaussian.m

@@ -18,5 +18,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = Gaussian(imageIn,sigmas,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('Gaussian',imageIn,sigmas,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('Gaussian',imageIn,sigmas,numIterations,device);
 end

src/MATLAB/+HIP/@Cuda/GetMinMax.m

@@ -10,5 +10,5 @@
 %    	maxValue = This is the highest value found in the array.
 %    
 function [minVal,maxVal] = GetMinMax(imageIn,device)
-    [minVal,maxVal] = HIP.Cuda.Mex('GetMinMax',imageIn,device);
+    [minVal,maxVal] = HIP.Cuda.HIP('GetMinMax',imageIn,device);
 end

src/MATLAB/+HIP/@Cuda/Help.m

 % Help - Print detailed usage information for the specified command.
 %    HIP.Cuda.Help([command])
 function Help(command)
-    HIP.Cuda.Mex('Help',command);
+    HIP.Cuda.HIP('Help',command);
 end

src/MATLAB/+HIP/@Cuda/HighPassFilter.m

@@ -14,5 +14,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = HighPassFilter(imageIn,sigmas,device)
-    [imageOut] = HIP.Cuda.Mex('HighPassFilter',imageIn,sigmas,device);
+    [imageOut] = HIP.Cuda.HIP('HighPassFilter',imageIn,sigmas,device);
 end

src/MATLAB/+HIP/@Cuda/IdentityFilter.m

@@ -11,5 +11,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = IdentityFilter(imageIn,device)
-    [imageOut] = HIP.Cuda.Mex('IdentityFilter',imageIn,device);
+    [imageOut] = HIP.Cuda.HIP('IdentityFilter',imageIn,device);
 end

src/MATLAB/+HIP/@Cuda/Info.m

@@ -7,5 +7,5 @@
 %       commandInfo.helpLines - Help string
 %    
 function [cmdInfo] = Info()
-    [cmdInfo] = HIP.Cuda.Mex('Info');
+    [cmdInfo] = HIP.Cuda.HIP('Info');
 end

src/MATLAB/+HIP/@Cuda/LoG.m

@@ -14,5 +14,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = LoG(imageIn,sigmas,device)
-    [imageOut] = HIP.Cuda.Mex('LoG',imageIn,sigmas,device);
+    [imageOut] = HIP.Cuda.HIP('LoG',imageIn,sigmas,device);
 end

src/MATLAB/+HIP/@Cuda/MaxFilter.m

@@ -19,5 +19,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = MaxFilter(imageIn,kernel,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('MaxFilter',imageIn,kernel,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('MaxFilter',imageIn,kernel,numIterations,device);
 end

src/MATLAB/+HIP/@Cuda/MeanFilter.m

@@ -19,5 +19,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = MeanFilter(imageIn,kernel,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('MeanFilter',imageIn,kernel,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('MeanFilter',imageIn,kernel,numIterations,device);
 end

src/MATLAB/+HIP/@Cuda/MedianFilter.m

@@ -19,5 +19,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = MedianFilter(imageIn,kernel,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('MedianFilter',imageIn,kernel,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('MedianFilter',imageIn,kernel,numIterations,device);
 end

src/MATLAB/+HIP/@Cuda/MinFilter.m

@@ -19,5 +19,5 @@
 %    	imageOut = This will be an array of the same type and shape as the input array.
 %    
 function [imageOut] = MinFilter(imageIn,kernel,numIterations,device)
-    [imageOut] = HIP.Cuda.Mex('MinFilter',imageIn,kernel,numIterations,device);
+    [imageOut] = HIP.Cuda.HIP('MinFilter',imageIn,kernel,numIterations,device);
 end