diff --git a/CMakeLists.txt b/CMakeLists.txt
index 966967a..74360af 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,5 +1,5 @@
 #Specify the version being used aswell as the language
-cmake_minimum_required(VERSION 2.8)
+cmake_minimum_required(VERSION 3.16)
 
 #Name your project here
 project(ga-gpu)
@@ -32,8 +32,8 @@ SET( CUDA_NVCC_FLAGS "--gpu-architecture=compute_50 --gpu-code=sm_50,compute_50"
 
 #find packages-----------------------------------
 #find OpenCV
-find_package(OpenCV REQUIRED)
-add_definitions(-DUSING_OPENCV)
+#find_package(OpenCV REQUIRED)
+#add_definitions(-DUSING_OPENCV)
 
 #find the pthreads package
 find_package(Threads)
diff --git a/FindSTIM.cmake b/FindSTIM.cmake
index a83481a..cb2bcc7 100644
--- a/FindSTIM.cmake
+++ b/FindSTIM.cmake
@@ -3,20 +3,14 @@
 
 include(FindPackageHandleStandardArgs)
 
-set(STIM_INCLUDE_DIR $ENV{STIMLIB_PATH})
+set(STIM_ROOT $ENV{STIM_ROOT})
 
-find_package_handle_standard_args(STIM DEFAULT_MSG STIM_INCLUDE_DIR)
+IF(NOT STIM_ROOT)
+    MESSAGE("ERROR: STIM_ROOT environment variable must be set!")
+ENDIF(NOT STIM_ROOT)
 
-if(STIM_FOUND)
-    set(STIM_INCLUDE_DIRS ${STIM_INCLUDE_DIR})
-elseif(STIM_FOUND)
-	#if the STIM library isn't found, download it
-	#file(REMOVE_RECURSE ${CMAKE_BINARY_DIR}/stimlib)	#remove the stimlib directory if it exists
-	#set(STIM_GIT "https://git.stim.ee.uh.edu/codebase/stimlib.git")
-	#execute_process(COMMAND git clone --depth 1 ${STIM_GIT} WORKING_DIRECTORY ${CMAKE_BINARY_DIR})
-	#set(STIM_INCLUDE_DIRS "${CMAKE_BINARY_DIR}/stimlib" CACHE TYPE PATH)
-	message("STIM library not found. Set the STIMLIB_PATH environment variable to the STIMLIB location.")
-	message("STIMLIB can be found here: https://git.stim.ee.uh.edu/codebase/stimlib")
-endif(STIM_FOUND)
+    FIND_PATH(STIM_INCLUDE_DIRS DOC "Path to STIM include directory."
+              NAMES stim/image/image.h
+              PATHS ${STIM_ROOT})
 
-find_package_handle_standard_args(STIM DEFAULT_MSG STIM_INCLUDE_DIR)
+find_package_handle_standard_args(STIM DEFAULT_MSG STIM_INCLUDE_DIRS)
diff --git a/src/ga_gpu.h b/src/ga_gpu.h
index e05bd6e..eaec051 100644
--- a/src/ga_gpu.h
+++ b/src/ga_gpu.h
@@ -4,7 +4,7 @@
 #include <iostream>
 #include <thread>
 #include <complex>
-#include <cv.h>
+//#include <cv.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <iostream>
diff --git a/src/main.cpp b/src/main.cpp
index 5c09727..71ac674 100644
--- a/src/main.cpp
+++ b/src/main.cpp
@@ -153,6 +153,7 @@ void gpuComputeEignS( size_t g, size_t fea){
 		std::cout<<std::endl;
 	}
 	
+	///DETERMINANT CURRENTLY REQUIRES OPENCV
 	cv::Mat newSw = cv::Mat((int)r, (int)r, CV_32FC1, nSw);		//within scatter of gnome g in the population 
 	cv::Mat newSb = cv::Mat((int)r, (int)r, CV_32FC1, nSb);		//within scatter of gnome g in the population 
 			
@@ -167,20 +168,6 @@ void gpuComputeEignS( size_t g, size_t fea){
 			std::cout << ga.P[ga.f * g + i] << ", ";
 		std::cout << std::endl;
 	}
-//	if(!isfinite(gnom.S[g])){
-//      std::cout<<"-----------------------------------------------"<<std::endl;
-//      std::cout<<"Displaying intermediate values of gnome for which score is non finite"<<std::endl;
-//     	std::cout<<"population "<<std::endl;
-//			for(int i = 0; i < ga.f; i++){
-//				std::cout<<"\t"<<ga.P[g * ga.f + i];
-//			}
-//      std::cout<<std::endl;
-//      std::cout<<"Sb determinant is "<<cv::determinant(newSb)<<"\t Sw determinant is "<<cv::determinant(newSb)<<std::endl;
-//     	std::cout<<"fisher ratio is "<<fisherRatio<<std::endl;
-//			std::cout<<"Score["<<g<<"]: "<< gnom.S[g]<<std::endl;	
-//      std::cout<<"------------------------------------------------"<<std::endl;
-//
-//	}
 		
 	
 	if(IPIV!= NULL) std::free(IPIV);	
@@ -223,8 +210,6 @@ void fitnessFunction( float* sb, float* sw, float* lda, float* M, float* cM, siz
 		}				
 		
 	// -----------------------   Linear discriminant Analysis   --------------------------------------
-		//timer.start();	
-		//structure is created to pass variable to thread function as it accepts only 3 arguments 
 		gnom.S = ga.S;
 		gnom.Sw = sw;
 		gnom.Sb = sb;
@@ -234,26 +219,13 @@ void fitnessFunction( float* sb, float* sw, float* lda, float* M, float* cM, siz
 		for (size_t i = 0; i<ga.p; i++){			
 			//calling function for eigencomputation
 			gpuComputeEignS(i, f);
-			//std::cout<<"Score["<<i<<"]: "<< ga.S[i]<<std::endl;
 		}	
 	
-		//std::vector<std::thread> threads;
-		//for (size_t g = 0; g<ga.p; g++){
-		//	//creating thread to do eigen computation 
-		//	threads.push_back(std::thread(gpuComputeEignS, g, f));	
-		//}	
-		//
-		//// loop again to join the threads
-		//for (auto& t : threads)
-		//	t.join();
-
 		const auto elapsed1 = timer.time_elapsed();
 		if(gen > ga.gnrtn - 2){
 			std::cout << "gpu_eigen time "<<std::chrono::duration_cast<std::chrono::microseconds>(elapsed1).count() << "us" << std::endl;
       profilefile<< "gpu_eigen time "<<std::chrono::duration_cast<std::chrono::microseconds>(elapsed1).count() << "us" << std::endl;
 		}
-		//ga.S = gnom.score;
-		//size_t bestGnomeIdx = ga.sortSIndx()[0];
 
 }//end of fitness function
 
@@ -276,7 +248,6 @@ void advertisement() {
 	std::cout << "=========================================================================" << std::endl;
 	std::cout << "Thank you for using the GA-GPU features selection for spectroscopic image!" << std::endl;
 	std::cout << "=========================================================================" << std::endl << std::endl;
-//	std::cout << args.str();
 }
 
 int main(int argc, char* argv[]){
@@ -335,8 +306,7 @@ int main(int argc, char* argv[]){
 	E.close();													//close the hyperspectral file
 	time_t t_end = time(NULL);
 	std::cout<<"Total time: "<<t_end - t_start<<" s"<<std::endl;	
-	//display_PixelfeatureNclass(ga.F, ga.T , B , 1);			//Print one value from feature matrix to debug feature loading
-	//std::cout << "pixel target is " << ga.T[0] << "  " << ga.T[1] << "  " << ga.T[tP - 2] << "  " << ga.T[tP - 1]<<std::endl;
+	
 ///--------------------------Genetic algorith configurations with defult paramets and from argument values---------------------
 	ga.f = args["features"].as_int(0);					//number of features to be selected by user default value is 10
 	ga.p = args["population"].as_int(0);				//population size to be selected by user default value is 1000
@@ -415,11 +385,6 @@ int main(int argc, char* argv[]){
 	HANDLE_ERROR(cudaGetDeviceProperties(&props, 0));
 	ga.gpuInitializationfrommain(M, cM, nP, tP, nC);
 
-//feture selection for class selected by user with user arguments (make it binary class data by making chosen class label as 1 and al other class labels 0 from multiclass data )
-//to select feature for all classes in joint class data using binary class system need to write a script with loop covering all classes
-	//if(binaryClass){									
-	//	binaryclassifier(binClassOne);
-	//}
 
 //============================= GA evolution by generations ====================================================
 	std::vector<unsigned int> bestgnome;												//holds best gnome after each generation evaluation 
@@ -468,9 +433,6 @@ int main(int argc, char* argv[]){
 	std::ofstream csv(outfile.c_str(), std::ios::out);			//open outfstream for outfile
 	size_t ldaindx = bestG_Indx * (nC-1) * ga.f ;				//Compute LDA basis index of best gnome
 	
-	//if(binaryClass){											//this option is for binary class feature selection from joint classes but not fully implemented
-	//	csv<<binClassOne<<std::endl;
-	//}
 
  //fitness values of best gnome is 
 	csv<<"best gnome's fitness value is "<<best_S[ga.gnrtn-1]<<std::endl;									//output fitness value of best gnome in last generation
--
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