Convenience fixes

Attempting to fix issues pointed out by Vadim Pisarevsky during the pull
request review. In particular, the following things are done:
*) The mechanism of debug info printing is changed and made more
procedure-style than the previous macro-style
*) z in solveLP() is now returned as a column-vector
*) Func parameter of solveLP() is now allowed to be column-vector, in
which case it is understood to be the transpose of what we need
*) Func and Constr now can contain floats, not only doubles (in the
former case the conversion is done via convertTo())
*)different constructor to allocate space for z in solveLP() is used,
making the size of z more explicit (this is just a notation change, not
functional, both constructors are achieving the same goal)
*) (big) mat.hpp and iostream headers are moved to precomp-headers from
optim.hpp

modules/optim/doc/linear_programming.rst

@@ -28,11 +28,11 @@ by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular
 
 .. ocv:function:: int optim::solveLP(const Mat& Func, const Mat& Constr, Mat& z)
 
-    :param Func: This row-vector corresponds to :math:`c` in the LP problem formulation (see above).
+    :param Func: This row-vector corresponds to :math:`c` in the LP problem formulation (see above). It should contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, in the latter case it is understood to correspond to :math:`c^T`.
 
-    :param Constr: *m*-by-*n\+1* matrix, whose rightmost column corresponds to :math:`b` in formulation above and the remaining to :math:`A`.
+    :param Constr: *m*-by-*n\+1* matrix, whose rightmost column corresponds to :math:`b` in formulation above and the remaining to :math:`A`. It should containt 32- or 64-bit floating point numbers.
 
-    :param z: The solution will be returned here as a row-vector - it corresponds to (transposed) :math:`c` in the formulation above.
+    :param z: The solution will be returned here as a column-vector - it corresponds to :math:`c` in the formulation above. It will contain 64-bit floating point numbers.
 
     :return: One of the return codes:
 

modules/optim/include/opencv2/optim.hpp

@@ -43,10 +43,6 @@
 #ifndef __OPENCV_OPTIM_HPP__
 #define __OPENCV_OPTIM_HPP__
 
-#include <iostream>
-#include "opencv2/core.hpp"
-#include "opencv2/core/mat.hpp"
-
 //uncomment the next line to print the debug info
 //#define ALEX_DEBUG
 

modules/optim/src/lpsolver.cpp

@@ -9,39 +9,39 @@ using std::vector;
 
 #ifdef ALEX_DEBUG
 #define dprintf(x) printf x
-#define print_matrix(x) do{\
-    printf("\ttype:%d vs %d,\tsize: %d-on-%d\n",(x).type(),CV_64FC1,(x).rows,(x).cols);\
-    for(int i=0;i<(x).rows;i++){\
-        printf("\t[");\
-        for(int j=0;j<(x).cols;j++){\
-            printf("%g, ",(x).at<double>(i,j));\
-        }\
-        printf("]\n");\
-    }\
-}while(0)
-#define print_simplex_state(c,b,v,N,B) do{\
-    printf("\tprint simplex state\n");\
-    \
-    printf("v=%g\n",(v));\
-    \
-    printf("here c goes\n");\
-    print_matrix((c));\
-    \
-    printf("non-basic: ");\
-    for (std::vector<int>::const_iterator it = (N).begin() ; it != (N).end(); ++it){\
-        printf("%d, ",*it);\
-    }\
-    printf("\n");\
-    \
-    printf("here b goes\n");\
-    print_matrix((b));\
-    printf("basic: ");\
-    \
-    for (std::vector<int>::const_iterator it = (B).begin() ; it != (B).end(); ++it){\
-        printf("%d, ",*it);\
-    }\
-    printf("\n");\
-}while(0)
+static void print_matrix(const Mat& x){
+    printf("\ttype:%d vs %d,\tsize: %d-on-%d\n",(x).type(),CV_64FC1,(x).rows,(x).cols);
+    for(int i=0;i<(x).rows;i++){
+        printf("\t[");
+        for(int j=0;j<(x).cols;j++){
+            printf("%g, ",(x).at<double>(i,j));
+        }
+        printf("]\n");
+    }
+}
+static void print_simplex_state(const Mat& c,const Mat& b,double v,const std::vector<int> N,const std::vector<int> B){
+    printf("\tprint simplex state\n");
+    
+    printf("v=%g\n",(v));
+    
+    printf("here c goes\n");
+    print_matrix((c));
+    
+    printf("non-basic: ");
+    for (std::vector<int>::const_iterator it = (N).begin() ; it != (N).end(); ++it){
+        printf("%d, ",*it);
+    }
+    printf("\n");
+    
+    printf("here b goes\n");
+    print_matrix((b));
+    printf("basic: ");
+    
+    for (std::vector<int>::const_iterator it = (B).begin() ; it != (B).end(); ++it){
+        printf("%d, ",*it);
+    }
+    printf("\n");
+}
 #else
 #define dprintf(x) do {} while (0)
 #define print_matrix(x) do {} while (0)
@@ -66,16 +66,36 @@ int solveLP(const Mat& Func, const Mat& Constr, Mat& z){
     dprintf(("call to solveLP\n"));
 
     //sanity check (size, type, no. of channels)
-    CV_Assert(Func.type()==CV_64FC1);
-    CV_Assert(Constr.type()==CV_64FC1);
-    CV_Assert(Func.rows==1);
-    CV_Assert(Constr.cols-Func.cols==1);
+    CV_Assert(Func.type()==CV_64FC1 || Func.type()==CV_32FC1);
+    CV_Assert(Constr.type()==CV_64FC1 || Constr.type()==CV_32FC1);
+    CV_Assert((Func.rows==1 && (Constr.cols-Func.cols==1))||
+            (Func.cols==1 && (Constr.cols-Func.rows==1)));
 
     //copy arguments for we will shall modify them
-    Mat_<double> bigC=Mat_<double>(1,Func.cols+1),
+    Mat_<double> bigC=Mat_<double>(1,(Func.rows==1?Func.cols:Func.rows)+1),
         bigB=Mat_<double>(Constr.rows,Constr.cols+1);
-    Func.copyTo(bigC.colRange(1,bigC.cols));
-    Constr.copyTo(bigB.colRange(1,bigB.cols));
+    if(Func.rows==1){
+        Func.convertTo(bigC.colRange(1,bigC.cols),CV_64FC1);
+    }else{
+        dprintf(("hi from other branch\n"));
+        Mat_<double> slice=bigC.colRange(1,bigC.cols);
+        MatIterator_<double> slice_iterator=slice.begin();
+        switch(Func.type()){
+            case CV_64FC1:
+                for(MatConstIterator_<double> it=Func.begin<double>();it!=Func.end<double>();it++,slice_iterator++){
+                    * slice_iterator= *it;
+                }
+                break;
+            case CV_32FC1:
+                for(MatConstIterator_<float> it=Func.begin<float>();it!=Func.end<double>();it++,slice_iterator++){
+                    * slice_iterator= *it;
+                }
+                break;
+        }
+        print_matrix(Func);
+        print_matrix(bigC);
+    }
+    Constr.convertTo(bigB.colRange(1,bigB.cols),CV_64FC1);
     double v=0;
     vector<int> N,B;
 
@@ -91,8 +111,7 @@ int solveLP(const Mat& Func, const Mat& Constr, Mat& z){
     }
 
     //return the optimal solution
-    const int z_size[]={1,c.cols};
-    z.create(2,z_size,CV_64FC1);
+    z.create(c.cols,1,CV_64FC1);
     MatIterator_<double> it=z.begin<double>();
     for(int i=1;i<=c.cols;i++,it++){
         std::vector<int>::iterator pos=B.begin();

modules/optim/src/precomp.hpp

@@ -43,6 +43,8 @@
 #ifndef __OPENCV_PRECOMP_H__
 #define __OPENCV_PRECOMP_H__
 
+#include "opencv2/core.hpp"
+#include "opencv2/core/mat.hpp"
 #include "opencv2/optim.hpp"
 
 #endif

modules/optim/test/test_lpsolver.cpp

@@ -1,17 +1,17 @@
 #include "test_precomp.hpp"
-#include "opencv2/optim.hpp"
+#include <iostream>
 
 TEST(Optim_LpSolver, regression_basic){
     cv::Mat A,B,z,etalon_z;
 
     if(true){
     //cormen's example #1
-    A=(cv::Mat_<double>(1,3)<<3,1,2);
+    A=(cv::Mat_<double>(3,1)<<3,1,2);
     B=(cv::Mat_<double>(3,4)<<1,1,3,30,2,2,5,24,4,1,2,36);
     std::cout<<"here A goes\n"<<A<<"\n";
     cv::optim::solveLP(A,B,z);
     std::cout<<"here z goes\n"<<z<<"\n";
-    etalon_z=(cv::Mat_<double>(1,3)<<8,4,0);
+    etalon_z=(cv::Mat_<double>(3,1)<<8,4,0);
     ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
     }
 
@@ -22,7 +22,7 @@ TEST(Optim_LpSolver, regression_basic){
     std::cout<<"here A goes\n"<<A<<"\n";
     cv::optim::solveLP(A,B,z);
     std::cout<<"here z goes\n"<<z<<"\n";
-    etalon_z=(cv::Mat_<double>(1,2)<<20,0);
+    etalon_z=(cv::Mat_<double>(2,1)<<20,0);
     ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
     }
 
@@ -33,7 +33,7 @@ TEST(Optim_LpSolver, regression_basic){
     std::cout<<"here A goes\n"<<A<<"\n";
     cv::optim::solveLP(A,B,z);
     std::cout<<"here z goes\n"<<z<<"\n";
-    etalon_z=(cv::Mat_<double>(1,2)<<1,0);
+    etalon_z=(cv::Mat_<double>(2,1)<<1,0);
     ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
     }
 }
@@ -48,7 +48,7 @@ TEST(Optim_LpSolver, regression_init_unfeasible){
     std::cout<<"here A goes\n"<<A<<"\n";
     cv::optim::solveLP(A,B,z);
     std::cout<<"here z goes\n"<<z<<"\n";
-    etalon_z=(cv::Mat_<double>(1,3)<<1250,1000,0);
+    etalon_z=(cv::Mat_<double>(3,1)<<1250,1000,0);
     ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
     }
 }
@@ -71,7 +71,7 @@ TEST(Optim_LpSolver, regression_multiple_solutions){
 
     if(true){
     //trivial example with multiple solutions
-    A=(cv::Mat_<double>(1,2)<<1,1);
+    A=(cv::Mat_<double>(2,1)<<1,1);
     B=(cv::Mat_<double>(1,3)<<1,1,1);
     std::cout<<"here A goes\n"<<A<<"\n";
     int res=cv::optim::solveLP(A,B,z);
@@ -85,7 +85,7 @@ TEST(Optim_LpSolver, regression_multiple_solutions){
     if(false){
     //cormen's example from chapter about initialize_simplex
     //online solver told it has inf many solutions, but I'm not sure
-    A=(cv::Mat_<double>(1,2)<<2,-1);
+    A=(cv::Mat_<double>(2,1)<<2,-1);
     B=(cv::Mat_<double>(2,3)<<2,-1,2,1,-5,-4);
     std::cout<<"here A goes\n"<<A<<"\n";
     int res=cv::optim::solveLP(A,B,z);
@@ -101,7 +101,7 @@ TEST(Optim_LpSolver, regression_cycling){
 
     if(true){
     //example with cycling from http://people.orie.cornell.edu/miketodd/or630/SimplexCyclingExample.pdf
-    A=(cv::Mat_<double>(1,4)<<10,-57,-9,-24);
+    A=(cv::Mat_<double>(4,1)<<10,-57,-9,-24);
     B=(cv::Mat_<double>(3,5)<<0.5,-5.5,-2.5,9,0,0.5,-1.5,-0.5,1,0,1,0,0,0,1);
     std::cout<<"here A goes\n"<<A<<"\n";
     int res=cv::optim::solveLP(A,B,z);

modules/optim/test/test_precomp.hpp

@@ -9,6 +9,8 @@
 #ifndef __OPENCV_TEST_PRECOMP_HPP__
 #define __OPENCV_TEST_PRECOMP_HPP__
 
+#include "opencv2/core.hpp"
+#include "opencv2/core/mat.hpp"
 #include "opencv2/ts.hpp"
 #include "opencv2/optim.hpp"