addressed issues raised in the PR

Author: sunitanyk

modules/alphamat/CMakeLists.txt

@@ -5,10 +5,6 @@ if(NOT HAVE_EIGEN)
   ocv_module_disable(alphamat)
 endif()
 
-cmake_minimum_required (VERSION 3.0)
-project (myproject)
-set (CMAKE_CXX_STANDARD 11)
-
 ocv_define_module(alphamat opencv_core
     opencv_imgproc
 )

modules/alphamat/include/opencv2/alphamat.hpp

@@ -0,0 +1,59 @@
+/*
+ By downloading, copying, installing or using the software you agree to this
+ license. If you do not agree to this license, do not download, install,
+ copy or use the software.
+                License Agreement
+        For Open Source Computer Vision Library
+                (3-clause BSD License)
+ Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+ 
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+ 
+    * Redistributions of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+ 
+    * Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+ 
+    * Neither the names of the copyright holders nor the names of the contributors
+ may be used to endorse or promote products derived from this software
+ without specific prior written permission.
+ 
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are
+ disclaimed. In no event shall copyright holders or contributors be liable for
+ any direct, indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+ */
+
+/** Information Flow algorithm implementaton for alphamatting */
+
+#ifndef _OPENCV_ALPHAMAT_HPP_
+#define _OPENCV_ALPHAMAT_HPP_
+
+/**
+ * @defgroup alphamat Alpha Matting
+ * This module is dedicated to compute alpha matting of images, given the input image and an input trimap.
+ * The samples directory includes easy examples of how to use the module.
+ *
+ * The implementation is based on Designing Effective Inter-Pixel Information Flow for Natural Image Matting by Yağız Aksoy, Tunç Ozan Aydın and Marc Pollefeys, CVPR 2019.
+ *
+ * This module has been originally developed by Muskaan Kularia and Sunita Nayak as a project
+ * for Google Summer of Code 2019 (GSoC 19).
+ *
+ */
+
+namespace cv{ namespace alphamat{
+
+CV_EXPORTS_W void infoFlow(Mat& image, Mat& tmap, Mat& result);
+
+}}
+#endif

modules/alphamat/include/opencv2/infoflow.hpp

@@ -4,10 +4,9 @@
 
 #include <iostream>
 #include "opencv2/highgui.hpp"
-#include <opencv2/core/base.hpp>
-#include <opencv2/core/utility.hpp>
+#include <opencv2/core.hpp>
 #include <opencv2/imgproc.hpp>
-#include <opencv2/infoflow.hpp>
+#include <opencv2/alphamat.hpp>
 
 using namespace std;
 using namespace cv;
@@ -28,7 +27,7 @@ int main(int argc, char *argv[])
 
     if (show_help)
     {
-        printf("\nThis sample demonstrates Information Flow alpha matting\n"
+        printf("\nThis sample demonstrates Information Flow Alpha Matting\n"
                "Call:\n"
                "    %s -img=<string> -tri=<string> [-out=<string>]\n\n", argv[0]);
         return 0;
@@ -75,7 +74,6 @@ int main(int argc, char *argv[])
         imwrite(result_path, result);
     }
 
-    imshow("Result Matte", result);
     return 0;
 
 }

modules/alphamat/samples/information_flow_matting.cpp

@@ -59,7 +59,7 @@
 #include <vector>
 
 /** Library version: 0xMmP (M=Major,m=minor,P=patch) */
-#define NANOFLANN_VERSION 0x130
+#define NANOFLANN_VERSION 0x132
 
 // Avoid conflicting declaration of min/max macros in windows headers
 #if !defined(NOMINMAX) &&                                                      \
@@ -469,7 +469,8 @@ struct SO2_Adaptor {
   /** Note: this assumes that input angles are already in the range [-pi,pi] */
   template <typename U, typename V>
   inline DistanceType accum_dist(const U a, const V b, const size_t) const {
-    DistanceType result = DistanceType(), PI = pi_const<DistanceType>();
+    DistanceType result = DistanceType();
+    DistanceType PI = pi_const<DistanceType>();
     result = b - a;
     if (result > PI)
       result -= 2 * PI;

modules/alphamat/samples/input_images/net.png

@@ -11,7 +11,7 @@
 #include "cm.hpp"
 
 namespace cv{
-  namespace alphamat{
+namespace alphamat{
 
 void generateFVectorCM(my_vector_of_vectors_t &samples, Mat &img)
 {
@@ -24,18 +24,17 @@ void generateFVectorCM(my_vector_of_vectors_t &samples, Mat &img)
 
   int i, j;
 
- for (i = 0; i < nRows; ++i)
- for (j = 0; j < nCols; ++j){
- samples[i*nCols+j].resize(dim);
- samples[i*nCols+j][0] = img.at<cv::Vec3b>(i, j)[0]/255.0;
- samples[i*nCols+j][1] = img.at<cv::Vec3b>(i, j)[1]/255.0;
- samples[i*nCols+j][2] = img.at<cv::Vec3b>(i, j)[2]/255.0;
- samples[i*nCols+j][3] = double(i)/nRows;
- samples[i*nCols+j][4] = double(j)/nCols;
- }
+  for (i = 0; i < nRows; ++i)
+     for (j = 0; j < nCols; ++j){
+         samples[i*nCols+j].resize(dim);
+         samples[i*nCols+j][0] = img.at<cv::Vec3b>(i, j)[0]/255.0;
+         samples[i*nCols+j][1] = img.at<cv::Vec3b>(i, j)[1]/255.0;
+         samples[i*nCols+j][2] = img.at<cv::Vec3b>(i, j)[2]/255.0;
+         samples[i*nCols+j][3] = double(i)/nRows;
+         samples[i*nCols+j][4] = double(j)/nCols;
+     }
 }
 
-
 void kdtree_CM(Mat &img, my_vector_of_vectors_t& indm, my_vector_of_vectors_t& samples, std::unordered_set<int>& unk)
 {
   // Generate feature vectors for intra U:

modules/alphamat/samples/input_images/plant.jpg

@@ -3,12 +3,18 @@
 // of this distribution and at http://opencv.org/license.html.
 
 #include "precomp.hpp"
-#include "opencv2/infoflow.hpp"
+#include "opencv2/alphamat.hpp"
 #include <fstream>
+#include <Eigen/Sparse>
+
+using namespace Eigen;
 
 namespace cv{
 namespace alphamat{
 
+void solve(SparseMatrix<double> Wcm,SparseMatrix<double> Wuu,SparseMatrix<double> Wl,SparseMatrix<double> Dcm,
+               SparseMatrix<double> Duu,SparseMatrix<double> Dl, SparseMatrix<double> T, Mat& wf, Mat& alpha);
+
 void solve(SparseMatrix<double> Wcm,SparseMatrix<double> Wuu,SparseMatrix<double> Wl,SparseMatrix<double> Dcm,
     SparseMatrix<double> Duu, SparseMatrix<double> Dl, SparseMatrix<double> T,
     Mat &wf, Mat &alpha){

modules/alphamat/samples/input_images/plant.png

@@ -10,24 +10,26 @@
 #include <unordered_set>
 #include <set>
 
-#include <opencv2/core/base.hpp>
-#include <opencv2/core/utility.hpp>
+#include <opencv2/core.hpp>
 #include <opencv2/imgproc.hpp>
 
-#include "nanoflann.hpp"
-#include "KDTreeVectorOfVectorsAdaptor.h"
+#include "3rdparty/nanoflann.hpp"
+#include "3rdparty/KDTreeVectorOfVectorsAdaptor.h"
+
+#ifdef HAVE_EIGEN
 #include <Eigen/Eigen>
 #include <opencv2/core/eigen.hpp>
+#include <Eigen/IterativeLinearSolvers>
+#endif
 
 namespace cv{
-	namespace alphamat{
-		const int dim = 5;  // dimension of feature vectors
-    }
+namespace alphamat{
+const int dim = 5;  // dimension of feature vectors        
+}
 }
 
 #include "intraU.hpp"
 #include "cm.hpp"
 #include "local_info.hpp"
-#include <Eigen/IterativeLinearSolvers>
 
 #endif

modules/alphamat/samples/input_images/plasticbag.png

@@ -11,19 +11,19 @@ Alphamatting is the problem of extracting the foreground from an image. The extr
 
 Given an input image and its corresponding trimap, we try to extract the foreground from the background. Following is an example:
 
-Input Image: ![](images/net_input_image.jpg)
-Input Trimap: ![](images/net_trimap.jpg)
-Output alpha Matte: ![](images/net_result.jpg)
+Input Image: ![](samples/input_images/plant.jpg)
+Input Trimap: ![](samples/trimaps/plant.png)
+Output alpha Matte: ![](samples/output_mattes/plant_result.jpg)
 
-This project is implementation of "[[Designing Effective Inter-Pixel Information Flow for Natural Image Matting](http://people.inf.ethz.ch/aksoyy/ifm/)]" by Yağız Aksoy, Tunç Ozan Aydın and Marc Pollefeys[1]. It required implementation of parts of other papers [2,3,4].
+This project is implementation of @cite aksoy2017designing . It required implementation of parts of other papers [2,3,4].
 
 # Building
 
 This module uses the Eigen package.
 
-Build the sample code of the alphamat module using the following cmake command, followed by  ```make``` command.
+Build the sample code of the alphamat module using the following cmake command, followed by  `make` command.
 ```
-cmake -DOPENCV_EXTRA_MODULES_PATH=<path to opencv_contrib modules> -Dopencv_alphamat=ON -DBUILD_EXAMPLES=ON ..
+cmake -DOPENCV_EXTRA_MODULES_PATH=<path to opencv_contrib modules> -DBUILD_EXAMPLES=ON ..
 
 make
 ```