Merge opencv master

.github/ISSUE_TEMPLATE.md

@@ -1,15 +1,8 @@
-<!--
-If you have a question rather than reporting a bug please go to http://answers.opencv.org where you get much faster responses.
-If you need further assistance please read [How To Contribute](https://github.com/opencv/opencv/wiki/How_to_contribute).
-
-This is a template helping you to create an issue which can be processed as quickly as possible. This is the bug reporting section for the OpenCV library.
--->
-
 ##### System information (version)
 <!-- Example
-- OpenCV => 3.1
+- OpenCV => 4.2
 - Operating System / Platform => Windows 64 Bit
-- Compiler => Visual Studio 2015
+- Compiler => Visual Studio 2017
 -->
 
 - OpenCV => :grey_question:
@@ -27,4 +20,44 @@ This is a template helping you to create an issue which can be processed as quic
     // C++ code example
     ```
  or attach as .txt or .zip file
--->
+-->
+
+##### Issue submission checklist
+
+ - [ ] I report the issue, it's not a question
+   <!--
+   OpenCV team works with answers.opencv.org, Stack Overflow and other communities
+   to discuss problems. Tickets with question without real issue statement will be
+   closed.
+   -->
+ - [ ] I checked the problem with documentation, FAQ, open issues,
+       answers.opencv.org, Stack Overflow, etc and have not found solution
+   <!--
+   Places to check:
+   * OpenCV documentation: https://docs.opencv.org
+   * FAQ page: https://github.com/opencv/opencv/wiki/FAQ
+   * OpenCV forum: https://answers.opencv.org
+   * OpenCV issue tracker: https://github.com/opencv/opencv/issues?q=is%3Aissue
+   * OpenCV Contrib issue tracker: https://github.com/opencv/opencv_contrib/issues?q=is%3Aissue
+   * Stack Overflow branch: https://stackoverflow.com/questions/tagged/opencv
+   -->
+ - [ ] I updated to latest OpenCV version and the issue is still there
+   <!--
+   master branch for OpenCV 4.x and 3.4 branch for OpenCV 3.x releases.
+   OpenCV team supports only latest release for each branch.
+   The ticket is closed, if the problem is not reproduced with modern version.
+   -->
+ - [ ] There is reproducer code and related data files: videos, images, onnx, etc
+   <!--
+   The best reproducer -- test case for OpenCV that we can add to the library.
+   Recommendations for media files and binary files:
+   * Try to reproduce the issue with images and videos in opencv_extra repository
+     to reduce attachment size
+   * Use PNG for images, if you report some CV related bug, but not image reader
+     issue
+   * Attach the image as archive to the ticket, if you report some reader issue.
+     Image hosting services compress images and it breaks the repro code.
+   * Provide ONNX file for some public model or ONNX file with with random weights,
+     if you report ONNX parsing or handling issue. Architecture details diagram
+     from netron tool can be very useful too. See https://lutzroeder.github.io/netron/
+   -->

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,9 +1,11 @@
-<!-- Please use this line to close one or multiple issues when this pullrequest gets merged
-You can add another line right under the first one:
-resolves #1234
-resolves #1235
--->
+### Pull Request Readiness Checklist
 
-### This pullrequest changes
+See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
 
-<!-- Please describe what your pullrequest is changing -->
+- [ ] I agree to contribute to the project under OpenCV (BSD) License.
+- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
+- [ ] The PR is proposed to proper branch
+- [ ] There is reference to original bug report and related work
+- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
+      Patch to opencv_extra has the same branch name.
+- [ ] The feature is well documented and sample code can be built with the project CMake

.travis.yml

@@ -4,7 +4,7 @@ compiler:
   - clang
 before_script:
   - cd ../
-  - git clone --branch 3.4 --depth=1 https://github.com/opencv/opencv.git
+  - git clone --branch master --depth=1 https://github.com/opencv/opencv.git
   - mkdir build-opencv
   - cd build-opencv
   - cmake 

README.md

@@ -31,6 +31,8 @@ use CMake's `BUILD_opencv_*` options. Like in this example:
 $ cmake -DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -DBUILD_opencv_legacy=OFF <opencv_source_directory>
 ```
 
+If you also want to build the samples from the "samples" folder of each module, also include the "-DBUILD_EXAMPLES=ON" option.
+
 If you prefer using the gui version of cmake (cmake-gui), then, you can add `opencv_contrib` modules within `opencv` core by doing the following:
 
 1. start cmake-gui

modules/README.md

@@ -10,6 +10,8 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **aruco**: ArUco and ChArUco Markers -- Augmented reality ArUco marker and "ChARUco" markers where ArUco markers embedded inside the white areas of the checker board.
 
+- **alphamat**: Computer Vision based Alpha Matting -- Given an input image and a trimap, generate an alpha matte.
+
 - **bgsegm**: Background segmentation algorithm combining statistical background image estimation and per-pixel Bayesian segmentation.
 
 - **bioinspired**: Biological Vision -- Biologically inspired vision model: minimize noise and luminance variance, transient event segmentation, high dynamic range tone mapping methods.
@@ -22,7 +24,9 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **datasets**: Datasets Reader -- Code for reading existing computer vision databases and samples of using the readers to train, test and run using that dataset's data.
 
-- **dnn_objdetect**: Object Detection using CNNs -- Implements compact CNN Model for object detection. Trained using Caffe but uses opencv_dnn modeule.
+- **dnn_objdetect**: Object Detection using CNNs -- Implements compact CNN Model for object detection. Trained using Caffe but uses opencv_dnn module.
+
+- **dnn_superres**: Superresolution using CNNs -- Contains four trained convolutional neural networks to upscale images.
 
 - **dnns_easily_fooled**: Subvert DNNs -- This code can use the activations in a network to fool the networks into recognizing something else.
 
@@ -48,13 +52,13 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **reg**: Image Registration -- Pixels based image registration for precise alignment. Follows the paper "Image Alignment and Stitching: A Tutorial", by Richard Szeliski.
 
-- **rgbd**: RGB-Depth Processing module -- Linemod 3D object recognition; Fast surface normals and 3D plane finding. 3D visual odometry
+- **rgbd**: RGB-Depth Processing module -- Linemod 3D object recognition; Fast surface normals and 3D plane finding. 3D visual odometry. 3d reconstruction using KinectFusion.
 
 - **saliency**: Saliency API -- Where humans would look in a scene. Has routines for static, motion and "objectness" saliency.
 
 - **sfm**: Structure from Motion -- This module contains algorithms to perform 3d reconstruction from 2d images. The core of the module is a light version of Libmv.
 
-- **stereo**: Stereo Correspondence -- Stereo matching done with different descriptors: Census / CS-Census / MCT / BRIEF / MV.
+- **stereo**: Stereo Correspondence -- Stereo matching done with different descriptors: Census / CS-Census / MCT / BRIEF / MV and dense stereo correspondence using Quasi Dense Stereo method.
 
 - **structured_light**: Structured Light Use -- How to generate and project gray code patterns and use them to find dense depth in a scene.
 

modules/alphamat/CMakeLists.txt

@@ -0,0 +1,9 @@
+if(NOT HAVE_EIGEN)
+  message(STATUS "Module opencv_alphamat disabled because the following dependencies are not found: Eigen")
+  ocv_module_disable(alphamat)
+endif()
+
+ocv_define_module(alphamat
+    opencv_core
+    opencv_imgproc
+)

modules/alphamat/README.md

@@ -0,0 +1,23 @@
+# Computer Vision based Alpha Matting
+
+This project was part of the Google Summer of Code 2019.
+
+####Student: Muskaan Kularia
+####Mentor: Sunita Nayak
+***
+Alphamatting is the problem of extracting the foreground from an image. Given the input of an image and its corresponding trimap, we try to extract the foreground from the background.
+
+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].
+
+
+## References
+
+[1] Yagiz Aksoy, Tunc Ozan Aydin, Marc Pollefeys, "[Designing Effective Inter-Pixel Information Flow for Natural Image Matting](http://people.inf.ethz.ch/aksoyy/ifm/)", CVPR, 2017.
+
+[2] Roweis, Sam T., and Lawrence K. Saul. "[Nonlinear dimensionality reduction by locally linear embedding](https://science.sciencemag.org/content/290/5500/2323)" Science 290.5500 (2000): 2323-2326.
+
+[3] Anat Levin, Dani Lischinski, Yair Weiss, "[A Closed Form Solution to Natural Image Matting](https://www.researchgate.net/publication/5764820_A_Closed-Form_Solution_to_Natural_Image_Matting)", IEEE TPAMI, 2008.
+
+[4] Qifeng Chen, Dingzeyu Li, Chi-Keung Tang, "[KNN Matting](http://dingzeyu.li/files/knn-matting-tpami.pdf)", IEEE TPAMI, 2013.
+
+[5] Yagiz Aksoy, "[Affinity Based Matting Toolbox](https://github.com/yaksoy/AffinityBasedMattingToolbox)".

modules/alphamat/doc/alphamat.bib

@@ -0,0 +1,26 @@
+@inproceedings{aksoy2017designing,
+  title={Designing effective inter-pixel information flow for natural image matting},
+  author={Aksoy, Yagiz and Ozan Aydin, Tunc and Pollefeys, Marc},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={29--37},
+  year={2017}
+}
+
+@article{roweis2000nonlinear,
+  title={Nonlinear dimensionality reduction by locally linear embedding},
+  author={Roweis, Sam T and Saul, Lawrence K},
+  journal={science},
+  volume={290},
+  number={5500},
+  pages={2323--2326},
+  year={2000},
+  publisher={American Association for the Advancement of Science}
+}
+
+@inproceedings{shahrian2013improving,
+  title={Improving image matting using comprehensive sampling sets},
+  author={Shahrian, Ehsan and Rajan, Deepu and Price, Brian and Cohen, Scott},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={636--643},
+  year={2013}
+}

modules/alphamat/include/opencv2/alphamat.hpp

@@ -0,0 +1,32 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+/** 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.
+ */
+
+namespace cv { namespace alphamat {
+//! @addtogroup alphamat
+//! @{
+
+/**
+ * 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).
+ *
+ */
+CV_EXPORTS_W void infoFlow(InputArray image, InputArray tmap, OutputArray result);
+
+//! @}
+}}  // namespace
+
+#endif

modules/alphamat/samples/information_flow_matting.cpp

@@ -0,0 +1,77 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include <iostream>
+#include "opencv2/highgui.hpp"
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/alphamat.hpp>
+
+using namespace std;
+using namespace cv;
+using namespace cv::alphamat;
+
+const char* keys =
+    "{img || input image name}"
+    "{tri || input trimap image name}"
+    "{out || output image name}"
+    "{help h || print help message}"
+;
+
+int main(int argc, char* argv[])
+{
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("This sample demonstrates Information Flow Alpha Matting");
+
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    string img_path = parser.get<std::string>("img");
+    string trimap_path = parser.get<std::string>("tri");
+    string result_path = parser.get<std::string>("out");
+
+    if (!parser.check()
+            || img_path.empty() || trimap_path.empty())
+    {
+        parser.printMessage();
+        parser.printErrors();
+        return 1;
+    }
+
+    Mat image, tmap;
+
+    image = imread(img_path, IMREAD_COLOR);  // Read the input image file
+    if (image.empty())
+    {
+        printf("Cannot read image file: '%s'\n", img_path.c_str());
+        return 1;
+    }
+
+    tmap = imread(trimap_path, IMREAD_GRAYSCALE);
+    if (tmap.empty())
+    {
+        printf("Cannot read trimap file: '%s'\n", trimap_path.c_str());
+        return 1;
+    }
+
+    Mat result;
+    infoFlow(image, tmap, result);
+
+    if (result_path.empty())
+    {
+        namedWindow("result alpha matte", WINDOW_NORMAL);
+        imshow("result alpha matte", result);
+        waitKey(0);
+    }
+    else
+    {
+        imwrite(result_path, result);
+        printf("Result saved: '%s'\n", result_path.c_str());
+    }
+
+    return 0;
+}