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Interactive Visual Debugging of Computer Vision applications {#tutorial_cvv_introduction}
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============================================================
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What is the most common way to debug computer vision applications? Usually the answer is temporary,
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hacked together, custom code that must be removed from the code for release compilation.
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In this tutorial we will show how to use the visual debugging features of the **cvv** module
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(*opencv2/cvv.hpp*) instead.
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Goals
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-----
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In this tutorial you will learn how to:
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- Add cvv debug calls to your application
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- Use the visual debug GUI
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- Enable and disable the visual debug features during compilation (with zero runtime overhead when
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disabled)
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Code
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----
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The example code
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- captures images (*videoio*), e.g. from a webcam,
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- applies some filters to each image (*imgproc*),
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- detects image features and matches them to the previous image (*features*).
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If the program is compiled without visual debugging (see CMakeLists.txt below) the only result is
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some information printed to the command line. We want to demonstrate how much debugging or
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development functionality is added by just a few lines of *cvv* commands.
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@includelineno cvv/samples/cvv_demo.cpp
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@code{.cmake}
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cmake_minimum_required(VERSION 2.8)
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project(cvvisual_test)
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SET(CMAKE_PREFIX_PATH ~/software/opencv/install)
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SET(CMAKE_CXX_COMPILER "g++-4.8")
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SET(CMAKE_CXX_FLAGS "-std=c++11 -O2 -pthread -Wall -Werror")
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# (un)set: cmake -DCVV_DEBUG_MODE=OFF ..
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OPTION(CVV_DEBUG_MODE "cvvisual-debug-mode" ON)
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if(CVV_DEBUG_MODE MATCHES ON)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCVVISUAL_DEBUGMODE")
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endif()
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FIND_PACKAGE(OpenCV REQUIRED)
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include_directories(${OpenCV_INCLUDE_DIRS})
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add_executable(cvvt main.cpp)
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target_link_libraries(cvvt
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opencv_core opencv_videoio opencv_imgproc opencv_features
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opencv_cvv
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)
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@endcode
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Explanation
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-----------
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-# We compile the program either using the above CmakeLists.txt with Option *CVV_DEBUG_MODE=ON*
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(*cmake -DCVV_DEBUG_MODE=ON*) or by adding the corresponding define *CVVISUAL_DEBUGMODE* to
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our compiler (e.g. *g++ -DCVVISUAL_DEBUGMODE*).
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-# The first cvv call simply shows the image (similar to *imshow*) with the imgIdString as comment.
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@code{.cpp}
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cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str());
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@endcode
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The image is added to the overview tab in the visual debug GUI and the cvv call blocks.
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The image can then be selected and viewed
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Whenever you want to continue in the code, i.e. unblock the cvv call, you can either continue
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until the next cvv call (*Step*), continue until the last cvv call (*\>\>*) or run the
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application until it exists (*Close*).
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We decide to press the green *Step* button.
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-# The next cvv calls are used to debug all kinds of filter operations, i.e. operations that take a
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picture as input and return a picture as output.
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@code{.cpp}
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cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray");
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@endcode
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As with every cvv call, you first end up in the overview.
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We decide not to care about the conversion to gray scale and press *Step*.
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@code{.cpp}
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cvv::debugFilter(imgGray, imgGraySmooth, CVVISUAL_LOCATION, "smoothed");
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@endcode
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If you open the filter call, you will end up in the so called "DefaultFilterView". Both images
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are shown next to each other and you can (synchronized) zoom into them.
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When you go to very high zoom levels, each pixel is annotated with its numeric values.
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We press *Step* twice and have a look at the dilated image.
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@code{.cpp}
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cvv::debugFilter(imgEdges, imgEdgesDilated, CVVISUAL_LOCATION, "dilated edges");
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@endcode
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The DefaultFilterView showing both images
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Now we use the *View* selector in the top right and select the "DualFilterView". We select
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"Changed Pixels" as filter and apply it (middle image).
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After we had a close look at these images, perhaps using different views, filters or other GUI
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features, we decide to let the program run through. Therefore we press the yellow *\>\>* button.
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The program will block at
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@code{.cpp}
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cvv::finalShow();
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@endcode
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and display the overview with everything that was passed to cvv in the meantime.
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-# The cvv debugDMatch call is used in a situation where there are two images each with a set of
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descriptors that are matched to each other.
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We pass both images, both sets of keypoints and their matching to the visual debug module.
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@code{.cpp}
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cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str());
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@endcode
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Since we want to have a look at matches, we use the filter capabilities (*\#type match*) in the
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overview to only show match calls.
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We want to have a closer look at one of them, e.g. to tune our parameters that use the matching.
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The view has various settings how to display keypoints and matches. Furthermore, there is a
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mouseover tooltip.
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We see (visual debugging!) that there are many bad matches. We decide that only 70% of the
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matches should be shown - those 70% with the lowest match distance.
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Having successfully reduced the visual distraction, we want to see more clearly what changed
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between the two images. We select the "TranslationMatchView" that shows to where the keypoint
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was matched in a different way.
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It is easy to see that the cup was moved to the left during the two images.
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Although, cvv is all about interactively *seeing* the computer vision bugs, this is complemented
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by a "RawView" that allows to have a look at the underlying numeric data.
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-# There are many more useful features contained in the cvv GUI. For instance, one can group the
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overview tab.
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Result
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------
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- By adding a view expressive lines to our computer vision program we can interactively debug it
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through different visualizations.
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- Once we are done developing/debugging we do not have to remove those lines. We simply disable
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cvv debugging (*cmake -DCVV_DEBUG_MODE=OFF* or g++ without *-DCVVISUAL_DEBUGMODE*) and our
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programs runs without any debug overhead.
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Enjoy computer vision!
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