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add a cascade classifier model visualisation tool for master branch

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This commit is contained in:
StevenPuttemans 2016-05-11 08:56:42 +02:00
parent af64ecdf25
commit 02fe93a399
3 changed files with 390 additions and 1 deletions
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2
apps/CMakeLists.txt
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@ -4,4 +4,4 @@ link_libraries(${OPENCV_LINKER_LIBS})
add_subdirectory(traincascade)
add_subdirectory(createsamples)
add_subdirectory(annotation)
add_subdirectory(interactive-calibration)
add_subdirectory(visualisation)

37
apps/visualisation/CMakeLists.txt Normal file
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@ -0,0 +1,37 @@
SET(OPENCV_VISUALISATION_DEPS opencv_core opencv_highgui opencv_imgproc opencv_videoio opencv_imgcodecs)
ocv_check_dependencies(${OPENCV_VISUALISATION_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(visualisation)
set(the_target opencv_visualisation)
ocv_target_include_directories(${the_target} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_target_include_modules_recurse(${the_target} ${OPENCV_VISUALISATION_DEPS})
file(GLOB SRCS *.cpp)
set(visualisation_files ${SRCS})
ocv_add_executable(${the_target} ${visualisation_files})
ocv_target_link_libraries(${the_target} ${OPENCV_VISUALISATION_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
OUTPUT_NAME "opencv_visualisation")
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "applications")
endif()
if(INSTALL_CREATE_DISTRIB)
if(BUILD_SHARED_LIBS)
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
endif()
else()
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
endif()

352
apps/visualisation/opencv_visualisation.cpp Normal file
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@ -0,0 +1,352 @@
////////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of the copyright holders may not 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 the Intel Corporation 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.
//
////////////////////////////////////////////////////////////////////////////////////////
/*****************************************************************************************************
Software for visualising cascade classifier models trained by OpenCV and to get a better
understanding of the used features.
USAGE:
./visualise_models -model <model.xml> -image <ref.png> -data <output folder>
LIMITS
- Use an absolute path for the output folder to ensure the tool works
- Only handles cascade classifier models
- Handles stumps only for the moment
- Needs a valid training/test sample window with the original model dimensions, passed as `ref.png`
- Can handle HAAR and LBP features
Created by: Puttemans Steven - April 2016
*****************************************************************************************************/
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/videoio.hpp>
#include <fstream>
#include <iostream>
using namespace std;
using namespace cv;
struct rect_data{
int x;
int y;
int w;
int h;
float weight;
};
int main( int argc, const char** argv )
{
// Read in the input arguments
string model = "";
string output_folder = "";
string image_ref = "";
for(int i = 1; i < argc; ++i )
{
if( !strcmp( argv[i], "-model" ) )
{
model = argv[++i];
}else if( !strcmp( argv[i], "-image" ) ){
image_ref = argv[++i];
}else if( !strcmp( argv[i], "-data" ) ){
output_folder = argv[++i];
}
}
// Value for timing
// You can increase this to have a better visualisation during the generation
int timing = 1;
// Value for cols of storing elements
int cols_prefered = 5;
// Open the XML model
FileStorage fs;
fs.open(model, FileStorage::READ);
// Get a the required information
// First decide which feature type we are using
FileNode cascade = fs["cascade"];
string feature_type = cascade["featureType"];
bool haar = false, lbp = false;
if (feature_type.compare("HAAR") == 0){
haar = true;
}
if (feature_type.compare("LBP") == 0){
lbp = true;
}
if ( feature_type.compare("HAAR") != 0 && feature_type.compare("LBP")){
cerr << "The model is not an HAAR or LBP feature based model!" << endl;
cerr << "Please select a model that can be visualized by the software." << endl;
return -1;
}
// We make a visualisation mask - which increases the window to make it at least a bit more visible
int resize_factor = 10;
int resize_storage_factor = 10;
Mat reference_image = imread(image_ref, IMREAD_GRAYSCALE );
Mat visualization;
resize(reference_image, visualization, Size(reference_image.cols * resize_factor, reference_image.rows * resize_factor));
// First recover for each stage the number of weak features and their index
// Important since it is NOT sequential when using LBP features
vector< vector<int> > stage_features;
FileNode stages = cascade["stages"];
FileNodeIterator it_stages = stages.begin(), it_stages_end = stages.end();
int idx = 0;
for( ; it_stages != it_stages_end; it_stages++, idx++ ){
vector<int> current_feature_indexes;
FileNode weak_classifiers = (*it_stages)["weakClassifiers"];
FileNodeIterator it_weak = weak_classifiers.begin(), it_weak_end = weak_classifiers.end();
vector<int> values;
for(int idy = 0; it_weak != it_weak_end; it_weak++, idy++ ){
(*it_weak)["internalNodes"] >> values;
current_feature_indexes.push_back( (int)values[2] );
}
stage_features.push_back(current_feature_indexes);
}
// If the output option has been chosen than we will store a combined image plane for
// each stage, containing all weak classifiers for that stage.
bool draw_planes = false;
stringstream output_video;
output_video << output_folder << "model_visualization.avi";
VideoWriter result_video;
if( output_folder.compare("") != 0 ){
draw_planes = true;
result_video.open(output_video.str(), VideoWriter::fourcc('X','V','I','D'), 15, Size(reference_image.cols * resize_factor, reference_image.rows * resize_factor), false);
}
if(haar){
// Grab the corresponding features dimensions and weights
FileNode features = cascade["features"];
vector< vector< rect_data > > feature_data;
FileNodeIterator it_features = features.begin(), it_features_end = features.end();
for(int idf = 0; it_features != it_features_end; it_features++, idf++ ){
vector< rect_data > current_feature_rectangles;
FileNode rectangles = (*it_features)["rects"];
int nrects = (int)rectangles.size();
for(int k = 0; k < nrects; k++){
rect_data current_data;
FileNode single_rect = rectangles[k];
current_data.x = (int)single_rect[0];
current_data.y = (int)single_rect[1];
current_data.w = (int)single_rect[2];
current_data.h = (int)single_rect[3];
current_data.weight = (float)single_rect[4];
current_feature_rectangles.push_back(current_data);
}
feature_data.push_back(current_feature_rectangles);
}
// Loop over each possible feature on its index, visualise on the mask and wait a bit,
// then continue to the next feature.
// If visualisations should be stored then do the in between calculations
Mat image_plane;
Mat metadata = Mat::zeros(150, 1000, CV_8UC1);
vector< rect_data > current_rects;
for(int sid = 0; sid < (int)stage_features.size(); sid ++){
if(draw_planes){
int features_nmbr = (int)stage_features[sid].size();
int cols = cols_prefered;
int rows = features_nmbr / cols;
if( (features_nmbr % cols) > 0){
rows++;
}
image_plane = Mat::zeros(reference_image.rows * resize_storage_factor * rows, reference_image.cols * resize_storage_factor * cols, CV_8UC1);
}
for(int fid = 0; fid < (int)stage_features[sid].size(); fid++){
stringstream meta1, meta2;
meta1 << "Stage " << sid << " / Feature " << fid;
meta2 << "Rectangles: ";
Mat temp_window = visualization.clone();
Mat temp_metadata = metadata.clone();
int current_feature_index = stage_features[sid][fid];
current_rects = feature_data[current_feature_index];
Mat single_feature = reference_image.clone();
resize(single_feature, single_feature, Size(), resize_storage_factor, resize_storage_factor);
for(int i = 0; i < (int)current_rects.size(); i++){
rect_data local = current_rects[i];
if(draw_planes){
if(local.weight >= 0){
rectangle(single_feature, Rect(local.x * resize_storage_factor, local.y * resize_storage_factor, local.w * resize_storage_factor, local.h * resize_storage_factor), Scalar(0), FILLED);
}else{
rectangle(single_feature, Rect(local.x * resize_storage_factor, local.y * resize_storage_factor, local.w * resize_storage_factor, local.h * resize_storage_factor), Scalar(255), FILLED);
}
}
Rect part(local.x * resize_factor, local.y * resize_factor, local.w * resize_factor, local.h * resize_factor);
meta2 << part << " (w " << local.weight << ") ";
if(local.weight >= 0){
rectangle(temp_window, part, Scalar(0), FILLED);
}else{
rectangle(temp_window, part, Scalar(255), FILLED);
}
}
imshow("features", temp_window);
putText(temp_window, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
result_video.write(temp_window);
// Copy the feature image if needed
if(draw_planes){
single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
}
putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
putText(temp_metadata, meta2.str(), Point(15,40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
imshow("metadata", temp_metadata);
waitKey(timing);
}
//Store the stage image if needed
if(draw_planes){
stringstream save_location;
save_location << output_folder << "stage_" << sid << ".png";
imwrite(save_location.str(), image_plane);
}
}
}
if(lbp){
// Grab the corresponding features dimensions and weights
FileNode features = cascade["features"];
vector<Rect> feature_data;
FileNodeIterator it_features = features.begin(), it_features_end = features.end();
for(int idf = 0; it_features != it_features_end; it_features++, idf++ ){
FileNode rectangle = (*it_features)["rect"];
Rect current_feature ((int)rectangle[0], (int)rectangle[1], (int)rectangle[2], (int)rectangle[3]);
feature_data.push_back(current_feature);
}
// Loop over each possible feature on its index, visualise on the mask and wait a bit,
// then continue to the next feature.
Mat image_plane;
Mat metadata = Mat::zeros(150, 1000, CV_8UC1);
for(int sid = 0; sid < (int)stage_features.size(); sid ++){
if(draw_planes){
int features_nmbr = (int)stage_features[sid].size();
int cols = cols_prefered;
int rows = features_nmbr / cols;
if( (features_nmbr % cols) > 0){
rows++;
}
image_plane = Mat::zeros(reference_image.rows * resize_storage_factor * rows, reference_image.cols * resize_storage_factor * cols, CV_8UC1);
}
for(int fid = 0; fid < (int)stage_features[sid].size(); fid++){
stringstream meta1, meta2;
meta1 << "Stage " << sid << " / Feature " << fid;
meta2 << "Rectangle: ";
Mat temp_window = visualization.clone();
Mat temp_metadata = metadata.clone();
int current_feature_index = stage_features[sid][fid];
Rect current_rect = feature_data[current_feature_index];
Mat single_feature = reference_image.clone();
resize(single_feature, single_feature, Size(), resize_storage_factor, resize_storage_factor);
// VISUALISATION
// The rectangle is the top left one of a 3x3 block LBP constructor
Rect resized(current_rect.x * resize_factor, current_rect.y * resize_factor, current_rect.width * resize_factor, current_rect.height * resize_factor);
meta2 << resized;
// Top left
rectangle(temp_window, resized, Scalar(255), 1);
// Top middle
rectangle(temp_window, Rect(resized.x + resized.width, resized.y, resized.width, resized.height), Scalar(255), 1);
// Top right
rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y, resized.width, resized.height), Scalar(255), 1);
// Middle left
rectangle(temp_window, Rect(resized.x, resized.y + resized.height, resized.width, resized.height), Scalar(255), 1);
// Middle middle
rectangle(temp_window, Rect(resized.x + resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(255), FILLED);
// Middle right
rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y + resized.height, resized.width, resized.height), Scalar(255), 1);
// Bottom left
rectangle(temp_window, Rect(resized.x, resized.y + 2*resized.height, resized.width, resized.height), Scalar(255), 1);
// Bottom middle
rectangle(temp_window, Rect(resized.x + resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(255), 1);
// Bottom right
rectangle(temp_window, Rect(resized.x + 2*resized.width, resized.y + 2*resized.height, resized.width, resized.height), Scalar(255), 1);
if(draw_planes){
Rect resized_inner(current_rect.x * resize_storage_factor, current_rect.y * resize_storage_factor, current_rect.width * resize_storage_factor, current_rect.height * resize_storage_factor);
// Top left
rectangle(single_feature, resized_inner, Scalar(255), 1);
// Top middle
rectangle(single_feature, Rect(resized_inner.x + resized_inner.width, resized_inner.y, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Top right
rectangle(single_feature, Rect(resized_inner.x + 2*resized_inner.width, resized_inner.y, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Middle left
rectangle(single_feature, Rect(resized_inner.x, resized_inner.y + resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Middle middle
rectangle(single_feature, Rect(resized_inner.x + resized_inner.width, resized_inner.y + resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), FILLED);
// Middle right
rectangle(single_feature, Rect(resized_inner.x + 2*resized_inner.width, resized_inner.y + resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Bottom left
rectangle(single_feature, Rect(resized_inner.x, resized_inner.y + 2*resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Bottom middle
rectangle(single_feature, Rect(resized_inner.x + resized_inner.width, resized_inner.y + 2*resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
// Bottom right
rectangle(single_feature, Rect(resized_inner.x + 2*resized_inner.width, resized_inner.y + 2*resized_inner.height, resized_inner.width, resized_inner.height), Scalar(255), 1);
single_feature.copyTo(image_plane(Rect(0 + (fid%cols_prefered)*single_feature.cols, 0 + (fid/cols_prefered) * single_feature.rows, single_feature.cols, single_feature.rows)));
}
putText(temp_metadata, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
putText(temp_metadata, meta2.str(), Point(15,40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
imshow("metadata", temp_metadata);
imshow("features", temp_window);
putText(temp_window, meta1.str(), Point(15,15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255));
result_video.write(temp_window);
waitKey(timing);
}
//Store the stage image if needed
if(draw_planes){
stringstream save_location;
save_location << output_folder << "stage_" << sid << ".png";
imwrite(save_location.str(), image_plane);
}
}
}
return 0;
}