Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
11243 Commits
6 Branches
126 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 17f53bc301
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '17f53bc301'
${ noResults }
opencv/samples/cpp/bagofwords_classification.cpp

2604 lines
114 KiB
Raw Blame History

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/ml/ml.hpp"
#include <fstream>
#include <iostream>
#include <memory>
#include <functional>
#if defined WIN32 || defined _WIN32
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#undef min
#undef max
#include "sys/types.h"
#endif
#include <sys/stat.h>
#define DEBUG_DESC_PROGRESS
using namespace cv;
using namespace std;
const string paramsFile = "params.xml";
const string vocabularyFile = "vocabulary.xml.gz";
const string bowImageDescriptorsDir = "/bowImageDescriptors";
const string svmsDir = "/svms";
const string plotsDir = "/plots";
static void help(char** argv)
{
cout << "\nThis program shows how to read in, train on and produce test results for the PASCAL VOC (Visual Object Challenge) data. \n"
<< "It shows how to use detectors, descriptors and recognition methods \n"
"Using OpenCV version %s\n" << CV_VERSION << "\n"
<< "Call: \n"
<< "Format:\n ./" << argv[0] << " [VOC path] [result directory] \n"
<< " or: \n"
<< " ./" << argv[0] << " [VOC path] [result directory] [feature detector] [descriptor extractor] [descriptor matcher] \n"
<< "\n"
<< "Input parameters: \n"
<< "[VOC path] Path to Pascal VOC data (e.g. /home/my/VOCdevkit/VOC2010). Note: VOC2007-VOC2010 are supported. \n"
<< "[result directory] Path to result diractory. Following folders will be created in [result directory]: \n"
<< " bowImageDescriptors - to store image descriptors, \n"
<< " svms - to store trained svms, \n"
<< " plots - to store files for plots creating. \n"
<< "[feature detector] Feature detector name (e.g. SURF, FAST...) - see createFeatureDetector() function in detectors.cpp \n"
<< " Currently 12/2010, this is FAST, STAR, SIFT, SURF, MSER, GFTT, HARRIS \n"
<< "[descriptor extractor] Descriptor extractor name (e.g. SURF, SIFT) - see createDescriptorExtractor() function in descriptors.cpp \n"
<< " Currently 12/2010, this is SURF, OpponentSIFT, SIFT, OpponentSURF, BRIEF \n"
<< "[descriptor matcher] Descriptor matcher name (e.g. BruteForce) - see createDescriptorMatcher() function in matchers.cpp \n"
<< " Currently 12/2010, this is BruteForce, BruteForce-L1, FlannBased, BruteForce-Hamming, BruteForce-HammingLUT \n"
<< "\n";
}
static void makeDir( const string& dir )
{
#if defined WIN32 || defined _WIN32
CreateDirectory( dir.c_str(), 0 );
#else
mkdir( dir.c_str(), S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH );
#endif
}
static void makeUsedDirs( const string& rootPath )
{
makeDir(rootPath + bowImageDescriptorsDir);
makeDir(rootPath + svmsDir);
makeDir(rootPath + plotsDir);
}
/****************************************************************************************\
* Classes to work with PASCAL VOC dataset *
\****************************************************************************************/
//
// TODO: refactor this part of the code
//
//used to specify the (sub-)dataset over which operations are performed
enum ObdDatasetType {CV_OBD_TRAIN, CV_OBD_TEST};
class ObdObject
{
public:
string object_class;
Rect boundingBox;
};
//extended object data specific to VOC
enum VocPose {CV_VOC_POSE_UNSPECIFIED, CV_VOC_POSE_FRONTAL, CV_VOC_POSE_REAR, CV_VOC_POSE_LEFT, CV_VOC_POSE_RIGHT};
class VocObjectData
{
public:
bool difficult;
bool occluded;
bool truncated;
VocPose pose;
};
//enum VocDataset {CV_VOC2007, CV_VOC2008, CV_VOC2009, CV_VOC2010};
enum VocPlotType {CV_VOC_PLOT_SCREEN, CV_VOC_PLOT_PNG};
enum VocGT {CV_VOC_GT_NONE, CV_VOC_GT_DIFFICULT, CV_VOC_GT_PRESENT};
enum VocConfCond {CV_VOC_CCOND_RECALL, CV_VOC_CCOND_SCORETHRESH};
enum VocTask {CV_VOC_TASK_CLASSIFICATION, CV_VOC_TASK_DETECTION};
class ObdImage
{
public:
ObdImage(string p_id, string p_path) : id(p_id), path(p_path) {}
string id;
string path;
};
//used by getDetectorGroundTruth to sort a two dimensional list of floats in descending order
class ObdScoreIndexSorter
{
public:
float score;
int image_idx;
int obj_idx;
bool operator < (const ObdScoreIndexSorter& compare) const {return (score < compare.score);}
};
class VocData
{
public:
VocData( const string& vocPath, bool useTestDataset )
{ initVoc( vocPath, useTestDataset ); }
~VocData(){}
/* functions for returning classification/object data for multiple images given an object class */
void getClassImages(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<char>& object_present);
void getClassObjects(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<vector<ObdObject> >& objects);
void getClassObjects(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<vector<ObdObject> >& objects, vector<vector<VocObjectData> >& object_data, vector<VocGT>& ground_truth);
/* functions for returning object data for a single image given an image id */
ObdImage getObjects(const string& id, vector<ObdObject>& objects);
ObdImage getObjects(const string& id, vector<ObdObject>& objects, vector<VocObjectData>& object_data);
ObdImage getObjects(const string& obj_class, const string& id, vector<ObdObject>& objects, vector<VocObjectData>& object_data, VocGT& ground_truth);
/* functions for returning the ground truth (present/absent) for groups of images */
void getClassifierGroundTruth(const string& obj_class, const vector<ObdImage>& images, vector<char>& ground_truth);
void getClassifierGroundTruth(const string& obj_class, const vector<string>& images, vector<char>& ground_truth);
int getDetectorGroundTruth(const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<vector<Rect> >& bounding_boxes, const vector<vector<float> >& scores, vector<vector<char> >& ground_truth, vector<vector<char> >& detection_difficult, bool ignore_difficult = true);
/* functions for writing VOC-compatible results files */
void writeClassifierResultsFile(const string& out_dir, const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<float>& scores, const int competition = 1, const bool overwrite_ifexists = false);
/* functions for calculating metrics from a set of classification/detection results */
string getResultsFilename(const string& obj_class, const VocTask task, const ObdDatasetType dataset, const int competition = -1, const int number = -1);
void calcClassifierPrecRecall(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap, vector<size_t>& ranking);
void calcClassifierPrecRecall(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap);
void calcClassifierPrecRecall(const string& input_file, vector<float>& precision, vector<float>& recall, float& ap, bool outputRankingFile = false);
/* functions for calculating confusion matrices */
void calcClassifierConfMatRow(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, const VocConfCond cond, const float threshold, vector<string>& output_headers, vector<float>& output_values);
void calcDetectorConfMatRow(const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<vector<float> >& scores, const vector<vector<Rect> >& bounding_boxes, const VocConfCond cond, const float threshold, vector<string>& output_headers, vector<float>& output_values, bool ignore_difficult = true);
/* functions for outputting gnuplot output files */
void savePrecRecallToGnuplot(const string& output_file, const vector<float>& precision, const vector<float>& recall, const float ap, const string title = string(), const VocPlotType plot_type = CV_VOC_PLOT_SCREEN);
/* functions for reading in result/ground truth files */
void readClassifierGroundTruth(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<char>& object_present);
void readClassifierResultsFile(const std:: string& input_file, vector<ObdImage>& images, vector<float>& scores);
void readDetectorResultsFile(const string& input_file, vector<ObdImage>& images, vector<vector<float> >& scores, vector<vector<Rect> >& bounding_boxes);
/* functions for getting dataset info */
const vector<string>& getObjectClasses();
string getResultsDirectory();
protected:
void initVoc( const string& vocPath, const bool useTestDataset );
void initVoc2007to2010( const string& vocPath, const bool useTestDataset);
void readClassifierGroundTruth(const string& filename, vector<string>& image_codes, vector<char>& object_present);
void readClassifierResultsFile(const string& input_file, vector<string>& image_codes, vector<float>& scores);
void readDetectorResultsFile(const string& input_file, vector<string>& image_codes, vector<vector<float> >& scores, vector<vector<Rect> >& bounding_boxes);
void extractVocObjects(const string filename, vector<ObdObject>& objects, vector<VocObjectData>& object_data);
string getImagePath(const string& input_str);
void getClassImages_impl(const string& obj_class, const string& dataset_str, vector<ObdImage>& images, vector<char>& object_present);
void calcPrecRecall_impl(const vector<char>& ground_truth, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap, vector<size_t>& ranking, int recall_normalization = -1);
//test two bounding boxes to see if they meet the overlap criteria defined in the VOC documentation
float testBoundingBoxesForOverlap(const Rect detection, const Rect ground_truth);
//extract class and dataset name from a VOC-standard classification/detection results filename
void extractDataFromResultsFilename(const string& input_file, string& class_name, string& dataset_name);
//get classifier ground truth for a single image
bool getClassifierGroundTruthImage(const string& obj_class, const string& id);
//utility functions
void getSortOrder(const vector<float>& values, vector<size_t>& order, bool descending = true);
int stringToInteger(const string input_str);
void readFileToString(const string filename, string& file_contents);
string integerToString(const int input_int);
string checkFilenamePathsep(const string filename, bool add_trailing_slash = false);
void convertImageCodesToObdImages(const vector<string>& image_codes, vector<ObdImage>& images);
int extractXMLBlock(const string src, const string tag, const int searchpos, string& tag_contents);
//utility sorter
struct orderingSorter
{
bool operator ()(std::pair<size_t, vector<float>::const_iterator> const& a, std::pair<size_t, vector<float>::const_iterator> const& b)
{
return (*a.second) > (*b.second);
}
};
//data members
string m_vocPath;
string m_vocName;
//string m_resPath;
string m_annotation_path;
string m_image_path;
string m_imageset_path;
string m_class_imageset_path;
vector<string> m_classifier_gt_all_ids;
vector<char> m_classifier_gt_all_present;
string m_classifier_gt_class;
//data members
string m_train_set;
string m_test_set;
vector<string> m_object_classes;
float m_min_overlap;
bool m_sampled_ap;
};
//Return the classification ground truth data for all images of a given VOC object class
//--------------------------------------------------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - dataset Specifies whether to extract images from the training or test set
//OUTPUTS:
// - images An array of ObdImage containing info of all images extracted from the ground truth file
// - object_present An array of bools specifying whether the object defined by 'obj_class' is present in each image or not
//NOTES:
// This function is primarily useful for the classification task, where only
// whether a given object is present or not in an image is required, and not each object instance's
// position etc.
void VocData::getClassImages(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<char>& object_present)
{
string dataset_str;
//generate the filename of the classification ground-truth textfile for the object class
if (dataset == CV_OBD_TRAIN)
{
dataset_str = m_train_set;
} else {
dataset_str = m_test_set;
}
getClassImages_impl(obj_class, dataset_str, images, object_present);
}
void VocData::getClassImages_impl(const string& obj_class, const string& dataset_str, vector<ObdImage>& images, vector<char>& object_present)
{
//generate the filename of the classification ground-truth textfile for the object class
string gtFilename = m_class_imageset_path;
gtFilename.replace(gtFilename.find("%s"),2,obj_class);
gtFilename.replace(gtFilename.find("%s"),2,dataset_str);
//parse the ground truth file, storing in two separate vectors
//for the image code and the ground truth value
vector<string> image_codes;
readClassifierGroundTruth(gtFilename, image_codes, object_present);
//prepare output arrays
images.clear();
convertImageCodesToObdImages(image_codes, images);
}
//Return the object data for all images of a given VOC object class
//-----------------------------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - dataset Specifies whether to extract images from the training or test set
//OUTPUTS:
// - images An array of ObdImage containing info of all images in chosen dataset (tag, path etc.)
// - objects Contains the extended object info (bounding box etc.) for each object instance in each image
// - object_data Contains VOC-specific extended object info (marked difficult etc.)
// - ground_truth Specifies whether there are any difficult/non-difficult instances of the current
// object class within each image
//NOTES:
// This function returns extended object information in addition to the absent/present
// classification data returned by getClassImages. The objects returned for each image in the 'objects'
// array are of all object classes present in the image, and not just the class defined by 'obj_class'.
// 'ground_truth' can be used to determine quickly whether an object instance of the given class is present
// in an image or not.
void VocData::getClassObjects(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<vector<ObdObject> >& objects)
{
vector<vector<VocObjectData> > object_data;
vector<VocGT> ground_truth;
getClassObjects(obj_class,dataset,images,objects,object_data,ground_truth);
}
void VocData::getClassObjects(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<vector<ObdObject> >& objects, vector<vector<VocObjectData> >& object_data, vector<VocGT>& ground_truth)
{
//generate the filename of the classification ground-truth textfile for the object class
string gtFilename = m_class_imageset_path;
gtFilename.replace(gtFilename.find("%s"),2,obj_class);
if (dataset == CV_OBD_TRAIN)
{
gtFilename.replace(gtFilename.find("%s"),2,m_train_set);
} else {
gtFilename.replace(gtFilename.find("%s"),2,m_test_set);
}
//parse the ground truth file, storing in two separate vectors
//for the image code and the ground truth value
vector<string> image_codes;
vector<char> object_present;
readClassifierGroundTruth(gtFilename, image_codes, object_present);
//prepare output arrays
images.clear();
objects.clear();
object_data.clear();
ground_truth.clear();
string annotationFilename;
vector<ObdObject> image_objects;
vector<VocObjectData> image_object_data;
VocGT image_gt;
//transfer to output arrays and read in object data for each image
for (size_t i = 0; i < image_codes.size(); ++i)
{
ObdImage image = getObjects(obj_class, image_codes[i], image_objects, image_object_data, image_gt);
images.push_back(image);
objects.push_back(image_objects);
object_data.push_back(image_object_data);
ground_truth.push_back(image_gt);
}
}
//Return ground truth data for the objects present in an image with a given UID
//-----------------------------------------------------------------------------
//INPUTS:
// - id VOC Dataset unique identifier (string code in form YYYY_XXXXXX where YYYY is the year)
//OUTPUTS:
// - obj_class (*3) Specifies the object class to use to resolve 'ground_truth'
// - objects Contains the extended object info (bounding box etc.) for each object in the image
// - object_data (*2,3) Contains VOC-specific extended object info (marked difficult etc.)
// - ground_truth (*3) Specifies whether there are any difficult/non-difficult instances of the current
// object class within the image
//RETURN VALUE:
// ObdImage containing path and other details of image file with given code
//NOTES:
// There are three versions of this function
// * One returns a simple array of objects given an id [1]
// * One returns the same as (1) plus VOC specific object data [2]
// * One returns the same as (2) plus the ground_truth flag. This also requires an extra input obj_class [3]
ObdImage VocData::getObjects(const string& id, vector<ObdObject>& objects)
{
vector<VocObjectData> object_data;
ObdImage image = getObjects(id, objects, object_data);
return image;
}
ObdImage VocData::getObjects(const string& id, vector<ObdObject>& objects, vector<VocObjectData>& object_data)
{
//first generate the filename of the annotation file
string annotationFilename = m_annotation_path;
annotationFilename.replace(annotationFilename.find("%s"),2,id);
//extract objects contained in the current image from the xml
extractVocObjects(annotationFilename,objects,object_data);
//generate image path from extracted string code
string path = getImagePath(id);
ObdImage image(id, path);
return image;
}
ObdImage VocData::getObjects(const string& obj_class, const string& id, vector<ObdObject>& objects, vector<VocObjectData>& object_data, VocGT& ground_truth)
{
//extract object data (except for ground truth flag)
ObdImage image = getObjects(id,objects,object_data);
//pregenerate a flag to indicate whether the current class is present or not in the image
ground_truth = CV_VOC_GT_NONE;
//iterate through all objects in current image
for (size_t j = 0; j < objects.size(); ++j)
{
if (objects[j].object_class == obj_class)
{
if (object_data[j].difficult == false)
{
//if at least one non-difficult example is present, this flag is always set to CV_VOC_GT_PRESENT
ground_truth = CV_VOC_GT_PRESENT;
break;
} else {
//set if at least one object instance is present, but it is marked difficult
ground_truth = CV_VOC_GT_DIFFICULT;
}
}
}
return image;
}
//Return ground truth data for the presence/absence of a given object class in an arbitrary array of images
//---------------------------------------------------------------------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - images An array of ObdImage OR strings containing the images for which ground truth
// will be computed
//OUTPUTS:
// - ground_truth An output array indicating the presence/absence of obj_class within each image
void VocData::getClassifierGroundTruth(const string& obj_class, const vector<ObdImage>& images, vector<char>& ground_truth)
{
vector<char>(images.size()).swap(ground_truth);
vector<ObdObject> objects;
vector<VocObjectData> object_data;
vector<char>::iterator gt_it = ground_truth.begin();
for (vector<ObdImage>::const_iterator it = images.begin(); it != images.end(); ++it, ++gt_it)
{
//getObjects(obj_class, it->id, objects, object_data, voc_ground_truth);
(*gt_it) = (getClassifierGroundTruthImage(obj_class, it->id));
}
}
void VocData::getClassifierGroundTruth(const string& obj_class, const vector<string>& images, vector<char>& ground_truth)
{
vector<char>(images.size()).swap(ground_truth);
vector<ObdObject> objects;
vector<VocObjectData> object_data;
vector<char>::iterator gt_it = ground_truth.begin();
for (vector<string>::const_iterator it = images.begin(); it != images.end(); ++it, ++gt_it)
{
//getObjects(obj_class, (*it), objects, object_data, voc_ground_truth);
(*gt_it) = (getClassifierGroundTruthImage(obj_class, (*it)));
}
}
//Return ground truth data for the accuracy of detection results
//--------------------------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - images An array of ObdImage containing the images for which ground truth
// will be computed
// - bounding_boxes A 2D input array containing the bounding box rects of the objects of
// obj_class which were detected in each image
//OUTPUTS:
// - ground_truth A 2D output array indicating whether each object detection was accurate
// or not
// - detection_difficult A 2D output array indicating whether the detection fired on an object
// marked as 'difficult'. This allows it to be ignored if necessary
// (the voc documentation specifies objects marked as difficult
// have no effects on the results and are effectively ignored)
// - (ignore_difficult) If set to true, objects marked as difficult will be ignored when returning
// the number of hits for p-r normalization (default = true)
//RETURN VALUE:
// Returns the number of object hits in total in the gt to allow proper normalization
// of a p-r curve
//NOTES:
// As stated in the VOC documentation, multiple detections of the same object in an image are
// considered FALSE detections e.g. 5 detections of a single object is counted as 1 correct
// detection and 4 false detections - it is the responsibility of the participant's system
// to filter multiple detections from its output
int VocData::getDetectorGroundTruth(const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<vector<Rect> >& bounding_boxes, const vector<vector<float> >& scores, vector<vector<char> >& ground_truth, vector<vector<char> >& detection_difficult, bool ignore_difficult)
{
int recall_normalization = 0;
/* first create a list of indices referring to the elements of bounding_boxes and scores in
* descending order of scores */
vector<ObdScoreIndexSorter> sorted_ids;
{
/* first count how many objects to allow preallocation */
size_t obj_count = 0;
CV_Assert(images.size() == bounding_boxes.size());
CV_Assert(scores.size() == bounding_boxes.size());
for (size_t im_idx = 0; im_idx < scores.size(); ++im_idx)
{
CV_Assert(scores[im_idx].size() == bounding_boxes[im_idx].size());
obj_count += scores[im_idx].size();
}
/* preallocate id vector */
sorted_ids.resize(obj_count);
/* now copy across scores and indexes to preallocated vector */
int flat_pos = 0;
for (size_t im_idx = 0; im_idx < scores.size(); ++im_idx)
{
for (size_t ob_idx = 0; ob_idx < scores[im_idx].size(); ++ob_idx)
{
sorted_ids[flat_pos].score = scores[im_idx][ob_idx];
sorted_ids[flat_pos].image_idx = (int)im_idx;
sorted_ids[flat_pos].obj_idx = (int)ob_idx;
++flat_pos;
}
}
/* and sort the vector in descending order of score */
std::sort(sorted_ids.begin(),sorted_ids.end());
std::reverse(sorted_ids.begin(),sorted_ids.end());
}
/* prepare ground truth + difficult vector (1st dimension) */
vector<vector<char> >(images.size()).swap(ground_truth);
vector<vector<char> >(images.size()).swap(detection_difficult);
vector<vector<char> > detected(images.size());
vector<vector<ObdObject> > img_objects(images.size());
vector<vector<VocObjectData> > img_object_data(images.size());
/* preload object ground truth bounding box data */
{
vector<vector<ObdObject> > img_objects_all(images.size());
vector<vector<VocObjectData> > img_object_data_all(images.size());
for (size_t image_idx = 0; image_idx < images.size(); ++image_idx)
{
/* prepopulate ground truth bounding boxes */
getObjects(images[image_idx].id, img_objects_all[image_idx], img_object_data_all[image_idx]);
/* meanwhile, also set length of target ground truth + difficult vector to same as number of object detections (2nd dimension) */
ground_truth[image_idx].resize(bounding_boxes[image_idx].size());
detection_difficult[image_idx].resize(bounding_boxes[image_idx].size());
}
/* save only instances of the object class concerned */
for (size_t image_idx = 0; image_idx < images.size(); ++image_idx)
{
for (size_t obj_idx = 0; obj_idx < img_objects_all[image_idx].size(); ++obj_idx)
{
if (img_objects_all[image_idx][obj_idx].object_class == obj_class)
{
img_objects[image_idx].push_back(img_objects_all[image_idx][obj_idx]);
img_object_data[image_idx].push_back(img_object_data_all[image_idx][obj_idx]);
}
}
detected[image_idx].resize(img_objects[image_idx].size(), false);
}
}
/* calculate the total number of objects in the ground truth for the current dataset */
{
vector<ObdImage> gt_images;
vector<char> gt_object_present;
getClassImages(obj_class, dataset, gt_images, gt_object_present);
for (size_t image_idx = 0; image_idx < gt_images.size(); ++image_idx)
{
vector<ObdObject> gt_img_objects;
vector<VocObjectData> gt_img_object_data;
getObjects(gt_images[image_idx].id, gt_img_objects, gt_img_object_data);
for (size_t obj_idx = 0; obj_idx < gt_img_objects.size(); ++obj_idx)
{
if (gt_img_objects[obj_idx].object_class == obj_class)
{
if ((gt_img_object_data[obj_idx].difficult == false) || (ignore_difficult == false))
++recall_normalization;
}
}
}
}
#ifdef PR_DEBUG
int printed_count = 0;
#endif
/* now iterate through detections in descending order of score, assigning to ground truth bounding boxes if possible */
for (size_t detect_idx = 0; detect_idx < sorted_ids.size(); ++detect_idx)
{
//read in indexes to make following code easier to read
int im_idx = sorted_ids[detect_idx].image_idx;
int ob_idx = sorted_ids[detect_idx].obj_idx;
//set ground truth for the current object to false by default
ground_truth[im_idx][ob_idx] = false;
detection_difficult[im_idx][ob_idx] = false;
float maxov = -1.0;
bool max_is_difficult = false;
int max_gt_obj_idx = -1;
//-- for each detected object iterate through objects present in the bounding box ground truth --
for (size_t gt_obj_idx = 0; gt_obj_idx < img_objects[im_idx].size(); ++gt_obj_idx)
{
if (detected[im_idx][gt_obj_idx] == false)
{
//check if the detected object and ground truth object overlap by a sufficient margin
float ov = testBoundingBoxesForOverlap(bounding_boxes[im_idx][ob_idx], img_objects[im_idx][gt_obj_idx].boundingBox);
if (ov != -1.0)
{
//if all conditions are met store the overlap score and index (as objects are assigned to the highest scoring match)
if (ov > maxov)
{
maxov = ov;
max_gt_obj_idx = (int)gt_obj_idx;
//store whether the maximum detection is marked as difficult or not
max_is_difficult = (img_object_data[im_idx][gt_obj_idx].difficult);
}
}
}
}
//-- if a match was found, set the ground truth of the current object to true --
if (maxov != -1.0)
{
CV_Assert(max_gt_obj_idx != -1);
ground_truth[im_idx][ob_idx] = true;
//store whether the maximum detection was marked as 'difficult' or not
detection_difficult[im_idx][ob_idx] = max_is_difficult;
//remove the ground truth object so it doesn't match with subsequent detected objects
//** this is the behaviour defined by the voc documentation **
detected[im_idx][max_gt_obj_idx] = true;
}
#ifdef PR_DEBUG
if (printed_count < 10)
{
cout << printed_count << ": id=" << images[im_idx].id << ", score=" << scores[im_idx][ob_idx] << " (" << ob_idx << ") [" << bounding_boxes[im_idx][ob_idx].x << "," <<
bounding_boxes[im_idx][ob_idx].y << "," << bounding_boxes[im_idx][ob_idx].width + bounding_boxes[im_idx][ob_idx].x <<
"," << bounding_boxes[im_idx][ob_idx].height + bounding_boxes[im_idx][ob_idx].y << "] detected=" << ground_truth[im_idx][ob_idx] <<
", difficult=" << detection_difficult[im_idx][ob_idx] << endl;
++printed_count;
/* print ground truth */
for (int gt_obj_idx = 0; gt_obj_idx < img_objects[im_idx].size(); ++gt_obj_idx)
{
cout << " GT: [" << img_objects[im_idx][gt_obj_idx].boundingBox.x << "," <<
img_objects[im_idx][gt_obj_idx].boundingBox.y << "," << img_objects[im_idx][gt_obj_idx].boundingBox.width + img_objects[im_idx][gt_obj_idx].boundingBox.x <<
"," << img_objects[im_idx][gt_obj_idx].boundingBox.height + img_objects[im_idx][gt_obj_idx].boundingBox.y << "]";
if (gt_obj_idx == max_gt_obj_idx) cout << " <--- (" << maxov << " overlap)";
cout << endl;
}
}
#endif
}
return recall_normalization;
}
//Write VOC-compliant classifier results file
//-------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - dataset Specifies whether working with the training or test set
// - images An array of ObdImage containing the images for which data will be saved to the result file
// - scores A corresponding array of confidence scores given a query
// - (competition) If specified, defines which competition the results are for (see VOC documentation - default 1)
//NOTES:
// The result file path and filename are determined automatically using m_results_directory as a base
void VocData::writeClassifierResultsFile( const string& out_dir, const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<float>& scores, const int competition, const bool overwrite_ifexists)
{
CV_Assert(images.size() == scores.size());
string output_file_base, output_file;
if (dataset == CV_OBD_TRAIN)
{
output_file_base = out_dir + "/comp" + integerToString(competition) + "_cls_" + m_train_set + "_" + obj_class;
} else {
output_file_base = out_dir + "/comp" + integerToString(competition) + "_cls_" + m_test_set + "_" + obj_class;
}
output_file = output_file_base + ".txt";
//check if file exists, and if so create a numbered new file instead
if (overwrite_ifexists == false)
{
struct stat stFileInfo;
if (stat(output_file.c_str(),&stFileInfo) == 0)
{
string output_file_new;
int filenum = 0;
do
{
++filenum;
output_file_new = output_file_base + "_" + integerToString(filenum);
output_file = output_file_new + ".txt";
} while (stat(output_file.c_str(),&stFileInfo) == 0);
}
}
//output data to file
std::ofstream result_file(output_file.c_str());
if (result_file.is_open())
{
for (size_t i = 0; i < images.size(); ++i)
{
result_file << images[i].id << " " << scores[i] << endl;
}
result_file.close();
} else {
string err_msg = "could not open classifier results file '" + output_file + "' for writing. Before running for the first time, a 'results' subdirectory should be created within the VOC dataset base directory. e.g. if the VOC data is stored in /VOC/VOC2010 then the path /VOC/results must be created.";
CV_Error(CV_StsError,err_msg.c_str());
}
}
//---------------------------------------
//CALCULATE METRICS FROM VOC RESULTS DATA
//---------------------------------------
//Utility function to construct a VOC-standard classification results filename
//----------------------------------------------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string
// - task Specifies whether to generate a filename for the classification or detection task
// - dataset Specifies whether working with the training or test set
// - (competition) If specified, defines which competition the results are for (see VOC documentation
// default of -1 means this is set to 1 for the classification task and 3 for the detection task)
// - (number) If specified and above 0, defines which of a number of duplicate results file produced for a given set of
// of settings should be used (this number will be added as a postfix to the filename)
//NOTES:
// This is primarily useful for returning the filename of a classification file previously computed using writeClassifierResultsFile
// for example when calling calcClassifierPrecRecall
string VocData::getResultsFilename(const string& obj_class, const VocTask task, const ObdDatasetType dataset, const int competition, const int number)
{
if ((competition < 1) && (competition != -1))
CV_Error(CV_StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
if ((number < 1) && (number != -1))
CV_Error(CV_StsBadArg,"number argument should be a positive non-zero number or -1 to accept the default");
string dset, task_type;
if (dataset == CV_OBD_TRAIN)
{
dset = m_train_set;
} else {
dset = m_test_set;
}
int comp = competition;
if (task == CV_VOC_TASK_CLASSIFICATION)
{
task_type = "cls";
if (comp == -1) comp = 1;
} else {
task_type = "det";
if (comp == -1) comp = 3;
}
stringstream ss;
if (number < 1)
{
ss << "comp" << comp << "_" << task_type << "_" << dset << "_" << obj_class << ".txt";
} else {
ss << "comp" << comp << "_" << task_type << "_" << dset << "_" << obj_class << "_" << number << ".txt";
}
string filename = ss.str();
return filename;
}
//Calculate metrics for classification results
//--------------------------------------------
//INPUTS:
// - ground_truth A vector of booleans determining whether the currently tested class is present in each input image
// - scores A vector containing the similarity score for each input image (higher is more similar)
//OUTPUTS:
// - precision A vector containing the precision calculated at each datapoint of a p-r curve generated from the result set
// - recall A vector containing the recall calculated at each datapoint of a p-r curve generated from the result set
// - ap The ap metric calculated from the result set
// - (ranking) A vector of the same length as 'ground_truth' and 'scores' containing the order of the indices in both of
// these arrays when sorting by the ranking score in descending order
//NOTES:
// The result file path and filename are determined automatically using m_results_directory as a base
void VocData::calcClassifierPrecRecall(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap, vector<size_t>& ranking)
{
vector<char> res_ground_truth;
getClassifierGroundTruth(obj_class, images, res_ground_truth);
calcPrecRecall_impl(res_ground_truth, scores, precision, recall, ap, ranking);
}
void VocData::calcClassifierPrecRecall(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap)
{
vector<char> res_ground_truth;
getClassifierGroundTruth(obj_class, images, res_ground_truth);
vector<size_t> ranking;
calcPrecRecall_impl(res_ground_truth, scores, precision, recall, ap, ranking);
}
//< Overloaded version which accepts VOC classification result file input instead of array of scores/ground truth >
//INPUTS:
// - input_file The path to the VOC standard results file to use for calculating precision/recall
// If a full path is not specified, it is assumed this file is in the VOC standard results directory
// A VOC standard filename can be retrieved (as used by writeClassifierResultsFile) by calling getClassifierResultsFilename
void VocData::calcClassifierPrecRecall(const string& input_file, vector<float>& precision, vector<float>& recall, float& ap, bool outputRankingFile)
{
//read in classification results file
vector<string> res_image_codes;
vector<float> res_scores;
string input_file_std = checkFilenamePathsep(input_file);
readClassifierResultsFile(input_file_std, res_image_codes, res_scores);
//extract the object class and dataset from the results file filename
string class_name, dataset_name;
extractDataFromResultsFilename(input_file_std, class_name, dataset_name);
//generate the ground truth for the images extracted from the results file
vector<char> res_ground_truth;
getClassifierGroundTruth(class_name, res_image_codes, res_ground_truth);
if (outputRankingFile)
{
/* 1. store sorting order by score (descending) in 'order' */
vector<std::pair<size_t, vector<float>::const_iterator> > order(res_scores.size());
size_t n = 0;
for (vector<float>::const_iterator it = res_scores.begin(); it != res_scores.end(); ++it, ++n)
order[n] = make_pair(n, it);
std::sort(order.begin(),order.end(),orderingSorter());
/* 2. save ranking results to text file */
string input_file_std1 = checkFilenamePathsep(input_file);
size_t fnamestart = input_file_std1.rfind("/");
string scoregt_file_str = input_file_std1.substr(0,fnamestart+1) + "scoregt_" + class_name + ".txt";
std::ofstream scoregt_file(scoregt_file_str.c_str());
if (scoregt_file.is_open())
{
for (size_t i = 0; i < res_scores.size(); ++i)
{
scoregt_file << res_image_codes[order[i].first] << " " << res_scores[order[i].first] << " " << res_ground_truth[order[i].first] << endl;
}
scoregt_file.close();
} else {
string err_msg = "could not open scoregt file '" + scoregt_file_str + "' for writing.";
CV_Error(CV_StsError,err_msg.c_str());
}
}
//finally, calculate precision+recall+ap
vector<size_t> ranking;
calcPrecRecall_impl(res_ground_truth,res_scores,precision,recall,ap,ranking);
}
//< Protected implementation of Precision-Recall calculation used by both calcClassifierPrecRecall and calcDetectorPrecRecall >
void VocData::calcPrecRecall_impl(const vector<char>& ground_truth, const vector<float>& scores, vector<float>& precision, vector<float>& recall, float& ap, vector<size_t>& ranking, int recall_normalization)
{
CV_Assert(ground_truth.size() == scores.size());
//add extra element for p-r at 0 recall (in case that first retrieved is positive)
vector<float>(scores.size()+1).swap(precision);
vector<float>(scores.size()+1).swap(recall);
// SORT RESULTS BY THEIR SCORE
/* 1. store sorting order in 'order' */
VocData::getSortOrder(scores, ranking);
#ifdef PR_DEBUG
std::ofstream scoregt_file("D:/pr.txt");
if (scoregt_file.is_open())
{
for (int i = 0; i < scores.size(); ++i)
{
scoregt_file << scores[ranking[i]] << " " << ground_truth[ranking[i]] << endl;
}
scoregt_file.close();
}
#endif
// CALCULATE PRECISION+RECALL
int retrieved_hits = 0;
int recall_norm;
if (recall_normalization != -1)
{
recall_norm = recall_normalization;
} else {
recall_norm = (int)std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
}
ap = 0;
recall[0] = 0;
for (size_t idx = 0; idx < ground_truth.size(); ++idx)
{
if (ground_truth[ranking[idx]] != 0) ++retrieved_hits;
precision[idx+1] = static_cast<float>(retrieved_hits)/static_cast<float>(idx+1);
recall[idx+1] = static_cast<float>(retrieved_hits)/static_cast<float>(recall_norm);
if (idx == 0)
{
//add further point at 0 recall with the same precision value as the first computed point
precision[idx] = precision[idx+1];
}
if (recall[idx+1] == 1.0)
{
//if recall = 1, then end early as all positive images have been found
recall.resize(idx+2);
precision.resize(idx+2);
break;
}
}
/* ap calculation */
if (m_sampled_ap == false)
{
// FOR VOC2010+ AP IS CALCULATED FROM ALL DATAPOINTS
/* make precision monotonically decreasing for purposes of calculating ap */
vector<float> precision_monot(precision.size());
vector<float>::iterator prec_m_it = precision_monot.begin();
for (vector<float>::iterator prec_it = precision.begin(); prec_it != precision.end(); ++prec_it, ++prec_m_it)
{
vector<float>::iterator max_elem;
max_elem = std::max_element(prec_it,precision.end());
(*prec_m_it) = (*max_elem);
}
/* calculate ap */
for (size_t idx = 0; idx < (recall.size()-1); ++idx)
{
ap += (recall[idx+1] - recall[idx])*precision_monot[idx+1] + //no need to take min of prec - is monotonically decreasing
0.5f*(recall[idx+1] - recall[idx])*std::abs(precision_monot[idx+1] - precision_monot[idx]);
}
} else {
// FOR BEFORE VOC2010 AP IS CALCULATED BY SAMPLING PRECISION AT RECALL 0.0,0.1,..,1.0
for (float recall_pos = 0.f; recall_pos <= 1.f; recall_pos += 0.1f)
{
//find iterator of the precision corresponding to the first recall >= recall_pos
vector<float>::iterator recall_it = recall.begin();
vector<float>::iterator prec_it = precision.begin();
while ((*recall_it) < recall_pos)
{
++recall_it;
++prec_it;
if (recall_it == recall.end()) break;
}
/* if no recall >= recall_pos found, this level of recall is never reached so stop adding to ap */
if (recall_it == recall.end()) break;
/* if the prec_it is valid, compute the max precision at this level of recall or higher */
vector<float>::iterator max_prec = std::max_element(prec_it,precision.end());
ap += (*max_prec)/11;
}
}
}
/* functions for calculating confusion matrix rows */
//Calculate rows of a confusion matrix
//------------------------------------
//INPUTS:
// - obj_class The VOC object class identifier string for the confusion matrix row to compute
// - images An array of ObdImage containing the images to use for the computation
// - scores A corresponding array of confidence scores for the presence of obj_class in each image
// - cond Defines whether to use a cut off point based on recall (CV_VOC_CCOND_RECALL) or score
// (CV_VOC_CCOND_SCORETHRESH) the latter is useful for classifier detections where positive
// values are positive detections and negative values are negative detections
// - threshold Threshold value for cond. In case of CV_VOC_CCOND_RECALL, is proportion recall (e.g. 0.5).
// In the case of CV_VOC_CCOND_SCORETHRESH is the value above which to count results.
//OUTPUTS:
// - output_headers An output vector of object class headers for the confusion matrix row
// - output_values An output vector of values for the confusion matrix row corresponding to the classes
// defined in output_headers
//NOTES:
// The methodology used by the classifier version of this function is that true positives have a single unit
// added to the obj_class column in the confusion matrix row, whereas false positives have a single unit
// distributed in proportion between all the columns in the confusion matrix row corresponding to the objects
// present in the image.
void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<ObdImage>& images, const vector<float>& scores, const VocConfCond cond, const float threshold, vector<string>& output_headers, vector<float>& output_values)
{
CV_Assert(images.size() == scores.size());
// SORT RESULTS BY THEIR SCORE
/* 1. store sorting order in 'ranking' */
vector<size_t> ranking;
VocData::getSortOrder(scores, ranking);
// CALCULATE CONFUSION MATRIX ENTRIES
/* prepare object category headers */
output_headers = m_object_classes;
vector<float>(output_headers.size(),0.0).swap(output_values);
/* find the index of the target object class in the headers for later use */
int target_idx;
{
vector<string>::iterator target_idx_it = std::find(output_headers.begin(),output_headers.end(),obj_class);
/* if the target class can not be found, raise an exception */
if (target_idx_it == output_headers.end())
{
string err_msg = "could not find the target object class '" + obj_class + "' in list of valid classes.";
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
target_idx = (int)std::distance(output_headers.begin(),target_idx_it);
}
/* prepare variables related to calculating recall if using the recall threshold */
int retrieved_hits = 0;
int total_relevant = 0;
if (cond == CV_VOC_CCOND_RECALL)
{
vector<char> ground_truth;
/* in order to calculate the total number of relevant images for normalization of recall
it's necessary to extract the ground truth for the images under consideration */
getClassifierGroundTruth(obj_class, images, ground_truth);
total_relevant = (int)std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
}
/* iterate through images */
vector<ObdObject> img_objects;
vector<VocObjectData> img_object_data;
int total_images = 0;
for (size_t image_idx = 0; image_idx < images.size(); ++image_idx)
{
/* if using the score as the break condition, check for it now */
if (cond == CV_VOC_CCOND_SCORETHRESH)
{
if (scores[ranking[image_idx]] <= threshold) break;
}
/* if continuing for this iteration, increment the image counter for later normalization */
++total_images;
/* for each image retrieve the objects contained */
getObjects(images[ranking[image_idx]].id, img_objects, img_object_data);
//check if the tested for object class is present
if (getClassifierGroundTruthImage(obj_class, images[ranking[image_idx]].id))
{
//if the target class is present, assign fully to the target class element in the confusion matrix row
output_values[target_idx] += 1.0;
if (cond == CV_VOC_CCOND_RECALL) ++retrieved_hits;
} else {
//first delete all objects marked as difficult
for (size_t obj_idx = 0; obj_idx < img_objects.size(); ++obj_idx)
{
if (img_object_data[obj_idx].difficult == true)
{
vector<ObdObject>::iterator it1 = img_objects.begin();
std::advance(it1,obj_idx);
img_objects.erase(it1);
vector<VocObjectData>::iterator it2 = img_object_data.begin();
std::advance(it2,obj_idx);
img_object_data.erase(it2);
--obj_idx;
}
}
//if the target class is not present, add values to the confusion matrix row in equal proportions to all objects present in the image
for (size_t obj_idx = 0; obj_idx < img_objects.size(); ++obj_idx)
{
//find the index of the currently considered object
vector<string>::iterator class_idx_it = std::find(output_headers.begin(),output_headers.end(),img_objects[obj_idx].object_class);
//if the class name extracted from the ground truth file could not be found in the list of available classes, raise an exception
if (class_idx_it == output_headers.end())
{
string err_msg = "could not find object class '" + img_objects[obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
//add to confusion matrix row in proportion
output_values[class_idx] += 1.f/static_cast<float>(img_objects.size());
}
}
//check break conditions if breaking on certain level of recall
if (cond == CV_VOC_CCOND_RECALL)
{
if(static_cast<float>(retrieved_hits)/static_cast<float>(total_relevant) >= threshold) break;
}
}
/* finally, normalize confusion matrix row */
for (vector<float>::iterator it = output_values.begin(); it < output_values.end(); ++it)
{
(*it) /= static_cast<float>(total_images);
}
}
// NOTE: doesn't ignore repeated detections
void VocData::calcDetectorConfMatRow(const string& obj_class, const ObdDatasetType dataset, const vector<ObdImage>& images, const vector<vector<float> >& scores, const vector<vector<Rect> >& bounding_boxes, const VocConfCond cond, const float threshold, vector<string>& output_headers, vector<float>& output_values, bool ignore_difficult)
{
CV_Assert(images.size() == scores.size());
CV_Assert(images.size() == bounding_boxes.size());
//collapse scores and ground_truth vectors into 1D vectors to allow ranking
/* define final flat vectors */
vector<string> images_flat;
vector<float> scores_flat;
vector<Rect> bounding_boxes_flat;
{
/* first count how many objects to allow preallocation */
int obj_count = 0;
CV_Assert(scores.size() == bounding_boxes.size());
for (size_t img_idx = 0; img_idx < scores.size(); ++img_idx)
{
CV_Assert(scores[img_idx].size() == bounding_boxes[img_idx].size());
for (size_t obj_idx = 0; obj_idx < scores[img_idx].size(); ++obj_idx)
{
++obj_count;
}
}
/* preallocate vectors */
images_flat.resize(obj_count);
scores_flat.resize(obj_count);
bounding_boxes_flat.resize(obj_count);
/* now copy across to preallocated vectors */
int flat_pos = 0;
for (size_t img_idx = 0; img_idx < scores.size(); ++img_idx)
{
for (size_t obj_idx = 0; obj_idx < scores[img_idx].size(); ++obj_idx)
{
images_flat[flat_pos] = images[img_idx].id;
scores_flat[flat_pos] = scores[img_idx][obj_idx];
bounding_boxes_flat[flat_pos] = bounding_boxes[img_idx][obj_idx];
++flat_pos;
}
}
}
// SORT RESULTS BY THEIR SCORE
/* 1. store sorting order in 'ranking' */
vector<size_t> ranking;
VocData::getSortOrder(scores_flat, ranking);
// CALCULATE CONFUSION MATRIX ENTRIES
/* prepare object category headers */
output_headers = m_object_classes;
output_headers.push_back("background");
vector<float>(output_headers.size(),0.0).swap(output_values);
/* prepare variables related to calculating recall if using the recall threshold */
int retrieved_hits = 0;
int total_relevant = 0;
if (cond == CV_VOC_CCOND_RECALL)
{
// vector<char> ground_truth;
// /* in order to calculate the total number of relevant images for normalization of recall
// it's necessary to extract the ground truth for the images under consideration */
// getClassifierGroundTruth(obj_class, images, ground_truth);
// total_relevant = std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<bool>(),true));
/* calculate the total number of objects in the ground truth for the current dataset */
vector<ObdImage> gt_images;
vector<char> gt_object_present;
getClassImages(obj_class, dataset, gt_images, gt_object_present);
for (size_t image_idx = 0; image_idx < gt_images.size(); ++image_idx)
{
vector<ObdObject> gt_img_objects;
vector<VocObjectData> gt_img_object_data;
getObjects(gt_images[image_idx].id, gt_img_objects, gt_img_object_data);
for (size_t obj_idx = 0; obj_idx < gt_img_objects.size(); ++obj_idx)
{
if (gt_img_objects[obj_idx].object_class == obj_class)
{
if ((gt_img_object_data[obj_idx].difficult == false) || (ignore_difficult == false))
++total_relevant;
}
}
}
}
/* iterate through objects */
vector<ObdObject> img_objects;
vector<VocObjectData> img_object_data;
int total_objects = 0;
for (size_t image_idx = 0; image_idx < images.size(); ++image_idx)
{
/* if using the score as the break condition, check for it now */
if (cond == CV_VOC_CCOND_SCORETHRESH)
{
if (scores_flat[ranking[image_idx]] <= threshold) break;
}
/* increment the image counter for later normalization */
++total_objects;
/* for each image retrieve the objects contained */
getObjects(images[ranking[image_idx]].id, img_objects, img_object_data);
//find the ground truth object which has the highest overlap score with the detected object
float maxov = -1.0;
int max_gt_obj_idx = -1;
//-- for each detected object iterate through objects present in ground truth --
for (size_t gt_obj_idx = 0; gt_obj_idx < img_objects.size(); ++gt_obj_idx)
{
//check difficulty flag
if (ignore_difficult || (img_object_data[gt_obj_idx].difficult == false))
{
//if the class matches, then check if the detected object and ground truth object overlap by a sufficient margin
float ov = testBoundingBoxesForOverlap(bounding_boxes_flat[ranking[image_idx]], img_objects[gt_obj_idx].boundingBox);
if (ov != -1.f)
{
//if all conditions are met store the overlap score and index (as objects are assigned to the highest scoring match)
if (ov > maxov)
{
maxov = ov;
max_gt_obj_idx = (int)gt_obj_idx;
}
}
}
}
//assign to appropriate object class if an object was detected
if (maxov != -1.0)
{
//find the index of the currently considered object
vector<string>::iterator class_idx_it = std::find(output_headers.begin(),output_headers.end(),img_objects[max_gt_obj_idx].object_class);
//if the class name extracted from the ground truth file could not be found in the list of available classes, raise an exception
if (class_idx_it == output_headers.end())
{
string err_msg = "could not find object class '" + img_objects[max_gt_obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
CV_Error(CV_StsError,err_msg.c_str());
}
/* convert iterator to index */
int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
//add to confusion matrix row in proportion
output_values[class_idx] += 1.0;
} else {
//otherwise assign to background class
output_values[output_values.size()-1] += 1.0;
}
//check break conditions if breaking on certain level of recall
if (cond == CV_VOC_CCOND_RECALL)
{
if(static_cast<float>(retrieved_hits)/static_cast<float>(total_relevant) >= threshold) break;
}
}
/* finally, normalize confusion matrix row */
for (vector<float>::iterator it = output_values.begin(); it < output_values.end(); ++it)
{
(*it) /= static_cast<float>(total_objects);
}
}
//Save Precision-Recall results to a p-r curve in GNUPlot format
//--------------------------------------------------------------
//INPUTS:
// - output_file The file to which to save the GNUPlot data file. If only a filename is specified, the data
// file is saved to the standard VOC results directory.
// - precision Vector of precisions as returned from calcClassifier/DetectorPrecRecall
// - recall Vector of recalls as returned from calcClassifier/DetectorPrecRecall
// - ap ap as returned from calcClassifier/DetectorPrecRecall
// - (title) Title to use for the plot (if not specified, just the ap is printed as the title)
// This also specifies the filename of the output file if printing to pdf
// - (plot_type) Specifies whether to instruct GNUPlot to save to a PDF file (CV_VOC_PLOT_PDF) or directly
// to screen (CV_VOC_PLOT_SCREEN) in the datafile
//NOTES:
// The GNUPlot data file can be executed using GNUPlot from the commandline in the following way:
// >> GNUPlot <output_file>
// This will then display the p-r curve on the screen or save it to a pdf file depending on plot_type
void VocData::savePrecRecallToGnuplot(const string& output_file, const vector<float>& precision, const vector<float>& recall, const float ap, const string title, const VocPlotType plot_type)
{
string output_file_std = checkFilenamePathsep(output_file);
//if no directory is specified, by default save the output file in the results directory
// if (output_file_std.find("/") == output_file_std.npos)
// {
// output_file_std = m_results_directory + output_file_std;
// }
std::ofstream plot_file(output_file_std.c_str());
if (plot_file.is_open())
{
plot_file << "set xrange [0:1]" << endl;
plot_file << "set yrange [0:1]" << endl;
plot_file << "set size square" << endl;
string title_text = title;
if (title_text.size() == 0) title_text = "Precision-Recall Curve";
plot_file << "set title \"" << title_text << " (ap: " << ap << ")\"" << endl;
plot_file << "set xlabel \"Recall\"" << endl;
plot_file << "set ylabel \"Precision\"" << endl;
plot_file << "set style data lines" << endl;
plot_file << "set nokey" << endl;
if (plot_type == CV_VOC_PLOT_PNG)
{
plot_file << "set terminal png" << endl;
string pdf_filename;
if (title.size() != 0)
{
pdf_filename = title;
} else {
pdf_filename = "prcurve";
}
plot_file << "set out \"" << title << ".png\"" << endl;
}
plot_file << "plot \"-\" using 1:2" << endl;
plot_file << "# X Y" << endl;
CV_Assert(precision.size() == recall.size());
for (size_t i = 0; i < precision.size(); ++i)
{
plot_file << " " << recall[i] << " " << precision[i] << endl;
}
plot_file << "end" << endl;
if (plot_type == CV_VOC_PLOT_SCREEN)
{
plot_file << "pause -1" << endl;
}
plot_file.close();
} else {
string err_msg = "could not open plot file '" + output_file_std + "' for writing.";
CV_Error(CV_StsError,err_msg.c_str());
}
}
void VocData::readClassifierGroundTruth(const string& obj_class, const ObdDatasetType dataset, vector<ObdImage>& images, vector<char>& object_present)
{
images.clear();
string gtFilename = m_class_imageset_path;
gtFilename.replace(gtFilename.find("%s"),2,obj_class);
if (dataset == CV_OBD_TRAIN)
{
gtFilename.replace(gtFilename.find("%s"),2,m_train_set);
} else {
gtFilename.replace(gtFilename.find("%s"),2,m_test_set);
}
vector<string> image_codes;
readClassifierGroundTruth(gtFilename, image_codes, object_present);
convertImageCodesToObdImages(image_codes, images);
}
void VocData::readClassifierResultsFile(const std:: string& input_file, vector<ObdImage>& images, vector<float>& scores)
{
images.clear();
string input_file_std = checkFilenamePathsep(input_file);
//if no directory is specified, by default search for the input file in the results directory
// if (input_file_std.find("/") == input_file_std.npos)
// {
// input_file_std = m_results_directory + input_file_std;
// }
vector<string> image_codes;
readClassifierResultsFile(input_file_std, image_codes, scores);
convertImageCodesToObdImages(image_codes, images);
}
void VocData::readDetectorResultsFile(const string& input_file, vector<ObdImage>& images, vector<vector<float> >& scores, vector<vector<Rect> >& bounding_boxes)
{
images.clear();
string input_file_std = checkFilenamePathsep(input_file);
//if no directory is specified, by default search for the input file in the results directory
// if (input_file_std.find("/") == input_file_std.npos)
// {
// input_file_std = m_results_directory + input_file_std;
// }
vector<string> image_codes;
readDetectorResultsFile(input_file_std, image_codes, scores, bounding_boxes);
convertImageCodesToObdImages(image_codes, images);
}
const vector<string>& VocData::getObjectClasses()
{
return m_object_classes;
}
//string VocData::getResultsDirectory()
//{
// return m_results_directory;
//}
//---------------------------------------------------------
// Protected Functions ------------------------------------
//---------------------------------------------------------
static string getVocName( const string& vocPath )
{
size_t found = vocPath.rfind( '/' );
if( found == string::npos )
{
found = vocPath.rfind( '\\' );
if( found == string::npos )
return vocPath;
}
return vocPath.substr(found + 1, vocPath.size() - found);
}
void VocData::initVoc( const string& vocPath, const bool useTestDataset )
{
initVoc2007to2010( vocPath, useTestDataset );
}
//Initialize file paths and settings for the VOC 2010 dataset
//-----------------------------------------------------------
void VocData::initVoc2007to2010( const string& vocPath, const bool useTestDataset )
{
//check format of root directory and modify if necessary
m_vocName = getVocName( vocPath );
CV_Assert( !m_vocName.compare("VOC2007") || !m_vocName.compare("VOC2008") ||
!m_vocName.compare("VOC2009") || !m_vocName.compare("VOC2010") );
m_vocPath = checkFilenamePathsep( vocPath, true );
if (useTestDataset)
{
m_train_set = "trainval";
m_test_set = "test";
} else {
m_train_set = "train";
m_test_set = "val";
}
// initialize main classification/detection challenge paths
m_annotation_path = m_vocPath + "/Annotations/%s.xml";
m_image_path = m_vocPath + "/JPEGImages/%s.jpg";
m_imageset_path = m_vocPath + "/ImageSets/Main/%s.txt";
m_class_imageset_path = m_vocPath + "/ImageSets/Main/%s_%s.txt";
//define available object_classes for VOC2010 dataset
m_object_classes.push_back("aeroplane");
m_object_classes.push_back("bicycle");
m_object_classes.push_back("bird");
m_object_classes.push_back("boat");
m_object_classes.push_back("bottle");
m_object_classes.push_back("bus");
m_object_classes.push_back("car");
m_object_classes.push_back("cat");
m_object_classes.push_back("chair");
m_object_classes.push_back("cow");
m_object_classes.push_back("diningtable");
m_object_classes.push_back("dog");
m_object_classes.push_back("horse");
m_object_classes.push_back("motorbike");
m_object_classes.push_back("person");
m_object_classes.push_back("pottedplant");
m_object_classes.push_back("sheep");
m_object_classes.push_back("sofa");
m_object_classes.push_back("train");
m_object_classes.push_back("tvmonitor");
m_min_overlap = 0.5;
//up until VOC 2010, ap was calculated by sampling p-r curve, not taking complete curve
m_sampled_ap = ((m_vocName == "VOC2007") || (m_vocName == "VOC2008") || (m_vocName == "VOC2009"));
}
//Read a VOC classification ground truth text file for a given object class and dataset
//-------------------------------------------------------------------------------------
//INPUTS:
// - filename The path of the text file to read
//OUTPUTS:
// - image_codes VOC image codes extracted from the GT file in the form 20XX_XXXXXX where the first four
// digits specify the year of the dataset, and the last group specifies a unique ID
// - object_present For each image in the 'image_codes' array, specifies whether the object class described
// in the loaded GT file is present or not
void VocData::readClassifierGroundTruth(const string& filename, vector<string>& image_codes, vector<char>& object_present)
{
image_codes.clear();
object_present.clear();
std::ifstream gtfile(filename.c_str());
if (!gtfile.is_open())
{
string err_msg = "could not open VOC ground truth textfile '" + filename + "'.";
CV_Error(CV_StsError,err_msg.c_str());
}
string line;
string image;
int obj_present = 0;
while (!gtfile.eof())
{
std::getline(gtfile,line);
std::istringstream iss(line);
iss >> image >> obj_present;
if (!iss.fail())
{
image_codes.push_back(image);
object_present.push_back(obj_present == 1);
} else {
if (!gtfile.eof()) CV_Error(CV_StsParseError,"error parsing VOC ground truth textfile.");
}
}
gtfile.close();
}
void VocData::readClassifierResultsFile(const string& input_file, vector<string>& image_codes, vector<float>& scores)
{
//check if results file exists
std::ifstream result_file(input_file.c_str());
if (result_file.is_open())
{
string line;
string image;
float score;
//read in the results file
while (!result_file.eof())
{
std::getline(result_file,line);
std::istringstream iss(line);
iss >> image >> score;
if (!iss.fail())
{
image_codes.push_back(image);
scores.push_back(score);
} else {
if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC classifier results file.");
}
}
result_file.close();
} else {
string err_msg = "could not open classifier results file '" + input_file + "' for reading.";
CV_Error(CV_StsError,err_msg.c_str());
}
}
void VocData::readDetectorResultsFile(const string& input_file, vector<string>& image_codes, vector<vector<float> >& scores, vector<vector<Rect> >& bounding_boxes)
{
image_codes.clear();
scores.clear();
bounding_boxes.clear();
//check if results file exists
std::ifstream result_file(input_file.c_str());
if (result_file.is_open())
{
string line;
string image;
Rect bounding_box;
float score;
//read in the results file
while (!result_file.eof())
{
std::getline(result_file,line);
std::istringstream iss(line);
iss >> image >> score >> bounding_box.x >> bounding_box.y >> bounding_box.width >> bounding_box.height;
if (!iss.fail())
{
//convert right and bottom positions to width and height
bounding_box.width -= bounding_box.x;
bounding_box.height -= bounding_box.y;
//convert to 0-indexing
bounding_box.x -= 1;
bounding_box.y -= 1;
//store in output vectors
/* first check if the current image code has been seen before */
vector<string>::iterator image_codes_it = std::find(image_codes.begin(),image_codes.end(),image);
if (image_codes_it == image_codes.end())
{
image_codes.push_back(image);
vector<float> score_vect(1);
score_vect[0] = score;
scores.push_back(score_vect);
vector<Rect> bounding_box_vect(1);
bounding_box_vect[0] = bounding_box;
bounding_boxes.push_back(bounding_box_vect);
} else {
/* if the image index has been seen before, add the current object below it in the 2D arrays */
int image_idx = (int)std::distance(image_codes.begin(),image_codes_it);
scores[image_idx].push_back(score);
bounding_boxes[image_idx].push_back(bounding_box);
}
} else {
if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC detector results file.");
}
}
result_file.close();
} else {
string err_msg = "could not open detector results file '" + input_file + "' for reading.";
CV_Error(CV_StsError,err_msg.c_str());
}
}
//Read a VOC annotation xml file for a given image
//------------------------------------------------
//INPUTS:
// - filename The path of the xml file to read
//OUTPUTS:
// - objects Array of VocObject describing all object instances present in the given image
void VocData::extractVocObjects(const string filename, vector<ObdObject>& objects, vector<VocObjectData>& object_data)
{
#ifdef PR_DEBUG
int block = 1;
cout << "SAMPLE VOC OBJECT EXTRACTION for " << filename << ":" << endl;
#endif
objects.clear();
object_data.clear();
string contents, object_contents, tag_contents;
readFileToString(filename, contents);
//keep on extracting 'object' blocks until no more can be found
if (extractXMLBlock(contents, "annotation", 0, contents) != -1)
{
int searchpos = 0;
searchpos = extractXMLBlock(contents, "object", searchpos, object_contents);
while (searchpos != -1)
{
#ifdef PR_DEBUG
cout << "SEARCHPOS:" << searchpos << endl;
cout << "start block " << block << " ---------" << endl;
cout << object_contents << endl;
cout << "end block " << block << " -----------" << endl;
++block;
#endif
ObdObject object;
VocObjectData object_d;
//object class -------------
if (extractXMLBlock(object_contents, "name", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <name> tag in object definition of '" + filename + "'");
object.object_class.swap(tag_contents);
//object bounding box -------------
int xmax, xmin, ymax, ymin;
if (extractXMLBlock(object_contents, "xmax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmax> tag in object definition of '" + filename + "'");
xmax = stringToInteger(tag_contents);
if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmin> tag in object definition of '" + filename + "'");
xmin = stringToInteger(tag_contents);
if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymax> tag in object definition of '" + filename + "'");
ymax = stringToInteger(tag_contents);
if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymin> tag in object definition of '" + filename + "'");
ymin = stringToInteger(tag_contents);
object.boundingBox.x = xmin-1; //convert to 0-based indexing
object.boundingBox.width = xmax - xmin;
object.boundingBox.y = ymin-1;
object.boundingBox.height = ymax - ymin;
CV_Assert(xmin != 0);
CV_Assert(xmax > xmin);
CV_Assert(ymin != 0);
CV_Assert(ymax > ymin);
//object tags -------------
if (extractXMLBlock(object_contents, "difficult", 0, tag_contents) != -1)
{
object_d.difficult = (tag_contents == "1");
} else object_d.difficult = false;
if (extractXMLBlock(object_contents, "occluded", 0, tag_contents) != -1)
{
object_d.occluded = (tag_contents == "1");
} else object_d.occluded = false;
if (extractXMLBlock(object_contents, "truncated", 0, tag_contents) != -1)
{
object_d.truncated = (tag_contents == "1");
} else object_d.truncated = false;
if (extractXMLBlock(object_contents, "pose", 0, tag_contents) != -1)
{
if (tag_contents == "Frontal") object_d.pose = CV_VOC_POSE_FRONTAL;
if (tag_contents == "Rear") object_d.pose = CV_VOC_POSE_REAR;
if (tag_contents == "Left") object_d.pose = CV_VOC_POSE_LEFT;
if (tag_contents == "Right") object_d.pose = CV_VOC_POSE_RIGHT;
}
//add to array of objects
objects.push_back(object);
object_data.push_back(object_d);
//extract next 'object' block from file if it exists
searchpos = extractXMLBlock(contents, "object", searchpos, object_contents);
}
}
}
//Converts an image identifier string in the format YYYY_XXXXXX to a single index integer of form XXXXXXYYYY
//where Y represents a year and returns the image path
//----------------------------------------------------------------------------------------------------------
string VocData::getImagePath(const string& input_str)
{
string path = m_image_path;
path.replace(path.find("%s"),2,input_str);
return path;
}
//Tests two boundary boxes for overlap (using the intersection over union metric) and returns the overlap if the objects
//defined by the two bounding boxes are considered to be matched according to the criterion outlined in
//the VOC documentation [namely intersection/union > some threshold] otherwise returns -1.0 (no match)
//----------------------------------------------------------------------------------------------------------
float VocData::testBoundingBoxesForOverlap(const Rect detection, const Rect ground_truth)
{
int detection_x2 = detection.x + detection.width;
int detection_y2 = detection.y + detection.height;
int ground_truth_x2 = ground_truth.x + ground_truth.width;
int ground_truth_y2 = ground_truth.y + ground_truth.height;
//first calculate the boundaries of the intersection of the rectangles
int intersection_x = std::max(detection.x, ground_truth.x); //rightmost left
int intersection_y = std::max(detection.y, ground_truth.y); //bottommost top
int intersection_x2 = std::min(detection_x2, ground_truth_x2); //leftmost right
int intersection_y2 = std::min(detection_y2, ground_truth_y2); //topmost bottom
//then calculate the width and height of the intersection rect
int intersection_width = intersection_x2 - intersection_x + 1;
int intersection_height = intersection_y2 - intersection_y + 1;
//if there is no overlap then return false straight away
if ((intersection_width <= 0) || (intersection_height <= 0)) return -1.0;
//otherwise calculate the intersection
int intersection_area = intersection_width*intersection_height;
//now calculate the union
int union_area = (detection.width+1)*(detection.height+1) + (ground_truth.width+1)*(ground_truth.height+1) - intersection_area;
//calculate the intersection over union and use as threshold as per VOC documentation
float overlap = static_cast<float>(intersection_area)/static_cast<float>(union_area);
if (overlap > m_min_overlap)
{
return overlap;
} else {
return -1.0;
}
}
//Extracts the object class and dataset from the filename of a VOC standard results text file, which takes
//the format 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'
//----------------------------------------------------------------------------------------------------------
void VocData::extractDataFromResultsFilename(const string& input_file, string& class_name, string& dataset_name)
{
string input_file_std = checkFilenamePathsep(input_file);
size_t fnamestart = input_file_std.rfind("/");
size_t fnameend = input_file_std.rfind(".txt");
if ((fnamestart == input_file_std.npos) || (fnameend == input_file_std.npos))
CV_Error(CV_StsError,"Could not extract filename of results file.");
++fnamestart;
if (fnamestart >= fnameend)
CV_Error(CV_StsError,"Could not extract filename of results file.");
//extract dataset and class names, triggering exception if the filename format is not correct
string filename = input_file_std.substr(fnamestart, fnameend-fnamestart);
size_t datasetstart = filename.find("_");
datasetstart = filename.find("_",datasetstart+1);
size_t classstart = filename.find("_",datasetstart+1);
//allow for appended index after a further '_' by discarding this part if it exists
size_t classend = filename.find("_",classstart+1);
if (classend == filename.npos) classend = filename.size();
if ((datasetstart == filename.npos) || (classstart == filename.npos))
CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
++datasetstart;
++classstart;
if (((datasetstart-classstart) < 1) || ((classend-datasetstart) < 1))
CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
dataset_name = filename.substr(datasetstart,classstart-datasetstart-1);
class_name = filename.substr(classstart,classend-classstart);
}
bool VocData::getClassifierGroundTruthImage(const string& obj_class, const string& id)
{
/* if the classifier ground truth data for all images of the current class has not been loaded yet, load it now */
if (m_classifier_gt_all_ids.empty() || (m_classifier_gt_class != obj_class))
{
m_classifier_gt_all_ids.clear();
m_classifier_gt_all_present.clear();
m_classifier_gt_class = obj_class;
for (int i=0; i<2; ++i) //run twice (once over test set and once over training set)
{
//generate the filename of the classification ground-truth textfile for the object class
string gtFilename = m_class_imageset_path;
gtFilename.replace(gtFilename.find("%s"),2,obj_class);
if (i == 0)
{
gtFilename.replace(gtFilename.find("%s"),2,m_train_set);
} else {
gtFilename.replace(gtFilename.find("%s"),2,m_test_set);
}
//parse the ground truth file, storing in two separate vectors
//for the image code and the ground truth value
vector<string> image_codes;
vector<char> object_present;
readClassifierGroundTruth(gtFilename, image_codes, object_present);
m_classifier_gt_all_ids.insert(m_classifier_gt_all_ids.end(),image_codes.begin(),image_codes.end());
m_classifier_gt_all_present.insert(m_classifier_gt_all_present.end(),object_present.begin(),object_present.end());
CV_Assert(m_classifier_gt_all_ids.size() == m_classifier_gt_all_present.size());
}
}
//search for the image code
vector<string>::iterator it = find (m_classifier_gt_all_ids.begin(), m_classifier_gt_all_ids.end(), id);
if (it != m_classifier_gt_all_ids.end())
{
//image found, so return corresponding ground truth
return m_classifier_gt_all_present[std::distance(m_classifier_gt_all_ids.begin(),it)] != 0;
} else {
string err_msg = "could not find classifier ground truth for image '" + id + "' and class '" + obj_class + "'";
CV_Error(CV_StsError,err_msg.c_str());
}
return true;
}
//-------------------------------------------------------------------
// Protected Functions (utility) ------------------------------------
//-------------------------------------------------------------------
//returns a vector containing indexes of the input vector in sorted ascending/descending order
void VocData::getSortOrder(const vector<float>& values, vector<size_t>& order, bool descending)
{
/* 1. store sorting order in 'order_pair' */
vector<std::pair<size_t, vector<float>::const_iterator> > order_pair(values.size());
size_t n = 0;
for (vector<float>::const_iterator it = values.begin(); it != values.end(); ++it, ++n)
order_pair[n] = make_pair(n, it);
std::sort(order_pair.begin(),order_pair.end(),orderingSorter());
if (descending == false) std::reverse(order_pair.begin(),order_pair.end());
vector<size_t>(order_pair.size()).swap(order);
for (size_t i = 0; i < order_pair.size(); ++i)
{
order[i] = order_pair[i].first;
}
}
void VocData::readFileToString(const string filename, string& file_contents)
{
std::ifstream ifs(filename.c_str());
if (!ifs.is_open()) CV_Error(CV_StsError,"could not open text file");
stringstream oss;
oss << ifs.rdbuf();
file_contents = oss.str();
}
int VocData::stringToInteger(const string input_str)
{
int result = 0;
stringstream ss(input_str);
if ((ss >> result).fail())
{
CV_Error(CV_StsBadArg,"could not perform string to integer conversion");
}
return result;
}
string VocData::integerToString(const int input_int)
{
string result;
stringstream ss;
if ((ss << input_int).fail())
{
CV_Error(CV_StsBadArg,"could not perform integer to string conversion");
}
result = ss.str();
return result;
}
string VocData::checkFilenamePathsep( const string filename, bool add_trailing_slash )
{
string filename_new = filename;
size_t pos = filename_new.find("\\\\");
while (pos != filename_new.npos)
{
filename_new.replace(pos,2,"/");
pos = filename_new.find("\\\\", pos);
}
pos = filename_new.find("\\");
while (pos != filename_new.npos)
{
filename_new.replace(pos,1,"/");
pos = filename_new.find("\\", pos);
}
if (add_trailing_slash)
{
//add training slash if this is missing
if (filename_new.rfind("/") != filename_new.length()-1) filename_new += "/";
}
return filename_new;
}
void VocData::convertImageCodesToObdImages(const vector<string>& image_codes, vector<ObdImage>& images)
{
images.clear();
images.reserve(image_codes.size());
string path;
//transfer to output arrays
for (size_t i = 0; i < image_codes.size(); ++i)
{
//generate image path and indices from extracted string code
path = getImagePath(image_codes[i]);
images.push_back(ObdImage(image_codes[i], path));
}
}
//Extract text from within a given tag from an XML file
//-----------------------------------------------------
//INPUTS:
// - src XML source file
// - tag XML tag delimiting block to extract
// - searchpos position within src at which to start search
//OUTPUTS:
// - tag_contents text extracted between <tag> and </tag> tags
//RETURN VALUE:
// - the position of the final character extracted in tag_contents within src
// (can be used to call extractXMLBlock recursively to extract multiple blocks)
// returns -1 if the tag could not be found
int VocData::extractXMLBlock(const string src, const string tag, const int searchpos, string& tag_contents)
{
size_t startpos, next_startpos, endpos;
int embed_count = 1;
//find position of opening tag
startpos = src.find("<" + tag + ">", searchpos);
if (startpos == string::npos) return -1;
//initialize endpos -
// start searching for end tag anywhere after opening tag
endpos = startpos;
//find position of next opening tag
next_startpos = src.find("<" + tag + ">", startpos+1);
//match opening tags with closing tags, and only
//accept final closing tag of same level as original
//opening tag
while (embed_count > 0)
{
endpos = src.find("</" + tag + ">", endpos+1);
if (endpos == string::npos) return -1;
//the next code is only executed if there are embedded tags with the same name
if (next_startpos != string::npos)
{
while (next_startpos<endpos)
{
//counting embedded start tags
++embed_count;
next_startpos = src.find("<" + tag + ">", next_startpos+1);
if (next_startpos == string::npos) break;
}
}
//passing end tag so decrement nesting level
--embed_count;
}
//finally, extract the tag region
startpos += tag.length() + 2;
if (startpos > src.length()) return -1;
if (endpos > src.length()) return -1;
tag_contents = src.substr(startpos,endpos-startpos);
return static_cast<int>(endpos);
}
/****************************************************************************************\
* Sample on image classification *
\****************************************************************************************/
//
// This part of the code was a little refactor
//
struct DDMParams
{
DDMParams() : detectorType("SURF"), descriptorType("SURF"), matcherType("BruteForce") {}
DDMParams( const string _detectorType, const string _descriptorType, const string& _matcherType ) :
detectorType(_detectorType), descriptorType(_descriptorType), matcherType(_matcherType){}
void read( const FileNode& fn )
{
fn["detectorType"] >> detectorType;
fn["descriptorType"] >> descriptorType;
fn["matcherType"] >> matcherType;
}
void write( FileStorage& fs ) const
{
fs << "detectorType" << detectorType;
fs << "descriptorType" << descriptorType;
fs << "matcherType" << matcherType;
}
void print() const
{
cout << "detectorType: " << detectorType << endl;
cout << "descriptorType: " << descriptorType << endl;
cout << "matcherType: " << matcherType << endl;
}
string detectorType;
string descriptorType;
string matcherType;
};
struct VocabTrainParams
{
VocabTrainParams() : trainObjClass("chair"), vocabSize(1000), memoryUse(200), descProportion(0.3f) {}
VocabTrainParams( const string _trainObjClass, size_t _vocabSize, size_t _memoryUse, float _descProportion ) :
trainObjClass(_trainObjClass), vocabSize((int)_vocabSize), memoryUse((int)_memoryUse), descProportion(_descProportion) {}
void read( const FileNode& fn )
{
fn["trainObjClass"] >> trainObjClass;
fn["vocabSize"] >> vocabSize;
fn["memoryUse"] >> memoryUse;
fn["descProportion"] >> descProportion;
}
void write( FileStorage& fs ) const
{
fs << "trainObjClass" << trainObjClass;
fs << "vocabSize" << vocabSize;
fs << "memoryUse" << memoryUse;
fs << "descProportion" << descProportion;
}
void print() const
{
cout << "trainObjClass: " << trainObjClass << endl;
cout << "vocabSize: " << vocabSize << endl;
cout << "memoryUse: " << memoryUse << endl;
cout << "descProportion: " << descProportion << endl;
}
string trainObjClass; // Object class used for training visual vocabulary.
// It shouldn't matter which object class is specified here - visual vocab will still be the same.
int vocabSize; //number of visual words in vocabulary to train
int memoryUse; // Memory to preallocate (in MB) when training vocab.
// Change this depending on the size of the dataset/available memory.
float descProportion; // Specifies the number of descriptors to use from each image as a proportion of the total num descs.
};
struct SVMTrainParamsExt
{
SVMTrainParamsExt() : descPercent(0.5f), targetRatio(0.4f), balanceClasses(true) {}
SVMTrainParamsExt( float _descPercent, float _targetRatio, bool _balanceClasses ) :
descPercent(_descPercent), targetRatio(_targetRatio), balanceClasses(_balanceClasses) {}
void read( const FileNode& fn )
{
fn["descPercent"] >> descPercent;
fn["targetRatio"] >> targetRatio;
fn["balanceClasses"] >> balanceClasses;
}
void write( FileStorage& fs ) const
{
fs << "descPercent" << descPercent;
fs << "targetRatio" << targetRatio;
fs << "balanceClasses" << balanceClasses;
}
void print() const
{
cout << "descPercent: " << descPercent << endl;
cout << "targetRatio: " << targetRatio << endl;
cout << "balanceClasses: " << balanceClasses << endl;
}
float descPercent; // Percentage of extracted descriptors to use for training.
float targetRatio; // Try to get this ratio of positive to negative samples (minimum).
bool balanceClasses; // Balance class weights by number of samples in each (if true cSvmTrainTargetRatio is ignored).
};
static void readUsedParams( const FileNode& fn, string& vocName, DDMParams& ddmParams, VocabTrainParams& vocabTrainParams, SVMTrainParamsExt& svmTrainParamsExt )
{
fn["vocName"] >> vocName;
FileNode currFn = fn;
currFn = fn["ddmParams"];
ddmParams.read( currFn );
currFn = fn["vocabTrainParams"];
vocabTrainParams.read( currFn );
currFn = fn["svmTrainParamsExt"];
svmTrainParamsExt.read( currFn );
}
static void writeUsedParams( FileStorage& fs, const string& vocName, const DDMParams& ddmParams, const VocabTrainParams& vocabTrainParams, const SVMTrainParamsExt& svmTrainParamsExt )
{
fs << "vocName" << vocName;
fs << "ddmParams" << "{";
ddmParams.write(fs);
fs << "}";
fs << "vocabTrainParams" << "{";
vocabTrainParams.write(fs);
fs << "}";
fs << "svmTrainParamsExt" << "{";
svmTrainParamsExt.write(fs);
fs << "}";
}
static void printUsedParams( const string& vocPath, const string& resDir,
const DDMParams& ddmParams, const VocabTrainParams& vocabTrainParams,
const SVMTrainParamsExt& svmTrainParamsExt )
{
cout << "CURRENT CONFIGURATION" << endl;
cout << "----------------------------------------------------------------" << endl;
cout << "vocPath: " << vocPath << endl;
cout << "resDir: " << resDir << endl;
cout << endl; ddmParams.print();
cout << endl; vocabTrainParams.print();
cout << endl; svmTrainParamsExt.print();
cout << "----------------------------------------------------------------" << endl << endl;
}
static bool readVocabulary( const string& filename, Mat& vocabulary )
{
cout << "Reading vocabulary...";
FileStorage fs( filename, FileStorage::READ );
if( fs.isOpened() )
{
fs["vocabulary"] >> vocabulary;
cout << "done" << endl;
return true;
}
return false;
}
static bool writeVocabulary( const string& filename, const Mat& vocabulary )
{
cout << "Saving vocabulary..." << endl;
FileStorage fs( filename, FileStorage::WRITE );
if( fs.isOpened() )
{
fs << "vocabulary" << vocabulary;
return true;
}
return false;
}
static Mat trainVocabulary( const string& filename, VocData& vocData, const VocabTrainParams& trainParams,
const Ptr<FeatureDetector>& fdetector, const Ptr<DescriptorExtractor>& dextractor )
{
Mat vocabulary;
if( !readVocabulary( filename, vocabulary) )
{
CV_Assert( dextractor->descriptorType() == CV_32FC1 );
const int elemSize = CV_ELEM_SIZE(dextractor->descriptorType());
const int descByteSize = dextractor->descriptorSize() * elemSize;
const int bytesInMB = 1048576;
const int maxDescCount = (trainParams.memoryUse * bytesInMB) / descByteSize; // Total number of descs to use for training.
cout << "Extracting VOC data..." << endl;
vector<ObdImage> images;
vector<char> objectPresent;
vocData.getClassImages( trainParams.trainObjClass, CV_OBD_TRAIN, images, objectPresent );
cout << "Computing descriptors..." << endl;
RNG& rng = theRNG();
TermCriteria terminate_criterion;
terminate_criterion.epsilon = FLT_EPSILON;
BOWKMeansTrainer bowTrainer( trainParams.vocabSize, terminate_criterion, 3, KMEANS_PP_CENTERS );
while( images.size() > 0 )
{
if( bowTrainer.descripotorsCount() > maxDescCount )
{
#ifdef DEBUG_DESC_PROGRESS
cout << "Breaking due to full memory ( descriptors count = " << bowTrainer.descripotorsCount()
<< "; descriptor size in bytes = " << descByteSize << "; all used memory = "
<< bowTrainer.descripotorsCount()*descByteSize << endl;
#endif
break;
}
// Randomly pick an image from the dataset which hasn't yet been seen
// and compute the descriptors from that image.
int randImgIdx = rng( (unsigned)images.size() );
Mat colorImage = imread( images[randImgIdx].path );
vector<KeyPoint> imageKeypoints;
fdetector->detect( colorImage, imageKeypoints );
Mat imageDescriptors;
dextractor->compute( colorImage, imageKeypoints, imageDescriptors );
//check that there were descriptors calculated for the current image
if( !imageDescriptors.empty() )
{
int descCount = imageDescriptors.rows;
// Extract trainParams.descProportion descriptors from the image, breaking if the 'allDescriptors' matrix becomes full
int descsToExtract = static_cast<int>(trainParams.descProportion * static_cast<float>(descCount));
// Fill mask of used descriptors
vector<char> usedMask( descCount, false );
fill( usedMask.begin(), usedMask.begin() + descsToExtract, true );
for( int i = 0; i < descCount; i++ )
{
int i1 = rng(descCount), i2 = rng(descCount);
char tmp = usedMask[i1]; usedMask[i1] = usedMask[i2]; usedMask[i2] = tmp;
}
for( int i = 0; i < descCount; i++ )
{
if( usedMask[i] && bowTrainer.descripotorsCount() < maxDescCount )
bowTrainer.add( imageDescriptors.row(i) );
}
}
#ifdef DEBUG_DESC_PROGRESS
cout << images.size() << " images left, " << images[randImgIdx].id << " processed - "
<</* descs_extracted << "/" << image_descriptors.rows << " extracted - " << */
cvRound((static_cast<double>(bowTrainer.descripotorsCount())/static_cast<double>(maxDescCount))*100.0)
<< " % memory used" << ( imageDescriptors.empty() ? " -> no descriptors extracted, skipping" : "") << endl;
#endif
// Delete the current element from images so it is not added again
images.erase( images.begin() + randImgIdx );
}
cout << "Maximum allowed descriptor count: " << maxDescCount << ", Actual descriptor count: " << bowTrainer.descripotorsCount() << endl;
cout << "Training vocabulary..." << endl;
vocabulary = bowTrainer.cluster();
if( !writeVocabulary(filename, vocabulary) )
{
cout << "Error: file " << filename << " can not be opened to write" << endl;
exit(-1);
}
}
return vocabulary;
}
static bool readBowImageDescriptor( const string& file, Mat& bowImageDescriptor )
{
FileStorage fs( file, FileStorage::READ );
if( fs.isOpened() )
{
fs["imageDescriptor"] >> bowImageDescriptor;
return true;
}
return false;
}
static bool writeBowImageDescriptor( const string& file, const Mat& bowImageDescriptor )
{
FileStorage fs( file, FileStorage::WRITE );
if( fs.isOpened() )
{
fs << "imageDescriptor" << bowImageDescriptor;
return true;
}
return false;
}
// Load in the bag of words vectors for a set of images, from file if possible
static void calculateImageDescriptors( const vector<ObdImage>& images, vector<Mat>& imageDescriptors,
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
const string& resPath )
{
CV_Assert( !bowExtractor->getVocabulary().empty() );
imageDescriptors.resize( images.size() );
for( size_t i = 0; i < images.size(); i++ )
{
string filename = resPath + bowImageDescriptorsDir + "/" + images[i].id + ".xml.gz";
if( readBowImageDescriptor( filename, imageDescriptors[i] ) )
{
#ifdef DEBUG_DESC_PROGRESS
cout << "Loaded bag of word vector for image " << i+1 << " of " << images.size() << " (" << images[i].id << ")" << endl;
#endif
}
else
{
Mat colorImage = imread( images[i].path );
#ifdef DEBUG_DESC_PROGRESS
cout << "Computing descriptors for image " << i+1 << " of " << images.size() << " (" << images[i].id << ")" << flush;
#endif
vector<KeyPoint> keypoints;
fdetector->detect( colorImage, keypoints );
#ifdef DEBUG_DESC_PROGRESS
cout << " + generating BoW vector" << std::flush;
#endif
bowExtractor->compute( colorImage, keypoints, imageDescriptors[i] );
#ifdef DEBUG_DESC_PROGRESS
cout << " ...DONE " << static_cast<int>(static_cast<float>(i+1)/static_cast<float>(images.size())*100.0)
<< " % complete" << endl;
#endif
if( !imageDescriptors[i].empty() )
{
if( !writeBowImageDescriptor( filename, imageDescriptors[i] ) )
{
cout << "Error: file " << filename << "can not be opened to write bow image descriptor" << endl;
exit(-1);
}
}
}
}
}
static void removeEmptyBowImageDescriptors( vector<ObdImage>& images, vector<Mat>& bowImageDescriptors,
vector<char>& objectPresent )
{
CV_Assert( !images.empty() );
for( int i = (int)images.size() - 1; i >= 0; i-- )
{
bool res = bowImageDescriptors[i].empty();
if( res )
{
cout << "Removing image " << images[i].id << " due to no descriptors..." << endl;
images.erase( images.begin() + i );
bowImageDescriptors.erase( bowImageDescriptors.begin() + i );
objectPresent.erase( objectPresent.begin() + i );
}
}
}
static void removeBowImageDescriptorsByCount( vector<ObdImage>& images, vector<Mat> bowImageDescriptors, vector<char> objectPresent,
const SVMTrainParamsExt& svmParamsExt, int descsToDelete )
{
RNG& rng = theRNG();
int pos_ex = (int)std::count( objectPresent.begin(), objectPresent.end(), (char)1 );
int neg_ex = (int)std::count( objectPresent.begin(), objectPresent.end(), (char)0 );
while( descsToDelete != 0 )
{
int randIdx = rng((unsigned)images.size());
// Prefer positive training examples according to svmParamsExt.targetRatio if required
if( objectPresent[randIdx] )
{
if( (static_cast<float>(pos_ex)/static_cast<float>(neg_ex+pos_ex) < svmParamsExt.targetRatio) &&
(neg_ex > 0) && (svmParamsExt.balanceClasses == false) )
{ continue; }
else
{ pos_ex--; }
}
else
{ neg_ex--; }
images.erase( images.begin() + randIdx );
bowImageDescriptors.erase( bowImageDescriptors.begin() + randIdx );
objectPresent.erase( objectPresent.begin() + randIdx );
descsToDelete--;
}
CV_Assert( bowImageDescriptors.size() == objectPresent.size() );
}
static void setSVMParams( CvSVMParams& svmParams, CvMat& class_wts_cv, const Mat& responses, bool balanceClasses )
{
int pos_ex = countNonZero(responses == 1);
int neg_ex = countNonZero(responses == -1);
cout << pos_ex << " positive training samples; " << neg_ex << " negative training samples" << endl;
svmParams.svm_type = CvSVM::C_SVC;
svmParams.kernel_type = CvSVM::RBF;
if( balanceClasses )
{
Mat class_wts( 2, 1, CV_32FC1 );
// The first training sample determines the '+1' class internally, even if it is negative,
// so store whether this is the case so that the class weights can be reversed accordingly.
bool reversed_classes = (responses.at<float>(0) < 0.f);
if( reversed_classes == false )
{
class_wts.at<float>(0) = static_cast<float>(pos_ex)/static_cast<float>(pos_ex+neg_ex); // weighting for costs of positive class + 1 (i.e. cost of false positive - larger gives greater cost)
class_wts.at<float>(1) = static_cast<float>(neg_ex)/static_cast<float>(pos_ex+neg_ex); // weighting for costs of negative class - 1 (i.e. cost of false negative)
}
else
{
class_wts.at<float>(0) = static_cast<float>(neg_ex)/static_cast<float>(pos_ex+neg_ex);
class_wts.at<float>(1) = static_cast<float>(pos_ex)/static_cast<float>(pos_ex+neg_ex);
}
class_wts_cv = class_wts;
svmParams.class_weights = &class_wts_cv;
}
}
static void setSVMTrainAutoParams( CvParamGrid& c_grid, CvParamGrid& gamma_grid,
CvParamGrid& p_grid, CvParamGrid& nu_grid,
CvParamGrid& coef_grid, CvParamGrid& degree_grid )
{
c_grid = CvSVM::get_default_grid(CvSVM::C);
gamma_grid = CvSVM::get_default_grid(CvSVM::GAMMA);
p_grid = CvSVM::get_default_grid(CvSVM::P);
p_grid.step = 0;
nu_grid = CvSVM::get_default_grid(CvSVM::NU);
nu_grid.step = 0;
coef_grid = CvSVM::get_default_grid(CvSVM::COEF);
coef_grid.step = 0;
degree_grid = CvSVM::get_default_grid(CvSVM::DEGREE);
degree_grid.step = 0;
}
static void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsExt, const string& objClassName, VocData& vocData,
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
const string& resPath )
{
/* first check if a previously trained svm for the current class has been saved to file */
string svmFilename = resPath + svmsDir + "/" + objClassName + ".xml.gz";
FileStorage fs( svmFilename, FileStorage::READ);
if( fs.isOpened() )
{
cout << "*** LOADING SVM CLASSIFIER FOR CLASS " << objClassName << " ***" << endl;
svm.load( svmFilename.c_str() );
}
else
{
cout << "*** TRAINING CLASSIFIER FOR CLASS " << objClassName << " ***" << endl;
cout << "CALCULATING BOW VECTORS FOR TRAINING SET OF " << objClassName << "..." << endl;
// Get classification ground truth for images in the training set
vector<ObdImage> images;
vector<Mat> bowImageDescriptors;
vector<char> objectPresent;
vocData.getClassImages( objClassName, CV_OBD_TRAIN, images, objectPresent );
// Compute the bag of words vector for each image in the training set.
calculateImageDescriptors( images, bowImageDescriptors, bowExtractor, fdetector, resPath );
// Remove any images for which descriptors could not be calculated
removeEmptyBowImageDescriptors( images, bowImageDescriptors, objectPresent );
CV_Assert( svmParamsExt.descPercent > 0.f && svmParamsExt.descPercent <= 1.f );
if( svmParamsExt.descPercent < 1.f )
{
int descsToDelete = static_cast<int>(static_cast<float>(images.size())*(1.0-svmParamsExt.descPercent));
cout << "Using " << (images.size() - descsToDelete) << " of " << images.size() <<
" descriptors for training (" << svmParamsExt.descPercent*100.0 << " %)" << endl;
removeBowImageDescriptorsByCount( images, bowImageDescriptors, objectPresent, svmParamsExt, descsToDelete );
}
// Prepare the input matrices for SVM training.
Mat trainData( (int)images.size(), bowExtractor->getVocabulary().rows, CV_32FC1 );
Mat responses( (int)images.size(), 1, CV_32SC1 );
// Transfer bag of words vectors and responses across to the training data matrices
for( size_t imageIdx = 0; imageIdx < images.size(); imageIdx++ )
{
// Transfer image descriptor (bag of words vector) to training data matrix
Mat submat = trainData.row((int)imageIdx);
if( bowImageDescriptors[imageIdx].cols != bowExtractor->descriptorSize() )
{
cout << "Error: computed bow image descriptor size " << bowImageDescriptors[imageIdx].cols
<< " differs from vocabulary size" << bowExtractor->getVocabulary().cols << endl;
exit(-1);
}
bowImageDescriptors[imageIdx].copyTo( submat );
// Set response value
responses.at<int>((int)imageIdx) = objectPresent[imageIdx] ? 1 : -1;
}
cout << "TRAINING SVM FOR CLASS ..." << objClassName << "..." << endl;
CvSVMParams svmParams;
CvMat class_wts_cv;
setSVMParams( svmParams, class_wts_cv, responses, svmParamsExt.balanceClasses );
CvParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid;
setSVMTrainAutoParams( c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
svm.train_auto( trainData, responses, Mat(), Mat(), svmParams, 10, c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
cout << "SVM TRAINING FOR CLASS " << objClassName << " COMPLETED" << endl;
svm.save( svmFilename.c_str() );
cout << "SAVED CLASSIFIER TO FILE" << endl;
}
}
static void computeConfidences( CvSVM& svm, const string& objClassName, VocData& vocData,
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
const string& resPath )
{
cout << "*** CALCULATING CONFIDENCES FOR CLASS " << objClassName << " ***" << endl;
cout << "CALCULATING BOW VECTORS FOR TEST SET OF " << objClassName << "..." << endl;
// Get classification ground truth for images in the test set
vector<ObdImage> images;
vector<Mat> bowImageDescriptors;
vector<char> objectPresent;
vocData.getClassImages( objClassName, CV_OBD_TEST, images, objectPresent );
// Compute the bag of words vector for each image in the test set
calculateImageDescriptors( images, bowImageDescriptors, bowExtractor, fdetector, resPath );
// Remove any images for which descriptors could not be calculated
removeEmptyBowImageDescriptors( images, bowImageDescriptors, objectPresent);
// Use the bag of words vectors to calculate classifier output for each image in test set
cout << "CALCULATING CONFIDENCE SCORES FOR CLASS " << objClassName << "..." << endl;
vector<float> confidences( images.size() );
float signMul = 1.f;
for( size_t imageIdx = 0; imageIdx < images.size(); imageIdx++ )
{
if( imageIdx == 0 )
{
// In the first iteration, determine the sign of the positive class
float classVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], false );
float scoreVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], true );
signMul = (classVal < 0) == (scoreVal < 0) ? 1.f : -1.f;
}
// svm output of decision function
confidences[imageIdx] = signMul * svm.predict( bowImageDescriptors[imageIdx], true );
}
cout << "WRITING QUERY RESULTS TO VOC RESULTS FILE FOR CLASS " << objClassName << "..." << endl;
vocData.writeClassifierResultsFile( resPath + plotsDir, objClassName, CV_OBD_TEST, images, confidences, 1, true );
cout << "DONE - " << objClassName << endl;
cout << "---------------------------------------------------------------" << endl;
}
static void computeGnuPlotOutput( const string& resPath, const string& objClassName, VocData& vocData )
{
vector<float> precision, recall;
float ap;
const string resultFile = vocData.getResultsFilename( objClassName, CV_VOC_TASK_CLASSIFICATION, CV_OBD_TEST);
const string plotFile = resultFile.substr(0, resultFile.size()-4) + ".plt";
cout << "Calculating precision recall curve for class '" <<objClassName << "'" << endl;
vocData.calcClassifierPrecRecall( resPath + plotsDir + "/" + resultFile, precision, recall, ap, true );
cout << "Outputting to GNUPlot file..." << endl;
vocData.savePrecRecallToGnuplot( resPath + plotsDir + "/" + plotFile, precision, recall, ap, objClassName, CV_VOC_PLOT_PNG );
}
int main(int argc, char** argv)
{
if( argc != 3 && argc != 6 )
{
help(argv);
return -1;
}
cv::initModule_nonfree();
const string vocPath = argv[1], resPath = argv[2];
// Read or set default parameters
string vocName;
DDMParams ddmParams;
VocabTrainParams vocabTrainParams;
SVMTrainParamsExt svmTrainParamsExt;
makeUsedDirs( resPath );
FileStorage paramsFS( resPath + "/" + paramsFile, FileStorage::READ );
if( paramsFS.isOpened() )
{
readUsedParams( paramsFS.root(), vocName, ddmParams, vocabTrainParams, svmTrainParamsExt );
CV_Assert( vocName == getVocName(vocPath) );
}
else
{
vocName = getVocName(vocPath);
if( argc!= 6 )
{
cout << "Feature detector, descriptor extractor, descriptor matcher must be set" << endl;
return -1;
}
ddmParams = DDMParams( argv[3], argv[4], argv[5] ); // from command line
// vocabTrainParams and svmTrainParamsExt is set by defaults
paramsFS.open( resPath + "/" + paramsFile, FileStorage::WRITE );
if( paramsFS.isOpened() )
{
writeUsedParams( paramsFS, vocName, ddmParams, vocabTrainParams, svmTrainParamsExt );
paramsFS.release();
}
else
{
cout << "File " << (resPath + "/" + paramsFile) << "can not be opened to write" << endl;
return -1;
}
}
// Create detector, descriptor, matcher.
Ptr<FeatureDetector> featureDetector = FeatureDetector::create( ddmParams.detectorType );
Ptr<DescriptorExtractor> descExtractor = DescriptorExtractor::create( ddmParams.descriptorType );
Ptr<BOWImgDescriptorExtractor> bowExtractor;
if( !featureDetector || !descExtractor )
{
cout << "featureDetector or descExtractor was not created" << endl;
return -1;
}
{
Ptr<DescriptorMatcher> descMatcher = DescriptorMatcher::create( ddmParams.matcherType );
if( !featureDetector || !descExtractor || !descMatcher )
{
cout << "descMatcher was not created" << endl;
return -1;
}
bowExtractor = makePtr<BOWImgDescriptorExtractor>( descExtractor, descMatcher );
}
// Print configuration to screen
printUsedParams( vocPath, resPath, ddmParams, vocabTrainParams, svmTrainParamsExt );
// Create object to work with VOC
VocData vocData( vocPath, false );
// 1. Train visual word vocabulary if a pre-calculated vocabulary file doesn't already exist from previous run
Mat vocabulary = trainVocabulary( resPath + "/" + vocabularyFile, vocData, vocabTrainParams,
featureDetector, descExtractor );
bowExtractor->setVocabulary( vocabulary );
// 2. Train a classifier and run a sample query for each object class
const vector<string>& objClasses = vocData.getObjectClasses(); // object class list
for( size_t classIdx = 0; classIdx < objClasses.size(); ++classIdx )
{
// Train a classifier on train dataset
CvSVM svm;
trainSVMClassifier( svm, svmTrainParamsExt, objClasses[classIdx], vocData,
bowExtractor, featureDetector, resPath );
// Now use the classifier over all images on the test dataset and rank according to score order
// also calculating precision-recall etc.
computeConfidences( svm, objClasses[classIdx], vocData,
bowExtractor, featureDetector, resPath );
// Calculate precision/recall/ap and use GNUPlot to output to a pdf file
computeGnuPlotOutput( resPath, objClasses[classIdx], vocData );
}
return 0;
}