-#include "opencv2/opencv_modules.hpp"
-#include "opencv2/imgcodecs.hpp"
-#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 cv::ml;
-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(Error::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(Error::StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
- if ((number < 1) && (number != -1))
- CV_Error(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::StsError,"missing <xmax> tag in object definition of '" + filename + "'");
- xmax = stringToInteger(tag_contents);
-
- if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <xmin> tag in object definition of '" + filename + "'");
- xmin = stringToInteger(tag_contents);
-
- if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <ymax> tag in object definition of '" + filename + "'");
- ymax = stringToInteger(tag_contents);
-
- if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(Error::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(Error::StsError,"Could not extract filename of results file.");
-
- ++fnamestart;
- if (fnamestart >= fnameend)
- CV_Error(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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(Error::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.descriptorsCount() > maxDescCount )
- {
-#ifdef DEBUG_DESC_PROGRESS
- cout << "Breaking due to full memory ( descriptors count = " << bowTrainer.descriptorsCount()
- << "; descriptor size in bytes = " << descByteSize << "; all used memory = "
- << bowTrainer.descriptorsCount()*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.descriptorsCount() < 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.descriptorsCount())/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.descriptorsCount() << 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( SVM::Params& svmParams, Mat& 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.svmType = SVM::C_SVC;
- svmParams.kernelType = SVM::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.classWeights = class_wts_cv;
- }
-}
-
-static void setSVMTrainAutoParams( ParamGrid& c_grid, ParamGrid& gamma_grid,
- ParamGrid& p_grid, ParamGrid& nu_grid,
- ParamGrid& coef_grid, ParamGrid& degree_grid )
-{
- c_grid = SVM::getDefaultGrid(SVM::C);
-
- gamma_grid = SVM::getDefaultGrid(SVM::GAMMA);
-
- p_grid = SVM::getDefaultGrid(SVM::P);
- p_grid.logStep = 0;
-
- nu_grid = SVM::getDefaultGrid(SVM::NU);
- nu_grid.logStep = 0;
-
- coef_grid = SVM::getDefaultGrid(SVM::COEF);
- coef_grid.logStep = 0;
-
- degree_grid = SVM::getDefaultGrid(SVM::DEGREE);
- degree_grid.logStep = 0;
-}
-
-static Ptr<SVM> trainSVMClassifier( 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";
- Ptr<SVM> svm;
-
- FileStorage fs( svmFilename, FileStorage::READ);
- if( fs.isOpened() )
- {
- cout << "*** LOADING SVM CLASSIFIER FOR CLASS " << objClassName << " ***" << endl;
- svm = StatModel::load<SVM>( svmFilename );
- }
- 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;
- SVM::Params svmParams;
- Mat class_wts_cv;
- setSVMParams( svmParams, class_wts_cv, responses, svmParamsExt.balanceClasses );
- svm = SVM::create(svmParams);
- ParamGrid 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->trainAuto(TrainData::create(trainData, ROW_SAMPLE, responses), 10,
- c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid);
- cout << "SVM TRAINING FOR CLASS " << objClassName << " COMPLETED" << endl;
-
- svm->save( svmFilename );
- cout << "SAVED CLASSIFIER TO FILE" << endl;
- }
- return svm;
-}
-
-static void computeConfidences( const Ptr<SVM>& 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], noArray(), 0 );
- float scoreVal = confidences[imageIdx] = svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
- signMul = (classVal < 0) == (scoreVal < 0) ? 1.f : -1.f;
- }
- // svm output of decision function
- confidences[imageIdx] = signMul * svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
- }
-
- 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
- Ptr<SVM> svm = trainSVMClassifier( 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;
-}