From: Francois Fleuret Date: Sun, 12 Oct 2008 12:45:05 +0000 (+0200) Subject: automatic commit X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=94ef84aef3a1845326c7c43970997b701a919bd6;p=folded-ctf.git automatic commit --- diff --git a/README.txt b/README.txt index 3bca7f0..c322ce7 100644 --- a/README.txt +++ b/README.txt @@ -1,31 +1,27 @@ -###################################################################### -## INTRODUCTION +I. INTRODUCTION - This is the C++ implementation of the folded hierarchy of - classifiers for cat detection described in + This is the open-source C++ implementation of the folded hierarchy + of classifiers for cat detection described in F. Fleuret and D. Geman, "Stationary Features and Cat Detection", Journal of Machine Learning Research (JMLR), 2008, to appear. - Please cite this paper when referring to this software. + Please use that citation when referring to this software. -###################################################################### -## INSTALLATION + Contact Francois Fleuret at fleuret@idiap.ch for comments and bug + reports. - This program was developed on Debian GNU/Linux computers with the - following main tool versions - - * GNU bash, version 3.2.39 - * g++ 4.3.2 - * gnuplot 4.2 patchlevel 4 +II. INSTALLATION If you have installed the RateMyKitten images provided on - http://www.idiap.ch/folded-ctf + http://www.idiap.ch/folded-ctf in the source directory, everything should work seamlessly by - invoking the ./run.sh script. It will + invoking the ./run.sh script. + + It will * Compile the source code entirely @@ -35,33 +31,42 @@ * Run 20 rounds of training / test (ten rounds for each of HB and H+B detectors with different random seeds) - You can also run the full thing with the following commands if you - have wget installed + You can run the full thing with the following commands if you have + wget installed - > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/folding-gpl.tgz + > wget http://www.idiap.ch/folded-ctf/data/folding-gpl.tgz > tar zxvf folding-gpl.tgz > cd folding - > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/rmk.tgz + > wget http://www.idiap.ch/folded-ctf/data/rmk.tgz > tar zxvf rmk.tgz > ./run.sh Note that every one of the twenty rounds of training/testing takes more than three days on a powerful PC. However, the script detects already running computations by looking at the presence of the - corresponding result directory. Hence, it can be run in parallel on - several machines as long as they see the same result directory. + corresponding result directories. Hence, it can be run in parallel + on several machines as long as they see the same result directory. When all or some of the experimental rounds are over, you can - generate the ROC curves by invoking the ./graph.sh script. + generate the ROC curves by invoking the ./graph.sh script. You need + a fairly recent version of Gnuplot. + + This program was developed on Debian GNU/Linux computers with the + following main tool versions + + * GNU bash, version 3.2.39 + * g++ 4.3.2 + * gnuplot 4.2 patchlevel 4 - You are welcome to send bug reports and comments to fleuret@idiap.ch + Due to approximations in the optimized arithmetic operations with + g++, results may vary with different versions of the compiler + and/or different levels of optimization. -###################################################################### -## PARAMETERS +III. PARAMETERS - To set the value of a parameter during an experiment, just add an - argument of the form --parameter-name=value before the commands that - should take into account that value. + To set the value of a parameter, just add an argument of the form + --parameter-name=value before the commands that should take it into + account. For every parameter below, the default value is given between parenthesis. @@ -80,12 +85,13 @@ * pool-name (no default) - Where are the data to use + The scene pool file name. * test-pool-name (no default) - Should we use a separate pool file, and ignore proportion-for-test - then. + Should we use a separate test pool file. If none is given, then + the test scenes are taken at random from the main pool file + according to proportion-for-test. * detector-name ("default.det") @@ -110,12 +116,12 @@ * tree-depth-max (1) Maximum depth of the decision trees used as weak learners in the - classifier. The default value corresponds to stumps. + classifier. The default value of 1 corresponds to stumps. * proportion-negative-cells-for-training (0.025) Overall proportion of negative cells to use during learning (we - sample among them) + sample among for boosting). * nb-negative-samples-per-positive (10) @@ -124,28 +130,27 @@ * nb-features-for-boosting-optimization (10000) - How many pose-indexed features to use at every step of boosting. + How many pose-indexed features to look at for optimization at + every step of boosting. * force-head-belly-independence ("no") Should we force the independence between the two levels of the detector (i.e. make an H+B detector) - * nb-weak-learners-per-classifier (10) + * nb-weak-learners-per-classifier (100) - This parameter corresponds to the value U in the JMLR paper, and - should be set to 100. + This parameter corresponds to the value U in the article. * nb-classifiers-per-level (25) - This parameter corresponds to the value B in the JMLR paper. + This parameter corresponds to the value B in the article. - * nb-levels (1) + * nb-levels (2) - How many levels in the hierarchy. This should be 2 for the JMLR - paper experiments. + How many levels in the hierarchy. - * proportion-for-train (0.5) + * proportion-for-train (0.75) The proportion of scenes from the pool to use for training. @@ -167,14 +172,16 @@ * write-parse-images ("no") Should we save one image for every test scene with the resulting - alarms. + alarms. This option generates a lot of images for every round and + is switched off by default. Switch it on to produce images such as + the full page of results in the paper. * write-tag-images ("no") Should we save the (very large) tag images when saving the materials. - * wanted-true-positive-rate (0.5) + * wanted-true-positive-rate (0.75) What is the target true positive rate. Note that this is the rate without post-processing and without pose tolerance in the @@ -208,10 +215,9 @@ * progress-bar ("yes") - Should we display a progress bar. + Should we display a progress bar during long computations. -###################################################################### -## COMMANDS +IV. COMMANDS * open-pool @@ -223,8 +229,8 @@ * compute-thresholds - Compute the thresholds of the detector classifiers to obtain the - required wanted-true-positive-rate + Compute the thresholds of the detector classifiers from the + validation set to obtain the required wanted-true-positive-rate. * test-detector @@ -234,8 +240,8 @@ Visit nb-wanted-true-positive-rates rates between 0 and wanted-true-positive-rate, for each compute the detector - thresholds on the validation set, estimate the error rate on the - test set. + thresholds on the validation set and estimate the error rate on + the test set. * write-detector @@ -247,7 +253,12 @@ * write-pool-images - Write PNG images of the scenes in the pool. + For every of the first nb-images of the pool, save one PNG image + with the ground truth, one with the corresponding referential at + the reference scale, and one with the feature material-feature-nb + from the detector. This last image is not saved if either no + detector has been read/trained or if no feature number has been + specified. -- Francois Fleuret diff --git a/global.cc b/global.cc index 8016c41..de53962 100644 --- a/global.cc +++ b/global.cc @@ -32,7 +32,7 @@ Global::~Global() { void Global::init_parser(ParamParser *parser) { // The nice level of the process - parser->add_association("niceness", "5", false); + parser->add_association("niceness", "15", false); // Seed to initialize the random generator parser->add_association("random-seed", "0", false); @@ -54,6 +54,8 @@ void Global::init_parser(ParamParser *parser) { // How many images to produce/process parser->add_association("nb-images", "-1", false); + // What is the number of the feature to show in the images + parser->add_association("material-feature-nb", "-1", false); // What is the maximum tree depth parser->add_association("tree-depth-max", "1", false); @@ -66,11 +68,11 @@ void Global::init_parser(ParamParser *parser) { // Do we allow head-belly registration parser->add_association("force-head-belly-independence", "no", false); // How many weak-learners in every classifier - parser->add_association("nb-weak-learners-per-classifier", "10", false); + parser->add_association("nb-weak-learners-per-classifier", "100", false); // How many classifiers per level parser->add_association("nb-classifiers-per-level", "25", false); // How many levels - parser->add_association("nb-levels", "1", false); + parser->add_association("nb-levels", "2", false); // Proportion of images from the pool to use for training parser->add_association("proportion-for-train", "0.5", false); @@ -90,7 +92,7 @@ void Global::init_parser(ParamParser *parser) { parser->add_association("write-tag-images", "no", false); // What is the wanted true overall positive rate - parser->add_association("wanted-true-positive-rate", "0.5", false); + parser->add_association("wanted-true-positive-rate", "0.75", false); // How many rates to try for the sequence of tests parser->add_association("nb-wanted-true-positive-rates", "10", false); @@ -143,6 +145,7 @@ void Global::read_parser(ParamParser *parser) { } nb_images = parser->get_association_int("nb-images"); + material_feature_nb = parser->get_association_int("material-feature-nb"); tree_depth_max = parser->get_association_int("tree-depth-max"); nb_weak_learners_per_classifier = parser->get_association_int("nb-weak-learners-per-classifier"); nb_classifiers_per_level = parser->get_association_int("nb-classifiers-per-level"); diff --git a/global.h b/global.h index fae2d81..afe9dd2 100644 --- a/global.h +++ b/global.h @@ -43,12 +43,10 @@ public: char detector_name[buffer_size]; char result_path[buffer_size]; - char materials_image_numbers[buffer_size]; - char materials_pf_numbers[buffer_size]; - int loss_type; int nb_images; + int material_feature_nb; int tree_depth_max; diff --git a/materials.cc b/materials.cc index 64729db..ad79830 100644 --- a/materials.cc +++ b/materials.cc @@ -75,26 +75,7 @@ void write_referential_png(char *filename, referential->draw(&result_sp, level); } - (*global.log_stream) << "Writing " << filename << endl; - result_sp.write_png(filename); -} - -void write_one_pi_feature_png(char *filename, - LabelledImage *image, - PoseCellHierarchy *hierarchy, - int nb_target, - int level, - PiFeature *pf) { - - PoseCell target_cell; - hierarchy->get_containing_cell(image, level, - image->get_target_pose(nb_target), &target_cell); - PiReferential referential(&target_cell); - RGBImage result(image->width(), image->height()); - image->to_rgb(&result); - RGBImageSubpixel result_sp(&result); - referential.draw(&result_sp, level); - // pf->draw(&result_sp, 255, 255, 0, &referential); + cout << "Writing " << filename << endl; result_sp.write_png(filename); } @@ -109,33 +90,35 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool, PoseCellHierarchy *hierarchy = new PoseCellHierarchy(pool); - for(int i = 0; i < min(global.nb_images, pool->nb_images()); i++) { - image = pool->grab_image(i); - RGBImage result(image->width(), image->height()); - image->to_rgb(&result); - RGBImageSubpixel result_sp(&result); - - if(global.pictures_for_article) { - for(int t = 0; t < image->nb_targets(); t++) { - image->get_target_pose(t)->draw(8, 255, 255, 255, - hierarchy->nb_levels() - 1, &result_sp); - + if(global.material_feature_nb < 0) { + for(int i = 0; i < min(global.nb_images, pool->nb_images()); i++) { + image = pool->grab_image(i); + RGBImage result(image->width(), image->height()); + image->to_rgb(&result); + RGBImageSubpixel result_sp(&result); + + if(global.pictures_for_article) { + for(int t = 0; t < image->nb_targets(); t++) { + image->get_target_pose(t)->draw(8, 255, 255, 255, + hierarchy->nb_levels() - 1, &result_sp); + + } + for(int t = 0; t < image->nb_targets(); t++) { + image->get_target_pose(t)->draw(4, 0, 0, 0, + hierarchy->nb_levels() - 1, &result_sp); + } + } else { + for(int t = 0; t < image->nb_targets(); t++) { + image->get_target_pose(t)->draw(4, 255, 128, 0, + hierarchy->nb_levels() - 1, &result_sp); + } } - for(int t = 0; t < image->nb_targets(); t++) { - image->get_target_pose(t)->draw(4, 0, 0, 0, - hierarchy->nb_levels() - 1, &result_sp); - } - } else { - for(int t = 0; t < image->nb_targets(); t++) { - image->get_target_pose(t)->draw(4, 255, 128, 0, - hierarchy->nb_levels() - 1, &result_sp); - } - } - sprintf(buffer, "/tmp/truth-%05d.png", i); - cout << "Writing " << buffer << endl; - result_sp.write_png(buffer); - pool->release_image(i); + sprintf(buffer, "/tmp/truth-%05d.png", i); + cout << "Writing " << buffer << endl; + result_sp.write_png(buffer); + pool->release_image(i); + } } for(int i = 0; i < min(global.nb_images, pool->nb_images()); i++) { @@ -145,8 +128,6 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool, image->to_rgb(&result); RGBImageSubpixel result_sp(&result); - int u = 0; - // image->compute_rich_structure(); for(int t = 0; t < image->nb_targets(); t++) { @@ -164,32 +145,26 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool, PiReferential referential(&target_cell); - sprintf(buffer, "/tmp/referential-%05d-%02d.png", i, u); image->compute_rich_structure(); - write_referential_png(buffer, hierarchy->nb_levels() - 1, image, &referential, 0); - - if(detector) { - int nb_features = 100; - for(int f = 0; f < nb_features; f++) - if(f == 0 || f == 50 || f == 53) { - int n_family, n_feature; - if(f < nb_features/2) { - n_family = 0; - n_feature = f; - } else { - n_family = detector->_nb_classifiers_per_level; - n_feature = f - nb_features/2; - } - pf = detector->_pi_feature_families[n_family]->get_feature(n_feature); - sprintf(buffer, "/tmp/pf-%05d-%02d-%03d.png", i, u, f); - write_referential_png(buffer, - hierarchy->nb_levels() - 1, - image, - &referential, - pf); - } + + if(global.material_feature_nb < 0) { + sprintf(buffer, "/tmp/referential-%05d-%02d.png", i, t); + write_referential_png(buffer, hierarchy->nb_levels() - 1, image, &referential, 0); + } else if(detector) { + int n_family = 0; + int n_feature = global.material_feature_nb; + while(n_feature > detector->_pi_feature_families[n_family]->nb_features()) { + n_family++; + n_feature -= detector->_pi_feature_families[n_family]->nb_features(); + } + pf = detector->_pi_feature_families[n_family]->get_feature(n_feature); + sprintf(buffer, "/tmp/pf-%05d-%02d-%05d.png", i, t, global.material_feature_nb); + write_referential_png(buffer, + hierarchy->nb_levels() - 1, + image, + &referential, + pf); } - u++; } pool->release_image(i); @@ -233,7 +208,6 @@ void write_image_with_detections(const char *filename, } } - (*global.log_stream) << "Writing " << filename << endl; - + cout << "Writing " << filename << endl; result_sp.write_png(filename); } diff --git a/materials.h b/materials.h index 4d6b337..a271a22 100644 --- a/materials.h +++ b/materials.h @@ -32,13 +32,6 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool, Detector *detector); -void write_one_pi_feature_png(char *filename, - LabelledImage *image, - PoseCellHierarchy *hierarchy, - int nb_target, - int level, - PiFeature *pf); - void write_image_with_detections(const char *filename, LabelledImage *image, PoseCellSet *detections, diff --git a/pose_cell_hierarchy.cc b/pose_cell_hierarchy.cc index 85a843a..b8c5eff 100644 --- a/pose_cell_hierarchy.cc +++ b/pose_cell_hierarchy.cc @@ -92,14 +92,6 @@ PoseCellHierarchy::PoseCellHierarchy(LabelledImagePool *train_pool) { scalar_t belly_ryc_min = belly_resolution * floor(belly_ryc.min / belly_resolution); int nb_belly_ryc = int(ceil((belly_ryc.max - belly_ryc_min) / belly_resolution)); - (*global.log_stream) << "belly_rxc = " << belly_rxc << endl - << "belly_rxc_min = " << belly_rxc_min << endl - << "belly_rxc_min + nb_belly_rxc * belly_resolution = " << belly_rxc_min + nb_belly_rxc * belly_resolution << endl - << endl - << "belly_ryc = " << belly_ryc << endl - << "belly_ryc_min = " << belly_ryc_min << endl - << "belly_ryc_min + nb_belly_ryc * belly_resolution = " << belly_ryc_min + nb_belly_ryc * belly_resolution << endl; - int used[nb_belly_rxc * nb_belly_rxc]; for(int k = 0; k < nb_belly_rxc * nb_belly_ryc; k++) { @@ -150,22 +142,6 @@ PoseCellHierarchy::PoseCellHierarchy(LabelledImagePool *train_pool) { _belly_cells = new RelativeBellyPoseCell[_nb_belly_cells]; - for(int j = 0; j < nb_belly_ryc; j++) { - for(int i = 0; i < nb_belly_rxc; i++) { - if(used[i + nb_belly_rxc * j]) { - if(sq(scalar_t(i) * belly_resolution + belly_resolution/2 + belly_rxc_min) + - sq(scalar_t(j) * belly_resolution + belly_resolution/2 + belly_ryc_min) <= 1) { - (*global.log_stream) << "*"; - } else { - (*global.log_stream) << "X"; - } - } else { - (*global.log_stream) << "."; - } - } - (*global.log_stream) << endl; - } - int k = 0; for(int j = 0; j < nb_belly_ryc; j++) { for(int i = 0; i < nb_belly_rxc; i++) { @@ -184,8 +160,6 @@ PoseCellHierarchy::PoseCellHierarchy(LabelledImagePool *train_pool) { } } } - - (*global.log_stream) << _nb_belly_cells << " belly cells." << endl; } PoseCellHierarchy::~PoseCellHierarchy() { diff --git a/run.sh b/run.sh index 13f3a8e..42dc58b 100755 --- a/run.sh +++ b/run.sh @@ -19,6 +19,7 @@ MAIN_URL="http://www.idiap.ch/folded-ctf" +###################################################################### # Compiling make -j -k @@ -81,53 +82,85 @@ fi RESULT_DIR=./results -if [[ ! -d ${RESULT_DIR} ]]; then - mkdir ${RESULT_DIR} -fi +case $1 in -for SEED in {0..9}; do + pics) - for MODE in hb h+b; do + SEED=0 - EXPERIMENT_RESULT_DIR="${RESULT_DIR}/${MODE}-${SEED}" + EXPERIMENT_RESULT_DIR="${RESULT_DIR}/hb-${SEED}" - mkdir ${EXPERIMENT_RESULT_DIR} 2> /dev/null + if [[ -d "${EXPERIMENT_RESULT_DIR}" ]]; then - if [[ $? == 0 ]]; then + for n in -1 0 2501 2504; do - OPTS="--random-seed=${SEED} --wanted-true-positive-rate=0.75" - - if [[ $MODE == "h+b" ]]; then - OPTS="${OPTS} --force-head-belly-independence=yes" - fi - - if [[ $1 == "valgrind" ]]; then - OPTS="${OPTS} --nb-classifiers-per-level=1 --nb-weak-learners-per-classifier=10" - OPTS="${OPTS} --proportion-for-train=0.1 --proportion-for-validation=0.025 --proportion-for-test=0.01" - OPTS="${OPTS} --wanted-true-positive-rate=0.1" - DEBUGGER="valgrind --db-attach=yes --leak-check=full --show-reachable=yes" - fi - - ${DEBUGGER} ./folding \ - --niceness=15 \ - --pool-name=${POOL_NAME} \ - --nb-levels=2 \ - --nb-classifiers-per-level=25 --nb-weak-learners-per-classifier=100 \ - --result-path=${EXPERIMENT_RESULT_DIR} \ - --detector-name=${EXPERIMENT_RESULT_DIR}/default.det \ - ${OPTS} \ - open-pool \ - train-detector \ - compute-thresholds \ - write-detector \ - sequence-test-detector | tee -a ${EXPERIMENT_RESULT_DIR}/stdout + ./folding --random-seed=${SEED} \ + --pool-name=${POOL_NAME} \ + --result-path=${EXPERIMENT_RESULT_DIR} \ + --detector-name=${EXPERIMENT_RESULT_DIR}/default.det \ + --nb-images=1 \ + --material-feature-nb=${n} \ + open-pool \ + read-detector \ + write-pool-images + + done else + echo "You have to run at least the first round completely to be able" >&2 + echo "to generate the pictures." >&2 + exit 1 + fi + + ;; - echo "${EXPERIMENT_RESULT_DIR} exists, aborting experiment." + valgrind|"") + if [[ ! -d ${RESULT_DIR} ]]; then + mkdir ${RESULT_DIR} fi - done + for SEED in {0..9}; do + + for MODE in hb h+b; do + + EXPERIMENT_RESULT_DIR="${RESULT_DIR}/${MODE}-${SEED}" + + mkdir ${EXPERIMENT_RESULT_DIR} 2> /dev/null + + if [[ $? == 0 ]]; then + + if [[ $MODE == "h+b" ]]; then + OPTS="${OPTS} --force-head-belly-independence=yes" + fi + + if [[ $1 == "valgrind" ]]; then + OPTS="${OPTS} --nb-classifiers-per-level=1 --nb-weak-learners-per-classifier=10" + OPTS="${OPTS} --proportion-for-train=0.1 --proportion-for-validation=0.025 --proportion-for-test=0.01" + OPTS="${OPTS} --wanted-true-positive-rate=0.1" + DEBUGGER="valgrind --db-attach=yes --leak-check=full --show-reachable=yes" + fi + + ${DEBUGGER} ./folding \ + --random-seed=${SEED} \ + --pool-name=${POOL_NAME} \ + --result-path=${EXPERIMENT_RESULT_DIR} \ + --detector-name=${EXPERIMENT_RESULT_DIR}/default.det \ + ${OPTS} \ + open-pool \ + train-detector \ + compute-thresholds \ + write-detector \ + sequence-test-detector | tee -a ${EXPERIMENT_RESULT_DIR}/stdout + + else + + echo "${EXPERIMENT_RESULT_DIR} exists, aborting experiment." + + fi + + done + + done -done +esac