文件列表:

fdtool (0, 2011-04-05)
fdtool\area.c (4366, 2011-04-03)
fdtool\auroc.m (1544, 2010-12-22)
fdtool\basicroc.m (1583, 2009-05-11)
fdtool\build_negatives.m (1673, 2011-03-23)
fdtool\chlbp.c (22810, 2011-01-16)
fdtool\chlbp_adaboost_binary_predict_cascade.c (17882, 2011-03-09)
fdtool\chlbp_adaboost_binary_train_cascade.c (15801, 2011-03-09)
fdtool\chlbp_gentleboost_binary_predict_cascade.c (18720, 2011-03-09)
fdtool\chlbp_gentleboost_binary_train_cascade.c (21224, 2011-03-09)
fdtool\d2uint8_image.m (115, 2009-03-09)
fdtool\demo_chlbp.m (21784, 2011-02-15)
fdtool\demo_detector_haar.m (2394, 2011-03-07)
fdtool\demo_detector_hmblbp.m (1697, 2011-04-05)
fdtool\demo_fine_threshold.m (4349, 2011-02-16)
fdtool\demo_haar.m (14784, 2011-02-15)
fdtool\demo_haar_mblbp_training.m (7021, 2011-02-16)
fdtool\demo_integral_histogram.m (2087, 2011-02-21)
fdtool\demo_mblbp.m (22820, 2011-03-21)
fdtool\demo_mblbp_variant_training.m (5577, 2011-02-20)
fdtool\demo_mlhmslbp_spyr_svm_training.m (16612, 2011-03-11)
fdtool\demo_type_cascade_scaling_vs_interp.m (1771, 2011-02-16)
fdtool\detector_haar.c (53842, 2011-03-24)
fdtool\detector_mblbp.c (44794, 2011-03-24)
fdtool\detector_mlhmslbp_spyr.c (77503, 2011-03-25)
fdtool\display_database.m (748, 2009-05-10)
fdtool\eval_chlbp.c (22822, 2011-03-09)
fdtool\eval_haar.c (28038, 2011-03-09)
fdtool\eval_haar_subwindow.c (26735, 2011-03-09)
fdtool\eval_hmblbp_spyr_subwindow.c (56219, 2011-03-29)
fdtool\eval_mblbp.c (23342, 2011-03-09)
fdtool\fast_haar_adaboost_binary_train_cascade.c (35416, 2011-03-16)
fdtool\fast_haar_ada_weaklearner.c (24575, 2011-03-16)
fdtool\fast_rotate.c (2341, 2011-03-13)
fdtool\fftw3.h (13996, 2010-12-21)
fdtool\generate_data_cascade.m (8864, 2011-03-02)
fdtool\generate_face_features.m (17829, 2011-03-24)
fdtool\generate_neg_features.m (8421, 2011-03-24)
fdtool\generate_pos_features.m (13041, 2011-03-24)
fdtool\getmapping.m (2757, 2009-01-18)
... ...

Faces detection toolbox v 0.2 ----------------------------- This toolbox provides some tools for faces detection/classification using Local Binary Patterns (and some variants) and Haar features. Face detection is performed by evaluating over multi-scans windows trained models with boosting approach (such Adaboosting, FastAdaboosting and Gentleboosting) or with Fast SVM solvers. The main objective of FDT is to deliver simple but efficient tools mainly written in C codes with a matlab interface and easy to modify. Installation ------------ This toolbox has been tested on Windows system and should also work for Linux plateform without any problem. Please run "setup_fdt" to install, compile each mex-files and add fdtool directory in the matlab path. Type "help mexme_fdt" for more compilation options. Please open *.m or *.c files to read full description/instruction of each function and main references. Run First --------- a) Play with "demo_detector_haar.m" or "demo_detector_hmblbp.m" for real-time face tracking. For windows system, you can use the included VCAPG2 webcam grabber. Otherwise and for Linux system, you must have the IMAQ Toolbox (getsnapshot function). b) View examples included in "train_cascade" for training a complete cascade with boosting algorithms (type: help train_cascade) c) View examples included in "train_histoint_feat_boost" for training fast Histogram integral LBP + Fast linear SVM (type: help train_histoint_feat_boost) VCAPG2 webcam grabber for windows --------------------------------- In order to compile vcapg2.cpp (webcam grabber for windows), please i) download the last Windows DDK at http://www.microsoft.com/downloads/en/details.aspx?displaylang=en&FamilyID=36a2630f-5d56-43b5-b996-7633f2ec14ff ii) copy qedit.h into c:\Program Files\Microsoft SDKs\Windows\vx.x\Include folder where x.x designs the DDK version (currently last is 7.1) iii) compile with mex vcapg2.cpp -I"c:\Program Files\Microsoft SDKs\Windows\vx.x\Include" Thanks to Pr. Fehn for the x*** adaptation of vcapg2 Training set ------------ i) Positives examples for boosting approaches Viola-Jones [4] and Jensen positives examples are included in this package in mat format, viola_24x24.mat and jensen_24x24.mat respectively where the size of each image is (24 x 24). ii) Positives examples for Histogram of feature + SVM Please download a face database, for example the MIT-CBCL at: http://cbcl.mit.edu/software-datasets/heisele/download/MIT-CBCL-facerec-database.zip Extract "training-synthetic" pgm files in "positives" dir. iii) Negatives examples A relative small "negatives" archive is also included and must be unpacked into "negatives" dir. 2 possibilities to retrieve more negatives pics in jpeg format: a)use "build_negatives.m" function or b)download the following zip file: http://c2inet.sce.ntu.edu.sg/Jianxin/RareEvent/nonface.zip and extract in "negatives" subfolder (be aware there are some positives faces in this zip) Demos ----- 10 demos are included. i) "demo_mblbp" ii) "demo_chlbp" iii) "demo_haar" vi) "demo_detector_haar" v) "demo_detector_hmblbp" vi) "demo_fine_threshold.m" vii) "demo_haar_mblbp_training.m" viii) "demo_mblbp.m" ix) "demo_mblbp_variant_training.m" x) "demo_type_cascade_scaling_vs_interp.m" Organization ------------ A) HAAR Features detector_haar Real-Time face detector based on Haar's features trained with boosting methods eval_haar Compute output of a trained strong classifier for new instances matrix X eval_haar_subwindow Compute output of a trained strong classifier for new instances matrix X at different scale fast_haar_ada_weaklearner Train ffast a Decision stump weaklearner with adaboost on Haar features. Assume normal pdf for positives ans negatives examples fast_haar_adaboost_binary_model_cascade Train a strong classifier with Fasthaaradaboosting on Haar features gui_features_dictionary GUI for creating patterns dictionary haar Compute the Haar features for a given set of featured defined by haar_featlist haar_ada_weaklearner Decision stump weaklearner for adaboosting on Haar features computed online haar_ada_weaklearner_memory Decision stump weaklearner for adaboosting on Haar features computed offline haar_adaboost_binary_train_cascade Train a strong classifier with Adaboosting on Haar features computed online haar_adaboost_binary_train_cascade_memory Train a strong classifier with Adaboosting on Haar features computed offline haar_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on Haar features computed online haar_adaboost_binary_predict_cascade_memory Predict label with trained model with Adaboosting on Haar features computed offline haar_featlist Compute Haar features parameters haar_gentle_weaklearner Decision stump weaklearner for gentleboosting on Haar features computed online haar_gentle_weaklearner_memory Decision stump weaklearner for gentleboosting on Haar features computed offline haar_gentleboost_binary_train_cascade Train a strong classifier with Gentleboosting on Haar features computed online haar_gentleboost_binary_train_cascade_memory Train a strong classifier with Gentleboosting on Haar features computed offline haar_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on Haar features computed online haar_gentleboost_binary_predict_cascade_memory Predict label with trained model with Gentleboosting on Haar features computed offline Haar_matG Sparse Haar Features matrix for fasthaaradaboosting haar_scale Haar features scaled to Faces database size B) CHLBP features chlbp Compute the chlbp features chlbp_adaboost_binary_train_cascade Train a strong classifier with Adaboosting on chlbp features chlbp_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on chlbp features chlbp_gentleboost_binary_train_cascade Train a strong classifier with Gentleboosting on chlbp features chlbp_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on chlbp features eval_chlbp Compute output of a trained strong classifier for new instances matrix X C) MBLBP features detector_mblbp Real-Time face detector based on mblbp's features trained with boosting methods eval_mblbp Compute output of a trained strong classifier for new images mblbp Compute the mblbp features mblbp_ada_weaklearner Decision stump weaklearner for adaboosting on mblbp features mblbp_adaboost_binary_train_cascade Train a strong classifier with Adaboosting on mblbp features mblbp_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on mblbp features mblbp_featlist Compute mblbp features parameters mblbp_gentle_weaklearner Decision stump weaklearner for gentlboosting on mblbp features mblbp_gentleboost_binary_train_cascade Train a strong classifier with Gentleboosting on mblbp features mblbp_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on mblbp features D) HMBLBP_spyr features detector_mlhmslbp_spyr Real-Time face detector based on histogram of LBP features trained with L-SVM method eval_hmblbp_spyr_subwindow Compute output of a trainedclassifier for new images E) MISCELLANEOUS area Compute area of rectangular ROI with Integral Image method auroc Compute the Area Under the ROC basicroc Compute ROC given true label and Outputs of Strong classifiers build_negatives Download from internet set of images used to construct negatives subwindows display_database Display all faces/non faces database fast_rotate Rotate UIUT8 grayscale image generate_data_cascade Generate Positives & Negatives examples for training a cascade with boosting methods generate_face_features Generate Positives & Negatives examples for training Histogram Integral features via Linear SVM generate_pos_features Generate Positives non-detection examples passing through current trained Linear SVM model generate_neg_features Generate Negatives false alarms examples passing through current trained Linear SVM model getmapping Mapping feature's values for CHLBP and MBLBP approaches (from Marko Heikkil and Timo Ahonen LBP toolbox) ieJPGSearch Included michaelB function to retrive URL links of images from Google image_integral_standard Standardize and compute Images Integral image_standard Standardize images imresize Resize UINT8 images by bilinear interpolation int8tosparse Convert a int matrix in a sparse matrix (thx J. Tursa) inv_integral_image Retrieve images from images integral jensen_24x24 Ole Jensen faces/non faces database mexme_fdt Script for compiling mex files nbfeat_haar Compute number of features given image database size and patterns plot_rectangle Display rectangles associated with detected faces rgb2gray Convert RGB image in gray format train_cascade Train cascade model with coventional/multi-exit asymetric boosting approach (see Pham Minh Tri works) train_histoint_feat Train model Via Linear SVM for Histogram of features with Fast Histogram Integral approach train_histoint_feat_boost Train model Via Linear SVM for Histogram of features with Fast Histogram Integral approach and boosting examples close of the margin train_stage_cascade Train a stage of the cascade vcapg2 Capture webcam frames (see Kazuyuki Kobayashi file from http://www.mathworks.com/matlabcentral/fileexchange/2939 ) viola_24x24 Viola-Jones faces/non faces database setup_fdt Install and setup the face detection toolbox Author : Sébastien PARIS : sebastien.paris@lsis.org for contact and bugs reporting ------- Initial release date : 02/20/2009 Main References : [1] R.E Schapire and al "Boosting the margin : A new explanation for the effectiveness of voting methods". ---------------- The annals of statistics, 1999 [2] Zhang, L. and Chu, R.F. and Xiang, S.M. and Liao, S.C. and Li, S.Z, "Face Detection Based on Multi-Block LBP Representation" ICB07 [3] C. Huang, H. Ai, Y. Li and S. Lao, "Learning sparse features in granular space for multi-view face detection", FG2006 [4] P.A Viola and M. Jones, "Robust real-time face detection", International Journal on Computer Vision, 2004 [5] M-T. Pham and all, "Detection with multi-exit asymetric boosting", CVPR'08 [6] Eanes Torres Pereira, Herman Martins Gomes, Joo Marques de Carvalho "Integral Local Binary Patterns: A Novel Approach Suitable for Texture-Based Object Detection Tasks" 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images Greetings to : i ) Ole Jensen for providing me his faces database and the merging detections algorithm for detector_haar and detector_mblbp, ------------ ii) Pham Minh Tri for his responses concerning Fastadaboosting and multi-exit asymetric boosting, iii) Pr Fehn for his modified version of vcapg2. iv) Zhu Jianqing for his conventional cascade and for provinding to me the URL for negatives picts Changelogs : ------------ v0.2 03/11 Major Update: - Changed Input parsing, only (1+1) imputs, a unique options/model structure must be given right now as imput - Inputs parsing more flexible and with more options - OpenMP support (Multicore) for faster training and detection - Better and faster cascade training algorithms (conventional and multi-exit cascade) - Better merging detections (removing internal subwindows inside a larger one) - Add haar_gentleboost_binary_train_cascade_memory.c, haar_gentleboost_binary_predict_cascade_memory.c, haar_gentle_weaklearner_memory.c haar_adaboost_binary_train_cascade_memory.c, haar_adaboost_binary_predict_cascade_memory.c and haar_ada_weaklearner_memory.c for fast and exact Haar training but for system with enough RAM (at least 8gb of RAM is required, see options.algoboost = 3,4) - Fix a crash for FastHaarAdaboosting with *** bits systems (add largeArrayDims option for sparse matrix) - Add fine_threshold option for FastHaarAdaboosting improving a little accuracy - Add tranpose option to speedup weaklearner training with pre-computed MBLBP and Haar features - Add probarotIpos, m_angle and sigma_angle for rotate positives examples with angle~N(m_angle, sigma_angle) in order to be more robust to face orientation during training phase - Add a novel approach based on Fast computation of Histograms of features (Histogram Integral) (LBP here) and trained by Linear SVM (still in early stage of testing) - Correct, add some comments and fix some typos (there are still a lot I know). To Do: - Mix Haar/MLBP features for boosting approaches - Add MBLDP (for MultiBlock Local Derivative Patterns) features for boosting approaches - Add other Histograms features: HOG, HMBLDP,etc... - Add multi-class support v0.1-0.1f - Fix bugs in fast_haar_ada_weaklearner and fast_haar_adaboost_binary_model_cascade when alpha = 0 (Thx to Zhu), minor cleanup. - Fix some small bugs, better Linux*** support, include vcapg2 modified for win***. - Correct minor crashes - Should correctly compile with LCC and prior release of Matlab (R13 and upper versions) (thx to Bruno Luong for his function) - Should compile on Win*** platerform (thx to Soeren Sproessig for report bug) - Typo corrections

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