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‎README.md

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# PeopleDetection-and-Facedetection-in-ROS

‎fdetection/CMakeLists.txt

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cmake_minimum_required(VERSION 2.8.3)
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project(fdetection)
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# Build options
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option(USE_OPENMP "Set to ON to build use openmp" ON)
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option(USE_SSE "Set to ON to build use SSE" ON)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
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if(WIN32 AND NOT CYGWIN)
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set(HOME $ENV{PROFILE})
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else()
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set(HOME $ENV{HOME})
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endif()
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# Use SSE
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if (USE_SSE)
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add_definitions(-DUSE_SSE)
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message(STATUS "Use SSE")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -msse4.1")
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endif()
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# Use OpenMP
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if (USE_OPENMP)
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find_package(OpenMP QUIET)
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if (OPENMP_FOUND)
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message(STATUS "Use OpenMP")
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add_definitions(-DUSE_OPENMP)
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set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
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set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
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endif()
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endif()
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find_package(catkin REQUIRED COMPONENTS
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roscpp
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rospy
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std_msgs
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sensor_msgs
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cv_bridge
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image_transport
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)
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set(OpenCV_DIR /opt/ros/kinetic/include)
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find_package(OpenCV REQUIRED)
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message("PB->OPENCV = ${OpenCV_INCLUDE_DIRS}")
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catkin_package(CATKIN_DEPENDS
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roscpp
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std_msgs
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sensor_msgs
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)
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include_directories(
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include
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${OpenCV_INCLUDE_DIRS}
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${catkin_INCLUDE_DIRS}
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)
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### find files
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file(GLOB_RECURSE HEADER_FILES ${CMAKE_CURRENT_SOURCE_DIR}/include/*.h*)
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file(GLOB_RECURSE SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/*.c*)
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add_executable(fdetect
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${HEADER_FILES}
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${SOURCE_FILES}
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)
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target_link_libraries(fdetect
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${catkin_LIBRARIES}
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${OpenCV_LIBRARIES}
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)
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‎fdetection/gakki.jpg

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‎fdetection/include/classifier.h

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/*
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*
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* This file is part of the open-source SeetaFace engine, which includes three modules:
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* SeetaFace Detection, SeetaFace Alignment, and SeetaFace Identification.
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*
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* This file is part of the SeetaFace Detection module, containing codes implementing the
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* face detection method described in the following paper:
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*
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*
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* Funnel-structured cascade for multi-view face detection with alignment awareness,
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* Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen.
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* In Neurocomputing (under review)
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*
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*
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* Copyright (C) 2016, Visual Information Processing and Learning (VIPL) group,
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* Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
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*
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* The codes are mainly developed by Shuzhe Wu (a Ph.D supervised by Prof. Shiguang Shan)
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*
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* As an open-source face recognition engine: you can redistribute SeetaFace source codes
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* and/or modify it under the terms of the BSD 2-Clause License.
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*
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* You should have received a copy of the BSD 2-Clause License along with the software.
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* If not, see < https://opensource.org/licenses/BSD-2-Clause>.
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*
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* Contact Info: you can send an email to [email protected] for any problems.
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*
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* Note: the above information must be kept whenever or wherever the codes are used.
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*
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*/
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#ifndef SEETA_FD_CLASSIFIER_H_
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#define SEETA_FD_CLASSIFIER_H_
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#include "common.h"
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#include "feature_map.h"
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namespace seeta {
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namespace fd {
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enum ClassifierType {
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LAB_Boosted_Classifier,
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SURF_MLP
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};
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class Classifier {
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public:
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Classifier() {}
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virtual ~Classifier() {}
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virtual void SetFeatureMap(seeta::fd::FeatureMap* feat_map) = 0;
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virtual bool Classify(float* score = nullptr, float* outputs = nullptr) = 0;
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virtual seeta::fd::ClassifierType type() = 0;
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DISABLE_COPY_AND_ASSIGN(Classifier);
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};
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} // namespace fd
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} // namespace seeta
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#endif // SEETA_FD_CLASSIFIER_H_

‎fdetection/include/classifier/lab_boosted_classifier.h

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/*
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*
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* This file is part of the open-source SeetaFace engine, which includes three modules:
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* SeetaFace Detection, SeetaFace Alignment, and SeetaFace Identification.
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*
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* This file is part of the SeetaFace Detection module, containing codes implementing the
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* face detection method described in the following paper:
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*
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*
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* Funnel-structured cascade for multi-view face detection with alignment awareness,
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* Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen.
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* In Neurocomputing (under review)
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*
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*
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* Copyright (C) 2016, Visual Information Processing and Learning (VIPL) group,
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* Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
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*
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* The codes are mainly developed by Shuzhe Wu (a Ph.D supervised by Prof. Shiguang Shan)
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*
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* As an open-source face recognition engine: you can redistribute SeetaFace source codes
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* and/or modify it under the terms of the BSD 2-Clause License.
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*
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* You should have received a copy of the BSD 2-Clause License along with the software.
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* If not, see < https://opensource.org/licenses/BSD-2-Clause>.
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*
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* Contact Info: you can send an email to [email protected] for any problems.
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*
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* Note: the above information must be kept whenever or wherever the codes are used.
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*
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*/
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#ifndef SEETA_FD_CLASSIFIER_LAB_BOOSTED_CLASSIFIER_H_
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#define SEETA_FD_CLASSIFIER_LAB_BOOSTED_CLASSIFIER_H_
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#include <algorithm>
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#include <cstdint>
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#include <memory>
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#include <vector>
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#include "classifier.h"
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#include "feat/lab_feature_map.h"
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namespace seeta {
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namespace fd {
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/**
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* @class LABBaseClassifier
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* @brief Base classifier using LAB feature.
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*/
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class LABBaseClassifier {
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public:
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LABBaseClassifier()
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: num_bin_(255), thresh_(0.0f) {
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weights_.resize(num_bin_ + 1);
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}
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~LABBaseClassifier() {}
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void SetWeights(const float* weights, int32_t num_bin);
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inline void SetThreshold(float thresh) { thresh_ = thresh; }
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inline int32_t num_bin() const { return num_bin_; }
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inline float weights(int32_t val) const { return weights_[val]; }
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inline float threshold() const { return thresh_; }
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private:
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int32_t num_bin_;
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std::vector<float> weights_;
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float thresh_;
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};
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/**
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* @class LABBoostedClassifier
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* @Brief A strong classifier constructed from base classifiers using LAB features.
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*/
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class LABBoostedClassifier : public Classifier {
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public:
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LABBoostedClassifier() : use_std_dev_(true) {}
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virtual ~LABBoostedClassifier() {}
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virtual bool Classify(float* score = nullptr, float* outputs = nullptr);
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inline virtual seeta::fd::ClassifierType type() {
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return seeta::fd::ClassifierType::LAB_Boosted_Classifier;
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}
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void AddFeature(int32_t x, int32_t y);
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void AddBaseClassifier(const float* weights, int32_t num_bin, float thresh);
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inline virtual void SetFeatureMap(seeta::fd::FeatureMap* featMap) {
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feat_map_ = dynamic_cast<seeta::fd::LABFeatureMap*>(featMap);
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}
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inline void SetUseStdDev(bool useStdDev) { use_std_dev_ = useStdDev; }
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private:
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static const int32_t kFeatGroupSize = 10;
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const float kStdDevThresh = 10.0f;
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std::vector<seeta::fd::LABFeature> feat_;
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std::vector<std::shared_ptr<seeta::fd::LABBaseClassifier> > base_classifiers_;
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seeta::fd::LABFeatureMap* feat_map_;
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bool use_std_dev_;
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};
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} // namespace fd
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} // namespace seeta
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#endif // SEETA_FD_CLASSIFIER_LAB_BOOSTED_CLASSIFIER_H_

‎fdetection/include/classifier/mlp.h

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/*
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*
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* This file is part of the open-source SeetaFace engine, which includes three modules:
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* SeetaFace Detection, SeetaFace Alignment, and SeetaFace Identification.
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*
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* This file is part of the SeetaFace Detection module, containing codes implementing the
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* face detection method described in the following paper:
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*
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*
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* Funnel-structured cascade for multi-view face detection with alignment awareness,
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* Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen.
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* In Neurocomputing (under review)
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*
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*
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* Copyright (C) 2016, Visual Information Processing and Learning (VIPL) group,
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* Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
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*
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* The codes are mainly developed by Shuzhe Wu (a Ph.D supervised by Prof. Shiguang Shan)
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*
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* As an open-source face recognition engine: you can redistribute SeetaFace source codes
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* and/or modify it under the terms of the BSD 2-Clause License.
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*
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* You should have received a copy of the BSD 2-Clause License along with the software.
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* If not, see < https://opensource.org/licenses/BSD-2-Clause>.
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*
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* Contact Info: you can send an email to [email protected] for any problems.
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*
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* Note: the above information must be kept whenever or wherever the codes are used.
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*
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*/
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#ifndef SEETA_FD_CLASSIFIER_MLP_H_
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#define SEETA_FD_CLASSIFIER_MLP_H_
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#include <cmath>
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#include <cstdint>
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#include <memory>
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#include <algorithm>
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#include <vector>
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#include "util/math_func.h"
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namespace seeta {
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namespace fd {
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class MLPLayer {
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public:
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explicit MLPLayer(int32_t act_func_type = 1)
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: input_dim_(0), output_dim_(0), act_func_type_(act_func_type) {}
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~MLPLayer() {}
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void Compute(const float* input, float* output);
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inline int32_t GetInputDim() const { return input_dim_; }
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inline int32_t GetOutputDim() const { return output_dim_; }
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inline void SetSize(int32_t inputDim, int32_t outputDim) {
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if (inputDim <= 0 || outputDim <= 0) {
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return; // @todo handle the errors!!!
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}
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input_dim_ = inputDim;
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output_dim_ = outputDim;
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weights_.resize(inputDim * outputDim);
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bias_.resize(outputDim);
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}
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inline void SetWeights(const float* weights, int32_t len) {
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if (weights == nullptr || len != input_dim_ * output_dim_) {
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return; // @todo handle the errors!!!
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}
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std::copy(weights, weights + input_dim_ * output_dim_, weights_.begin());
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}
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inline void SetBias(const float* bias, int32_t len) {
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if (bias == nullptr || len != output_dim_) {
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return; // @todo handle the errors!!!
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}
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std::copy(bias, bias + output_dim_, bias_.begin());
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}
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private:
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inline float Sigmoid(float x) {
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return 1.0f / (1.0f + std::exp(x));
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}
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inline float ReLU(float x) {
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return (x > 0.0f ? x : 0.0f);
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}
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private:
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int32_t act_func_type_;
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int32_t input_dim_;
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int32_t output_dim_;
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std::vector<float> weights_;
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std::vector<float> bias_;
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};
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class MLP {
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public:
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MLP() {}
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~MLP() {}
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void Compute(const float* input, float* output);
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inline int32_t GetInputDim() const {
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return layers_[0]->GetInputDim();
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}
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inline int32_t GetOutputDim() const {
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return layers_.back()->GetOutputDim();
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}
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inline int32_t GetLayerNum() const {
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return static_cast<int32_t>(layers_.size());
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}
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void AddLayer(int32_t inputDim, int32_t outputDim, const float* weights,
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const float* bias, bool is_output = false);
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private:
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std::vector<std::shared_ptr<seeta::fd::MLPLayer> > layers_;
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std::vector<float> layer_buf_[2];
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};
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} // namespace fd
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} // namespace seeta
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#endif // SEETA_FD_CLASSIFIER_MLP_H_

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