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add minimal functions of EigenFaces

This commit is contained in:
ser1zw 2013-02-24 02:57:21 +09:00
parent 2ffd26a887
commit 336e9c4ad2
7 changed files with 250 additions and 4 deletions

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@ -109,6 +109,8 @@ ext/opencv/cvutils.cpp
ext/opencv/cvutils.h
ext/opencv/cvvideowriter.cpp
ext/opencv/cvvideowriter.h
ext/opencv/eigenfaces.cpp
ext/opencv/eigenfaces.h
ext/opencv/gui.cpp
ext/opencv/gui.h
ext/opencv/iplconvkernel.cpp
@ -216,6 +218,7 @@ test/test_cvsurfpoint.rb
test/test_cvtermcriteria.rb
test/test_cvtwopoints.rb
test/test_cvvideowriter.rb
test/test_eigenfaces.rb
test/test_iplconvkernel.rb
test/test_iplimage.rb
test/test_mouseevent.rb

143
ext/opencv/eigenfaces.cpp Normal file
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@ -0,0 +1,143 @@
/************************************************************
eigenfaces.cpp -
$Author: ser1zw $
Copyright (C) 2013 ser1zw
************************************************************/
#include <stdio.h>
#include "eigenfaces.h"
/*
* Document-class: OpenCV::EigenFaces
*
*/
__NAMESPACE_BEGIN_OPENCV
__NAMESPACE_BEGIN_EIGENFACES
VALUE rb_klass;
std::map<long, cv::Ptr<cv::FaceRecognizer> > ptr_guard_map;
VALUE
rb_class()
{
return rb_klass;
}
void
release_facerecognizer(void *ptr) {
long key = (long)ptr;
ptr_guard_map[key].release();
ptr_guard_map.erase(key);
}
VALUE
rb_allocate(VALUE klass)
{
return Data_Wrap_Struct(klass, 0, release_facerecognizer, NULL);
}
/*
* call-seq:
* EigenFaces.new(num_components=0, threshold=DBL_MAX)
*/
VALUE
rb_initialize(int argc, VALUE argv[], VALUE self)
{
VALUE num_components_val, threshold_val;
rb_scan_args(argc, argv, "02", &num_components_val, &threshold_val);
int num_components = NIL_P(num_components_val) ? 0 : NUM2INT(num_components_val);
double threshold = NIL_P(threshold_val) ? DBL_MAX : NUM2DBL(threshold_val);
free(DATA_PTR(self));
cv::Ptr<cv::FaceRecognizer> ptr = cv::createEigenFaceRecognizer(num_components, threshold);
DATA_PTR(self) = ptr;
long key = (long)(DATA_PTR(self));
ptr_guard_map[key] = ptr; // To avoid cv::Ptr's GC
return self;
}
/*
* call-seq:
* train(src, labels)
*
* Trains a FaceRecognizer with given data and associated labels.
*/
VALUE
rb_train(VALUE self, VALUE src, VALUE labels)
{
Check_Type(src, T_ARRAY);
Check_Type(labels, T_ARRAY);
VALUE *src_ptr = RARRAY_PTR(src);
int src_size = RARRAY_LEN(src);
std::vector<cv::Mat> images;
for (int i = 0; i < src_size; i++) {
images.push_back(cv::Mat(CVMAT_WITH_CHECK(src_ptr[i])));
}
VALUE *labels_ptr = RARRAY_PTR(labels);
int labels_size = RARRAY_LEN(labels);
std::vector<int> local_labels;
for (int i = 0; i < labels_size; i++) {
local_labels.push_back(NUM2INT(labels_ptr[i]));
}
cv::FaceRecognizer *self_ptr = FACERECOGNIZER(self);
try {
self_ptr->train(images, local_labels);
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return Qnil;
}
/*
* call-seq:
* predict(src)
*
* Predicts a label and associated confidence (e.g. distance) for a given input image.
*/
VALUE
rb_predict(VALUE self, VALUE src)
{
cv::Mat mat = cv::Mat(CVMAT_WITH_CHECK(src));
cv::FaceRecognizer *self_ptr = FACERECOGNIZER(self);
int label;
try {
label = self_ptr->predict(mat);
}
catch (cv::Exception& e) {
raise_cverror(e);
}
return INT2NUM(label);
}
void
define_ruby_class()
{
if (rb_klass)
return;
/*
* opencv = rb_define_module("OpenCV");
*
* note: this comment is used by rdoc.
*/
VALUE opencv = rb_module_opencv();
rb_klass = rb_define_class_under(opencv, "EigenFaces", rb_cObject);
rb_define_alloc_func(rb_klass, rb_allocate);
rb_define_private_method(rb_klass, "initialize", RUBY_METHOD_FUNC(rb_initialize), -1);
rb_define_method(rb_klass, "train", RUBY_METHOD_FUNC(rb_train), 2);
rb_define_method(rb_klass, "predict", RUBY_METHOD_FUNC(rb_predict), 1);
}
__NAMESPACE_END_EIGENFACES
__NAMESPACE_END_OPENCV

42
ext/opencv/eigenfaces.h Normal file
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@ -0,0 +1,42 @@
/************************************************************
eigenfacerecognizer.h
$Author: ser1zw $
Copyright (C) 2013 ser1zw
************************************************************/
#ifndef RUBY_OPENCV_EIGENFACES_H
#define RUBY_OPENCV_EIGENFACES_H
#include "opencv.h"
#define __NAMESPACE_BEGIN_EIGENFACES namespace cEigenFaces {
#define __NAMESPACE_END_EIGENFACES }
__NAMESPACE_BEGIN_OPENCV
__NAMESPACE_BEGIN_EIGENFACES
VALUE rb_class();
void define_ruby_class();
VALUE rb_allocate(VALUE klass);
VALUE rb_initialize(int argc, VALUE argv[], VALUE self);
VALUE rb_train(VALUE self, VALUE src, VALUE labels);
__NAMESPACE_END_EIGENFACES
inline cv::FaceRecognizer*
FACERECOGNIZER(VALUE object)
{
cv::FaceRecognizer *ptr;
Data_Get_Struct(object, cv::FaceRecognizer, ptr);
return ptr;
}
__NAMESPACE_END_OPENCV
#endif // RUBY_OPENCV_EIGENFACES_H

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@ -706,6 +706,9 @@ extern "C" {
mOpenCV::cCvConnectedComp::define_ruby_class();
mOpenCV::cCvAvgComp::define_ruby_class();
mOpenCV::cCvHaarClassifierCascade::define_ruby_class();
mOpenCV::cEigenFaces::define_ruby_class();
mOpenCV::mGUI::define_ruby_module();
mOpenCV::mGUI::cWindow::define_ruby_class();
mOpenCV::mGUI::cTrackbar::define_ruby_class();

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@ -130,6 +130,8 @@ extern "C" {
#include "cvfeaturetree.h"
#include "eigenfaces.h"
// GUI
#include "gui.h"
#include "window.h"

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53
test/test_eigenfaces.rb Executable file
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@ -0,0 +1,53 @@
#!/usr/bin/env ruby
# -*- mode: ruby; coding: utf-8-unix -*-
require 'test/unit'
require 'opencv'
require File.expand_path(File.dirname(__FILE__)) + '/helper'
include OpenCV
# Tests for OpenCV::EigenFaces
class TestEigenFaces < OpenCVTestCase
def setup
@eigenfaces = EigenFaces.new
end
def test_initialize
[EigenFaces.new, EigenFaces.new(1), EigenFaces.new(1, 99999)].each { |ef|
assert_equal(EigenFaces, ef.class)
}
assert_raise(TypeError) {
EigenFaces.new(DUMMY_OBJ)
}
assert_raise(TypeError) {
EigenFaces.new(1, DUMMY_OBJ)
}
end
def test_train
img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
assert_nil(@eigenfaces.train([img], [1]))
assert_raise(TypeError) {
@eigenfaces.train(DUMMY_OBJ, [1])
}
assert_raise(TypeError) {
@eigenfaces.train([img], DUMMY_OBJ)
}
end
def test_predict
img = CvMat.load(FILENAME_LENA256x256, CV_LOAD_IMAGE_GRAYSCALE)
label = 1
@eigenfaces.train([img], [label])
assert_equal(label, @eigenfaces.predict(img))
assert_raise(TypeError) {
@eigenfaces.predict(DUMMY_OBJ)
}
end
end