/*
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If you do not agree to this license, do not download, install,
copy or use the software.
License Agreement
For Open Source Computer Vision Library
(3-clause BSD License)
Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
Copyright (C) 2015, OpenCV Foundation, all rights reserved.
Copyright (C) 2015, Itseez Inc., all rights reserved.
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Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
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this list of conditions and the following disclaimer in the documentation
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* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
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This software is provided by the copyright holders and contributors "as is" and
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In no event shall copyright holders or contributors be liable for any direct,
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*/
#include "precomp.hpp"
using std::cerr;
using std::endl;
using std::vector;
using std::string;
namespace cv {
namespace xobjdetect {
static vector<Mat> sample_patches(
const string& path,
int n_rows,
int n_cols,
size_t n_patches)
{
vector<String> filenames;
glob(path, filenames);
vector<Mat> patches;
size_t patch_count = 0;
for (size_t i = 0; i < filenames.size(); ++i) {
Mat img = imread(filenames[i], IMREAD_GRAYSCALE);
for (int row = 0; row + n_rows < img.rows; row += n_rows) {
for (int col = 0; col + n_cols < img.cols; col += n_cols) {
patches.push_back(img(Rect(col, row, n_cols, n_rows)).clone());
patch_count += 1;
if (patch_count == n_patches) {
goto sampling_finished;
}
}
}
}
sampling_finished:
return patches;
}
static vector<Mat> read_imgs(const string& path)
{
vector<String> filenames;
glob(path, filenames);
vector<Mat> imgs;
for (size_t i = 0; i < filenames.size(); ++i) {
imgs.push_back(imread(filenames[i], IMREAD_GRAYSCALE));
}
return imgs;
}
void WBDetectorImpl::read(const FileNode& node)
{
boost_.read(node);
}
void WBDetectorImpl::write(FileStorage &fs) const
{
boost_.write(fs);
}
void WBDetectorImpl::train(
const string& pos_samples_path,
const string& neg_imgs_path)
{
vector<Mat> pos_imgs = read_imgs(pos_samples_path);
vector<Mat> neg_imgs = sample_patches(neg_imgs_path, 24, 24, pos_imgs.size() * 10);
assert(pos_imgs.size());
assert(neg_imgs.size());
int n_features;
Mat pos_data, neg_data;
Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create();
eval->init(CvFeatureParams::create(), 1, Size(24, 24));
n_features = eval->getNumFeatures();
const int stages[] = {64, 128, 256, 512, 1024};
const int stage_count = sizeof(stages) / sizeof(*stages);
const int stage_neg = (int)(pos_imgs.size() * 5);
const int max_per_image = 100;
const float scales_arr[] = {.3f, .4f, .5f, .6f, .7f, .8f, .9f, 1.0f};
const vector<float> scales(scales_arr,
scales_arr + sizeof(scales_arr) / sizeof(*scales_arr));
vector<String> neg_filenames;
glob(neg_imgs_path, neg_filenames);
for (int i = 0; i < stage_count; ++i) {
cerr << "compute features" << endl;
pos_data = Mat1b(n_features, (int)pos_imgs.size());
neg_data = Mat1b(n_features, (int)neg_imgs.size());
for (size_t k = 0; k < pos_imgs.size(); ++k) {
eval->setImage(pos_imgs[k], +1, 0, boost_.get_feature_indices());
for (int j = 0; j < n_features; ++j) {
pos_data.at<uchar>(j, (int)k) = (uchar)(*eval)(j);
}
}
for (size_t k = 0; k < neg_imgs.size(); ++k) {
eval->setImage(neg_imgs[k], 0, 0, boost_.get_feature_indices());
for (int j = 0; j < n_features; ++j) {
neg_data.at<uchar>(j, (int)k) = (uchar)(*eval)(j);
}
}
boost_.reset(stages[i]);
boost_.fit(pos_data, neg_data);
if (i + 1 == stage_count) {
break;
}
int bootstrap_count = 0;
size_t img_i = 0;
for (; img_i < neg_filenames.size(); ++img_i) {
cerr << "win " << bootstrap_count << "/" << stage_neg
<< " img " << (img_i + 1) << "/" << neg_filenames.size() << "\r";
Mat img = imread(neg_filenames[img_i], IMREAD_GRAYSCALE);
vector<Rect> bboxes;
Mat1f confidences;
boost_.detect(eval, img, scales, bboxes, confidences);
if (confidences.rows > 0) {
Mat1i indices;
sortIdx(confidences, indices,
CV_SORT_EVERY_COLUMN + CV_SORT_DESCENDING);
int win_count = min(max_per_image, confidences.rows);
win_count = min(win_count, stage_neg - bootstrap_count);
Mat window;
for (int k = 0; k < win_count; ++k) {
resize(img(bboxes[indices(k, 0)]), window, Size(24, 24), 0, 0, INTER_LINEAR_EXACT);
neg_imgs.push_back(window.clone());
bootstrap_count += 1;
}
if (bootstrap_count >= stage_neg) {
break;
}
}
}
cerr << "bootstrapped " << bootstrap_count << " windows from "
<< (img_i + 1) << " images" << endl;
}
}
void WBDetectorImpl::detect(
const Mat& img,
vector<Rect> &bboxes,
vector<double> &confidences)
{
Mat test_img = img.clone();
bboxes.clear();
confidences.clear();
vector<float> scales;
for (float scale = 0.2f; scale < 1.2f; scale *= 1.1f) {
scales.push_back(scale);
}
Ptr<CvFeatureParams> params = CvFeatureParams::create();
Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create();
eval->init(params, 1, Size(24, 24));
boost_.detect(eval, img, scales, bboxes, confidences);
assert(confidences.size() == bboxes.size());
}
Ptr<WBDetector>
WBDetector::create()
{
return Ptr<WBDetector>(new WBDetectorImpl());
}
}
}