wbdetector.cpp

/*
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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], CV_LOAD_IMAGE_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], CV_LOAD_IMAGE_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], CV_LOAD_IMAGE_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));
                    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());
}

}
}