void prepareMeasurement(const ed::EntityConstPtr& e, cv::Mat& view_color_img, cv::Mat& view_depth_img, cv::Mat& mask, cv::Rect& bouding_box) {

    // Get the best measurement from the entity
    ed::MeasurementConstPtr msr = e->lastMeasurement();
    if (!msr)
        return;

    int min_x, max_x, min_y, max_y;

    // create a view
    rgbd::View view(*msr->image(), msr->image()->getRGBImage().cols);

    // get depth image
    view_depth_img = msr->image()->getDepthImage();

    // get color image
    const cv::Mat& color_image = msr->image()->getRGBImage();

    // crop it to match the view
    view_color_img = cv::Mat(color_image(cv::Rect(0, 0, view.getWidth(), view.getHeight())));

//    std::cout << "image: " << cropped_image.cols << "x" << cropped_image.rows << std::endl;

    // initialize bounding box points
    max_x = 0;
    max_y = 0;
    min_x = view.getWidth();
    min_y = view.getHeight();

    // initialize mask, all 0s
    mask = cv::Mat::zeros(view.getHeight(), view.getWidth(), CV_8UC1);

    // Iterate over all points in the mask
    for(ed::ImageMask::const_iterator it = msr->imageMask().begin(view.getWidth()); it != msr->imageMask().end(); ++it)
    {
        // mask's (x, y) coordinate in the depth image
        const cv::Point2i& p_2d = *it;

        // paint a mask
        mask.at<unsigned char>(*it) = 255;

        // update the boundary coordinates
        if (min_x > p_2d.x) min_x = p_2d.x;
        if (max_x < p_2d.x) max_x = p_2d.x;
        if (min_y > p_2d.y) min_y = p_2d.y;
        if (max_y < p_2d.y) max_y = p_2d.y;
    }

    bouding_box = cv::Rect(min_x, min_y, max_x - min_x, max_y - min_y);
}
Esempio n. 2
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void HumanContourMatcher::process(ed::EntityConstPtr e, tue::Configuration& result) const
{
    // if initialization failed, return
    if (!init_success_)
        return;

    // Get the best measurement from the entity
    ed::MeasurementConstPtr msr = e->lastMeasurement();
    if (!msr)
        return;

    float depth_sum = 0;
    float avg_depht;
    float classification_error = 0;
    float classification_deviation = 0;
    std::string classification_stance;
    uint point_counter = 0;
    bool is_human = false;

    // Get the depth image from the measurement
    const cv::Mat& depth_image = msr->image()->getDepthImage();
    const cv::Mat& color_image = msr->image()->getRGBImage();

    cv::Mat mask_cv = cv::Mat::zeros(depth_image.rows, depth_image.cols, CV_8UC1);

    // Iterate over all points in the mask. You must specify the width of the image on which you
    // want to apply the mask (see the begin(...) method).
    for(ed::ImageMask::const_iterator it = msr->imageMask().begin(depth_image.cols); it != msr->imageMask().end(); ++it)
    {
        // mask's (x, y) coordinate in the depth image
        const cv::Point2i& p_2d = *it;

        // TODO dont creat a Mat mask, just give human_classifier_.Classify a vector of 2D points!
        // paint a mask
        mask_cv.at<unsigned char>(p_2d) = 255;

        // calculate measurement average depth
        depth_sum += depth_image.at<float>(p_2d);
        point_counter++;
    }

    avg_depht = depth_sum/(float)point_counter;

    is_human = human_classifier_.Classify(depth_image, color_image, mask_cv, avg_depht, classification_error, classification_deviation, classification_stance);

    // ----------------------- assert results -----------------------

    // create group if it doesnt exist
    if (!result.readGroup("perception_result", tue::OPTIONAL))
    {
        result.writeGroup("perception_result");
    }

    // assert the results to the entity
    result.writeGroup(kModuleName);
    result.setValue("label", "Human Shape");

    if (classification_error > 0){
        result.setValue("stance", classification_stance);
        result.setValue("error", classification_error);
        result.setValue("deviation", classification_deviation);
    }

    if(is_human)
    {
        result.setValue("score", 1.0);
    }else{
        result.setValue("score", 0.0);
    }

    result.endGroup();  // close human_contour_matcher group
    result.endGroup();  // close perception_result group
}