apollo::perception::trafficlight::Select类 参考

#include <select.h>

apollo::perception::trafficlight::Select 的协作图:

Public 成员函数
	Select ()=default
	Construct a new select object.
bool	Init (int rows, int cols)
	Initialize select object parameters.
void	SelectTrafficLights (const std::vector< base::TrafficLightPtr > &refined_bboxes, std::vector< base::TrafficLightPtr > *hdmap_bboxes)
	Choose traffic lights using Gaussian calculation scores.
double	Calc2dGaussianScore (base::Point2DI p1, base::Point2DI p2, float sigma1, float sigma2)
	Calculate 2d Gaussian score.

详细描述

在文件 select.h 第 29 行定义.

构造及析构函数说明

Select()

apollo::perception::trafficlight::Select::Select ( )

default

Construct a new select object.

成员函数说明

Calc2dGaussianScore()

double apollo::perception::trafficlight::Select::Calc2dGaussianScore	(	base::Point2DI	p1,
		base::Point2DI	p2,
		float	sigma1,
		float	sigma2
	)

Calculate 2d Gaussian score.

参数

p1
p2
sigma1
sigma2

返回

double

在文件 select.cc 第 34 行定义.

                                                                                               {
    return std::exp(
            -0.5
            * (static_cast<float>((p1.x - p2.x) * (p1.x - p2.x)) / (sigma1 * sigma1)
               + (static_cast<float>((p1.y - p2.y) * (p1.y - p2.y)) / (sigma2 * sigma2))));
}

Init()

bool apollo::perception::trafficlight::Select::Init	(	int	rows,
		int	cols
	)

Initialize select object parameters.

参数

rows
cols

返回

true

false

在文件 select.cc 第 24 行定义.

                                    {
    if (rows < 0 || cols < 0) {
        return false;
    }
 
    munkres_.costs()->Reserve(rows, cols);
 
    return true;
}

SelectTrafficLights()

void apollo::perception::trafficlight::Select::SelectTrafficLights	(	const std::vector< base::TrafficLightPtr > &	refined_bboxes,
		std::vector< base::TrafficLightPtr > *	hdmap_bboxes
	)

Choose traffic lights using Gaussian calculation scores.

参数

refined_bboxes
hdmap_bboxes

在文件 select.cc 第 41 行定义.

                                                      {
    std::vector<std::pair<size_t, size_t>> assignments;
    munkres_.costs()->Resize(hdmap_bboxes->size(), refined_bboxes.size());
 
    for (size_t row = 0; row < hdmap_bboxes->size(); ++row) {
        auto center_hd = (*hdmap_bboxes)[row]->region.detection_roi.Center();
        if ((*hdmap_bboxes)[row]->region.outside_image) {
            AINFO << "projection_roi outside image, set score to 0.";
            for (size_t col = 0; col < refined_bboxes.size(); ++col) {
                (*munkres_.costs())(row, col) = 0.0;
            }
            continue;
        }
        for (size_t col = 0; col < refined_bboxes.size(); ++col) {
            float gaussian_score = 100.0f;
            auto center_refine = refined_bboxes[col]->region.detection_roi.Center();
            // use gaussian score as metrics of distance and width
            double distance_score = Calc2dGaussianScore(center_hd, center_refine, gaussian_score, gaussian_score);
 
            double max_score = 0.9;
            auto detect_score = refined_bboxes[col]->region.detect_score;
            double detection_score = detect_score > max_score ? max_score : detect_score;
 
            double distance_weight = 0.7;
            double detection_weight = 1 - distance_weight;
            (*munkres_.costs())(row, col)
                    = static_cast<float>(detection_weight * detection_score + distance_weight * distance_score);
            const auto &crop_roi = (*hdmap_bboxes)[row]->region.crop_roi;
            const auto &detection_roi = refined_bboxes[col]->region.detection_roi;
            if ((detection_roi & crop_roi) != detection_roi) {
                AINFO << "detection_roi outside crop_roi, set score to 0."
                      << " detection_roi: " << detection_roi.x << " " << detection_roi.y << " " << detection_roi.width
                      << " " << detection_roi.height << " crop_roi: " << crop_roi.x << " " << crop_roi.y << " "
                      << crop_roi.width << " " << crop_roi.height;
                (*munkres_.costs())(row, col) = 0.0;
            }
            AINFO << "score " << (*munkres_.costs())(row, col);
        }
    }
 
    munkres_.Maximize(&assignments);
 
    for (size_t i = 0; i < hdmap_bboxes->size(); ++i) {
        (*hdmap_bboxes)[i]->region.is_selected = false;
        (*hdmap_bboxes)[i]->region.is_detected = false;
    }
 
    for (size_t i = 0; i < assignments.size(); ++i) {
        if (static_cast<size_t>(assignments[i].first) >= hdmap_bboxes->size()
            || static_cast<size_t>(
                    assignments[i].second >= refined_bboxes.size()
                    || (*hdmap_bboxes)[assignments[i].first]->region.is_selected
                    || refined_bboxes[assignments[i].second]->region.is_selected)) {
        } else {
            auto &refined_bbox_region = refined_bboxes[assignments[i].second]->region;
            auto &hdmap_bbox_region = (*hdmap_bboxes)[assignments[i].first]->region;
            refined_bbox_region.is_selected = true;
            hdmap_bbox_region.is_selected = true;
 
            const auto &crop_roi = hdmap_bbox_region.crop_roi;
            const auto &detection_roi = refined_bbox_region.detection_roi;
            bool outside_crop_roi = ((crop_roi & detection_roi) != detection_roi);
            if (hdmap_bbox_region.outside_image || outside_crop_roi) {
                hdmap_bbox_region.is_detected = false;
            } else {
                hdmap_bbox_region.detection_roi = refined_bbox_region.detection_roi;
                hdmap_bbox_region.detect_class_id = refined_bbox_region.detect_class_id;
                hdmap_bbox_region.detect_score = refined_bbox_region.detect_score;
                hdmap_bbox_region.is_detected = refined_bbox_region.is_detected;
                hdmap_bbox_region.is_selected = refined_bbox_region.is_selected;
            }
        }
    }
 
    for (size_t i = 0; i < hdmap_bboxes->size(); ++i) {
        AINFO << "hdmap_bboxes-" << i << ":"
              << " projection_roi: " << (*hdmap_bboxes)[i]->region.projection_roi.ToStr()
              << " detection_roi: " << (*hdmap_bboxes)[i]->region.detection_roi.ToStr();
    }
}

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该类的文档由以下文件生成:

• modules/perception/traffic_light_detection/algorithm/select.h
• modules/perception/traffic_light_detection/algorithm/select.cc