02e5e637
 Zhao Shuaihua
增加行人/非机动车占机动车道(50...
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#include "road_seg_3cls_statistics.h"
#include "omp.h"
Road3clsSegProcess::Road3clsSegProcess(){
m_max_batchsize = 16;
}
Road3clsSegProcess::~Road3clsSegProcess(){
release();
}
cv::Mat Road3clsSegProcess::mask_to_rgb(cv::Mat img, cv::Mat mask) {
cv::Mat masks = img.clone();
int reg_cls = 4;
for (int i = 0; i < masks.rows; i++) {
for (int j = 0; j < masks.cols; j++) {
for (int k = 1; k < reg_cls; k++) {
if (mask.at<int>(i,j) == k) {
masks.at<cv::Vec3b>(i,j)[0] = seg_colors[k][0];
masks.at<cv::Vec3b>(i,j)[1] = seg_colors[k][1];
masks.at<cv::Vec3b>(i,j)[2] = seg_colors[k][2];
}
}
}
}
return masks;
}
float Road3clsSegProcess::contourArea(std::vector<cv::Point> contour, cv::Point2f& center) {
cv::RotatedRect rect = cv::minAreaRect(contour); // 最小外接矩形 rect[0]中心点 rect[1]宽 高 rect[2]旋转角度
center = rect.center;
return cv::contourArea(contour);
}
void Road3clsSegProcess::lanes_process(const rs3cls_lane* lanes, int lane_count, std::vector<std::pair<std::vector<cv::Point>, int>>& combined, float scale_w, float scale_h) {
std::vector<std::vector<cv::Point> > lanes_xys;
std::vector<int> lanes_cls;
for (int i = 0; i < lane_count; i++) {
std::vector<cv::Point> xys;
for (int j = 0; j < lanes[i].num_points; j++) {
int x = static_cast<int>(lanes[i].points[j].x_ * scale_w);
int y = static_cast<int>(lanes[i].points[j].y_ * scale_h);
if (x > 0 && y > 0) {
xys.emplace_back(x, y);
}
}
if (!xys.empty()) {
lanes_xys.push_back(xys);
lanes_cls.push_back(lanes[i].cls);
}
}
for (size_t i = 0; i < lanes_xys.size(); ++i) {
combined.push_back(std::make_pair(lanes_xys[i], lanes_cls[i]));
}
if (!combined.empty()) {
//按车道线起点坐标排序,相应的类别顺序也会变化以保证标签对齐
std::sort(combined.begin(), combined.end(), [](const std::pair<std::vector<cv::Point>, int>& a, const std::pair<std::vector<cv::Point>, int>& b) {
return a.first[0].x < b.first[0].x;
});
}
}
cv::Mat Road3clsSegProcess::imshow_lanes(cv::Mat img, const rs3cls_lane* lanes, int lane_count) {
float scale_w = img.cols / 640.0;
float scale_h = img.rows / 360.0;
std::vector<std::pair<std::vector<cv::Point>, int>> combined;
lanes_process(lanes, lane_count, combined, scale_w, scale_h);
for (const auto& lane_info : combined) {
const auto& xys = lane_info.first;
int cls = lane_info.second;
cv::Scalar color(lane_colors[cls][0],lane_colors[cls][1],lane_colors[cls][2]);
for (size_t i = 1; i < xys.size(); ++i) {
cv::line(img, xys[i - 1], xys[i], color, 4);
}
}
return img;
}
int Road3clsSegProcess::Mask2LanePoints(const cv::Mat& pred, std::vector<std::vector<cv::Point>>&lanes, std::vector<int>& cats) {
std::vector<int> labels = {9, 10, 11, 12, 13, 14};
for(auto cat: labels) {
cv::Mat b_masks, measure_labels, stats, centroids;
cv::compare(pred, cat, b_masks, cv::CMP_EQ); //将pred元素逐个和cat比较，相同255，不同0
// cv::threshold(pred, b_masks, 126, 255, cv::THRESH_OTSU); //生成二值图
// 连通域计算 b_masks:闭操作后的二值图像 measure_labels:和原图一样大的标记图 centroids:nccomps×2的矩阵,表示每个连通域的质心(x, y)
// stats:nccomps×5的矩阵 表示每个连通区域的外接矩形和面积(x, y, w, h, area)，索引0是背景信息
int nccomps = cv::connectedComponentsWithStats (b_masks, measure_labels, stats, centroids, 4); //8
for(int cv_measure_id = 1; cv_measure_id < nccomps; cv_measure_id++ ) { //跳过背景信息0
cv::Mat cv_measure_mask;
cv::compare(measure_labels, cv_measure_id, cv_measure_mask, cv::CMP_EQ);
std::vector< std::vector< cv::Point> > contours;
cv::findContours(cv_measure_mask,contours,cv::noArray(),cv::RETR_TREE,cv::CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> contours3cls_poly;
for (int j = 0; j < contours.size();j++) {
float area = cv::contourArea(contours[j]); if (area < 60) continue; //30
cv::approxPolyDP(cv::Mat(contours[j]), contours3cls_poly, 6, true); //减小epsilon提高拟合精度(需要调试，epsilon过小会使多边形不够简化，单条线变多条)
if (contours3cls_poly.size() == 1) continue;
lanes.push_back(contours3cls_poly);
cats.push_back(cat);
}
}
}
return 0;
}
cv::Mat Road3clsSegProcess::seg_post_process(bool large_resolution, unsigned char *seg_array, std::vector<std::pair<std::vector<cv::Point>, int>> combined, std::vector<std::vector<cv::Point>> &poly_masks, std::vector<int> ®ion_classes, std::vector<std::vector<cv::Point>> &lanes, std::vector<int> &cats, std::map<double, int> &x_sort) {
std::vector<int> pred_cls;
int h = 360, w = 640;
cv::Mat lanes_masks = cv::Mat_<int>(h,w); //正常分割结果
cv::Mat mask_rmlane = cv::Mat_<int>(h,w); //车道线区域置为背景
cv::Mat solve_masks = cv::Mat_<int>(h,w); //计算主行驶区域用(虚线及减速标线归入行车道)
int step_size = h*w;
int seg_min_region_area = 512; //1024
for (int i = 0; i < h; ++i) {
for (int j = 0; j < w; ++j) {
int max_cls = seg_array[(i * w + j)];
lanes_masks.ptr<int>(i)[j] = max_cls;
pred_cls.push_back(max_cls);
mask_rmlane.ptr<int>(i)[j] = max_cls;
solve_masks.ptr<int>(i)[j] = max_cls;
}
}
/* 求背景区域--mask车道区域，场景变化用
cv::compare(lanes_masks, 0, background_mask, cv::CMP_EQ); //将lanes_masks元素逐个和0比较，相同255，不同0*/
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02e5e637
Zhao Shuaihua
增加行人/非机动车占机动车道(50...
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//--mask远处区域------------------------------------------------
cv::Mat mask_black = mask_rmlane.clone();
if (large_resolution) mask_black(cv::Rect(0, 0, w, int(h * 0.14))) = 0;
else mask_black(cv::Rect(0, 0, w, int(h * 0.22))) = 0;
mask_black(cv::Rect(0, int(h * 0.95), w, int(h * 0.05))) = 0;
mask_black(cv::Rect(0, 0, int(w * 0.02), h)) = 0;
mask_black(cv::Rect(int(w * 0.95), 0, int(w * 0.05), h)) = 0;
mask_rmlane = mask_black;
//-------------------------------------------------------------------------
#endif
//2.去重获取预测到的类别
std::vector<int> labels(pred_cls);
std::sort(labels.begin(),labels.end());
labels.erase(std::unique(labels.begin(),labels.end()),labels.end());
// //4.求车道线区域
// int flag = Mask2LanePoints(lanes_masks, lanes, cats); ///////////////////////// 车道线如何与mask结合
//5.求道路区域
int count = 0;
for(auto cat: labels) {
// std::cout << cat << std::endl;
cv::Mat b_masks, measure_labels, stats, centroids;
cv::Mat n_masks = cv::Mat_<int>(h,w);
n_masks = cv::Scalar(255);
cv::compare(mask_rmlane, cat, b_masks, cv::CMP_EQ); //将mask_rmlane元素逐个和cat比较，相同255，不同0
//连通域计算
int nccomps = cv::connectedComponentsWithStats (b_masks, measure_labels, stats, centroids, 8);
for(int i = 1; i < nccomps; i++ ) { //跳过背景信息0
//移除过小的区域，并将对应位置置为0
if (stats.at<int>(i, cv::CC_STAT_AREA) < seg_min_region_area) {
cv::Mat comparison_result;
cv::compare(measure_labels, cv::Scalar(i), comparison_result, cv::CMP_EQ); //相等为255不等为0
n_masks.setTo(0, comparison_result); // 将comparison_result中非零区域置为0
cv::bitwise_and(mask_rmlane,n_masks,mask_rmlane);
continue;
}
double centr_x = centroids.at<double>(i, 0);
double centr_y = centroids.at<double>(i, 1);
int region_class = cat;
// printf("region_class: %d\n", region_class);
cv::Mat region_mask;
cv::bitwise_and(measure_labels,n_masks,measure_labels);/////
cv::compare(measure_labels, i, region_mask, cv::CMP_EQ);
std::vector< std::vector< cv::Point> > contours;
cv::findContours(region_mask,contours,cv::noArray(),cv::RETR_TREE,cv::CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> contours3cls_poly;
for (int j = 0; j < contours.size();j++) {
cv::approxPolyDP(cv::Mat(contours[j]), contours3cls_poly, 10, true);
if (contours3cls_poly.size() <= 2) continue;
poly_masks.push_back(contours3cls_poly);
region_classes.push_back(cat);
if (x_sort.count(centr_x)) centr_x += 0.0001;
x_sort.insert(std::make_pair(centr_x, count));
++ count;
}
}
}
return mask_rmlane;
}
/* 算法初始化 */
int Road3clsSegProcess::init(int gpu_id, string models_dir) {
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02e5e637
Zhao Shuaihua
增加行人/非机动车占机动车道(50...
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LOG_INFO("road_3clsseg 版本：{} 模型路径：{}", rs3cls_get_version(), model_path);
rs3cls_param param;
char modelNames[100];
strcpy(modelNames, model_path.c_str());
param.modelNames = modelNames;
param.thresld = 0.25;
param.devId = gpu_id;
m_devId = param.devId;
ACL_CALL(aclrtSetDevice(m_devId), ACL_SUCCESS, -1);
ACL_CALL(aclrtCreateContext(&m_algorthim_ctx, m_devId), ACL_SUCCESS, -1);
int ret = rs3cls_init(&m_seg_handle, param);
if(ret != 0) {
LOG_ERROR("Init Road3clsSegSdk failed error code is {}", ret);
return -1;
}
return 0;
}
/* 算法计算 */
int Road3clsSegProcess::process_gpu(sy_img * batch_img, vector<DeviceMemory*> vec_segMem, vector<string>& tasklist,
vector<vector<vector<int>>>& traffic_region, vector<vector<int>>& labels)
{
int batchsize = tasklist.size();
traffic_region.resize(batchsize), labels.resize(batchsize);
rs3cls_result result[batchsize];
int fea_size = SEG_IMG_RES_W * SEG_IMG_RES_H;
for (int b = 0; b < batchsize; b++) {
result[b].seg_array = new unsigned char[fea_size];
}
/* 路数太多时 按照最大batchsize数 拆批次运行 */
int ret = aclrtSetCurrentContext(m_algorthim_ctx);
if (ACL_SUCCESS != ret) {
return false;
}
int cur_batch_size = m_max_batchsize;
int cycleTimes = batchsize / cur_batch_size + (batchsize % cur_batch_size == 0 ? 0 : 1);
for (int c = 0; c < cycleTimes; c++) {
int real_batchsize = c == cycleTimes - 1 ? (batchsize - cur_batch_size*c) : cur_batch_size;
int startbatch = c*cur_batch_size;
rs3cls_result *real_res = result + startbatch;
ret = rs3cls_batch(m_seg_handle, batch_img + startbatch, real_batchsize, real_res);
}
for (int b = 0; b < batchsize; b++) {
std::vector<std::pair<std::vector<cv::Point>, int>> combined;
lanes_process(result[b].reg_array, result[b].lane_count, combined);
std::vector<std::vector<cv::Point>> poly_masks, lanes;
std::vector<int> region_classes, cats;
std::map<double, int> x_sort;
bool large_resolution = false;
if (batch_img[b].w_ > 1920) large_resolution = true;
cv::Mat seg_output = seg_post_process(large_resolution, result[b].seg_array, combined, poly_masks, region_classes, lanes, cats, x_sort); //m_masks:mask前的结果 poly_masks后的结果
float width_ratio = batch_img[b].w_*1.0 / SEG_IMG_RES_W;
float height_ratio = batch_img[b].h_*1.0 / SEG_IMG_RES_H;
for (int i = 0; i < lanes.size(); ++i) {
std::vector<cv::Point> points = lanes[i];
int lane_cls = cats[i];
vector<int> cur_region;
for (int j = 0; j < points.size(); ++j) {
int px = points[j].x*width_ratio; int py = points[j].y*height_ratio;
cur_region.push_back(px); cur_region.push_back(py);
}
traffic_region[b].push_back(cur_region); // 存储区域
labels[b].push_back(lane_cls); // 存储类别
}
for (int i = 0; i < poly_masks.size(); ++i) {
std::vector<cv::Point> points = poly_masks[i];
int seg_cls = region_classes[i];
vector<int> cur_region;
for (int j = 0; j < points.size(); ++j) {
int px = points[j].x*width_ratio; int py = points[j].y*height_ratio;
cur_region.push_back(px); cur_region.push_back(py);
}
traffic_region[b].push_back(cur_region); // 存储区域
labels[b].push_back(seg_cls); // 存储类别
}
#if 0
{
jpegUtil.jpeg_init(m_devId);
std::string file_path = "/data/shzhao/vpt_ascend/bin/res/seg3cls/";
auto time_now = std::chrono::system_clock::now();
std::string cur_timestamp_us = std::to_string(std::chrono::duration_cast<std::chrono::microseconds>(time_now.time_since_epoch()).count());
std::string img_filename = file_path + cur_timestamp_us + "_" + tasklist[b] + ".jpg";
vpc_img_info src_img_info = VPCUtil::vpc_devMem2vpcImg(vec_segMem[b]);
bool bSaved = jpegUtil.jpeg_encode(src_img_info.pic_desc, img_filename);
std::cout << bSaved << " " << img_filename << std::endl;
cv::Mat cur_img = cv::imread(img_filename);
cv::resize(cur_img, cur_img, cv::Size(640,360), 0, 0, cv::INTER_LINEAR);
cv::Mat vis_image = mask_to_rgb(cur_img, seg_output);
cv::imwrite(img_filename, vis_image);
VPCUtil::vpc_img_release(src_img_info);
jpegUtil.jpeg_release();
}
#endif
std::vector<std::vector<cv::Point>>().swap(poly_masks);
std::vector<std::vector<cv::Point>>().swap(lanes);
std::vector<int>().swap(region_classes);
std::vector<int>().swap(cats);
}
for (int b = 0; b < batchsize; b++) {
delete[] result[b].seg_array; result[b].seg_array = NULL;
}
return 0;
}
/* 算法句柄 资源释放 */
void Road3clsSegProcess::release(){
if (m_seg_handle){
rs3cls_release(&m_seg_handle);
m_seg_handle = NULL;
}
if(m_algorthim_ctx){
aclrtDestroyContext(m_algorthim_ctx);
}
}
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