void BlobDetection::handleOnImageReceived(const sensor_msgs::ImageConstPtr &msg)
{
auto image = cv_bridge::toCvShare(msg, "bgr8")->image;
showImage(image, "original");
cv::Mat lightCorrectedImage;
pCorrection->correct(image, lightCorrectedImage);
// showImage(lightCorrectedImage, "corrected", true);
detectWalls(image, msg->header.stamp);
auto results = detectObjects(lightCorrectedImage);
for (auto& result : results) {
object_detection_blob::BlobObject rmsg;
rmsg.header.seq++;
rmsg.header.stamp = msg->header.stamp;
rmsg.header.frame_id = "image";
rmsg.object_type = result.sType;
rmsg.center_x = result.sObjectRect.center.x;
rmsg.center_y = result.sObjectRect.center.y;
rmsg.size_x = result.sObjectRect.size.width;
rmsg.size_y = result.sObjectRect.size.height;
rmsg.img_width = lightCorrectedImage.cols;
rmsg.img_height = lightCorrectedImage.rows;
objectPublisher.publish(rmsg);
}
}
void FindLargestObject(const cv::Mat &img, cv::CascadeClassifier &cascade, cv::Rect &largestObject, int scaledWidth)
{
int flags = cv::CASCADE_FIND_BIGGEST_OBJECT;
Size minFeatureSize = Size(20, 20);
float searchScaleFactor = 1.1f;
int minNeighbors = 3;
std::vector<cv::Rect> objects;
detectObjects(img, cascade, objects, scaledWidth, flags, minFeatureSize, searchScaleFactor, minNeighbors);
if (objects.size() > 0) {
largestObject = (cv::Rect)objects.at(0);
}
else {
largestObject = cv::Rect(-1,-1,-1,-1);
}
}
int hrt_main (int argc, char **argv)
{
int flag;
int i;
// detection parameters
float scaleFactor = 1.2;
int minNeighbours = 3;
int size;
printf("-- entering main function --\r\n");
printf("-- loading image --\r\n");
MyImage imageObj;
MyImage *image = &imageObj;
flag = readPgm((char *)INPUT_FILENAME, image);
if (flag == -1)
{
printf( "Unable to open input image\n");
return 1;
}
printf("-- loading cascade classifier --\r\n");
myCascade cascadeObj;
myCascade *cascade = &cascadeObj;
MySize minSize = {20, 20};
MySize maxSize = {0, 0};
// classifier properties
cascade->n_stages=25;
cascade->total_nodes=2913;
cascade->orig_window_size.height = 24;
cascade->orig_window_size.width = 24;
readTextClassifier(cascade);
struct MyRect *result[NUM] = {};
//Load the program
hrt_cell_load_program_id(CELL, haar);
hrt_scalar_store(CELL, MyImage, myimage, image);
hrt_indexed_store(CELL, MySize, mysize, 0, minSize);
hrt_indexed_store(CELL, MySize, mysize, 1, maxSize);
hrt_scalar_store(CELL, myCascade, mycascade, cascade);
hrt_scalar_store(CELL, float, scalefactor, scaleFactor);
hrt_scalar_store(CELL, int, minneighbours, minNeighbours);
hrt_scalar_store(CELL, MyRect, myrect, result);
printf("-- detecting faces --\r\n");
//size = detectObjects(image, minSize, maxSize, cascade, scaleFactor, minNeighbours, result);
detectObjects();
size = hrt_scalar_load(CELL, int, size);
printf("-- drawing boxes --\r\n");
for(i = 0; i < NUM; i++ )
{
if ( result[i] != NULL) {
struct MyRect *r = result[i];
drawRectangle(image, r);
}
else
break;
}
printf("-- saving output --\r\n");
flag = writePgm((char *)OUTPUT_FILENAME, image);
printf("-- image saved --\r\n");
// delete image and free classifier
releaseTextClassifier(cascade);
freeImage(image);
return 0;
}
