srrc_simple_obj_detector. Simplest object detector.

The goal was to find a way to discover partially shadowed objects and the precache together with hook as single blobs to make object size estimation more precise and to determine hook orientation for precache. It appeared this simple filter does this trick good enough compared to other solutions I saw.

It turns out that the following simple function is already a good detector for srrc samples we have:

cv::Mat makeObjectMask( cv::Mat const& src_bgr_image
                      , int saturation_threshold = 168
                      , int brightness_threshold = 20)
{
  cv::Mat hsv_image = src_bgr_image.clone();
  cv::cvtColor(hsv_image, hsv_image, cv::COLOR_BGR2HSV);
  std::vector<cv::Mat> hsv_channels;
  cv::split(hsv_image, hsv_channels);
  return (hsv_channels[1] < saturation_threshold) & (hsv_channels[2] >= brightness_threshold);
}

Of course this simple rule will not work on sand or asphalt surface or with objects colored by other paints or with different camera settings or lightning conditions.

Examples of results:

every blob that's not exactly black is an object candidate. no farther filtering used.

Using for processing jpg files:

0. check out this repo into your catkin_ws and catkin_make
1. open file test_simple_detector_node.launch
2. replace the value for parameter src_image_src_folder with path to your folder containing jpg files
3. replace the value for parameter output_folder with the path you want to output the processed images to (it will be created if it does not exist). Everytime you run this node in this folder will be automatically created a subfolder with the name generated from timestamp where the result images are output, so it's ok to specify the value for output_folder only once and then use it unchanged.
4. call roslaunch srrc_simple_obj_detector test_simple_detector_node.launch and wait.

Using for processing videostream:

0. check out this repo into your catkin_ws and catkin_make
1. open file simple_detector_node.launch
2. replace the value for parameter input_image_topic with the name of your image topic
3. if the image topic is publishing uncompressed images set the value for parameter input_image_transfer_hint to 'raw'
4. call roslaunch srrc_simple_obj_detector simple_detector_node.launch
5. call `rqt_image_view' (f.e.) to see the published processed images.