re-arrange, use vector<double> to facilitate pass back to python

This commit is contained in:
Jack Culpepper 2015-03-12 07:39:42 +00:00
parent 154f9e4931
commit 9b78932ab1
2 changed files with 34 additions and 31 deletions

View File

@ -350,6 +350,18 @@ object_detector<scan_fhog_pyramid<pyramid_down<6>>>.")
ensures \n\
- This function runs the object detector on the input image and returns \n\
a list of detections. \n\
- Upsamples the image upsample_num_times before running the basic \n\
detector. If you don't know how many times you want to upsample then \n\
don't provide a value for upsample_num_times and an appropriate \n\
default will be used.")
.def("run", run_rect_detector, (arg("image"), arg("upsample_num_times")),
"requires \n\
- image is a numpy ndarray containing either an 8bit grayscale or RGB \n\
image. \n\
- upsample_num_times >= 0 \n\
ensures \n\
- This function runs the object detector on the input image and returns \n\
a tuple of (list of detections, list of scores, list of weight_indices). \n\
- Upsamples the image upsample_num_times before running the basic \n\
detector. If you don't know how many times you want to upsample then \n\
don't provide a value for upsample_num_times and an appropriate \n\
@ -383,18 +395,6 @@ ensures \n\
ensures \n\
- This function runs the object detector on the input image and returns \n\
a list of detections.")
.def("run", &type::run_detector3, (arg("image"), arg("upsample_num_times")),
"requires \n\
- image is a numpy ndarray containing either an 8bit grayscale or RGB \n\
image. \n\
- upsample_num_times >= 0 \n\
ensures \n\
- This function runs the object detector on the input image and returns \n\
a tuple of (list of detections, list of scores, list of weight_indices). \n\
- Upsamples the image upsample_num_times before running the basic \n\
detector. If you don't know how many times you want to upsample then \n\
don't provide a value for upsample_num_times and an appropriate \n\
default will be used.")
.def("save", save_simple_object_detector_py, (arg("detector_output_filename")), "Save a simple_object_detector to the provided path.")
.def_pickle(serialize_pickle<type>());
}

View File

@ -17,7 +17,7 @@ namespace dlib
std::vector<rect_detection>& rect_detections,
std::vector<rectangle>& rectangles,
std::vector<double>& detection_confidences,
std::vector<int>& weight_indices
std::vector<double>& weight_indices
)
{
rectangles.clear();
@ -37,7 +37,7 @@ namespace dlib
boost::python::object img,
const unsigned int upsampling_amount,
std::vector<double>& detection_confidences,
std::vector<int>& weight_indices
std::vector<double>& weight_indices
)
{
pyramid_down<2> pyr;
@ -111,6 +111,24 @@ namespace dlib
}
}
inline boost::python::tuple run_rect_detector (
dlib::simple_object_detector& detector,
boost::python::object img,
const unsigned int upsampling_amount)
{
boost::python::tuple t;
std::vector<double> detection_confidences;
std::vector<double> weight_indices;
std::vector<rectangle> rectangles;
rectangles = run_detector_with_upscale(detector, img, upsampling_amount,
detection_confidences, weight_indices);
return boost::python::make_tuple(rectangles,
detection_confidences, weight_indices);
}
struct simple_object_detector_py
{
simple_object_detector detector;
@ -124,7 +142,7 @@ namespace dlib
const unsigned int upsampling_amount_)
{
std::vector<double> detection_confidences;
std::vector<int> weight_indices;
std::vector<double> weight_indices;
return run_detector_with_upscale(detector, img, upsampling_amount_,
detection_confidences, weight_indices);
@ -133,27 +151,12 @@ namespace dlib
std::vector<dlib::rectangle> run_detector2 (boost::python::object img)
{
std::vector<double> detection_confidences;
std::vector<int> weight_indices;
std::vector<double> weight_indices;
return run_detector_with_upscale(detector, img, upsampling_amount,
detection_confidences, weight_indices);
}
boost::python::tuple run_detector3 (boost::python::object img,
const unsigned int upsampling_amount_)
{
boost::python::tuple t;
std::vector<double> detection_confidences;
std::vector<int> weight_indices;
std::vector<rectangle> rectangles;
rectangles = run_detector_with_upscale(detector, img, upsampling_amount,
detection_confidences, weight_indices);
return boost::python::make_tuple(rectangles,
detection_confidences, weight_indices);
}
};
}