// Copyright (C) 2018 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include "opaque_types.h"
#include <dlib/python.h>
#include "dlib/pixel.h"
#include <dlib/image_transforms.h>
#include <dlib/image_processing.h>
using namespace dlib;
using namespace std;
namespace py = pybind11;
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_resize_image (
const numpy_image<T>& img,
unsigned long rows,
unsigned long cols
)
{
numpy_image<T> out;
set_image_size(out, rows, cols);
resize_image(img, out);
return out;
}
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_equalize_histogram (
const numpy_image<T>& img
)
{
numpy_image<T> out;
equalize_histogram(img,out);
return out;
}
// ----------------------------------------------------------------------------------------
template <typename T>
line ht_get_line (
const hough_transform& ht,
const dlib::vector<T,2>& p
)
{
DLIB_CASSERT(get_rect(ht).contains(p));
auto temp = ht.get_line(p);
return line(temp.first, temp.second);
}
template <typename T>
double ht_get_line_angle_in_degrees (
const hough_transform& ht,
const dlib::vector<T,2>& p
)
{
DLIB_CASSERT(get_rect(ht).contains(p));
return ht.get_line_angle_in_degrees(p);
}
template <typename T>
py::tuple ht_get_line_properties (
const hough_transform& ht,
const dlib::vector<T,2>& p
)
{
DLIB_CASSERT(get_rect(ht).contains(p));
double angle_in_degrees;
double radius;
ht.get_line_properties(p, angle_in_degrees, radius);
return py::make_tuple(angle_in_degrees, radius);
}
point ht_get_best_hough_point (
hough_transform& ht,
const point& p,
const numpy_image<float>& himg
)
{
DLIB_CASSERT(num_rows(himg) == ht.size() && num_columns(himg) == ht.size() &&
get_rect(ht).contains(p) == true,
"\t point hough_transform::get_best_hough_point()"
<< "\n\t Invalid arguments given to this function."
<< "\n\t num_rows(himg): " << num_rows(himg)
<< "\n\t num_columns(himg): " << num_columns(himg)
<< "\n\t size(): " << ht.size()
<< "\n\t p: " << p
);
return ht.get_best_hough_point(p,himg);
}
template <
typename T
>
numpy_image<float> compute_ht (
const hough_transform& ht,
const numpy_image<T>& img,
const rectangle& box
)
{
numpy_image<float> out;
ht(img, box, out);
return out;
}
template <
typename T
>
numpy_image<float> compute_ht2 (
const hough_transform& ht,
const numpy_image<T>& img
)
{
numpy_image<float> out;
ht(img, out);
return out;
}
template <
typename T
>
py::list ht_find_pixels_voting_for_lines (
const hough_transform& ht,
const numpy_image<T>& img,
const rectangle& box,
const std::vector<point>& hough_points,
const unsigned long angle_window_size = 1,
const unsigned long radius_window_size = 1
)
{
return vector_to_python_list(ht.find_pixels_voting_for_lines(img, box, hough_points, angle_window_size, radius_window_size));
}
template <
typename T
>
py::list ht_find_pixels_voting_for_lines2 (
const hough_transform& ht,
const numpy_image<T>& img,
const std::vector<point>& hough_points,
const unsigned long angle_window_size = 1,
const unsigned long radius_window_size = 1
)
{
return vector_to_python_list(ht.find_pixels_voting_for_lines(img, hough_points, angle_window_size, radius_window_size));
}
std::vector<point> ht_find_strong_hough_points(
hough_transform& ht,
const numpy_image<float>& himg,
const float hough_count_thresh,
const double angle_nms_thresh,
const double radius_nms_thresh
)
{
return ht.find_strong_hough_points(himg, hough_count_thresh, angle_nms_thresh, radius_nms_thresh);
}
// ----------------------------------------------------------------------------------------
void register_hough_transform(py::module& m)
{
const char* class_docs =
"This object is a tool for computing the line finding version of the Hough transform \n\
given some kind of edge detection image as input. It also allows the edge pixels \n\
to be weighted such that higher weighted edge pixels contribute correspondingly \n\
more to the output of the Hough transform, allowing stronger edges to create \n\
correspondingly stronger line detections in the final Hough transform.";
const char* doc_constr =
"requires \n\
- size_ > 0 \n\
ensures \n\
- This object will compute Hough transforms that are size_ by size_ pixels. \n\
This is in terms of both the Hough accumulator array size as well as the \n\
input image size. \n\
- size() == size_";
/*!
requires
- size_ > 0
ensures
- This object will compute Hough transforms that are size_ by size_ pixels.
This is in terms of both the Hough accumulator array size as well as the
input image size.
- size() == size_
!*/
py::class_<hough_transform>(m, "hough_transform", class_docs)
.def(py::init<unsigned long>(), doc_constr, py::arg("size_"))
.def_property_readonly("size", &hough_transform::size,
"returns the size of the Hough transforms generated by this object. In particular, this object creates Hough transform images that are size by size pixels in size.")
.def("get_line", &ht_get_line<long>, py::arg("p"))
.def("get_line", &ht_get_line<double>, py::arg("p"),
"requires \n\
- rectangle(0,0,size-1,size-1).contains(p) == true \n\
(i.e. p must be a point inside the Hough accumulator array) \n\
ensures \n\
- returns the line segment in the original image space corresponding \n\
to Hough transform point p. \n\
- The returned points are inside rectangle(0,0,size-1,size-1).")
/*!
requires
- rectangle(0,0,size-1,size-1).contains(p) == true
(i.e. p must be a point inside the Hough accumulator array)
ensures
- returns the line segment in the original image space corresponding
to Hough transform point p.
- The returned points are inside rectangle(0,0,size-1,size-1).
!*/
.def("get_line_angle_in_degrees", &ht_get_line_angle_in_degrees<long>, py::arg("p"))
.def("get_line_angle_in_degrees", &ht_get_line_angle_in_degrees<double>, py::arg("p"),
"requires \n\
- rectangle(0,0,size-1,size-1).contains(p) == true \n\
(i.e. p must be a point inside the Hough accumulator array) \n\
ensures \n\
- returns the angle, in degrees, of the line corresponding to the Hough \n\
transform point p.")
/*!
requires
- rectangle(0,0,size-1,size-1).contains(p) == true
(i.e. p must be a point inside the Hough accumulator array)
ensures
- returns the angle, in degrees, of the line corresponding to the Hough
transform point p.
!*/
.def("get_line_properties", &ht_get_line_properties<long>, py::arg("p"))
.def("get_line_properties", &ht_get_line_properties<double>, py::arg("p"),
"requires \n\
- rectangle(0,0,size-1,size-1).contains(p) == true \n\
(i.e. p must be a point inside the Hough accumulator array) \n\
ensures \n\
- Converts a point in the Hough transform space into an angle, in degrees, \n\
and a radius, measured in pixels from the center of the input image. \n\
- let ANGLE_IN_DEGREES == the angle of the line corresponding to the Hough \n\
transform point p. Moreover: -90 <= ANGLE_IN_DEGREES < 90. \n\
- RADIUS == the distance from the center of the input image, measured in \n\
pixels, and the line corresponding to the Hough transform point p. \n\
Moreover: -sqrt(size*size/2) <= RADIUS <= sqrt(size*size/2) \n\
- returns a tuple of (ANGLE_IN_DEGREES, RADIUS)" )
/*!
requires
- rectangle(0,0,size-1,size-1).contains(p) == true
(i.e. p must be a point inside the Hough accumulator array)
ensures
- Converts a point in the Hough transform space into an angle, in degrees,
and a radius, measured in pixels from the center of the input image.
- let ANGLE_IN_DEGREES == the angle of the line corresponding to the Hough
transform point p. Moreover: -90 <= ANGLE_IN_DEGREES < 90.
- RADIUS == the distance from the center of the input image, measured in
pixels, and the line corresponding to the Hough transform point p.
Moreover: -sqrt(size*size/2) <= RADIUS <= sqrt(size*size/2)
- returns a tuple of (ANGLE_IN_DEGREES, RADIUS)
!*/
.def("get_best_hough_point", &ht_get_best_hough_point, py::arg("p"), py::arg("himg"),
"requires \n\
- himg has size rows and columns. \n\
- rectangle(0,0,size-1,size-1).contains(p) == true \n\
ensures \n\
- This function interprets himg as a Hough image and p as a point in the \n\
original image space. Given this, it finds the maximum scoring line that \n\
passes though p. That is, it checks all the Hough accumulator bins in \n\
himg corresponding to lines though p and returns the location with the \n\
largest score. \n\
- returns a point X such that get_rect(himg).contains(X) == true")
/*!
requires
- himg has size rows and columns.
- rectangle(0,0,size-1,size-1).contains(p) == true
ensures
- This function interprets himg as a Hough image and p as a point in the
original image space. Given this, it finds the maximum scoring line that
passes though p. That is, it checks all the Hough accumulator bins in
himg corresponding to lines though p and returns the location with the
largest score.
- returns a point X such that get_rect(himg).contains(X) == true
!*/
.def("__call__", &compute_ht<uint8_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<uint16_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<uint32_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<uint64_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<int8_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<int16_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<int32_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<int64_t>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<float>, py::arg("img"), py::arg("box"))
.def("__call__", &compute_ht<double>, py::arg("img"), py::arg("box"),
"requires \n\
- box.width() == size \n\
- box.height() == size \n\
ensures \n\
- Computes the Hough transform of the part of img contained within box. \n\
In particular, we do a grayscale version of the Hough transform where any \n\
non-zero pixel in img is treated as a potential component of a line and \n\
accumulated into the returned Hough accumulator image. However, rather than \n\
adding 1 to each relevant accumulator bin we add the value of the pixel \n\
in img to each Hough accumulator bin. This means that, if all the \n\
pixels in img are 0 or 1 then this routine performs a normal Hough \n\
transform. However, if some pixels have larger values then they will be \n\
weighted correspondingly more in the resulting Hough transform. \n\
- The returned hough transform image will be size rows by size columns. \n\
- The returned image is the Hough transform of the part of img contained in \n\
box. Each point in the Hough image corresponds to a line in the input box. \n\
In particular, the line for hough_image[y][x] is given by get_line(point(x,y)). \n\
Also, when viewing the Hough image, the x-axis gives the angle of the line \n\
and the y-axis the distance of the line from the center of the box. The \n\
conversion between Hough coordinates and angle and pixel distance can be \n\
obtained by calling get_line_properties()." )
/*!
requires
- box.width() == size
- box.height() == size
ensures
- Computes the Hough transform of the part of img contained within box.
In particular, we do a grayscale version of the Hough transform where any
non-zero pixel in img is treated as a potential component of a line and
accumulated into the returned Hough accumulator image. However, rather than
adding 1 to each relevant accumulator bin we add the value of the pixel
in img to each Hough accumulator bin. This means that, if all the
pixels in img are 0 or 1 then this routine performs a normal Hough
transform. However, if some pixels have larger values then they will be
weighted correspondingly more in the resulting Hough transform.
- The returned hough transform image will be size rows by size columns.
- The returned image is the Hough transform of the part of img contained in
box. Each point in the Hough image corresponds to a line in the input box.
In particular, the line for hough_image[y][x] is given by get_line(point(x,y)).
Also, when viewing the Hough image, the x-axis gives the angle of the line
and the y-axis the distance of the line from the center of the box. The
conversion between Hough coordinates and angle and pixel distance can be
obtained by calling get_line_properties().
!*/
.def("__call__", &compute_ht2<uint8_t>, py::arg("img"))
.def("__call__", &compute_ht2<uint16_t>, py::arg("img"))
.def("__call__", &compute_ht2<uint32_t>, py::arg("img"))
.def("__call__", &compute_ht2<uint64_t>, py::arg("img"))
.def("__call__", &compute_ht2<int8_t>, py::arg("img"))
.def("__call__", &compute_ht2<int16_t>, py::arg("img"))
.def("__call__", &compute_ht2<int32_t>, py::arg("img"))
.def("__call__", &compute_ht2<int64_t>, py::arg("img"))
.def("__call__", &compute_ht2<float>, py::arg("img"))
.def("__call__", &compute_ht2<double>, py::arg("img"),
" simply performs: return self(img, get_rect(img)). That is, just runs the hough transform on the whole input image.")
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<uint8_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<uint16_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<uint32_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<uint64_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<int8_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<int16_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<int32_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<int64_t>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<float>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines<double>, py::arg("img"), py::arg("box"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1,
"requires \n\
- box.width() == size \n\
- box.height() == size \n\
- for all valid i: \n\
- rectangle(0,0,size-1,size-1).contains(hough_points[i]) == true \n\
(i.e. hough_points must contain points in the output Hough transform \n\
space generated by this object.) \n\
- angle_window_size >= 1 \n\
- radius_window_size >= 1 \n\
ensures \n\
- This function computes the Hough transform of the part of img contained \n\
within box. It does the same computation as __call__() defined above, \n\
except instead of accumulating into an image we create an explicit list \n\
of all the points in img that contributed to each line (i.e each point in \n\
the Hough image). To do this we take a list of Hough points as input and \n\
only record hits on these specifically identified Hough points. A \n\
typical use of find_pixels_voting_for_lines() is to first run the normal \n\
Hough transform using __call__(), then find the lines you are interested \n\
in, and then call find_pixels_voting_for_lines() to determine which \n\
pixels in the input image belong to those lines. \n\
- This routine returns a vector, CONSTITUENT_POINTS, with the following \n\
properties: \n\
- CONSTITUENT_POINTS.size == hough_points.size \n\
- for all valid i: \n\
- Let HP[i] = centered_rect(hough_points[i], angle_window_size, radius_window_size) \n\
- Any point in img with a non-zero value that lies on a line \n\
corresponding to one of the Hough points in HP[i] is added to \n\
CONSTITUENT_POINTS[i]. Therefore, when this routine finishes, \n\
#CONSTITUENT_POINTS[i] will contain all the points in img that \n\
voted for the lines associated with the Hough accumulator bins in \n\
HP[i]. \n\
- #CONSTITUENT_POINTS[i].size == the number of points in img that \n\
voted for any of the lines HP[i] in Hough space. Note, however, \n\
that if angle_window_size or radius_window_size are made so large \n\
that HP[i] overlaps HP[j] for i!=j then the overlapping regions \n\
of Hough space are assigned to HP[i] or HP[j] arbitrarily. \n\
That is, we treat HP[i] and HP[j] as disjoint even if their boxes \n\
overlap. In this case, the overlapping region is assigned to \n\
either HP[i] or HP[j] in an arbitrary manner." )
/*!
requires
- box.width() == size
- box.height() == size
- for all valid i:
- rectangle(0,0,size-1,size-1).contains(hough_points[i]) == true
(i.e. hough_points must contain points in the output Hough transform
space generated by this object.)
- angle_window_size >= 1
- radius_window_size >= 1
ensures
- This function computes the Hough transform of the part of img contained
within box. It does the same computation as __call__() defined above,
except instead of accumulating into an image we create an explicit list
of all the points in img that contributed to each line (i.e each point in
the Hough image). To do this we take a list of Hough points as input and
only record hits on these specifically identified Hough points. A
typical use of find_pixels_voting_for_lines() is to first run the normal
Hough transform using __call__(), then find the lines you are interested
in, and then call find_pixels_voting_for_lines() to determine which
pixels in the input image belong to those lines.
- This routine returns a vector, CONSTITUENT_POINTS, with the following
properties:
- CONSTITUENT_POINTS.size == hough_points.size
- for all valid i:
- Let HP[i] = centered_rect(hough_points[i], angle_window_size, radius_window_size)
- Any point in img with a non-zero value that lies on a line
corresponding to one of the Hough points in HP[i] is added to
CONSTITUENT_POINTS[i]. Therefore, when this routine finishes,
#CONSTITUENT_POINTS[i] will contain all the points in img that
voted for the lines associated with the Hough accumulator bins in
HP[i].
- #CONSTITUENT_POINTS[i].size == the number of points in img that
voted for any of the lines HP[i] in Hough space. Note, however,
that if angle_window_size or radius_window_size are made so large
that HP[i] overlaps HP[j] for i!=j then the overlapping regions
of Hough space are assigned to HP[i] or HP[j] arbitrarily.
That is, we treat HP[i] and HP[j] as disjoint even if their boxes
overlap. In this case, the overlapping region is assigned to
either HP[i] or HP[j] in an arbitrary manner.
!*/
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<uint8_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<uint16_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<uint32_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<uint64_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<int8_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<int16_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<int32_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<int64_t>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<float>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1)
.def("find_pixels_voting_for_lines", &ht_find_pixels_voting_for_lines2<double>, py::arg("img"), py::arg("hough_points"), py::arg("angle_window_size")=1, py::arg("radius_window_size")=1,
" performs: return find_pixels_voting_for_lines(img, get_rect(img), hough_points, angle_window_size, radius_window_size); \n\
That is, just runs the routine on the whole input image." )
.def("find_strong_hough_points", &ht_find_strong_hough_points, py::arg("himg"), py::arg("hough_count_thresh"), py::arg("angle_nms_thresh"), py::arg("radius_nms_thresh"),
"requires \n\
- himg has size() rows and columns. \n\
- angle_nms_thresh >= 0 \n\
- radius_nms_thresh >= 0 \n\
ensures \n\
- This routine finds strong lines in a Hough transform and performs \n\
non-maximum suppression on the detected lines. Recall that each point in \n\
Hough space is associated with a line. Therefore, this routine finds all \n\
the pixels in himg (a Hough transform image) with values >= \n\
hough_count_thresh and performs non-maximum suppression on the \n\
identified list of pixels. It does this by discarding lines that are \n\
within angle_nms_thresh degrees of a stronger line or within \n\
radius_nms_thresh distance (in terms of radius as defined by \n\
get_line_properties()) to a stronger Hough point. \n\
- The identified lines are returned as a list of coordinates in himg." );
/*!
requires
- himg has size() rows and columns.
- angle_nms_thresh >= 0
- radius_nms_thresh >= 0
ensures
- This routine finds strong lines in a Hough transform and performs
non-maximum suppression on the detected lines. Recall that each point in
Hough space is associated with a line. Therefore, this routine finds all
the pixels in himg (a Hough transform image) with values >=
hough_count_thresh and performs non-maximum suppression on the
identified list of pixels. It does this by discarding lines that are
within angle_nms_thresh degrees of a stronger line or within
radius_nms_thresh distance (in terms of radius as defined by
get_line_properties()) to a stronger Hough point.
- The identified lines are returned as a list of coordinates in himg.
!*/
m.def("get_rect", [](const hough_transform& ht){ return get_rect(ht); },
"returns a rectangle(0,0,ht.size()-1,ht.size()-1). Therefore, it is the rectangle that bounds the Hough transform image.",
py::arg("ht") );
}
// ----------------------------------------------------------------------------------------
std::vector<point> py_remove_incoherent_edge_pixels (
const std::vector<point>& line,
const numpy_image<float>& horz_gradient,
const numpy_image<float>& vert_gradient,
double angle_threshold
)
{
DLIB_CASSERT(num_rows(horz_gradient) == num_rows(vert_gradient));
DLIB_CASSERT(num_columns(horz_gradient) == num_columns(vert_gradient));
DLIB_CASSERT(angle_threshold >= 0);
for (auto& p : line)
DLIB_CASSERT(get_rect(horz_gradient).contains(p), "All line points must be inside the given images.");
return remove_incoherent_edge_pixels(line, horz_gradient, vert_gradient, angle_threshold);
}
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_transform_image (
const numpy_image<T>& img,
const point_transform_projective& map_point,
long rows,
long columns
)
{
DLIB_CASSERT(rows > 0 && columns > 0, "The requested output image dimensions are invalid.");
numpy_image<T> out(rows, columns);
transform_image(img, out, interpolate_bilinear(), map_point);
return out;
}
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_extract_image_4points (
const numpy_image<T>& img,
const py::list& corners,
long rows,
long columns
)
{
DLIB_CASSERT(rows >= 0);
DLIB_CASSERT(columns >= 0);
DLIB_CASSERT(len(corners) == 4);
numpy_image<T> out;
set_image_size(out, rows, columns);
try
{
extract_image_4points(img, out, python_list_to_array<dpoint,4>(corners));
return out;
}
catch (py::cast_error&){}
try
{
extract_image_4points(img, out, python_list_to_array<line,4>(corners));
return out;
}
catch(py::cast_error&)
{
throw dlib::error("extract_image_4points() requires the corners argument to be a list of 4 dpoints or 4 lines.");
}
}
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_mbd (
const numpy_image<T>& img,
size_t iterations,
bool do_left_right_scans
)
{
numpy_image<T> out;
min_barrier_distance(img, out, iterations, do_left_right_scans);
return out;
}
numpy_image<unsigned char> py_mbd2 (
const numpy_image<rgb_pixel>& img,
size_t iterations,
bool do_left_right_scans
)
{
numpy_image<unsigned char> out;
min_barrier_distance(img, out, iterations, do_left_right_scans);
return out;
}
// ----------------------------------------------------------------------------------------
template <typename T>
numpy_image<T> py_extract_image_chip (
const numpy_image<T>& img,
const chip_details& chip_location
)
{
numpy_image<T> out;
extract_image_chip(img, chip_location, out);
return out;
}
template <typename T>
py::list py_extract_image_chips (
const numpy_image<T>& img,
const py::list& chip_locations
)
{
dlib::array<numpy_image<T>> out;
extract_image_chips(img, python_list_to_vector<chip_details>(chip_locations), out);
py::list ret;
for (auto& i : out)
ret.append(i);
return ret;
}
// ----------------------------------------------------------------------------------------
void register_extract_image_chip (py::module& m)
{
const char* class_docs =
"WHAT THIS OBJECT REPRESENTS \n\
This is a simple tool for passing in a pair of row and column values to the \n\
chip_details constructor.";
auto print_chip_dims_str = [](const chip_dims& d)
{
std::ostringstream sout;
sout << "rows="<< d.rows << ", cols=" << d.cols;
return sout.str();
};
auto print_chip_dims_repr = [](const chip_dims& d)
{
std::ostringstream sout;
sout << "chip_dims(rows="<< d.rows << ", cols=" << d.cols << ")";
return sout.str();
};
py::class_<chip_dims>(m, "chip_dims", class_docs)
.def(py::init<unsigned long, unsigned long>(), py::arg("rows"), py::arg("cols"))
.def("__str__", print_chip_dims_str)
.def("__repr__", print_chip_dims_repr)
.def_readwrite("rows", &chip_dims::rows)
.def_readwrite("cols", &chip_dims::cols);
auto print_chip_details_str = [](const chip_details& d)
{
std::ostringstream sout;
sout << "rect=" << d.rect << ", angle="<< d.angle << ", rows="<< d.rows << ", cols=" << d.cols;
return sout.str();
};
auto print_chip_details_repr = [](const chip_details& d)
{
std::ostringstream sout;
sout << "chip_details(rect=drectangle("
<< d.rect.left()<<","<<d.rect.top()<<","<<d.rect.right()<<","<<d.rect.bottom()
<<"), angle="<< d.angle << ", dims=chip_dims(rows="<< d.rows << ", cols=" << d.cols << "))";
return sout.str();
};
class_docs =
"WHAT THIS OBJECT REPRESENTS \n\
This object describes where an image chip is to be extracted from within \n\
another image. In particular, it specifies that the image chip is \n\
contained within the rectangle self.rect and that prior to extraction the \n\
image should be rotated counter-clockwise by self.angle radians. Finally, \n\
the extracted chip should have self.rows rows and self.cols columns in it \n\
regardless of the shape of self.rect. This means that the extracted chip \n\
will be stretched to fit via bilinear interpolation when necessary." ;
/*!
WHAT THIS OBJECT REPRESENTS
This object describes where an image chip is to be extracted from within
another image. In particular, it specifies that the image chip is
contained within the rectangle self.rect and that prior to extraction the
image should be rotated counter-clockwise by self.angle radians. Finally,
the extracted chip should have self.rows rows and self.cols columns in it
regardless of the shape of self.rect. This means that the extracted chip
will be stretched to fit via bilinear interpolation when necessary.
!*/
py::class_<chip_details>(m, "chip_details", class_docs)
.def(py::init<drectangle>(), py::arg("rect"))
.def(py::init<rectangle>(), py::arg("rect"),
"ensures \n\
- self.rect == rect_ \n\
- self.angle == 0 \n\
- self.rows == rect.height() \n\
- self.cols == rect.width()"
/*!
ensures
- self.rect == rect_
- self.angle == 0
- self.rows == rect.height()
- self.cols == rect.width()
!*/
)
.def(py::init<drectangle,unsigned long>(), py::arg("rect"), py::arg("size"))
.def(py::init<rectangle,unsigned long>(), py::arg("rect"), py::arg("size"),
"ensures \n\
- self.rect == rect \n\
- self.angle == 0 \n\
- self.rows and self.cols is set such that the total size of the chip is as close \n\
to size as possible but still matches the aspect ratio of rect. \n\
- As long as size and the aspect ratio of of rect stays constant then \n\
self.rows and self.cols will always have the same values. This means \n\
that, for example, if you want all your chips to have the same dimensions \n\
then ensure that size is always the same and also that rect always has \n\
the same aspect ratio. Otherwise the calculated values of self.rows and \n\
self.cols may be different for different chips. Alternatively, you can \n\
use the chip_details constructor below that lets you specify the exact \n\
values for rows and cols."
/*!
ensures
- self.rect == rect
- self.angle == 0
- self.rows and self.cols is set such that the total size of the chip is as close
to size as possible but still matches the aspect ratio of rect.
- As long as size and the aspect ratio of of rect stays constant then
self.rows and self.cols will always have the same values. This means
that, for example, if you want all your chips to have the same dimensions
then ensure that size is always the same and also that rect always has
the same aspect ratio. Otherwise the calculated values of self.rows and
self.cols may be different for different chips. Alternatively, you can
use the chip_details constructor below that lets you specify the exact
values for rows and cols.
!*/
)
.def(py::init<drectangle,unsigned long,double>(), py::arg("rect"), py::arg("size"), py::arg("angle"))
.def(py::init<rectangle,unsigned long,double>(), py::arg("rect"), py::arg("size"), py::arg("angle"),
"ensures \n\
- self.rect == rect \n\
- self.angle == angle \n\
- self.rows and self.cols is set such that the total size of the chip is as \n\
close to size as possible but still matches the aspect ratio of rect. \n\
- As long as size and the aspect ratio of of rect stays constant then \n\
self.rows and self.cols will always have the same values. This means \n\
that, for example, if you want all your chips to have the same dimensions \n\
then ensure that size is always the same and also that rect always has \n\
the same aspect ratio. Otherwise the calculated values of self.rows and \n\
self.cols may be different for different chips. Alternatively, you can \n\
use the chip_details constructor below that lets you specify the exact \n\
values for rows and cols."
/*!
ensures
- self.rect == rect
- self.angle == angle
- self.rows and self.cols is set such that the total size of the chip is as
close to size as possible but still matches the aspect ratio of rect.
- As long as size and the aspect ratio of of rect stays constant then
self.rows and self.cols will always have the same values. This means
that, for example, if you want all your chips to have the same dimensions
then ensure that size is always the same and also that rect always has
the same aspect ratio. Otherwise the calculated values of self.rows and
self.cols may be different for different chips. Alternatively, you can
use the chip_details constructor below that lets you specify the exact
values for rows and cols.
!*/
)
.def(py::init<drectangle,chip_dims>(), py::arg("rect"), py::arg("dims"))
.def(py::init<rectangle,chip_dims>(), py::arg("rect"), py::arg("dims"),
"ensures \n\
- self.rect == rect \n\
- self.angle == 0 \n\
- self.rows == dims.rows \n\
- self.cols == dims.cols"
/*!
ensures
- self.rect == rect
- self.angle == 0
- self.rows == dims.rows
- self.cols == dims.cols
!*/
)
.def(py::init<drectangle,chip_dims,double>(), py::arg("rect"), py::arg("dims"), py::arg("angle"))
.def(py::init<rectangle,chip_dims,double>(), py::arg("rect"), py::arg("dims"), py::arg("angle"),
"ensures \n\
- self.rect == rect \n\
- self.angle == angle \n\
- self.rows == dims.rows \n\
- self.cols == dims.cols"
/*!
ensures
- self.rect == rect
- self.angle == angle
- self.rows == dims.rows
- self.cols == dims.cols
!*/
)
.def(py::init<std::vector<dpoint>,std::vector<dpoint>,chip_dims>(), py::arg("chip_points"), py::arg("img_points"), py::arg("dims"))
.def(py::init<std::vector<point>,std::vector<point>,chip_dims>(), py::arg("chip_points"), py::arg("img_points"), py::arg("dims"),
"requires \n\
- len(chip_points) == len(img_points) \n\
- len(chip_points) >= 2 \n\
ensures \n\
- The chip will be extracted such that the pixel locations chip_points[i] \n\
in the chip are mapped to img_points[i] in the original image by a \n\
similarity transform. That is, if you know the pixelwize mapping you \n\
want between the chip and the original image then you use this function \n\
of chip_details constructor to define the mapping. \n\
- self.rows == dims.rows \n\
- self.cols == dims.cols \n\
- self.rect and self.angle are computed based on the given size of the output chip \n\
(specified by dims) and the similarity transform between the chip and \n\
image (specified by chip_points and img_points)."
/*!
requires
- len(chip_points) == len(img_points)
- len(chip_points) >= 2
ensures
- The chip will be extracted such that the pixel locations chip_points[i]
in the chip are mapped to img_points[i] in the original image by a
similarity transform. That is, if you know the pixelwize mapping you
want between the chip and the original image then you use this function
of chip_details constructor to define the mapping.
- self.rows == dims.rows
- self.cols == dims.cols
- self.rect and self.angle are computed based on the given size of the output chip
(specified by dims) and the similarity transform between the chip and
image (specified by chip_points and img_points).
!*/
)
.def("__str__", print_chip_details_str)
.def("__repr__", print_chip_details_repr)
.def_readwrite("rect", &chip_details::rect)
.def_readwrite("angle", &chip_details::angle)
.def_readwrite("rows", &chip_details::rows)
.def_readwrite("cols", &chip_details::cols);
m.def("extract_image_chip", &py_extract_image_chip<uint8_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<uint16_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<uint32_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<uint64_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<int8_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<int16_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<int32_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<int64_t>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<float>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<double>, py::arg("img"), py::arg("chip_location"));
m.def("extract_image_chip", &py_extract_image_chip<rgb_pixel>, py::arg("img"), py::arg("chip_location"),
" This routine is just like extract_image_chips() except it takes a single \n"
" chip_details object and returns a single chip image rather than a list of images."
);
m.def("extract_image_chips", &py_extract_image_chips<uint8_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<uint16_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<uint32_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<uint64_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<int8_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<int16_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<int32_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<int64_t>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<float>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<double>, py::arg("img"), py::arg("chip_locations"));
m.def("extract_image_chips", &py_extract_image_chips<rgb_pixel>, py::arg("img"), py::arg("chip_locations"),
"requires \n\
- for all valid i: \n\
- chip_locations[i].rect.is_empty() == false \n\
- chip_locations[i].rows*chip_locations[i].cols != 0 \n\
ensures \n\
- This function extracts \"chips\" from an image. That is, it takes a list of \n\
rectangular sub-windows (i.e. chips) within an image and extracts those \n\
sub-windows, storing each into its own image. It also scales and rotates the \n\
image chips according to the instructions inside each chip_details object. \n\
It uses bilinear interpolation. \n\
- The extracted image chips are returned in a python list of numpy arrays. The \n\
length of the returned array is len(chip_locations). \n\
- Let CHIPS be the returned array, then we have: \n\
- for all valid i: \n\
- #CHIPS[i] == The image chip extracted from the position \n\
chip_locations[i].rect in img. \n\
- #CHIPS[i].shape(0) == chip_locations[i].rows \n\
- #CHIPS[i].shape(1) == chip_locations[i].cols \n\
- The image will have been rotated counter-clockwise by \n\
chip_locations[i].angle radians, around the center of \n\
chip_locations[i].rect, before the chip was extracted. \n\
- Any pixels in an image chip that go outside img are set to 0 (i.e. black)."
/*!
requires
- for all valid i:
- chip_locations[i].rect.is_empty() == false
- chip_locations[i].rows*chip_locations[i].cols != 0
ensures
- This function extracts "chips" from an image. That is, it takes a list of
rectangular sub-windows (i.e. chips) within an image and extracts those
sub-windows, storing each into its own image. It also scales and rotates the
image chips according to the instructions inside each chip_details object.
It uses bilinear interpolation.
- The extracted image chips are returned in a python list of numpy arrays. The
length of the returned array is len(chip_locations).
- Let CHIPS be the returned array, then we have:
- for all valid i:
- #CHIPS[i] == The image chip extracted from the position
chip_locations[i].rect in img.
- #CHIPS[i].shape(0) == chip_locations[i].rows
- #CHIPS[i].shape(1) == chip_locations[i].cols
- The image will have been rotated counter-clockwise by
chip_locations[i].angle radians, around the center of
chip_locations[i].rect, before the chip was extracted.
- Any pixels in an image chip that go outside img are set to 0 (i.e. black).
!*/
);
}
// ----------------------------------------------------------------------------------------
py::array py_tile_images (
const py::list& images
)
{
DLIB_CASSERT(len(images) > 0);
if (is_image<rgb_pixel>(images[0].cast<py::array>()))
{
std::vector<numpy_image<rgb_pixel>> tmp(len(images));
for (size_t i = 0; i < tmp.size(); ++i)
assign_image(tmp[i], images[i].cast<py::array>());
return numpy_image<rgb_pixel>(tile_images(tmp));
}
else
{
std::vector<numpy_image<unsigned char>> tmp(len(images));
for (size_t i = 0; i < tmp.size(); ++i)
assign_image(tmp[i], images[i].cast<py::array>());
return numpy_image<unsigned char>(tile_images(tmp));
}
}
// ----------------------------------------------------------------------------------------
template <typename T>
py::array_t<unsigned long> py_get_histogram (
const numpy_image<T>& img,
size_t hist_size
)
{
matrix<unsigned long,1> hist;
get_histogram(img,hist,hist_size);
return numpy_image<unsigned long>(std::move(hist)).squeeze();
}
// ----------------------------------------------------------------------------------------
py::array py_sub_image (
const py::array& img,
const rectangle& win
)
{
DLIB_CASSERT(img.ndim() >= 2);
auto width_step = img.strides(0);
const long nr = img.shape(0);
const long nc = img.shape(1);
rectangle rect(0,0,nc-1,nr-1);
rect = rect.intersect(win);
std::vector<size_t> shape(img.ndim()), strides(img.ndim());
for (size_t i = 0; i < shape.size(); ++i)
{
shape[i] = img.shape(i);
strides[i] = img.strides(i);
}
shape[0] = rect.height();
shape[1] = rect.width();
size_t itemsize = img.itemsize();
for (size_t i = 1; i < strides.size(); ++i)
itemsize *= strides[i];
const void* data = (char*)img.data() + itemsize*rect.left() + rect.top()*strides[0];
return py::array(img.dtype(), shape, strides, data, img);
}
py::array py_sub_image2 (
const py::tuple& image_and_rect_tuple
)
{
DLIB_CASSERT(len(image_and_rect_tuple) == 2);
return py_sub_image(image_and_rect_tuple[0].cast<py::array>(), image_and_rect_tuple[1].cast<rectangle>());
}
// ----------------------------------------------------------------------------------------
template <typename T>
dpoint py_max_point(const numpy_image<T>& img)
{
DLIB_CASSERT(img.size() != 0);
return max_point(mat(img));
}
template <typename T>
dpoint py_max_point_interpolated(const numpy_image<T>& img)
{
DLIB_CASSERT(img.size() != 0);
return max_point_interpolated(mat(img));
}
// ----------------------------------------------------------------------------------------
template <typename T>
void py_zero_border_pixels (
numpy_image<T>& img,
long x_border_size,
long y_border_size
)
{
zero_border_pixels(img, x_border_size, y_border_size);
}
template <typename T>
void py_zero_border_pixels2 (
numpy_image<T>& img,
const rectangle& inside
)
{
zero_border_pixels(img, inside);
}
// ----------------------------------------------------------------------------------------
void bind_image_classes2(py::module& m)
{
const char* docs = "Resizes img, using bilinear interpolation, to have the indicated number of rows and columns.";
m.def("resize_image", &py_resize_image<uint8_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<uint16_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<uint32_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<uint64_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<int8_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<int16_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<int32_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<int64_t>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<float>, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<double>, docs, py::arg("img"), py::arg("rows"), py::arg("cols"));
m.def("resize_image", &py_resize_image<rgb_pixel>, docs, py::arg("img"), py::arg("rows"), py::arg("cols"));
register_extract_image_chip(m);
m.def("sub_image", &py_sub_image, py::arg("img"), py::arg("rect"),
"Returns a new numpy array that references the sub window in img defined by rect. \n\
If rect is larger than img then rect is cropped so that it does not go outside img. \n\
Therefore, this routine is equivalent to performing: \n\
win = get_rect(img).intersect(rect) \n\
subimg = img[win.top():win.bottom()-1,win.left():win.right()-1]"
/*!
Returns a new numpy array that references the sub window in img defined by rect.
If rect is larger than img then rect is cropped so that it does not go outside img.
Therefore, this routine is equivalent to performing:
win = get_rect(img).intersect(rect)
subimg = img[win.top():win.bottom()-1,win.left():win.right()-1]
!*/
);
m.def("sub_image", &py_sub_image2, py::arg("image_and_rect_tuple"),
"Performs: return sub_image(image_and_rect_tuple[0], image_and_rect_tuple[1])");
m.def("get_histogram", &py_get_histogram<uint8_t>, py::arg("img"), py::arg("hist_size"));
m.def("get_histogram", &py_get_histogram<uint16_t>, py::arg("img"), py::arg("hist_size"));
m.def("get_histogram", &py_get_histogram<uint32_t>, py::arg("img"), py::arg("hist_size"));
m.def("get_histogram", &py_get_histogram<uint64_t>, py::arg("img"), py::arg("hist_size"),
"ensures \n\
- Returns a numpy array, HIST, that contains a histogram of the pixels in img. \n\
In particular, we will have: \n\
- len(HIST) == hist_size \n\
- for all valid i: \n\
- HIST[i] == the number of times a pixel with intensity i appears in img."
/*!
ensures
- Returns a numpy array, HIST, that contains a histogram of the pixels in img.
In particular, we will have:
- len(HIST) == hist_size
- for all valid i:
- HIST[i] == the number of times a pixel with intensity i appears in img.
!*/
);
m.def("tile_images", py_tile_images, py::arg("images"),
"requires \n\
- images is a list of numpy arrays that can be interpreted as images. They \n\
must all be the same type of image as well. \n\
ensures \n\
- This function takes the given images and tiles them into a single large \n\
square image and returns this new big tiled image. Therefore, it is a \n\
useful method to visualize many small images at once."
/*!
requires
- images is a list of numpy arrays that can be interpreted as images. They
must all be the same type of image as well.
ensures
- This function takes the given images and tiles them into a single large
square image and returns this new big tiled image. Therefore, it is a
useful method to visualize many small images at once.
!*/
);
docs = "Returns a histogram equalized version of img.";
m.def("equalize_histogram", &py_equalize_histogram<uint8_t>, py::arg("img"));
m.def("equalize_histogram", &py_equalize_histogram<uint16_t>, docs, py::arg("img"));
m.def("min_barrier_distance", &py_mbd<uint8_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<uint16_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<uint32_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<uint64_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<int8_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<int16_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<int32_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<int64_t>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<float>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd<double>, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true);
m.def("min_barrier_distance", &py_mbd2, py::arg("img"), py::arg("iterations")=10, py::arg("do_left_right_scans")=true,
"requires \n\
- iterations > 0 \n\
ensures \n\
- This function implements the salient object detection method described in the paper: \n\
\"Minimum barrier salient object detection at 80 fps\" by Zhang, Jianming, et al. \n\
In particular, we compute the minimum barrier distance between the borders of \n\
the image and all the other pixels. The resulting image is returned. Note that \n\
the paper talks about a bunch of other things you could do beyond computing \n\
the minimum barrier distance, but this function doesn't do any of that. It's \n\
just the vanilla MBD. \n\
- We will perform iterations iterations of MBD passes over the image. Larger \n\
values might give better results but run slower. \n\
- During each MBD iteration we make raster scans over the image. These pass \n\
from top->bottom, bottom->top, left->right, and right->left. If \n\
do_left_right_scans==false then the left/right passes are not executed. \n\
Skipping them makes the algorithm about 2x faster but might reduce the \n\
quality of the output."
/*!
requires
- iterations > 0
ensures
- This function implements the salient object detection method described in the paper:
"Minimum barrier salient object detection at 80 fps" by Zhang, Jianming, et al.
In particular, we compute the minimum barrier distance between the borders of
the image and all the other pixels. The resulting image is returned. Note that
the paper talks about a bunch of other things you could do beyond computing
the minimum barrier distance, but this function doesn't do any of that. It's
just the vanilla MBD.
- We will perform iterations iterations of MBD passes over the image. Larger
values might give better results but run slower.
- During each MBD iteration we make raster scans over the image. These pass
from top->bottom, bottom->top, left->right, and right->left. If
do_left_right_scans==false then the left/right passes are not executed.
Skipping them makes the algorithm about 2x faster but might reduce the
quality of the output.
!*/
);
register_hough_transform(m);
m.def("normalize_image_gradients", normalize_image_gradients<numpy_image<double>>, py::arg("img1"), py::arg("img2"));
m.def("normalize_image_gradients", normalize_image_gradients<numpy_image<float>>, py::arg("img1"), py::arg("img2"),
"requires \n\
- img1 and img2 have the same dimensions. \n\
ensures \n\
- This function assumes img1 and img2 are the two gradient images produced by a \n\
function like sobel_edge_detector(). It then unit normalizes the gradient \n\
vectors. That is, for all valid r and c, this function ensures that: \n\
- img1[r][c]*img1[r][c] + img2[r][c]*img2[r][c] == 1 \n\
unless both img1[r][c] and img2[r][c] were 0 initially, then they stay zero.");
/*!
requires
- img1 and img2 have the same dimensions.
ensures
- This function assumes img1 and img2 are the two gradient images produced by a
function like sobel_edge_detector(). It then unit normalizes the gradient
vectors. That is, for all valid r and c, this function ensures that:
- img1[r][c]*img1[r][c] + img2[r][c]*img2[r][c] == 1
unless both img1[r][c] and img2[r][c] were 0 initially, then they stay zero.
!*/
m.def("remove_incoherent_edge_pixels", &py_remove_incoherent_edge_pixels, py::arg("line"), py::arg("horz_gradient"),
py::arg("vert_gradient"), py::arg("angle_thresh"),
"requires \n\
- horz_gradient and vert_gradient have the same dimensions. \n\
- horz_gradient and vert_gradient represent unit normalized vectors. That is, \n\
you should have called normalize_image_gradients(horz_gradient,vert_gradient) \n\
or otherwise caused all the gradients to have unit norm. \n\
- for all valid i: \n\
get_rect(horz_gradient).contains(line[i]) \n\
ensures \n\
- This routine looks at all the points in the given line and discards the ones that \n\
have outlying gradient directions. To be specific, this routine returns a set \n\
of points PTS such that: \n\
- for all valid i,j: \n\
- The difference in angle between the gradients for PTS[i] and PTS[j] is \n\
less than angle_threshold degrees. \n\
- len(PTS) <= len(line) \n\
- PTS is just line with some elements removed." );
/*!
requires
- horz_gradient and vert_gradient have the same dimensions.
- horz_gradient and vert_gradient represent unit normalized vectors. That is,
you should have called normalize_image_gradients(horz_gradient,vert_gradient)
or otherwise caused all the gradients to have unit norm.
- for all valid i:
get_rect(horz_gradient).contains(line[i])
ensures
- This routine looks at all the points in the given line and discards the ones that
have outlying gradient directions. To be specific, this routine returns a set
of points PTS such that:
- for all valid i,j:
- The difference in angle between the gradients for PTS[i] and PTS[j] is
less than angle_threshold degrees.
- len(PTS) <= len(line)
- PTS is just line with some elements removed.
!*/
py::register_exception<no_convex_quadrilateral>(m, "no_convex_quadrilateral");
m.def("extract_image_4points", &py_extract_image_4points<uint8_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<uint16_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<uint32_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<uint64_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<int8_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<int16_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<int32_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<int64_t>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<float>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<double>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"));
m.def("extract_image_4points", &py_extract_image_4points<rgb_pixel>, py::arg("img"), py::arg("corners"), py::arg("rows"), py::arg("columns"),
"requires \n\
- corners is a list of dpoint or line objects. \n\
- len(corners) == 4 \n\
- rows >= 0 \n\
- columns >= 0 \n\
ensures \n\
- The returned image has the given number of rows and columns. \n\
- if (corners contains dpoints) then \n\
- The 4 points in corners define a convex quadrilateral and this function \n\
extracts that part of the input image img and returns it. Therefore, \n\
each corner of the quadrilateral is associated to a corner of the \n\
extracted image and bilinear interpolation and a projective mapping is \n\
used to transform the pixels in the quadrilateral into the output image. \n\
To determine which corners of the quadrilateral map to which corners of \n\
the returned image we fit the tightest possible rectangle to the \n\
quadrilateral and map its vertices to their nearest rectangle corners. \n\
These corners are then trivially mapped to the output image (i.e. upper \n\
left corner to upper left corner, upper right corner to upper right \n\
corner, etc.). \n\
- else \n\
- This routine finds the 4 intersecting points of the given lines which \n\
form a convex quadrilateral and uses them as described above to extract \n\
an image. i.e. It just then calls: extract_image_4points(img, \n\
intersections_between_lines, rows, columns). \n\
- If no convex quadrilateral can be made from the given lines then this \n\
routine throws no_convex_quadrilateral."
/*!
requires
- corners is a list of dpoint or line objects.
- len(corners) == 4
- rows >= 0
- columns >= 0
ensures
- The returned image has the given number of rows and columns.
- if (corners contains dpoints) then
- The 4 points in corners define a convex quadrilateral and this function
extracts that part of the input image img and returns it. Therefore,
each corner of the quadrilateral is associated to a corner of the
extracted image and bilinear interpolation and a projective mapping is
used to transform the pixels in the quadrilateral into the output image.
To determine which corners of the quadrilateral map to which corners of
the returned image we fit the tightest possible rectangle to the
quadrilateral and map its vertices to their nearest rectangle corners.
These corners are then trivially mapped to the output image (i.e. upper
left corner to upper left corner, upper right corner to upper right
corner, etc.).
- else
- This routine finds the 4 intersecting points of the given lines which
form a convex quadrilateral and uses them as described above to extract
an image. i.e. It just then calls: extract_image_4points(img,
intersections_between_lines, rows, columns).
- If no convex quadrilateral can be made from the given lines then this
routine throws no_convex_quadrilateral.
!*/
);
m.def("transform_image", &py_transform_image<uint8_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<uint16_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<uint32_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<uint64_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<int8_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<int16_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<int32_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<int64_t>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<float>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<double>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"));
m.def("transform_image", &py_transform_image<rgb_pixel>, py::arg("img"), py::arg("map_point"), py::arg("rows"), py::arg("columns"),
"requires \n\
- rows > 0 \n\
- columns > 0 \n\
ensures \n\
- Returns an image that is the given rows by columns in size and contains a \n\
transformed part of img. To do this, we interpret map_point as a mapping \n\
from pixels in the returned image to pixels in the input img. transform_image() \n\
uses this mapping and bilinear interpolation to fill the output image with an \n\
interpolated copy of img. \n\
- Any locations in the output image that map to pixels outside img are set to 0."
/*!
requires
- rows > 0
- columns > 0
ensures
- Returns an image that is the given rows by columns in size and contains a
transformed part of img. To do this, we interpret map_point as a mapping
from pixels in the returned image to pixels in the input img. transform_image()
uses this mapping and bilinear interpolation to fill the output image with an
interpolated copy of img.
- Any locations in the output image that map to pixels outside img are set to 0.
!*/
);
m.def("max_point", &py_max_point<uint8_t>, py::arg("img"));
m.def("max_point", &py_max_point<uint16_t>, py::arg("img"));
m.def("max_point", &py_max_point<uint32_t>, py::arg("img"));
m.def("max_point", &py_max_point<uint64_t>, py::arg("img"));
m.def("max_point", &py_max_point<int8_t>, py::arg("img"));
m.def("max_point", &py_max_point<int16_t>, py::arg("img"));
m.def("max_point", &py_max_point<int32_t>, py::arg("img"));
m.def("max_point", &py_max_point<int64_t>, py::arg("img"));
m.def("max_point", &py_max_point<float>, py::arg("img"));
m.def("max_point", &py_max_point<double>, py::arg("img"),
"requires \n\
- m.size > 0 \n\
ensures \n\
- returns the location of the maximum element of the array, that is, if the \n\
returned point is P then it will be the case that: img[P.y,P.x] == img.max()."
/*!
requires
- m.size > 0
ensures
- returns the location of the maximum element of the array, that is, if the
returned point is P then it will be the case that: img[P.y,P.x] == img.max().
!*/
);
m.def("max_point_interpolated", &py_max_point_interpolated<uint8_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<uint16_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<uint32_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<uint64_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<int8_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<int16_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<int32_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<int64_t>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<float>, py::arg("img"));
m.def("max_point_interpolated", &py_max_point_interpolated<double>, py::arg("img"),
"requires \n\
- m.size > 0 \n\
ensures \n\
- Like max_point(), this function finds the location in m with the largest \n\
value. However, we additionally use some quadratic interpolation to find the \n\
location of the maximum point with sub-pixel accuracy. Therefore, the \n\
returned point is equal to max_point(m) + some small sub-pixel delta."
/*!
requires
- m.size > 0
ensures
- Like max_point(), this function finds the location in m with the largest
value. However, we additionally use some quadratic interpolation to find the
location of the maximum point with sub-pixel accuracy. Therefore, the
returned point is equal to max_point(m) + some small sub-pixel delta.
!*/
);
m.def("zero_border_pixels", &py_zero_border_pixels<uint8_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<uint16_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<uint32_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<uint64_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<int8_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<int16_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<int32_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<int64_t>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<float>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<double>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"));
m.def("zero_border_pixels", &py_zero_border_pixels<rgb_pixel>, py::arg("img"), py::arg("x_border_size"), py::arg("y_border_size"),
"requires \n\
- x_border_size >= 0 \n\
- y_border_size >= 0 \n\
ensures \n\
- The size and shape of img isn't changed by this function. \n\
- for all valid r such that r+y_border_size or r-y_border_size gives an invalid row \n\
- for all valid c such that c+x_border_size or c-x_border_size gives an invalid column \n\
- assigns the pixel img[r][c] to 0. \n\
(i.e. assigns 0 to every pixel in the border of img)"
/*!
requires
- x_border_size >= 0
- y_border_size >= 0
ensures
- The size and shape of img isn't changed by this function.
- for all valid r such that r+y_border_size or r-y_border_size gives an invalid row
- for all valid c such that c+x_border_size or c-x_border_size gives an invalid column
- assigns the pixel img[r][c] to 0.
(i.e. assigns 0 to every pixel in the border of img)
!*/
);
m.def("zero_border_pixels", &py_zero_border_pixels2<uint8_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<uint16_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<uint32_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<uint64_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<int8_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<int16_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<int32_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<int64_t>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<float>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<double>, py::arg("img"), py::arg("inside"));
m.def("zero_border_pixels", &py_zero_border_pixels2<rgb_pixel>, py::arg("img"), py::arg("inside"),
"ensures \n\
- The size and shape of img isn't changed by this function. \n\
- All the pixels in img that are not contained inside the inside rectangle \n\
given to this function are set to 0. That is, anything not \"inside\" is on \n\
the border and set to 0."
/*!
ensures
- The size and shape of img isn't changed by this function.
- All the pixels in img that are not contained inside the inside rectangle
given to this function are set to 0. That is, anything not "inside" is on
the border and set to 0.
!*/
);
}