updated docs

tools/python/src/image2.cpp

@@ -190,35 +190,35 @@ ensures \n\
     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.")
+            "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, py::arg("p"),
 "requires \n\
-    - rectangle(0,0,size()-1,size()-1).contains(p) == true \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).") 
+    - The returned points are inside rectangle(0,0,size-1,size-1).") 
     /*!
         requires
-            - rectangle(0,0,size()-1,size()-1).contains(p) == true
+            - 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).
+            - The returned points are inside rectangle(0,0,size-1,size-1).
     !*/
 
         .def("get_line_angle_in_degrees", &ht_get_line_angle_in_degrees, py::arg("p"),
 "requires \n\
-    - rectangle(0,0,size()-1,size()-1).contains(p) == true \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
+            - 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
@@ -228,7 +228,7 @@ ensures \n\
 
         .def("get_line_properties", &ht_get_line_properties, py::arg("p"),
 "requires \n\
-    - rectangle(0,0,size()-1,size()-1).contains(p) == true \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\
@@ -237,11 +237,11 @@ ensures \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\
+      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
+            - 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,
@@ -250,14 +250,14 @@ ensures \n\
               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)
+              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\
+    - 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\
@@ -267,8 +267,8 @@ ensures \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
+            - 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
@@ -289,8 +289,8 @@ ensures \n\
         .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\
+    - 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\
@@ -301,7 +301,7 @@ ensures \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 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\
@@ -311,8 +311,8 @@ ensures \n\
       obtained by calling get_line_properties()." )
     /*!
         requires
-            - box.width() == size()
-            - box.height() == size()
+            - 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
@@ -323,7 +323,7 @@ ensures \n\
               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 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)). 
@@ -356,10 +356,10 @@ ensures \n\
         .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\
+    - 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\
+        - 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\
@@ -377,7 +377,7 @@ ensures \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\
+        - 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\
@@ -386,7 +386,7 @@ ensures \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\
+            - #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\
@@ -396,10 +396,10 @@ ensures \n\
               CONSTITUENT_POINTS." )
     /*!
         requires
-            - box.width() == size()
-            - box.height() == size()
+            - box.width() == size
+            - box.height() == size
             - for all valid i:
-                - rectangle(0,0,size()-1,size()-1).contains(hough_points[i]) == true
+                - 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
@@ -417,7 +417,7 @@ ensures \n\
               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()
+                - 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
@@ -426,7 +426,7 @@ ensures \n\
                       #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
+                    - #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