Merge remote-tracking branch 'upstream/master'

modules/imgproc/doc/feature_detection.rst

@@ -496,6 +496,110 @@ And this is the output of the above program in case of the probabilistic Hough t
 
 .. image:: pics/houghp.png
 
+.. seealso::
+
+    :ocv:class:`LineSegmentDetector`
+
+
+
+LineSegmentDetector
+-------------------
+Line segment detector class, following the algorithm described at [Rafael12]_.
+
+.. ocv:class:: LineSegmentDetector : public Algorithm
+
+
+createLineSegmentDetectorPtr
+----------------------------
+Creates a smart pointer to a LineSegmentDetector object and initializes it.
+
+.. ocv:function:: Ptr<LineSegmentDetector> createLineSegmentDetectorPtr(int _refine = LSD_REFINE_STD, double _scale = 0.8, double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024)
+
+    :param _refine: The way found lines will be refined:
+
+        * **LSD_REFINE_NONE** - No refinement applied.
+
+        * **LSD_REFINE_STD**  - Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
+
+        * **LSD_REFINE_ADV**  - Advanced refinement. Number of false alarms is calculated, lines are refined through increase of precision, decrement in size, etc.
+
+    :param scale: The scale of the image that will be used to find the lines. Range (0..1].
+
+    :param sigma_scale: Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
+
+    :param quant: Bound to the quantization error on the gradient norm.
+
+    :param ang_th: Gradient angle tolerance in degrees.
+
+    :param log_eps: Detection threshold: -log10(NFA) > log_eps. Used only when advancent refinement is chosen.
+
+    :param density_th: Minimal density of aligned region points in the enclosing rectangle.
+
+    :param n_bins: Number of bins in pseudo-ordering of gradient modulus.
+
+The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
+
+
+LineSegmentDetector::detect
+---------------------------
+Finds lines in the input image. See the lsd_lines.cpp sample for possible usage.
+
+.. ocv:function:: void LineSegmentDetector::detect(const InputArray _image, OutputArray _lines, OutputArray width = noArray(), OutputArray prec = noArray(), OutputArray nfa = noArray())
+
+    :param _image A grayscale (CV_8UC1) input image.
+        If only a roi needs to be selected, use ::
+        lsd_ptr->detect(image(roi), lines, ...);
+        lines += Scalar(roi.x, roi.y, roi.x, roi.y);
+
+    :param lines: A vector of Vec4i elements specifying the beginning and ending point of a line. Where Vec4i is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.
+
+    :param width: Vector of widths of the regions, where the lines are found. E.g. Width of line.
+
+    :param prec: Vector of precisions with which the lines are found.
+
+    :param nfa: Vector containing number of false alarms in the line region, with precision of 10%. The bigger the value, logarithmically better the detection.
+
+        * -1 corresponds to 10 mean false alarms
+
+        * 0 corresponds to 1 mean false alarm
+
+        * 1 corresponds to 0.1 mean false alarms
+
+    This vector will be calculated only when the objects type is LSD_REFINE_ADV.
+
+This is the output of the default parameters of the algorithm on the above shown image.
+
+.. image:: pics/building_lsd.png
+
+.. note::
+
+   * An example using the LineSegmentDetector can be found at opencv_source_code/samples/cpp/lsd_lines.cpp
+
+LineSegmentDetector::drawSegments
+---------------------------------
+Draws the line segments on a given image.
+
+.. ocv:function:: void LineSegmentDetector::drawSegments(InputOutputArray _image, InputArray lines)
+
+    :param image: The image, where the liens will be drawn. Should be bigger or equal to the image, where the lines were found.
+
+    :param lines: A vector of the lines that needed to be drawn.
+
+
+LineSegmentDetector::compareSegments
+------------------------------------
+Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
+
+.. ocv:function:: int LineSegmentDetector::compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray())
+
+    :param size: The size of the image, where lines1 and lines2 were found.
+
+    :param lines1: The first group of lines that needs to be drawn. It is visualized in blue color.
+
+    :param lines2: The second group of lines. They visualized in red color.
+
+    :param image: Optional image, where the lines will be drawn. The image should be color in order for lines1 and lines2 to be drawn in the above mentioned colors.
+
 
 
 preCornerDetect
@@ -542,3 +646,5 @@ The corners can be found as local maximums of the functions, as shown below: ::
 .. [Shi94] J. Shi and C. Tomasi. *Good Features to Track*. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, June 1994.
 
 .. [Yuen90] Yuen, H. K. and Princen, J. and Illingworth, J. and Kittler, J., *Comparative study of Hough transform methods for circle finding*. Image Vision Comput. 8 1, pp 71–77 (1990)
+
+.. [Rafael12] Rafael Grompone von Gioi, Jérémie Jakubowicz, Jean-Michel Morel, and Gregory Randall, LSD: a Line Segment Detector, Image Processing On Line, vol. 2012. http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd

modules/imgproc/doc/filtering.rst

@@ -412,6 +412,28 @@ http://www.dai.ed.ac.uk/CVonline/LOCAL\_COPIES/MANDUCHI1/Bilateral\_Filtering.ht
 This filter does not work inplace.
 
 
+adaptiveBilateralFilter
+-----------------------
+Applies the adaptive bilateral filter to an image.
+
+.. ocv:function:: void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize, double sigmaSpace, Point anchor=Point(-1, -1), int borderType=BORDER_DEFAULT )
+
+.. ocv:pyfunction:: cv2.adaptiveBilateralFilter(src, ksize, sigmaSpace[, dst[, anchor[, borderType]]]) -> dst
+
+    :param src: Source 8-bit, 1-channel or 3-channel image.
+
+    :param dst: Destination image of the same size and type as  ``src`` .
+
+    :param ksize: filter kernel size.
+
+    :param sigmaSpace: Filter sigma in the coordinate space. It has similar meaning with ``sigmaSpace`` in ``bilateralFilter``.
+
+    :param anchor: anchor point; default value ``Point(-1,-1)`` means that the anchor is at the kernel center. Only default value is supported now.
+
+    :param borderType: border mode used to extrapolate pixels outside of the image.
+
+The function applies adaptive bilateral filtering to the input image. This filter is similar to ``bilateralFilter``, in that dissimilarity from and distance to the center pixel is punished. Instead of using ``sigmaColor``, we employ the variance of pixel values in the neighbourhood.
+
 
 
 blur