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ximgproc: new EdgeBoxes
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amroamroamro committed Feb 26, 2018
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278 changes: 278 additions & 0 deletions opencv_contrib/+cv/EdgeBoxes.m
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classdef EdgeBoxes < handle
%EDGEBOXES Class implementing Edge Boxes algorithm
%
% Algorithm from [ZitnickECCV14edgeBoxes].
%
% ## References
% [ZitnickECCV14edgeBoxes]:
% > C. Lawrence Zitnick and Piotr Dollar. "Edge boxes: Locating object
% > proposals from edges". In ECCV, 2014.
% > [PDF](https://www.microsoft.com/en-us/research/wp-content/uploads/2014/09/ZitnickDollarECCV14edgeBoxes.pdf)
%
% See also: cv.EdgeBoxes.EdgeBoxes, cv.StructuredEdgeDetection
%

properties (SetAccess = private)
% Object ID
id
end

properties (Dependent)
% The step size of sliding window search.
Alpha
% The NMS threshold for object proposals.
Beta
% The adaptation rate for NMS threshold.
Eta
% The min score of boxes to detect.
MinScore
% The max number of boxes to detect.
MaxBoxes
% The edge min magnitude.
EdgeMinMag
% The edge merge threshold.
EdgeMergeThr
% The cluster min magnitude.
ClusterMinMag
% The max aspect ratio of boxes.
MaxAspectRatio
% The minimum area of boxes.
MinBoxArea
% The affinity sensitivity.
Gamma
% The scale sensitivity.
Kappa
end

%% EdgeBoxes
methods
function this = EdgeBoxes(varargin)
%EDGEBOXES Creates instance of Edgeboxes
%
% obj = cv.EdgeBoxes()
% obj = cv.EdgeBoxes('OptionName',optionValue, ...)
%
% ## Options
% * __Alpha__ step size of sliding window search. default 0.65
% * __Beta__ NMS threshold for object proposals. default 0.75
% * __Eta__ adaptation rate for NMS threshold. default 1
% * __MinScore__ min score of boxes to detect. default 0.01
% * __MaxBoxes__ max number of boxes to detect. default 10000
% * __EdgeMinMag__ edge min magnitude. Increase to trade off
% accuracy for speed. default 0.1
% * __EdgeMergeThr__ edge merge threshold. Increase to trade off
% accuracy for speed. default 0.5
% * __ClusterMinMag__ cluster min magnitude. Increase to trade off
% accuracy for speed. default 0.5
% * __MaxAspectRatio__ max aspect ratio of boxes. default 3
% * __MinBoxArea__ minimum area of boxes. default 1000
% * __Gamma__ affinity sensitivity. default 2
% * __Kappa__ scale sensitivity. default 1.5
%
% See also: cv.EdgeBoxes.getBoundingBoxes
%
this.id = EdgeBoxes_(0, 'new', varargin{:});
end

function delete(this)
%DELETE Destructor
%
% obj.delete()
%
% See also: cv.EdgeBoxes
%
if isempty(this.id), return; end
EdgeBoxes_(this.id, 'delete');
end

function boxes = getBoundingBoxes(this, edgeMap, orientationMap)
%GETBOUNDINGBOXES Returns array containing proposal boxes.
%
% boxes = obj.getBoundingBoxes(edgeMap, orientationMap)
%
% ## Input
% * __edgeMap__ edge image.
% * __orientationMap__ orientation map.
%
% ## Output
% * __boxes__ proposal boxes.
%
% See also: cv.EdgeBoxes.amFilter
%
boxes = EdgeBoxes_(this.id, 'getBoundingBoxes', edgeMap, orientationMap);
end
end

%% Algorithm
methods (Hidden)
function clear(this)
%CLEAR Clears the algorithm state
%
% obj.clear()
%
% See also: cv.EdgeBoxes.empty, cv.EdgeBoxes.load
%
EdgeBoxes_(this.id, 'clear');
end

function b = empty(this)
%EMPTY Checks if algorithm object is empty
%
% b = obj.empty()
%
% ## Output
% * __b__ Returns true if the algorithm object is empty
% (e.g. in the very beginning or after unsuccessful read).
%
% See also: cv.EdgeBoxes.clear, cv.EdgeBoxes.load
%
b = EdgeBoxes_(this.id, 'empty');
end

function save(this, filename)
%SAVE Saves the algorithm parameters to a file
%
% obj.save(filename)
%
% ## Input
% * __filename__ Name of the file to save to.
%
% This method stores the algorithm parameters in the specified
% XML or YAML file.
%
% See also: cv.EdgeBoxes.load
%
EdgeBoxes_(this.id, 'save', filename);
end

function load(this, fname_or_str, varargin)
%LOAD Loads algorithm from a file or a string
%
% obj.load(fname)
% obj.load(str, 'FromString',true)
% obj.load(..., 'OptionName',optionValue, ...)
%
% ## Input
% * __fname__ Name of the file to read.
% * __str__ String containing the serialized model you want to
% load.
%
% ## Options
% * __ObjName__ The optional name of the node to read (if empty,
% the first top-level node will be used). default empty
% * __FromString__ Logical flag to indicate whether the input is a
% filename or a string containing the serialized model.
% default false
%
% This method reads algorithm parameters from the specified XML or
% YAML file (either from disk or serialized string). The previous
% algorithm state is discarded.
%
% See also: cv.EdgeBoxes.save
%
EdgeBoxes_(this.id, 'load', fname_or_str, varargin{:});
end

function name = getDefaultName(this)
%GETDEFAULTNAME Returns the algorithm string identifier
%
% name = obj.getDefaultName()
%
% ## Output
% * __name__ This string is used as top level XML/YML node tag
% when the object is saved to a file or string.
%
% See also: cv.EdgeBoxes.save, cv.EdgeBoxes.load
%
name = EdgeBoxes_(this.id, 'getDefaultName');
end
end

%% Getters/Setters
methods
function value = get.Alpha(this)
value = EdgeBoxes_(this.id, 'get', 'Alpha');
end
function set.Alpha(this, value)
EdgeBoxes_(this.id, 'set', 'Alpha', value);
end

function value = get.Beta(this)
value = EdgeBoxes_(this.id, 'get', 'Beta');
end
function set.Beta(this, value)
EdgeBoxes_(this.id, 'set', 'Beta', value);
end

function value = get.Eta(this)
value = EdgeBoxes_(this.id, 'get', 'Eta');
end
function set.Eta(this, value)
EdgeBoxes_(this.id, 'set', 'Eta', value);
end

function value = get.MinScore(this)
value = EdgeBoxes_(this.id, 'get', 'MinScore');
end
function set.MinScore(this, value)
EdgeBoxes_(this.id, 'set', 'MinScore', value);
end

function value = get.MaxBoxes(this)
value = EdgeBoxes_(this.id, 'get', 'MaxBoxes');
end
function set.MaxBoxes(this, value)
EdgeBoxes_(this.id, 'set', 'MaxBoxes', value);
end

function value = get.EdgeMinMag(this)
value = EdgeBoxes_(this.id, 'get', 'EdgeMinMag');
end
function set.EdgeMinMag(this, value)
EdgeBoxes_(this.id, 'set', 'EdgeMinMag', value);
end

function value = get.EdgeMergeThr(this)
value = EdgeBoxes_(this.id, 'get', 'EdgeMergeThr');
end
function set.EdgeMergeThr(this, value)
EdgeBoxes_(this.id, 'set', 'EdgeMergeThr', value);
end

function value = get.ClusterMinMag(this)
value = EdgeBoxes_(this.id, 'get', 'ClusterMinMag');
end
function set.ClusterMinMag(this, value)
EdgeBoxes_(this.id, 'set', 'ClusterMinMag', value);
end

function value = get.MaxAspectRatio(this)
value = EdgeBoxes_(this.id, 'get', 'MaxAspectRatio');
end
function set.MaxAspectRatio(this, value)
EdgeBoxes_(this.id, 'set', 'MaxAspectRatio', value);
end

function value = get.MinBoxArea(this)
value = EdgeBoxes_(this.id, 'get', 'MinBoxArea');
end
function set.MinBoxArea(this, value)
EdgeBoxes_(this.id, 'set', 'MinBoxArea', value);
end

function value = get.Gamma(this)
value = EdgeBoxes_(this.id, 'get', 'Gamma');
end
function set.Gamma(this, value)
EdgeBoxes_(this.id, 'set', 'Gamma', value);
end

function value = get.Kappa(this)
value = EdgeBoxes_(this.id, 'get', 'Kappa');
end
function set.Kappa(this, value)
EdgeBoxes_(this.id, 'set', 'Kappa', value);
end
end

end
19 changes: 15 additions & 4 deletions opencv_contrib/samples/structured_edge_detection_demo.m
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%% Structured Edge Detection demo
% This sample demonstrates structured forests for fast edge detection.
% This sample demonstrates structured forests for fast edge detection, and
% edgeboxes.
%
% The structered edge demo requires you to provide a model.
% This demo downloads a model from the opencv_extra repository on Github.
%
% Sources:
%
% * <https://github.com/opencv/opencv_contrib/blob/3.3.0/modules/ximgproc/samples/structured_edge_detection.cpp>
% * <https://github.com/opencv/opencv_contrib/blob/3.4.0/modules/ximgproc/samples/structured_edge_detection.cpp>
% * <https://github.com/opencv/opencv_contrib/blob/3.4.0/modules/ximgproc/samples/edgeboxes_demo.cpp>
% * <https://github.com/opencv/opencv_contrib/blob/3.4.0/modules/ximgproc/samples/edgeboxes_demo.py>
%

%% Load image
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%%
% suppress edges
edge_nms = pDollar.edgesNms(edges, orientation_map);
edges_nms = pDollar.edgesNms(edges, orientation_map);

%%
% generate object bounding box proposals using edges
ebx = cv.EdgeBoxes('MaxBoxes',30);
boxes = ebx.getBoundingBoxes(edges_nms, orientation_map);
out = cv.rectangle(img, boxes, 'Color',[0 255 0], 'LineType','AA');

%% Display result
figure('Position',[200 200 800 600])
subplot(221), imshow(img), title('image')
subplot(222), imshow(e8u), title('edges')
subplot(223), imshow(edge_nms), title('edges NMS')
subplot(223), imshow(edges_nms), title('edges NMS')
subplot(224), imshow(out), title('object proposals')
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