gusucode.com > vision工具箱matlab源码程序 > vision/+vision/en/PeopleDetector.m
classdef PeopleDetector< matlab.System
%PeopleDetector Detect upright people using HOG features
% DETECTOR = vision.PeopleDetector creates a System object that
% detects upright, unoccluded, people. The detector uses Histogram of
% Oriented Gradient (HOG) features and a trained Support Vector
% Machine (SVM) classifier.
%
% DETECTOR = vision.PeopleDetector(MODEL) creates a System object and
% sets the ClassificationModel to MODEL. The input MODEL can be
% either 'UprightPeople_128x64' or 'UprightPeople_96x48'.
%
% DETECTOR = vision.PeopleDetector(...,Name,Value) configures the
% System object properties, specified as one or more name-value pair
% arguments. Unspecified properties have default values.
%
% Step method syntax:
%
% BBOXES = step(DETECTOR,I) performs multi-scale object detection on
% the input image, I, and returns, BBOXES, an M-by-4 matrix defining
% M bounding boxes containing the detected people. Each row in BBOXES
% is a four-element vector, [x y width height], that specifies the
% upper left corner and size of a bounding box in pixels. When no
% people are detected, BBOXES is empty. I must be a grayscale or
% truecolor (RGB) image.
%
% [BBOXES, SCORES] = step(DETECTOR,I) returns the detection SCORES
% for each bounding box in an M-by-1 vector. The SCORES are positive
% values. Larger score values indicate a higher confidence in the
% detection.
%
% [...] = step(DETECTOR, I, ROI) detects people within the
% rectangular search region specified by ROI. ROI must be a 4-element
% vector, [x y width height], that defines a rectangular region of
% interest within image I. The 'UseROI' property must be true to use
% this syntax.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% PeopleDetector methods:
%
% step - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create people object detector object with same property
% values
% isLocked - Locked status (logical)
%
% PeopleDetector properties:
%
% ClassificationModel - Name of the classification model
% ClassificationThreshold - People classification threshold
% MinSize - Size of the smallest region containing a
% person
% MaxSize - Size of the biggest region containing a
% person
% ScaleFactor - Scaling for multi-scale object detection
% WindowStride - Detection window stride
% MergeDetections - Control whether similar detections are
% merged
% UseROI - Detect people within a region of interest
%
% % Example: Detect people
% % ----------------------------
%
% % Create people detector
% peopleDetector = vision.PeopleDetector;
%
% I = imread('visionteam1.jpg');
% [bboxes, scores] = step(peopleDetector, I); % Detect people
%
% % Annotate detected people
% I = insertObjectAnnotation(I, 'rectangle', bboxes, scores);
% figure, imshow(I)
% title('Detected people and detection scores');
%
% See also detectPeopleACF, vision.CascadeObjectDetector,
% insertObjectAnnotation, extractHOGFeatures
% Copyright 2012-2016 The MathWorks, Inc.
methods
function out=PeopleDetector
%PeopleDetector Detect upright people using HOG features
% DETECTOR = vision.PeopleDetector creates a System object that
% detects upright, unoccluded, people. The detector uses Histogram of
% Oriented Gradient (HOG) features and a trained Support Vector
% Machine (SVM) classifier.
%
% DETECTOR = vision.PeopleDetector(MODEL) creates a System object and
% sets the ClassificationModel to MODEL. The input MODEL can be
% either 'UprightPeople_128x64' or 'UprightPeople_96x48'.
%
% DETECTOR = vision.PeopleDetector(...,Name,Value) configures the
% System object properties, specified as one or more name-value pair
% arguments. Unspecified properties have default values.
%
% Step method syntax:
%
% BBOXES = step(DETECTOR,I) performs multi-scale object detection on
% the input image, I, and returns, BBOXES, an M-by-4 matrix defining
% M bounding boxes containing the detected people. Each row in BBOXES
% is a four-element vector, [x y width height], that specifies the
% upper left corner and size of a bounding box in pixels. When no
% people are detected, BBOXES is empty. I must be a grayscale or
% truecolor (RGB) image.
%
% [BBOXES, SCORES] = step(DETECTOR,I) returns the detection SCORES
% for each bounding box in an M-by-1 vector. The SCORES are positive
% values. Larger score values indicate a higher confidence in the
% detection.
%
% [...] = step(DETECTOR, I, ROI) detects people within the
% rectangular search region specified by ROI. ROI must be a 4-element
% vector, [x y width height], that defines a rectangular region of
% interest within image I. The 'UseROI' property must be true to use
% this syntax.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% PeopleDetector methods:
%
% step - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create people object detector object with same property
% values
% isLocked - Locked status (logical)
%
% PeopleDetector properties:
%
% ClassificationModel - Name of the classification model
% ClassificationThreshold - People classification threshold
% MinSize - Size of the smallest region containing a
% person
% MaxSize - Size of the biggest region containing a
% person
% ScaleFactor - Scaling for multi-scale object detection
% WindowStride - Detection window stride
% MergeDetections - Control whether similar detections are
% merged
% UseROI - Detect people within a region of interest
%
% % Example: Detect people
% % ----------------------------
%
% % Create people detector
% peopleDetector = vision.PeopleDetector;
%
% I = imread('visionteam1.jpg');
% [bboxes, scores] = step(peopleDetector, I); % Detect people
%
% % Annotate detected people
% I = insertObjectAnnotation(I, 'rectangle', bboxes, scores);
% figure, imshow(I)
% title('Detected people and detection scores');
%
% See also detectPeopleACF, vision.CascadeObjectDetector,
% insertObjectAnnotation, extractHOGFeatures
end
function getNumInputsImpl(in) %#ok<MANU>
end
function getNumOutputsImpl(in) %#ok<MANU>
end
function loadObjectImpl(in) %#ok<MANU>
end
function releaseImpl(in) %#ok<MANU>
end
function saveObjectImpl(in) %#ok<MANU>
end
function setupModel(in) %#ok<MANU>
% setup the HOG people detector
end
function stepImpl(in) %#ok<MANU>
end
function validateInputsImpl(in) %#ok<MANU>
end
function validatePropertiesImpl(in) %#ok<MANU>
% validate that MinSize is greater than or equal to the minimum
% object size used to train the classification model
end
end
methods (Abstract)
end
properties
%ClassificationModel Name of the classification model
% Specify the name of the model as a string. Valid values for
% this property are 'UprightPeople_128x64' and
% 'UprightPeople_96x48', where the image size used for training
% is 128-by-64 and 96-by-48 pixels, respectively. Note that the
% images used to train the models include background pixels
% around the actual person. Therefore, the actual size of a
% detected person is smaller than the training image size.
%
% Default: 'UprightPeople_128x64'
ClassificationModel;
%ClassificationThreshold People classification threshold
% Specify a threshold value as a non-negative scalar. Use this
% threshold to control the classification of individual image
% sub-regions as person or non-person during multi-scale object
% detection. Increase this threshold in situations where many
% false detections occur. This property is tunable. Typical
% values range from 0 to 4.
%
% Default: 1
ClassificationThreshold;
%MaxSize Size of the biggest region containing a person
% Specify the size of the biggest region containing a person, in
% pixels, as a two-element vector, [height width]. When you know
% the maximum person size to detect, you can reduce computation
% time by setting this property to a value smaller than the size
% of the input image. When you do not specify this property, the
% object sets it to the input image size. This property is
% tunable.
%
% Default: []
MaxSize;
%MergeDetections Control whether similar detections are merged
% Specify a logical scalar to control whether similar detections
% are merged using a mean shift based algorithm. Set this
% property to false to output unprocessed bounding boxes and
% detection scores. This is useful if you want to perform a
% custom merge operation.
%
% Default: true
MergeDetections;
%MinSize Size of the smallest region containing a person
% Specify the size of the smallest region containing a person, in
% pixels, as a two-element vector, [height width]. When you know
% the minimum person size to detect, you can reduce computation
% time by setting this property to a value larger than the image
% size used to train the classification model. When you do not
% specify this property, the object sets it to the image size
% used to train the classification model. This property is
% tunable.
%
% Default: []
MinSize;
%ScaleFactor Scaling for multi-scale object detection
% Specify the factor used to incrementally scale the detection
% resolution between MinSize and MaxSize. The ScaleFactor must be
% greater than or equal to 1.0001. At each increment, N, the
% detection resolution is
%
% round(TrainingSize*(ScaleFactor^N)).
%
% The TrainingSize is [128 64] for the 'UprightPeople_128x64'
% model and [96 48] for the 'UprightPeople_96x48' model.
% Decreasing the scale factor can increase the detection
% accuracy. However, doing so increases the computation time.
% This property is tunable.
%
% Default: 1.05
ScaleFactor;
% UseROI Detect objects within a ROI
% Set to true to detect objects within a rectangular region of
% interest within I.
%
% Default: false
UseROI;
%WindowStride Detection window stride
% Specify a scalar or a two-element vector [x y] in pixels for
% the detection window stride. The object uses the window stride
% to slide the detection window across the image. When you
% specify this value as a vector, the first and second elements
% are the stride size in the x and y directions. When you specify
% this value as a scalar, the stride is the same for both x and
% y. Decreasing the window stride can increase the detection
% accuracy. However, doing so increases computation time.
% Increasing the window stride beyond [8 8] can lead to a greater
% number of missed detections. This property is tunable.
%
% Default: [8 8]
WindowStride;
end
end