1 files changed, 79 insertions, 0 deletions
diff --git a/macros/peopleDetector.sci b/macros/peopleDetector.sci
new file mode 100644
index 0000000..1770ae3
--- /dev/null
+++ b/macros/peopleDetector.sci
@@ -0,0 +1,79 @@
+function bboxes = peopleDetector(image, varargin)
+// Detects people in an image
+//
+// Calling Sequence
+// [bboxes] = peopleDetector(image)
+// [bboxes] = peopleDetector(image, ["Parameter1", value1, ["Parameter2", value2 ... ]])
+//
+// Parameters
+// image : input image 
+// hitThreshold : threshold for distance between features and SVM classifying plane. Default value is 0.
+// winStride : Window stride. Multiple of block stride. Default value (8, 8)
+// padding : Default value (16, 16).
+// scale : Coefficient of the detection window increase. Default value 1.05
+// finalThreshold : Coefficient to regulate the similarity threshold. Some people may cover more boxex. To regulate that it is used. Default value 2.
+// useMeanshiftGrouping : Default value false.
+// bboxes : M * 4 matrix denoting M bounding boxes for detected people
+//
+// Description
+// The peopleDetector function detects standing people in the image using the Histogram of Oriented Gradient (HOG) features and a trained Support Vector Machine
+// (SVM) classifier. It detects standing peole in the image. It returns M * 4 matrix having M detected people in the image. Each row of matrix contains
+// [x, y, width, height] field. x, y represent the upper left corner point of the bounding box. width and height represent the width and height of the bounding box
+// respectively. 
+//
+// Examples
+// I = imread('standing.jpg')
+// [bboxes] = peopleDetector(I)
+// I = imread('standing.jpg')
+// [bboxes] = peopleDetector(I, "scale", 1.02)
+//
+// Authors
+// Suraj Prakash
+
+
+
+	[ lhs rhs ] = argn(0)
+	
+	if lhs > 1 then
+		error(msprintf("Too many output arguments"))
+	end
+	
+	if rhs > 13 then
+		error(msprintf("Too many input arguments"))
+	end
+	
+	if modulo(rhs, 2) <> 1 then
+		error(msprintf("Wrong number of input arguments"))
+	end
+	
+	image_list = mattolist(image)
+	
+	if rhs == 1 then
+		
+		bboxes = opencv_peopleDetector(image_list)
+		
+	elseif rhs == 3 then
+		
+		bboxes = opencv_peopleDetecotr(image_list, varargin(1), varargin(2))
+	
+	elseif rhs == 5 then
+		
+		bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4))
+	
+	elseif rhs == 7 then
+		
+		bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6))
+	
+	elseif rhs == 9 then
+		
+		bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varagin(7), varagin(8))
+		
+	elseif rhs == 11 then
+		
+		bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10))
+	
+	elseif rhs == 13 then
+		bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10), varargin(11), varargin(12))
+	end
+	
+endfunction