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11 // For Open Source Computer Vision Library
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41 //M*/
42
43 #include "precomp.hpp"
44
45 namespace cv
46 {
47
48 using std::vector;
49
~Feature2D()50 Feature2D::~Feature2D() {}
51
52 /*
53 * Detect keypoints in an image.
54 * image The image.
55 * keypoints The detected keypoints.
56 * mask Mask specifying where to look for keypoints (optional). Must be a char
57 * matrix with non-zero values in the region of interest.
58 */
detect(InputArray image,std::vector<KeyPoint> & keypoints,InputArray mask)59 void Feature2D::detect( InputArray image,
60 std::vector<KeyPoint>& keypoints,
61 InputArray mask )
62 {
63 if( image.empty() )
64 {
65 keypoints.clear();
66 return;
67 }
68 detectAndCompute(image, mask, keypoints, noArray(), false);
69 }
70
71
detect(InputArrayOfArrays _images,std::vector<std::vector<KeyPoint>> & keypoints,InputArrayOfArrays _masks)72 void Feature2D::detect( InputArrayOfArrays _images,
73 std::vector<std::vector<KeyPoint> >& keypoints,
74 InputArrayOfArrays _masks )
75 {
76 vector<Mat> images, masks;
77
78 _images.getMatVector(images);
79 size_t i, nimages = images.size();
80
81 if( !_masks.empty() )
82 {
83 _masks.getMatVector(masks);
84 CV_Assert(masks.size() == nimages);
85 }
86
87 keypoints.resize(nimages);
88
89 for( i = 0; i < nimages; i++ )
90 {
91 detect(images[i], keypoints[i], masks.empty() ? Mat() : masks[i] );
92 }
93 }
94
95 /*
96 * Compute the descriptors for a set of keypoints in an image.
97 * image The image.
98 * keypoints The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
99 * descriptors Copmputed descriptors. Row i is the descriptor for keypoint i.
100 */
compute(InputArray image,std::vector<KeyPoint> & keypoints,OutputArray descriptors)101 void Feature2D::compute( InputArray image,
102 std::vector<KeyPoint>& keypoints,
103 OutputArray descriptors )
104 {
105 if( image.empty() )
106 {
107 descriptors.release();
108 return;
109 }
110 detectAndCompute(image, noArray(), keypoints, descriptors, true);
111 }
112
compute(InputArrayOfArrays _images,std::vector<std::vector<KeyPoint>> & keypoints,OutputArrayOfArrays _descriptors)113 void Feature2D::compute( InputArrayOfArrays _images,
114 std::vector<std::vector<KeyPoint> >& keypoints,
115 OutputArrayOfArrays _descriptors )
116 {
117 if( !_descriptors.needed() )
118 return;
119
120 vector<Mat> images;
121
122 _images.getMatVector(images);
123 size_t i, nimages = images.size();
124
125 CV_Assert( keypoints.size() == nimages );
126 CV_Assert( _descriptors.kind() == _InputArray::STD_VECTOR_MAT );
127
128 vector<Mat>& descriptors = *(vector<Mat>*)_descriptors.getObj();
129 descriptors.resize(nimages);
130
131 for( i = 0; i < nimages; i++ )
132 {
133 compute(images[i], keypoints[i], descriptors[i]);
134 }
135 }
136
137
138 /* Detects keypoints and computes the descriptors */
detectAndCompute(InputArray,InputArray,std::vector<KeyPoint> &,OutputArray,bool)139 void Feature2D::detectAndCompute( InputArray, InputArray,
140 std::vector<KeyPoint>&,
141 OutputArray,
142 bool )
143 {
144 CV_Error(Error::StsNotImplemented, "");
145 }
146
descriptorSize() const147 int Feature2D::descriptorSize() const
148 {
149 return 0;
150 }
151
descriptorType() const152 int Feature2D::descriptorType() const
153 {
154 return CV_32F;
155 }
156
defaultNorm() const157 int Feature2D::defaultNorm() const
158 {
159 int tp = descriptorType();
160 return tp == CV_8U ? NORM_HAMMING : NORM_L2;
161 }
162
163 // Return true if detector object is empty
empty() const164 bool Feature2D::empty() const
165 {
166 return true;
167 }
168
169 }
170