Settings.h
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1 /*
2 Copyright (c) 2011-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
3 All rights reserved.
4 
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27 
28 #ifndef SETTINGS_H_
29 #define SETTINGS_H_
30 
31 #include "find_object/FindObjectExp.h" // DLL export/import defines
32 #include "find_object/Version.h" // DLL export/import defines
33 
34 #include <QtCore/QMap>
35 #include <QtCore/QVariant>
36 #include <QtCore/QByteArray>
37 #include <opencv2/features2d/features2d.hpp>
38 
39 namespace find_object {
40 
41 class Feature2D;
42 
43 typedef QMap<QString, QVariant> ParametersMap; // Key, value
44 typedef QMap<QString, QString> ParametersType; // Key, type
45 typedef QMap<QString, QString> DescriptionsMap; // Key, description
46 
47 typedef unsigned int uint;
48 
49 // MACRO BEGIN
50 
51 #define PARAMETER_GETTER_bool(PREFIX, NAME) \
52  static bool get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toBool();}
53 #define PARAMETER_GETTER_int(PREFIX, NAME) \
54  static int get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toInt();}
55 #define PARAMETER_GETTER_uint(PREFIX, NAME) \
56  static uint get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toUInt();}
57 #define PARAMETER_GETTER_float(PREFIX, NAME) \
58  static float get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toFloat();}
59 #define PARAMETER_GETTER_double(PREFIX, NAME) \
60  static double get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toDouble();}
61 #define PARAMETER_GETTER_QString(PREFIX, NAME) \
62  static QString get##PREFIX##_##NAME() {return parameters_.value(#PREFIX "/" #NAME).toString();}
63 
64 #define PARAMETER(PREFIX, NAME, TYPE, DEFAULT_VALUE, DESCRIPTION) \
65  public: \
66  static QString k##PREFIX##_##NAME() {return QString(#PREFIX "/" #NAME);} \
67  static TYPE default##PREFIX##_##NAME() {return DEFAULT_VALUE;} \
68  static QString type##PREFIX##_##NAME() {return QString(#TYPE);} \
69  static QString description##PREFIX##_##NAME() {return QString(DESCRIPTION);} \
70  PARAMETER_GETTER_##TYPE(PREFIX, NAME) \
71  static void set##PREFIX##_##NAME(const TYPE & value) {parameters_[#PREFIX "/" #NAME] = value;} \
72  private: \
73  class Dummy##PREFIX##_##NAME { \
74  public: \
75  Dummy##PREFIX##_##NAME() { \
76  defaultParameters_.insert(#PREFIX "/" #NAME, QVariant(DEFAULT_VALUE)); \
77  parameters_.insert(#PREFIX "/" #NAME, DEFAULT_VALUE); \
78  parametersType_.insert(#PREFIX "/" #NAME, #TYPE); \
79  descriptions_.insert(#PREFIX "/" #NAME, DESCRIPTION);} \
80  }; \
81  Dummy##PREFIX##_##NAME dummy##PREFIX##_##NAME;
82 
83 #define PARAMETER_COND(PREFIX, NAME, TYPE, COND, DEFAULT_VALUE1, DEFAULT_VALUE2, DESCRIPTION) \
84  public: \
85  static QString k##PREFIX##_##NAME() {return QString(#PREFIX "/" #NAME);} \
86  static TYPE default##PREFIX##_##NAME() {return COND?DEFAULT_VALUE1:DEFAULT_VALUE2;} \
87  static QString type##PREFIX##_##NAME() {return QString(#TYPE);} \
88  static QString description##PREFIX##_##NAME() {return QString(DESCRIPTION);} \
89  PARAMETER_GETTER_##TYPE(PREFIX, NAME) \
90  static void set##PREFIX##_##NAME(const TYPE & value) {parameters_[#PREFIX "/" #NAME] = value;} \
91  private: \
92  class Dummy##PREFIX##_##NAME { \
93  public: \
94  Dummy##PREFIX##_##NAME() { \
95  defaultParameters_.insert(#PREFIX "/" #NAME, QVariant(COND?DEFAULT_VALUE1:DEFAULT_VALUE2)); \
96  parameters_.insert(#PREFIX "/" #NAME, COND?DEFAULT_VALUE1:DEFAULT_VALUE2); \
97  parametersType_.insert(#PREFIX "/" #NAME, #TYPE); \
98  descriptions_.insert(#PREFIX "/" #NAME, DESCRIPTION);} \
99  }; \
100  Dummy##PREFIX##_##NAME dummy##PREFIX##_##NAME;
101 // MACRO END
102 
103 class FINDOBJECT_EXP Settings
104 {
105  PARAMETER(Camera, 1deviceId, int, 0, "Device ID (default 0).");
106  PARAMETER(Camera, 2imageWidth, int, 0, "Image width (0 means default width from camera).");
107  PARAMETER(Camera, 3imageHeight, int, 0, "Image height (0 means default height from camera).");
108  PARAMETER(Camera, 4imageRate, double, 10.0, "Image rate in Hz (0 Hz means as fast as possible)."); // Hz
109  PARAMETER(Camera, 5mediaPath, QString, "", "Video file or directory of images. If set, the camera is not used. See General->videoFormats and General->imageFormats for available formats.");
110  PARAMETER(Camera, 6useTcpCamera, bool, false, "Use TCP/IP input camera.");
111  PARAMETER(Camera, 8port, int, 0, "The images server's port when useTcpCamera is checked. Only one client at the same time is allowed.");
112  PARAMETER(Camera, 9queueSize, int, 1, "Maximum images buffered from TCP. If 0, all images are buffered.");
113 
114  //List format : [Index:item0;item1;item3;...]
115 
116  PARAMETER_COND(Feature2D, 1Detector, QString, FINDOBJECT_NONFREE, "7:Dense;Fast;GFTT;MSER;ORB;SIFT;Star;SURF;BRISK;AGAST;KAZE;AKAZE" , "4:Dense;Fast;GFTT;MSER;ORB;SIFT;Star;SURF;BRISK;AGAST;KAZE;AKAZE", "Keypoint detector.");
117  PARAMETER_COND(Feature2D, 2Descriptor, QString, FINDOBJECT_NONFREE, "3:Brief;ORB;SIFT;SURF;BRISK;FREAK;KAZE;AKAZE;LUCID;LATCH;DAISY", "1:Brief;ORB;SIFT;SURF;BRISK;FREAK;KAZE;AKAZE;LUCID;LATCH;DAISY", "Keypoint descriptor.");
118  PARAMETER(Feature2D, 3MaxFeatures, int, 0, "Maximum features per image. If the number of features extracted is over this threshold, only X features with the highest response are kept. 0 means all features are kept.");
119  PARAMETER(Feature2D, 4Affine, bool, false, "(ASIFT) Extract features on multiple affine transformations of the image.");
120  PARAMETER(Feature2D, 5AffineCount, int, 6, "(ASIFT) Higher the value, more affine transformations will be done.");
121  PARAMETER(Feature2D, 6SubPix, bool, false, "Refines the corner locations. With SIFT/SURF, features are already subpixel, so no need to activate this.");
122  PARAMETER(Feature2D, 7SubPixWinSize, int, 3, "Half of the side length of the search window. For example, if winSize=Size(5,5) , then a 5*2+1 x 5*2+1 = 11 x 11 search window is used.");
123  PARAMETER(Feature2D, 8SubPixIterations, int, 30, "The process of corner position refinement stops after X iterations.");
124  PARAMETER(Feature2D, 9SubPixEps, float, 0.02f, "The process of corner position refinement stops when the corner position moves by less than epsilon on some iteration.");
125 
126  PARAMETER(Feature2D, Brief_bytes, int, 32, "Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.");
127 
128 #if CV_MAJOR_VERSION < 3
129  PARAMETER(Feature2D, Dense_initFeatureScale, float, 1.f, "");
130  PARAMETER(Feature2D, Dense_featureScaleLevels, int, 1, "");
131  PARAMETER(Feature2D, Dense_featureScaleMul, float, 0.1f, "");
132  PARAMETER(Feature2D, Dense_initXyStep, int, 6, "");
133  PARAMETER(Feature2D, Dense_initImgBound, int, 0, "");
134  PARAMETER(Feature2D, Dense_varyXyStepWithScale, bool, true, "");
135  PARAMETER(Feature2D, Dense_varyImgBoundWithScale, bool, false, "");
136 #endif
137 
138  PARAMETER(Feature2D, Fast_threshold, int, 10, "Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.");
139  PARAMETER(Feature2D, Fast_nonmaxSuppression, bool, true, "If true, non-maximum suppression is applied to detected corners (keypoints).");
140  PARAMETER(Feature2D, Fast_gpu, bool, false, "GPU-FAST: Use GPU version of FAST. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
141  PARAMETER(Feature2D, Fast_keypointsRatio, double, 0.05, "Used with FAST GPU (OpenCV 2).");
142  PARAMETER(Feature2D, Fast_maxNpoints, int, 5000, "Used with FAST GPU (OpenCV 3).");
143 
144  PARAMETER(Feature2D, AGAST_threshold, int, 10, "Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.");
145  PARAMETER(Feature2D, AGAST_nonmaxSuppression, bool, true, "If true, non-maximum suppression is applied to detected corners (keypoints).");
146 
147  PARAMETER(Feature2D, KAZE_extended, bool, false, "Set to enable extraction of extended (128-byte) descriptor.");
148  PARAMETER(Feature2D, KAZE_upright, bool, false, "Set to enable use of upright descriptors (non rotation-invariant).");
149  PARAMETER(Feature2D, KAZE_threshold, float, 0.001f, "Detector response threshold to accept point");
150  PARAMETER(Feature2D, KAZE_nOctaves, int, 4, "Maximum octave evolution of the image.");
151  PARAMETER(Feature2D, KAZE_nOctaveLayers, int, 4, "Default number of sublevels per scale level.");
152 
153  PARAMETER(Feature2D, AKAZE_descriptorSize, int, 0, "Size of the descriptor in bits. 0 -> Full size.");
154  PARAMETER(Feature2D, AKAZE_descriptorChannels, int, 3, "Number of channels in the descriptor (1, 2, 3).");
155  PARAMETER(Feature2D, AKAZE_threshold, float, 0.001f, "Detector response threshold to accept point.");
156  PARAMETER(Feature2D, AKAZE_nOctaves, int, 4, "Maximum octave evolution of the image.");
157  PARAMETER(Feature2D, AKAZE_nOctaveLayers, int, 4, "Default number of sublevels per scale level.");
158 
159  PARAMETER(Feature2D, GFTT_maxCorners, int, 1000, "Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.");
160  PARAMETER(Feature2D, GFTT_qualityLevel, double, 0.01, "Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.");
161  PARAMETER(Feature2D, GFTT_minDistance, double, 1, "Minimum possible Euclidean distance between the returned corners.");
162  PARAMETER(Feature2D, GFTT_blockSize, int, 3, "Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs.");
163  PARAMETER(Feature2D, GFTT_useHarrisDetector, bool, false, "Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.");
164  PARAMETER(Feature2D, GFTT_k, double, 0.04, "Free parameter of the Harris detector.");
165 
166  PARAMETER(Feature2D, ORB_nFeatures, int, 500, "The maximum number of features to retain.");
167  PARAMETER(Feature2D, ORB_scaleFactor, float, 1.2f, "Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.");
168  PARAMETER(Feature2D, ORB_nLevels, int, 8, "The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).");
169  PARAMETER(Feature2D, ORB_edgeThreshold, int, 31, "This is size of the border where the features are not detected. It should roughly match the patchSize parameter.");
170  PARAMETER(Feature2D, ORB_firstLevel, int, 0, "It should be 0 in the current implementation.");
171  PARAMETER(Feature2D, ORB_WTA_K, int, 2, "The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).");
172  PARAMETER(Feature2D, ORB_scoreType, int, 0, "The default HARRIS_SCORE=0 means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE=1 is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.");
173  PARAMETER(Feature2D, ORB_patchSize, int, 31, "size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.");
174  PARAMETER(Feature2D, ORB_gpu, bool, false, "GPU-ORB: Use GPU version of ORB. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
175  PARAMETER(Feature2D, ORB_blurForDescriptor, bool, false, "GPU-ORB: blurForDescriptor parameter (OpenCV 3).");
176 
177  PARAMETER(Feature2D, MSER_delta, int, 5, "");
178  PARAMETER(Feature2D, MSER_minArea, int, 60, "");
179  PARAMETER(Feature2D, MSER_maxArea, int, 14400, "");
180  PARAMETER(Feature2D, MSER_maxVariation, double, 0.25, "");
181  PARAMETER(Feature2D, MSER_minDiversity, double, 0.2, "");
182  PARAMETER(Feature2D, MSER_maxEvolution, int, 200, "");
183  PARAMETER(Feature2D, MSER_areaThreshold, double, 1.01, "");
184  PARAMETER(Feature2D, MSER_minMargin, double, 0.003, "");
185  PARAMETER(Feature2D, MSER_edgeBlurSize, int, 5, "");
186 
187  PARAMETER(Feature2D, SIFT_nfeatures, int, 0, "The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast).");
188  PARAMETER(Feature2D, SIFT_nOctaveLayers, int, 3, "The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.");
189  PARAMETER(Feature2D, SIFT_contrastThreshold, double, 0.04, "The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.");
190  PARAMETER(Feature2D, SIFT_edgeThreshold, double, 10, "The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).");
191  PARAMETER(Feature2D, SIFT_sigma, double, 1.6, "The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.");
192  PARAMETER(Feature2D, SIFT_rootSIFT, bool, false, "RootSIFT descriptors.");
193 
194  PARAMETER(Feature2D, SURF_hessianThreshold, double, 600.0, "Threshold for hessian keypoint detector used in SURF.");
195  PARAMETER(Feature2D, SURF_nOctaves, int, 4, "Number of pyramid octaves the keypoint detector will use.");
196  PARAMETER(Feature2D, SURF_nOctaveLayers, int, 2, "Number of octave layers within each octave.");
197  PARAMETER(Feature2D, SURF_extended, bool, true, "Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).");
198  PARAMETER(Feature2D, SURF_upright, bool, false, "Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).");
199  PARAMETER(Feature2D, SURF_gpu, bool, false, "GPU-SURF: Use GPU version of SURF. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
200  PARAMETER(Feature2D, SURF_keypointsRatio, float, 0.01f, "Used with SURF GPU.");
201 
202  PARAMETER(Feature2D, Star_maxSize, int, 45, "");
203  PARAMETER(Feature2D, Star_responseThreshold, int, 30, "");
204  PARAMETER(Feature2D, Star_lineThresholdProjected, int, 10, "");
205  PARAMETER(Feature2D, Star_lineThresholdBinarized, int, 8, "");
206  PARAMETER(Feature2D, Star_suppressNonmaxSize, int, 5, "");
207 
208  PARAMETER(Feature2D, BRISK_thresh, int, 30, "FAST/AGAST detection threshold score.");
209  PARAMETER(Feature2D, BRISK_octaves, int, 3, "Detection octaves. Use 0 to do single scale.");
210  PARAMETER(Feature2D, BRISK_patternScale, float, 1.0f, "Apply this scale to the pattern used for sampling the neighbourhood of a keypoint.");
211 
212  PARAMETER(Feature2D, FREAK_orientationNormalized, bool, true, "Enable orientation normalization.");
213  PARAMETER(Feature2D, FREAK_scaleNormalized, bool, true, "Enable scale normalization.");
214  PARAMETER(Feature2D, FREAK_patternScale, float, 22.0f, "Scaling of the description pattern.");
215  PARAMETER(Feature2D, FREAK_nOctaves, int, 4, "Number of octaves covered by the detected keypoints.");
216 
217  PARAMETER(Feature2D, LUCID_kernel, int, 1, "Kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth.");
218  PARAMETER(Feature2D, LUCID_blur_kernel, int, 2, "Kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth.");
219 
220  PARAMETER(Feature2D, LATCH_bytes, int, 32, "Size of the descriptor - can be 64, 32, 16, 8, 4, 2 or 1.");
221  PARAMETER(Feature2D, LATCH_rotationInvariance, bool, true, "Whether or not the descriptor should compansate for orientation changes.");
222  PARAMETER(Feature2D, LATCH_half_ssd_size, int, 3, "The size of half of the mini-patches size. For example, if we would like to compare triplets of patches of size 7x7x then the half_ssd_size should be (7-1)/2 = 3.");
223 
224  PARAMETER(Feature2D, DAISY_radius, float, 15, "Radius of the descriptor at the initial scale.");
225  PARAMETER(Feature2D, DAISY_q_radius, int, 3, "Amount of radial range division quantity.");
226  PARAMETER(Feature2D, DAISY_q_theta, int, 8, "Amount of angular range division quantity.");
227  PARAMETER(Feature2D, DAISY_q_hist, int, 8, "Amount of gradient orientations range division quantity.");
228  PARAMETER(Feature2D, DAISY_interpolation, bool, true, "Switch to disable interpolation for speed improvement at minor quality loss.");
229  PARAMETER(Feature2D, DAISY_use_orientation, bool, false, "Sample patterns using keypoints orientation, disabled by default.");
230 
231  PARAMETER_COND(NearestNeighbor, 1Strategy, QString, FINDOBJECT_NONFREE, "1:Linear;KDTree;KMeans;Composite;Autotuned;Lsh;BruteForce", "6:Linear;KDTree;KMeans;Composite;Autotuned;Lsh;BruteForce", "Nearest neighbor strategy.");
232  PARAMETER_COND(NearestNeighbor, 2Distance_type, QString, FINDOBJECT_NONFREE, "0:EUCLIDEAN_L2;MANHATTAN_L1;MINKOWSKI;MAX;HIST_INTERSECT;HELLINGER;CHI_SQUARE_CS;KULLBACK_LEIBLER_KL;HAMMING", "1:EUCLIDEAN_L2;MANHATTAN_L1;MINKOWSKI;MAX;HIST_INTERSECT;HELLINGER;CHI_SQUARE_CS;KULLBACK_LEIBLER_KL;HAMMING", "Distance type.");
233  PARAMETER(NearestNeighbor, 3nndrRatioUsed, bool, true, "Nearest neighbor distance ratio approach to accept the best match.");
234  PARAMETER(NearestNeighbor, 4nndrRatio, float, 0.8f, "Nearest neighbor distance ratio.");
235  PARAMETER(NearestNeighbor, 5minDistanceUsed, bool, false, "Minimum distance with the nearest descriptor to accept a match.");
236  PARAMETER(NearestNeighbor, 6minDistance, float, 1.6f, "Minimum distance. You can look at top of this panel where minimum and maximum distances are shown to properly set this parameter depending of the descriptor used.");
237  PARAMETER(NearestNeighbor, 7ConvertBinToFloat, bool, false, "Convert binary descriptor to float before quantization, so you can use FLANN strategies with them.");
238 
239 
240  PARAMETER(NearestNeighbor, BruteForce_gpu, bool, false, "Brute force GPU");
241 
242  PARAMETER(NearestNeighbor, search_checks, int, 32, "The number of times the tree(s) in the index should be recursively traversed. A higher value for this parameter would give better search precision, but also take more time. If automatic configuration was used when the index was created, the number of checks required to achieve the specified precision was also computed, in which case this parameter is ignored.");
243  PARAMETER(NearestNeighbor, search_eps, float, 0, "");
244  PARAMETER(NearestNeighbor, search_sorted, bool, true, "");
245 
246  PARAMETER(NearestNeighbor, KDTree_trees, int, 4, "The number of parallel kd-trees to use. Good values are in the range [1..16].");
247 
248  PARAMETER(NearestNeighbor, Composite_trees, int, 4, "The number of parallel kd-trees to use. Good values are in the range [1..16].");
249  PARAMETER(NearestNeighbor, Composite_branching, int, 32, "The branching factor to use for the hierarchical k-means tree.");
250  PARAMETER(NearestNeighbor, Composite_iterations, int, 11, "The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence.");
251  PARAMETER(NearestNeighbor, Composite_centers_init, QString, "0:RANDOM;GONZALES;KMEANSPP", "The algorithm to use for selecting the initial centers when performing a k-means clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 ).");
252  PARAMETER(NearestNeighbor, Composite_cb_index, double, 0.2, "This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.");
253 
254  PARAMETER(NearestNeighbor, Autotuned_target_precision, double, 0.8, "Is a number between 0 and 1 specifying the percentage of the approximate nearest-neighbor searches that return the exact nearest-neighbor. Using a higher value for this parameter gives more accurate results, but the search takes longer. The optimum value usually depends on the application.");
255  PARAMETER(NearestNeighbor, Autotuned_build_weight, double, 0.01, "Specifies the importance of the index build time raported to the nearest-neighbor search time. In some applications it’s acceptable for the index build step to take a long time if the subsequent searches in the index can be performed very fast. In other applications it’s required that the index be build as fast as possible even if that leads to slightly longer search times.");
256  PARAMETER(NearestNeighbor, Autotuned_memory_weight, double, 0, "Is used to specify the tradeoff between time (index build time and search time) and memory used by the index. A value less than 1 gives more importance to the time spent and a value greater than 1 gives more importance to the memory usage.");
257  PARAMETER(NearestNeighbor, Autotuned_sample_fraction, double, 0.1, "Is a number between 0 and 1 indicating what fraction of the dataset to use in the automatic parameter configuration algorithm. Running the algorithm on the full dataset gives the most accurate results, but for very large datasets can take longer than desired. In such case using just a fraction of the data helps speeding up this algorithm while still giving good approximations of the optimum parameters.");
258 
259  PARAMETER(NearestNeighbor, KMeans_branching, int, 32, "The branching factor to use for the hierarchical k-means tree.");
260  PARAMETER(NearestNeighbor, KMeans_iterations, int, 11, "The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence.");
261  PARAMETER(NearestNeighbor, KMeans_centers_init, QString, "0:RANDOM;GONZALES;KMEANSPP", "The algorithm to use for selecting the initial centers when performing a k-means clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 ).");
262  PARAMETER(NearestNeighbor, KMeans_cb_index, double, 0.2, "This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.");
263 
264  PARAMETER(NearestNeighbor, Lsh_table_number, int, 12, "The number of hash tables to use (between 10 and 30 usually).");
265  PARAMETER(NearestNeighbor, Lsh_key_size, int, 20, "The size of the hash key in bits (between 10 and 20 usually).");
266  PARAMETER(NearestNeighbor, Lsh_multi_probe_level, int, 2, "The number of bits to shift to check for neighboring buckets (0 is regular LSH, 2 is recommended).");
267 
268  PARAMETER(General, autoStartCamera, bool, false, "Automatically start the camera when the application is opened.");
269  PARAMETER(General, autoUpdateObjects, bool, true, "Automatically update objects on every parameter changes, otherwise you would need to press \"Update objects\" on the objects panel.");
270  PARAMETER(General, nextObjID, uint, 1, "Next object ID to use.");
271  PARAMETER(General, imageFormats, QString, "*.png *.jpg *.bmp *.tiff *.ppm *.pgm", "Image formats supported.");
272  PARAMETER(General, videoFormats, QString, "*.avi *.m4v *.mp4", "Video formats supported.");
273  PARAMETER(General, mirrorView, bool, false, "Flip the camera image horizontally (like all webcam applications).");
274  PARAMETER(General, invertedSearch, bool, true, "Instead of matching descriptors from the objects to those in a vocabulary created with descriptors extracted from the scene, we create a vocabulary from all the objects' descriptors and we match scene's descriptors to this vocabulary. It is the inverted search mode.");
275  PARAMETER(General, controlsShown, bool, false, "Show play/image seek controls (useful with video file and directory of images modes).");
276  PARAMETER(General, threads, int, 1, "Number of threads used for objects matching and homography computation. 0 means as many threads as objects. On InvertedSearch mode, multi-threading has only effect on homography computation.");
277  PARAMETER(General, multiDetection, bool, false, "Multiple detection of the same object.");
278  PARAMETER(General, multiDetectionRadius, int, 30, "Ignore detection of the same object in X pixels radius of the previous detections.");
279  PARAMETER(General, port, int, 0, "Port on objects detected are published. If port=0, a port is chosen automatically.")
280  PARAMETER(General, autoScroll, bool, true, "Auto scroll to detected object in Objects panel.");
281  PARAMETER(General, vocabularyFixed, bool, false, "If the vocabulary is fixed, no new words will be added to it when adding new objects.");
282  PARAMETER(General, vocabularyIncremental, bool, false, "The vocabulary is created incrementally. When new objects are added, their descriptors are compared to those already in vocabulary to find if the visual word already exist or not. \"NearestNeighbor/nndrRatio\" and \"NearestNeighbor/minDistance\" are used to compare descriptors.");
283  PARAMETER(General, vocabularyUpdateMinWords, int, 2000, "When the vocabulary is incremental (see \"General/vocabularyIncremental\"), after X words added to vocabulary, the internal index is updated with new words. This parameter lets avoiding to reconstruct the whole nearest neighbor index after each time descriptors of an object are added to vocabulary. 0 means no incremental update.");
284  PARAMETER(General, sendNoObjDetectedEvents, bool, true, "When there are no objects detected, send an empty object detection event.");
285  PARAMETER(General, autoPauseOnDetection, bool, false, "Auto pause the camera when an object is detected.");
286  PARAMETER(General, autoScreenshotPath, QString, "", "Path to a directory to save screenshot of the current camera view when there is a detection.");
287  PARAMETER(General, debug, bool, false, "Show debug logs on terminal.");
288 
289  PARAMETER(Homography, homographyComputed, bool, true, "Compute homography? On ROS, this is required to publish objects detected.");
290  PARAMETER(Homography, method, QString, "1:LMEDS;RANSAC;RHO", "Type of the robust estimation algorithm: least-median algorithm or RANSAC algorithm.");
291  PARAMETER(Homography, ransacReprojThr, double, 3.0, "Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC method only). It usually makes sense to set this parameter somewhere in the range of 1 to 10.");
292 #if CV_MAJOR_VERSION >= 3
293  PARAMETER(Homography, maxIterations, int, 2000, "The maximum number of RANSAC iterations, 2000 is the maximum it can be.");
294  PARAMETER(Homography, confidence, double, 0.995, "Confidence level, between 0 and 1.");
295 #endif
296  PARAMETER(Homography, minimumInliers, int, 10, "Minimum inliers to accept the homography. Value must be >= 4.");
297  PARAMETER(Homography, ignoreWhenAllInliers, bool, false, "Ignore homography when all features are inliers (sometimes when the homography doesn't converge, it returns the best homography with all features as inliers).");
298  PARAMETER(Homography, rectBorderWidth, int, 4, "Homography rectangle border width.");
299  PARAMETER(Homography, allCornersVisible, bool, false, "All corners of the detected object must be visible in the scene.");
300  PARAMETER(Homography, minAngle, int, 0, "(Degrees) Homography minimum angle. Set 0 to disable. When the angle is very small, this is a good indication that the homography is wrong. A good value is over 60 degrees.");
301  PARAMETER(Homography, opticalFlow, bool, false, "Activate optical flow to refine matched features before computing the homography.");
302  PARAMETER(Homography, opticalFlowWinSize, int, 16, "Size of the search window at each pyramid level.");
303  PARAMETER(Homography, opticalFlowMaxLevel, int, 3, "0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.");
304  PARAMETER(Homography, opticalFlowIterations, int, 30, "Specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations).");
305  PARAMETER(Homography, opticalFlowEps, float, 0.01f, "Specifying the termination criteria of the iterative search algorithm (when the search window moves by less than epsilon).");
306 
307 public:
308  virtual ~Settings(){}
309 
310  static QString workingDirectory();
311  static QString iniDefaultPath();
312  static QString iniDefaultFileName() {return "config.ini";}
313  static QString iniPath();
314 
315  static void init(const QString & fileName);
316 
317  static void loadSettings(const QString & fileName = QString());
318  static void loadWindowSettings(QByteArray & windowGeometry, QByteArray & windowState, const QString & fileName = QString());
319  static void saveSettings(const QString & fileName = QString());
320  static void saveWindowSettings(const QByteArray & windowGeometry, const QByteArray & windowState, const QString & fileName = QString());
321 
322  static const ParametersMap & getDefaultParameters() {return defaultParameters_;}
323  static const ParametersMap & getParameters() {return parameters_;}
324  static const ParametersType & getParametersType() {return parametersType_;}
325  static const DescriptionsMap & getDescriptions() {return descriptions_;}
326  static void setParameter(const QString & key, const QVariant & value) {if(parameters_.contains(key))parameters_[key] = value;}
327  static void resetParameter(const QString & key) {if(defaultParameters_.contains(key)) parameters_.insert(key, defaultParameters_.value(key));}
328  static QVariant getParameter(const QString & key) {return parameters_.value(key, QVariant());}
329 
330  static Feature2D * createKeypointDetector();
331  static Feature2D * createDescriptorExtractor();
332 
333  static QString currentDescriptorType();
334  static QString currentDetectorType();
335  static QString currentNearestNeighborType();
336 
337  static bool isBruteForceNearestNeighbor();
338  static cv::flann::IndexParams * createFlannIndexParams();
339  static cvflann::flann_distance_t getFlannDistanceType();
340 
341  static int getHomographyMethod();
342 
343 private:
344  Settings(){}
345 
346 private:
347  static ParametersMap defaultParameters_;
348  static ParametersMap parameters_;
349  static ParametersType parametersType_;
350  static DescriptionsMap descriptions_;
351  static Settings dummyInit_;
352  static QString iniPath_;
353 };
354 
355 class Feature2D
356 {
357 public:
358 #if CV_MAJOR_VERSION < 3
359  Feature2D(cv::Ptr<cv::FeatureDetector> featureDetector);
360  Feature2D(cv::Ptr<cv::DescriptorExtractor> descriptorExtractor);
361 #endif
362  Feature2D(cv::Ptr<cv::Feature2D> feature2D);
363  Feature2D() {}
364  virtual ~Feature2D() {}
365 
366  virtual void detect(const cv::Mat & image,
367  std::vector<cv::KeyPoint> & keypoints,
368  const cv::Mat & mask = cv::Mat());
369 
370  virtual void compute(const cv::Mat & image,
371  std::vector<cv::KeyPoint> & keypoints,
372  cv::Mat & descriptors);
373 
374  virtual void detectAndCompute(const cv::Mat & image,
375  std::vector<cv::KeyPoint> & keypoints,
376  cv::Mat & descriptors,
377  const cv::Mat & mask = cv::Mat());
378 
379 private:
380 #if CV_MAJOR_VERSION < 3
381  cv::Ptr<cv::FeatureDetector> featureDetector_;
382  cv::Ptr<cv::DescriptorExtractor> descriptorExtractor_;
383 #endif
384  cv::Ptr<cv::Feature2D> feature2D_;
385 };
386 
387 } // namespace find_object
388 
389 #endif /* SETTINGS_H_ */
find_object::Feature2D
Definition: Settings.h:355
PARAMETER_COND
#define PARAMETER_COND(PREFIX, NAME, TYPE, COND, DEFAULT_VALUE1, DEFAULT_VALUE2, DESCRIPTION)
Definition: Settings.h:83
find_object::Settings::Settings
Settings()
Definition: Settings.h:344
find_object::Settings::~Settings
virtual ~Settings()
Definition: Settings.h:308
find_object::Settings::getParameter
static QVariant getParameter(const QString &key)
Definition: Settings.h:328
f
f
find_object::Camera
Definition: Camera.h:42
find_object::Settings::getParametersType
static const ParametersType & getParametersType()
Definition: Settings.h:324
FINDOBJECT_EXP
#define FINDOBJECT_EXP
Definition: FindObjectExp.h:38
find_object::Feature2D::featureDetector_
cv::Ptr< cv::FeatureDetector > featureDetector_
Definition: Settings.h:381
find_object::Settings::parametersType_
static ParametersType parametersType_
Definition: Settings.h:349
find_object
Definition: Camera.h:38
find_object::Settings::getParameters
static const ParametersMap & getParameters()
Definition: Settings.h:323
find_object::Settings::descriptions_
static DescriptionsMap descriptions_
Definition: Settings.h:350
find_object::Feature2D::~Feature2D
virtual ~Feature2D()
Definition: Settings.h:364
find_object::Settings::iniPath_
static QString iniPath_
Definition: Settings.h:352
find_object::ParametersType
QMap< QString, QString > ParametersType
Definition: Settings.h:44
find_object::Settings::setParameter
static void setParameter(const QString &key, const QVariant &value)
Definition: Settings.h:326
PARAMETER
#define PARAMETER(PREFIX, NAME, TYPE, DEFAULT_VALUE, DESCRIPTION)
Definition: Settings.h:64
find_object::Settings::getDescriptions
static const DescriptionsMap & getDescriptions()
Definition: Settings.h:325
find_object::ParametersMap
QMap< QString, QVariant > ParametersMap
Definition: Settings.h:41
find_object::Settings::parameters_
static ParametersMap parameters_
Definition: Settings.h:348
find_object::Settings::dummyInit_
static Settings dummyInit_
Definition: Settings.h:351
find_object::Settings
Definition: Settings.h:103
find_object::DescriptionsMap
QMap< QString, QString > DescriptionsMap
Definition: Settings.h:45
find_object::Feature2D::Feature2D
Feature2D()
Definition: Settings.h:363
find_object::Settings::iniDefaultFileName
static QString iniDefaultFileName()
Definition: Settings.h:312
find_object::Settings::defaultParameters_
static ParametersMap defaultParameters_
Definition: Settings.h:347
find_object::Settings::getDefaultParameters
static const ParametersMap & getDefaultParameters()
Definition: Settings.h:322
find_object::Feature2D::descriptorExtractor_
cv::Ptr< cv::DescriptorExtractor > descriptorExtractor_
Definition: Settings.h:382
find_object::Feature2D::feature2D_
cv::Ptr< cv::Feature2D > feature2D_
Definition: Settings.h:384
find_object::uint
unsigned int uint
Definition: Settings.h:47
find_object::Settings::resetParameter
static void resetParameter(const QString &key)
Definition: Settings.h:327

find_object_2d
Author(s): Mathieu Labbe
autogenerated on Mon Jun 10 2019 13:21:31