find_object::Settings Member List

This is the complete list of members for find_object::Settings, including all inherited members.

createDescriptorExtractor()find_object::Settingsstatic
createFlannIndexParams()find_object::Settingsstatic
createKeypointDetector()find_object::Settingsstatic
currentDescriptorType()find_object::Settingsstatic
currentDetectorType()find_object::Settingsstatic
currentNearestNeighborType()find_object::Settingsstatic
defaultParameters_find_object::Settingsprivatestatic
descriptions_find_object::Settingsprivatestatic
dummyInit_find_object::Settingsprivatestatic
getDefaultParameters()find_object::Settingsinlinestatic
getDescriptions()find_object::Settingsinlinestatic
getFlannDistanceType()find_object::Settingsstatic
getHomographyMethod()find_object::Settingsstatic
getParameter(const QString &key)find_object::Settingsinlinestatic
getParameters()find_object::Settingsinlinestatic
getParametersType()find_object::Settingsinlinestatic
iniDefaultFileName()find_object::Settingsinlinestatic
iniDefaultPath()find_object::Settingsstatic
iniPath()find_object::Settingsstatic
iniPath_find_object::Settingsprivatestatic
init(const QString &fileName)find_object::Settingsstatic
isBruteForceNearestNeighbor()find_object::Settingsstatic
loadSettings(const QString &fileName=QString())find_object::Settingsstatic
loadWindowSettings(QByteArray &windowGeometry, QByteArray &windowState, const QString &fileName=QString())find_object::Settingsstatic
PARAMETER(Camera, 1deviceId, int, 0,"Device ID (default 0).")find_object::Settingsprivate
PARAMETER(Camera, 2imageWidth, int, 0,"Image width (0 means default width from camera).")find_object::Settingsprivate
PARAMETER(Camera, 3imageHeight, int, 0,"Image height (0 means default height from camera).")find_object::Settingsprivate
PARAMETER(Camera, 4imageRate, double, 10.0,"Image rate in Hz (0 Hz means as fast as possible).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Camera, 6useTcpCamera, bool, false,"Use TCP/IP input camera.")find_object::Settingsprivate
PARAMETER(Camera, 8port, int, 0,"The images server's port when useTcpCamera is checked. Only one client at the same time is allowed.")find_object::Settingsprivate
PARAMETER(Camera, 9queueSize, int, 1,"Maximum images buffered from TCP. If 0, all images are buffered.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, 4Affine, bool, false,"(ASIFT) Extract features on multiple affine transformations of the image.")find_object::Settingsprivate
PARAMETER(Feature2D, 5AffineCount, int, 6,"(ASIFT) Higher the value, more affine transformations will be done.")find_object::Settingsprivate
PARAMETER(Feature2D, 6SubPix, bool, false,"Refines the corner locations. With SIFT/SURF, features are already subpixel, so no need to activate this.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, 8SubPixIterations, int, 30,"The process of corner position refinement stops after X iterations.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, Brief_bytes, int, 32,"Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_initFeatureScale, float, 1.f,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_featureScaleLevels, int, 1,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_featureScaleMul, float, 0.1f,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_initXyStep, int, 6,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_initImgBound, int, 0,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_varyXyStepWithScale, bool, true,"")find_object::Settingsprivate
PARAMETER(Feature2D, Dense_varyImgBoundWithScale, bool, false,"")find_object::Settingsprivate
PARAMETER(Feature2D, Fast_threshold, int, 10,"Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.")find_object::Settingsprivate
PARAMETER(Feature2D, Fast_nonmaxSuppression, bool, true,"If true, non-maximum suppression is applied to detected corners (keypoints).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, Fast_keypointsRatio, double, 0.05,"Used with FAST GPU (OpenCV 2).")find_object::Settingsprivate
PARAMETER(Feature2D, Fast_maxNpoints, int, 5000,"Used with FAST GPU (OpenCV 3).")find_object::Settingsprivate
PARAMETER(Feature2D, AGAST_threshold, int, 10,"Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.")find_object::Settingsprivate
PARAMETER(Feature2D, AGAST_nonmaxSuppression, bool, true,"If true, non-maximum suppression is applied to detected corners (keypoints).")find_object::Settingsprivate
PARAMETER(Feature2D, KAZE_extended, bool, false,"Set to enable extraction of extended (128-byte) descriptor.")find_object::Settingsprivate
PARAMETER(Feature2D, KAZE_upright, bool, false,"Set to enable use of upright descriptors (non rotation-invariant).")find_object::Settingsprivate
PARAMETER(Feature2D, KAZE_threshold, float, 0.001f,"Detector response threshold to accept point")find_object::Settingsprivate
PARAMETER(Feature2D, KAZE_nOctaves, int, 4,"Maximum octave evolution of the image.")find_object::Settingsprivate
PARAMETER(Feature2D, KAZE_nOctaveLayers, int, 4,"Default number of sublevels per scale level.")find_object::Settingsprivate
PARAMETER(Feature2D, AKAZE_descriptorSize, int, 0,"Size of the descriptor in bits. 0 -> Full size.")find_object::Settingsprivate
PARAMETER(Feature2D, AKAZE_descriptorChannels, int, 3,"Number of channels in the descriptor (1, 2, 3).")find_object::Settingsprivate
PARAMETER(Feature2D, AKAZE_threshold, float, 0.001f,"Detector response threshold to accept point.")find_object::Settingsprivate
PARAMETER(Feature2D, AKAZE_nOctaves, int, 4,"Maximum octave evolution of the image.")find_object::Settingsprivate
PARAMETER(Feature2D, AKAZE_nOctaveLayers, int, 4,"Default number of sublevels per scale level.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, GFTT_minDistance, double, 1,"Minimum possible Euclidean distance between the returned corners.")find_object::Settingsprivate
PARAMETER(Feature2D, GFTT_blockSize, int, 3,"Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs.")find_object::Settingsprivate
PARAMETER(Feature2D, GFTT_useHarrisDetector, bool, false,"Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.")find_object::Settingsprivate
PARAMETER(Feature2D, GFTT_k, double, 0.04,"Free parameter of the Harris detector.")find_object::Settingsprivate
PARAMETER(Feature2D, ORB_nFeatures, int, 500,"The maximum number of features to retain.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, ORB_firstLevel, int, 0,"It should be 0 in the current implementation.")find_object::Settingsprivate
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).")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, ORB_blurForDescriptor, bool, false,"GPU-ORB: blurForDescriptor parameter (OpenCV 3).")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_delta, int, 5,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_minArea, int, 60,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_maxArea, int, 14400,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_maxVariation, double, 0.25,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_minDiversity, double, 0.2,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_maxEvolution, int, 200,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_areaThreshold, double, 1.01,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_minMargin, double, 0.003,"")find_object::Settingsprivate
PARAMETER(Feature2D, MSER_edgeBlurSize, int, 5,"")find_object::Settingsprivate
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).")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, SIFT_rootSIFT, bool, false,"RootSIFT descriptors.")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_hessianThreshold, double, 600.0,"Threshold for hessian keypoint detector used in SURF.")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_nOctaves, int, 4,"Number of pyramid octaves the keypoint detector will use.")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_nOctaveLayers, int, 2,"Number of octave layers within each octave.")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_extended, bool, true,"Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_upright, bool, false,"Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, SURF_keypointsRatio, float, 0.01f,"Used with SURF GPU.")find_object::Settingsprivate
PARAMETER(Feature2D, Star_maxSize, int, 45,"")find_object::Settingsprivate
PARAMETER(Feature2D, Star_responseThreshold, int, 30,"")find_object::Settingsprivate
PARAMETER(Feature2D, Star_lineThresholdProjected, int, 10,"")find_object::Settingsprivate
PARAMETER(Feature2D, Star_lineThresholdBinarized, int, 8,"")find_object::Settingsprivate
PARAMETER(Feature2D, Star_suppressNonmaxSize, int, 5,"")find_object::Settingsprivate
PARAMETER(Feature2D, BRISK_thresh, int, 30,"FAST/AGAST detection threshold score.")find_object::Settingsprivate
PARAMETER(Feature2D, BRISK_octaves, int, 3,"Detection octaves. Use 0 to do single scale.")find_object::Settingsprivate
PARAMETER(Feature2D, BRISK_patternScale, float, 1.0f,"Apply this scale to the pattern used for sampling the neighbourhood of a keypoint.")find_object::Settingsprivate
PARAMETER(Feature2D, FREAK_orientationNormalized, bool, true,"Enable orientation normalization.")find_object::Settingsprivate
PARAMETER(Feature2D, FREAK_scaleNormalized, bool, true,"Enable scale normalization.")find_object::Settingsprivate
PARAMETER(Feature2D, FREAK_patternScale, float, 22.0f,"Scaling of the description pattern.")find_object::Settingsprivate
PARAMETER(Feature2D, FREAK_nOctaves, int, 4,"Number of octaves covered by the detected keypoints.")find_object::Settingsprivate
PARAMETER(Feature2D, LUCID_kernel, int, 1,"Kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, LATCH_bytes, int, 32,"Size of the descriptor - can be 64, 32, 16, 8, 4, 2 or 1.")find_object::Settingsprivate
PARAMETER(Feature2D, LATCH_rotationInvariance, bool, true,"Whether or not the descriptor should compansate for orientation changes.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_radius, float, 15,"Radius of the descriptor at the initial scale.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_q_radius, int, 3,"Amount of radial range division quantity.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_q_theta, int, 8,"Amount of angular range division quantity.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_q_hist, int, 8,"Amount of gradient orientations range division quantity.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_interpolation, bool, true,"Switch to disable interpolation for speed improvement at minor quality loss.")find_object::Settingsprivate
PARAMETER(Feature2D, DAISY_use_orientation, bool, false,"Sample patterns using keypoints orientation, disabled by default.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, 3nndrRatioUsed, bool, true,"Nearest neighbor distance ratio approach to accept the best match.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, 4nndrRatio, float, 0.8f,"Nearest neighbor distance ratio.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, 5minDistanceUsed, bool, false,"Minimum distance with the nearest descriptor to accept a match.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, 7ConvertBinToFloat, bool, false,"Convert binary descriptor to float before quantization, so you can use FLANN strategies with them.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, BruteForce_gpu, bool, false,"Brute force GPU")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, search_eps, float, 0,"")find_object::Settingsprivate
PARAMETER(NearestNeighbor, search_sorted, bool, true,"")find_object::Settingsprivate
PARAMETER(NearestNeighbor, KDTree_trees, int, 4,"The number of parallel kd-trees to use. Good values are in the range [1..16].")find_object::Settingsprivate
PARAMETER(NearestNeighbor, Composite_trees, int, 4,"The number of parallel kd-trees to use. Good values are in the range [1..16].")find_object::Settingsprivate
PARAMETER(NearestNeighbor, Composite_branching, int, 32,"The branching factor to use for the hierarchical k-means tree.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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 ).")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, KMeans_branching, int, 32,"The branching factor to use for the hierarchical k-means tree.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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 ).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(NearestNeighbor, Lsh_table_number, int, 12,"The number of hash tables to use (between 10 and 30 usually).")find_object::Settingsprivate
PARAMETER(NearestNeighbor, Lsh_key_size, int, 20,"The size of the hash key in bits (between 10 and 20 usually).")find_object::Settingsprivate
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).")find_object::Settingsprivate
PARAMETER(General, autoStartCamera, bool, false,"Automatically start the camera when the application is opened.")find_object::Settingsprivate
PARAMETER(General, autoUpdateObjects, bool, true,"Automatically update objects on every parameter changes, otherwise you would need to press \"Update objects\" on the objects panel.")find_object::Settingsprivate
PARAMETER(General, nextObjID, uint, 1,"Next object ID to use.")find_object::Settingsprivate
PARAMETER(General, imageFormats, QString,"*.png *.jpg *.bmp *.tiff *.ppm *.pgm","Image formats supported.")find_object::Settingsprivate
PARAMETER(General, videoFormats, QString,"*.avi *.m4v *.mp4","Video formats supported.")find_object::Settingsprivate
PARAMETER(General, mirrorView, bool, false,"Flip the camera image horizontally (like all webcam applications).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(General, controlsShown, bool, false,"Show play/image seek controls (useful with video file and directory of images modes).")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(General, multiDetection, bool, false,"Multiple detection of the same object.")find_object::Settingsprivate
PARAMETER(General, multiDetectionRadius, int, 30,"Ignore detection of the same object in X pixels radius of the previous detections.")find_object::Settingsprivate
PARAMETER(General, autoScroll, bool, true,"Auto scroll to detected object in Objects panel.")find_object::Settingsprivate
PARAMETER(General, vocabularyFixed, bool, false,"If the vocabulary is fixed, no new words will be added to it when adding new objects.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(General, sendNoObjDetectedEvents, bool, true,"When there are no objects detected, send an empty object detection event.")find_object::Settingsprivate
PARAMETER(General, autoPauseOnDetection, bool, false,"Auto pause the camera when an object is detected.")find_object::Settingsprivate
PARAMETER(General, autoScreenshotPath, QString,"","Path to a directory to save screenshot of the current camera view when there is a detection.")find_object::Settingsprivate
PARAMETER(General, debug, bool, false,"Show debug logs on terminal.")find_object::Settingsprivate
PARAMETER(Homography, homographyComputed, bool, true,"Compute homography? On ROS, this is required to publish objects detected.")find_object::Settingsprivate
PARAMETER(Homography, method, QString,"1:LMEDS;RANSAC;RHO","Type of the robust estimation algorithm: least-median algorithm or RANSAC algorithm.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Homography, minimumInliers, int, 10,"Minimum inliers to accept the homography. Value must be >= 4.")find_object::Settingsprivate
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).")find_object::Settingsprivate
PARAMETER(Homography, rectBorderWidth, int, 4,"Homography rectangle border width.")find_object::Settingsprivate
PARAMETER(Homography, allCornersVisible, bool, false,"All corners of the detected object must be visible in the scene.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Homography, opticalFlow, bool, false,"Activate optical flow to refine matched features before computing the homography.")find_object::Settingsprivate
PARAMETER(Homography, opticalFlowWinSize, int, 16,"Size of the search window at each pyramid level.")find_object::Settingsprivate
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.")find_object::Settingsprivate
PARAMETER(Homography, opticalFlowIterations, int, 30,"Specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations).")find_object::Settingsprivate
PARAMETER(Homography, opticalFlowEps, float, 0.01f,"Specifying the termination criteria of the iterative search algorithm (when the search window moves by less than epsilon).")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
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.")find_object::Settingsprivate
parameters_find_object::Settingsprivatestatic
parametersType_find_object::Settingsprivatestatic
resetParameter(const QString &key)find_object::Settingsinlinestatic
saveSettings(const QString &fileName=QString())find_object::Settingsstatic
saveWindowSettings(const QByteArray &windowGeometry, const QByteArray &windowState, const QString &fileName=QString())find_object::Settingsstatic
setParameter(const QString &key, const QVariant &value)find_object::Settingsinlinestatic
Settings()find_object::Settingsinlineprivate
workingDirectory()find_object::Settingsstatic
~Settings()find_object::Settingsinlinevirtual


find_object_2d
Author(s): Mathieu Labbe
autogenerated on Thu Jun 6 2019 19:22:27