1 // This file is part of OpenCV project.
2 // It is subject to the license terms in the LICENSE file found in the top-level directory
3 // of this distribution and at http://opencv.org/license.html
4 #ifndef OPENCV_OBJDETECT_ARUCO_DETECTOR_HPP
5 #define OPENCV_OBJDETECT_ARUCO_DETECTOR_HPP
7 #include <opencv2/objdetect/aruco_dictionary.hpp>
8 #include <opencv2/objdetect/aruco_board.hpp>
13 //! @addtogroup objdetect_aruco
16 enum CornerRefineMethod{
17 CORNER_REFINE_NONE, ///< Tag and corners detection based on the ArUco approach
18 CORNER_REFINE_SUBPIX, ///< ArUco approach and refine the corners locations using corner subpixel accuracy
19 CORNER_REFINE_CONTOUR, ///< ArUco approach and refine the corners locations using the contour-points line fitting
20 CORNER_REFINE_APRILTAG, ///< Tag and corners detection based on the AprilTag 2 approach @cite wang2016iros
23 /** @brief struct DetectorParameters is used by ArucoDetector
25 struct CV_EXPORTS_W_SIMPLE DetectorParameters {
26 CV_WRAP DetectorParameters() {
27 adaptiveThreshWinSizeMin = 3;
28 adaptiveThreshWinSizeMax = 23;
29 adaptiveThreshWinSizeStep = 10;
30 adaptiveThreshConstant = 7;
31 minMarkerPerimeterRate = 0.03;
32 maxMarkerPerimeterRate = 4.;
33 polygonalApproxAccuracyRate = 0.03;
34 minCornerDistanceRate = 0.05;
35 minDistanceToBorder = 3;
36 minMarkerDistanceRate = 0.05;
37 cornerRefinementMethod = CORNER_REFINE_NONE;
38 cornerRefinementWinSize = 5;
39 cornerRefinementMaxIterations = 30;
40 cornerRefinementMinAccuracy = 0.1;
42 perspectiveRemovePixelPerCell = 4;
43 perspectiveRemoveIgnoredMarginPerCell = 0.13;
44 maxErroneousBitsInBorderRate = 0.35;
46 errorCorrectionRate = 0.6;
47 aprilTagQuadDecimate = 0.0;
48 aprilTagQuadSigma = 0.0;
49 aprilTagMinClusterPixels = 5;
50 aprilTagMaxNmaxima = 10;
51 aprilTagCriticalRad = (float)(10* CV_PI /180);
52 aprilTagMaxLineFitMse = 10.0;
53 aprilTagMinWhiteBlackDiff = 5;
55 detectInvertedMarker = false;
56 useAruco3Detection = false;
57 minSideLengthCanonicalImg = 32;
58 minMarkerLengthRatioOriginalImg = 0.0;
61 /** @brief Read a new set of DetectorParameters from FileNode (use FileStorage.root()).
63 CV_WRAP bool readDetectorParameters(const FileNode& fn);
65 /** @brief Write a set of DetectorParameters to FileStorage
67 CV_WRAP bool writeDetectorParameters(FileStorage& fs, const String& name = String());
69 /// minimum window size for adaptive thresholding before finding contours (default 3).
70 CV_PROP_RW int adaptiveThreshWinSizeMin;
72 /// maximum window size for adaptive thresholding before finding contours (default 23).
73 CV_PROP_RW int adaptiveThreshWinSizeMax;
75 /// increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax during the thresholding (default 10).
76 CV_PROP_RW int adaptiveThreshWinSizeStep;
78 /// constant for adaptive thresholding before finding contours (default 7)
79 CV_PROP_RW double adaptiveThreshConstant;
81 /** @brief determine minimum perimeter for marker contour to be detected.
83 * This is defined as a rate respect to the maximum dimension of the input image (default 0.03).
85 CV_PROP_RW double minMarkerPerimeterRate;
87 /** @brief determine maximum perimeter for marker contour to be detected.
89 * This is defined as a rate respect to the maximum dimension of the input image (default 4.0).
91 CV_PROP_RW double maxMarkerPerimeterRate;
93 /// minimum accuracy during the polygonal approximation process to determine which contours are squares. (default 0.03)
94 CV_PROP_RW double polygonalApproxAccuracyRate;
96 /// minimum distance between corners for detected markers relative to its perimeter (default 0.05)
97 CV_PROP_RW double minCornerDistanceRate;
99 /// minimum distance of any corner to the image border for detected markers (in pixels) (default 3)
100 CV_PROP_RW int minDistanceToBorder;
102 /** @brief minimum mean distance beetween two marker corners to be considered imilar, so that the smaller one is removed.
104 * The rate is relative to the smaller perimeter of the two markers (default 0.05).
106 CV_PROP_RW double minMarkerDistanceRate;
108 /** @brief default value CORNER_REFINE_NONE */
109 CV_PROP_RW CornerRefineMethod cornerRefinementMethod;
111 /// window size for the corner refinement process (in pixels) (default 5).
112 CV_PROP_RW int cornerRefinementWinSize;
114 /// maximum number of iterations for stop criteria of the corner refinement process (default 30).
115 CV_PROP_RW int cornerRefinementMaxIterations;
117 /// minimum error for the stop cristeria of the corner refinement process (default: 0.1)
118 CV_PROP_RW double cornerRefinementMinAccuracy;
120 /// number of bits of the marker border, i.e. marker border width (default 1).
121 CV_PROP_RW int markerBorderBits;
123 /// number of bits (per dimension) for each cell of the marker when removing the perspective (default 4).
124 CV_PROP_RW int perspectiveRemovePixelPerCell;
126 /** @brief width of the margin of pixels on each cell not considered for the determination of the cell bit.
128 * Represents the rate respect to the total size of the cell, i.e. perspectiveRemovePixelPerCell (default 0.13)
130 CV_PROP_RW double perspectiveRemoveIgnoredMarginPerCell;
132 /** @brief maximum number of accepted erroneous bits in the border (i.e. number of allowed white bits in the border).
134 * Represented as a rate respect to the total number of bits per marker (default 0.35).
136 CV_PROP_RW double maxErroneousBitsInBorderRate;
138 /** @brief minimun standard deviation in pixels values during the decodification step to apply Otsu
139 * thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher than 128 or not) (default 5.0)
141 CV_PROP_RW double minOtsuStdDev;
143 /// error correction rate respect to the maximun error correction capability for each dictionary (default 0.6).
144 CV_PROP_RW double errorCorrectionRate;
146 /** @brief April :: User-configurable parameters.
148 * Detection of quads can be done on a lower-resolution image, improving speed at a cost of
149 * pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still
151 CV_PROP_RW float aprilTagQuadDecimate;
153 /// what Gaussian blur should be applied to the segmented image (used for quad detection?)
154 CV_PROP_RW float aprilTagQuadSigma;
156 // April :: Internal variables
157 /// reject quads containing too few pixels (default 5).
158 CV_PROP_RW int aprilTagMinClusterPixels;
160 /// how many corner candidates to consider when segmenting a group of pixels into a quad (default 10).
161 CV_PROP_RW int aprilTagMaxNmaxima;
163 /** @brief reject quads where pairs of edges have angles that are close to straight or close to 180 degrees.
165 * Zero means that no quads are rejected. (In radians) (default 10*PI/180)
167 CV_PROP_RW float aprilTagCriticalRad;
169 /// when fitting lines to the contours, what is the maximum mean squared error
170 CV_PROP_RW float aprilTagMaxLineFitMse;
172 /** @brief add an extra check that the white model must be (overall) brighter than the black model.
174 * When we build our model of black & white pixels, we add an extra check that the white model must be (overall)
175 * brighter than the black model. How much brighter? (in pixel values, [0,255]), (default 5)
177 CV_PROP_RW int aprilTagMinWhiteBlackDiff;
179 /// should the thresholded image be deglitched? Only useful for very noisy images (default 0).
180 CV_PROP_RW int aprilTagDeglitch;
182 /** @brief to check if there is a white marker.
184 * In order to generate a "white" marker just invert a normal marker by using a tilde, ~markerImage. (default false)
186 CV_PROP_RW bool detectInvertedMarker;
188 /** @brief enable the new and faster Aruco detection strategy.
190 * Proposed in the paper:
191 * Romero-Ramirez et al: Speeded up detection of squared fiducial markers (2018)
192 * https://www.researchgate.net/publication/325787310_Speeded_Up_Detection_of_Squared_Fiducial_Markers
194 CV_PROP_RW bool useAruco3Detection;
196 /// minimum side length of a marker in the canonical image. Latter is the binarized image in which contours are searched.
197 CV_PROP_RW int minSideLengthCanonicalImg;
199 /// range [0,1], eq (2) from paper. The parameter tau_i has a direct influence on the processing speed.
200 CV_PROP_RW float minMarkerLengthRatioOriginalImg;
203 /** @brief struct RefineParameters is used by ArucoDetector
205 struct CV_EXPORTS_W_SIMPLE RefineParameters {
206 CV_WRAP RefineParameters(float minRepDistance = 10.f, float errorCorrectionRate = 3.f, bool checkAllOrders = true);
209 /** @brief Read a new set of RefineParameters from FileNode (use FileStorage.root()).
211 CV_WRAP bool readRefineParameters(const FileNode& fn);
213 /** @brief Write a set of RefineParameters to FileStorage
215 CV_WRAP bool writeRefineParameters(FileStorage& fs, const String& name = String());
217 /** @brief minRepDistance minimum distance between the corners of the rejected candidate and the reprojected marker
218 in order to consider it as a correspondence.
220 CV_PROP_RW float minRepDistance;
222 /** @brief minRepDistance rate of allowed erroneous bits respect to the error correction capability of the used dictionary.
224 * -1 ignores the error correction step.
226 CV_PROP_RW float errorCorrectionRate;
228 /** @brief checkAllOrders consider the four posible corner orders in the rejectedCorners array.
230 * If it set to false, only the provided corner order is considered (default true).
232 CV_PROP_RW bool checkAllOrders;
235 /** @brief The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method.
237 * After detecting some markers in the image, you can try to find undetected markers from this dictionary with
238 * refineDetectedMarkers() method.
240 * @see DetectorParameters, RefineParameters
242 class CV_EXPORTS_W ArucoDetector : public Algorithm
245 /** @brief Basic ArucoDetector constructor
247 * @param dictionary indicates the type of markers that will be searched
248 * @param detectorParams marker detection parameters
249 * @param refineParams marker refine detection parameters
251 CV_WRAP ArucoDetector(const Dictionary &dictionary = getPredefinedDictionary(cv::aruco::DICT_4X4_50),
252 const DetectorParameters &detectorParams = DetectorParameters(),
253 const RefineParameters& refineParams = RefineParameters());
255 /** @brief Basic marker detection
257 * @param image input image
258 * @param corners vector of detected marker corners. For each marker, its four corners
259 * are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
260 * the dimensions of this array is Nx4. The order of the corners is clockwise.
261 * @param ids vector of identifiers of the detected markers. The identifier is of type int
262 * (e.g. std::vector<int>). For N detected markers, the size of ids is also N.
263 * The identifiers have the same order than the markers in the imgPoints array.
264 * @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a
265 * correct codification. Useful for debugging purposes.
267 * Performs marker detection in the input image. Only markers included in the specific dictionary
268 * are searched. For each detected marker, it returns the 2D position of its corner in the image
269 * and its corresponding identifier.
270 * Note that this function does not perform pose estimation.
271 * @note The function does not correct lens distortion or takes it into account. It's recommended to undistort
272 * input image with corresponging camera model, if camera parameters are known
273 * @sa undistort, estimatePoseSingleMarkers, estimatePoseBoard
275 CV_WRAP void detectMarkers(InputArray image, OutputArrayOfArrays corners, OutputArray ids,
276 OutputArrayOfArrays rejectedImgPoints = noArray()) const;
278 /** @brief Refind not detected markers based on the already detected and the board layout
280 * @param image input image
281 * @param board layout of markers in the board.
282 * @param detectedCorners vector of already detected marker corners.
283 * @param detectedIds vector of already detected marker identifiers.
284 * @param rejectedCorners vector of rejected candidates during the marker detection process.
285 * @param cameraMatrix optional input 3x3 floating-point camera matrix
286 * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
287 * @param distCoeffs optional vector of distortion coefficients
288 * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
289 * @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the
290 * original rejectedCorners array.
292 * This function tries to find markers that were not detected in the basic detecMarkers function.
293 * First, based on the current detected marker and the board layout, the function interpolates
294 * the position of the missing markers. Then it tries to find correspondence between the reprojected
295 * markers and the rejected candidates based on the minRepDistance and errorCorrectionRate parameters.
296 * If camera parameters and distortion coefficients are provided, missing markers are reprojected
297 * using projectPoint function. If not, missing marker projections are interpolated using global
298 * homography, and all the marker corners in the board must have the same Z coordinate.
300 CV_WRAP void refineDetectedMarkers(InputArray image, const Board &board,
301 InputOutputArrayOfArrays detectedCorners,
302 InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners,
303 InputArray cameraMatrix = noArray(), InputArray distCoeffs = noArray(),
304 OutputArray recoveredIdxs = noArray()) const;
306 CV_WRAP const Dictionary& getDictionary() const;
307 CV_WRAP void setDictionary(const Dictionary& dictionary);
309 CV_WRAP const DetectorParameters& getDetectorParameters() const;
310 CV_WRAP void setDetectorParameters(const DetectorParameters& detectorParameters);
312 CV_WRAP const RefineParameters& getRefineParameters() const;
313 CV_WRAP void setRefineParameters(const RefineParameters& refineParameters);
315 /** @brief Stores algorithm parameters in a file storage
317 virtual void write(FileStorage& fs) const override;
319 /** @brief simplified API for language bindings
321 CV_WRAP inline void write(FileStorage& fs, const String& name) { Algorithm::write(fs, name); }
323 /** @brief Reads algorithm parameters from a file storage
325 CV_WRAP virtual void read(const FileNode& fn) override;
327 struct ArucoDetectorImpl;
328 Ptr<ArucoDetectorImpl> arucoDetectorImpl;
331 /** @brief Draw detected markers in image
333 * @param image input/output image. It must have 1 or 3 channels. The number of channels is not altered.
334 * @param corners positions of marker corners on input image.
335 * (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the dimensions of
336 * this array should be Nx4. The order of the corners should be clockwise.
337 * @param ids vector of identifiers for markers in markersCorners .
338 * Optional, if not provided, ids are not painted.
339 * @param borderColor color of marker borders. Rest of colors (text color and first corner color)
340 * are calculated based on this one to improve visualization.
342 * Given an array of detected marker corners and its corresponding ids, this functions draws
343 * the markers in the image. The marker borders are painted and the markers identifiers if provided.
344 * Useful for debugging purposes.
346 CV_EXPORTS_W void drawDetectedMarkers(InputOutputArray image, InputArrayOfArrays corners,
347 InputArray ids = noArray(), Scalar borderColor = Scalar(0, 255, 0));
349 /** @brief Generate a canonical marker image
351 * @param dictionary dictionary of markers indicating the type of markers
352 * @param id identifier of the marker that will be returned. It has to be a valid id in the specified dictionary.
353 * @param sidePixels size of the image in pixels
354 * @param img output image with the marker
355 * @param borderBits width of the marker border.
357 * This function returns a marker image in its canonical form (i.e. ready to be printed)
359 CV_EXPORTS_W void generateImageMarker(const Dictionary &dictionary, int id, int sidePixels, OutputArray img,