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view mupdf-source/thirdparty/tesseract/src/ccstruct/linlsq.h @ 46:7ee69f120f19 default tip
>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
| author | Franz Glasner <fzglas.hg@dom66.de> |
|---|---|
| date | Sat, 11 Oct 2025 17:17:30 +0200 |
| parents | b50eed0cc0ef |
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/********************************************************************** * File: linlsq.h (Formerly llsq.h) * Description: Linear Least squares fitting code. * Author: Ray Smith * * (C) Copyright 1991, Hewlett-Packard Ltd. ** Licensed under the Apache License, Version 2.0 (the "License"); ** you may not use this file except in compliance with the License. ** You may obtain a copy of the License at ** http://www.apache.org/licenses/LICENSE-2.0 ** Unless required by applicable law or agreed to in writing, software ** distributed under the License is distributed on an "AS IS" BASIS, ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ** See the License for the specific language governing permissions and ** limitations under the License. * **********************************************************************/ #ifndef TESSERACT_CCSTRUCT_LINLSQ_H_ #define TESSERACT_CCSTRUCT_LINLSQ_H_ #include "points.h" // for FCOORD #include <algorithm> // for std::nth_element #include <cstdint> // for int32_t namespace tesseract { class TESS_API LLSQ { public: LLSQ() { // constructor clear(); // set to zeros } void clear(); // initialize // Adds an element with a weight of 1. void add(double x, double y); // Adds an element with a specified weight. void add(double x, double y, double weight); // Adds a whole LLSQ. void add(const LLSQ &other); // Deletes an element with a weight of 1. void remove(double x, double y); int32_t count() const { // no of elements return static_cast<int>(total_weight + 0.5); } double m() const; // get gradient double c(double m) const; // get constant double rms(double m, double c) const; // get error double pearson() const; // get correlation coefficient. // Returns the x,y means as an FCOORD. FCOORD mean_point() const; // Returns the average sum of squared perpendicular error from a line // through mean_point() in the direction dir. double rms_orth(const FCOORD &dir) const; // Returns the direction of the fitted line as a unit vector, using the // least mean squared perpendicular distance. The line runs through the // mean_point, i.e. a point p on the line is given by: // p = mean_point() + lambda * vector_fit() for some real number lambda. // Note that the result (0<=x<=1, -1<=y<=-1) is directionally ambiguous // and may be negated without changing its meaning, since a line is only // unique to a range of pi radians. // Modernists prefer to think of this as an Eigenvalue problem, but // Pearson had the simple solution in 1901. // // Note that this is equivalent to returning the Principal Component in PCA, // or the eigenvector corresponding to the largest eigenvalue in the // covariance matrix. FCOORD vector_fit() const; // Returns the covariance. double covariance() const { if (total_weight > 0.0) { return (sigxy - sigx * sigy / total_weight) / total_weight; } else { return 0.0; } } double x_variance() const { if (total_weight > 0.0) { return (sigxx - sigx * sigx / total_weight) / total_weight; } else { return 0.0; } } double y_variance() const { if (total_weight > 0.0) { return (sigyy - sigy * sigy / total_weight) / total_weight; } else { return 0.0; } } private: double total_weight; // no of elements or sum of weights. double sigx; // sum of x double sigy; // sum of y double sigxx; // sum x squared double sigxy; // sum of xy double sigyy; // sum y squared }; // Returns the median value of the vector, given that the values are // circular, with the given modulus. Values may be signed or unsigned, // eg range from -pi to pi (modulus 2pi) or from 0 to 2pi (modulus 2pi). // NOTE that the array is shuffled, but the time taken is linear. // An assumption is made that most of the values are spread over no more than // half the range, but wrap-around is accounted for if the median is near // the wrap-around point. // Cannot be a member of vector, as it makes heavy use of LLSQ. // T must be an integer or float/double type. template <typename T> T MedianOfCircularValues(T modulus, std::vector<T> &v) { LLSQ stats; T halfrange = static_cast<T>(modulus / 2); auto num_elements = v.size(); for (auto i : v) { stats.add(i, i + halfrange); } bool offset_needed = stats.y_variance() < stats.x_variance(); if (offset_needed) { for (auto i : v) { i += halfrange; } } auto median_index = num_elements / 2; std::nth_element(v.begin(), v.begin() + median_index, v.end()); if (offset_needed) { for (auto i : v) { i -= halfrange; } } return v[median_index]; } } // namespace tesseract #endif // TESSERACT_CCSTRUCT_LINLSQ_H_
