Alexander Alekhin
6 years ago
commit
e28e3c9491
37 changed files with 1158 additions and 438 deletions
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#include "opencv2/core.hpp" |
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#include "opencv2/highgui.hpp" |
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#include "opencv2/imgcodecs.hpp" |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/ml.hpp" |
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#include <algorithm> |
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#include <iostream> |
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#include <vector> |
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using namespace cv; |
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using namespace std; |
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const int SZ = 20; // size of each digit is SZ x SZ
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const int CLASS_N = 10; |
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const char* DIGITS_FN = "digits.png"; |
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static void help() |
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{ |
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cout << |
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"\n" |
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"SVM and KNearest digit recognition.\n" |
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"\n" |
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"Sample loads a dataset of handwritten digits from 'digits.png'.\n" |
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"Then it trains a SVM and KNearest classifiers on it and evaluates\n" |
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"their accuracy.\n" |
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"\n" |
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"Following preprocessing is applied to the dataset:\n" |
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" - Moment-based image deskew (see deskew())\n" |
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" - Digit images are split into 4 10x10 cells and 16-bin\n" |
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" histogram of oriented gradients is computed for each\n" |
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" cell\n" |
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" - Transform histograms to space with Hellinger metric (see [1] (RootSIFT))\n" |
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"\n" |
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"\n" |
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"[1] R. Arandjelovic, A. Zisserman\n" |
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" \"Three things everyone should know to improve object retrieval\"\n" |
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" http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf\n" |
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"\n" |
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"Usage:\n" |
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" ./digits\n" << endl; |
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} |
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static void split2d(const Mat& image, const Size cell_size, vector<Mat>& cells) |
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{ |
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int height = image.rows; |
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int width = image.cols; |
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int sx = cell_size.width; |
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int sy = cell_size.height; |
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cells.clear(); |
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for (int i = 0; i < height; i += sy) |
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{ |
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for (int j = 0; j < width; j += sx) |
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{ |
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cells.push_back(image(Rect(j, i, sx, sy))); |
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} |
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} |
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} |
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static void load_digits(const char* fn, vector<Mat>& digits, vector<int>& labels) |
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{ |
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digits.clear(); |
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labels.clear(); |
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String filename = samples::findFile(fn); |
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cout << "Loading " << filename << " ..." << endl; |
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Mat digits_img = imread(filename, IMREAD_GRAYSCALE); |
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split2d(digits_img, Size(SZ, SZ), digits); |
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for (int i = 0; i < CLASS_N; i++) |
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{ |
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for (size_t j = 0; j < digits.size() / CLASS_N; j++) |
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{ |
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labels.push_back(i); |
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} |
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} |
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} |
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static void deskew(const Mat& img, Mat& deskewed_img) |
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{ |
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Moments m = moments(img); |
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if (abs(m.mu02) < 0.01) |
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{ |
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deskewed_img = img.clone(); |
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return; |
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} |
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float skew = (float)(m.mu11 / m.mu02); |
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float M_vals[2][3] = {{1, skew, -0.5f * SZ * skew}, {0, 1, 0}}; |
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Mat M(Size(3, 2), CV_32F); |
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for (int i = 0; i < M.rows; i++) |
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{ |
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for (int j = 0; j < M.cols; j++) |
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{ |
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M.at<float>(i, j) = M_vals[i][j]; |
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} |
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} |
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warpAffine(img, deskewed_img, M, Size(SZ, SZ), WARP_INVERSE_MAP | INTER_LINEAR); |
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} |
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static void mosaic(const int width, const vector<Mat>& images, Mat& grid) |
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{ |
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int mat_width = SZ * width; |
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int mat_height = SZ * (int)ceil((double)images.size() / width); |
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if (!images.empty()) |
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{ |
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grid = Mat(Size(mat_width, mat_height), images[0].type()); |
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for (size_t i = 0; i < images.size(); i++) |
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{ |
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Mat location_on_grid = grid(Rect(SZ * ((int)i % width), SZ * ((int)i / width), SZ, SZ)); |
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images[i].copyTo(location_on_grid); |
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} |
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} |
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} |
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static void evaluate_model(const vector<float>& predictions, const vector<Mat>& digits, const vector<int>& labels, Mat& mos) |
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{ |
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double err = 0; |
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for (size_t i = 0; i < predictions.size(); i++) |
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{ |
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if ((int)predictions[i] != labels[i]) |
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{ |
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err++; |
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} |
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} |
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err /= predictions.size(); |
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cout << format("error: %.2f %%", err * 100) << endl; |
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int confusion[10][10] = {}; |
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for (size_t i = 0; i < labels.size(); i++) |
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{ |
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confusion[labels[i]][(int)predictions[i]]++; |
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} |
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cout << "confusion matrix:" << endl; |
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for (int i = 0; i < 10; i++) |
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{ |
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for (int j = 0; j < 10; j++) |
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{ |
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cout << format("%2d ", confusion[i][j]); |
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} |
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cout << endl; |
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} |
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cout << endl; |
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vector<Mat> vis; |
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for (size_t i = 0; i < digits.size(); i++) |
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{ |
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Mat img; |
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cvtColor(digits[i], img, COLOR_GRAY2BGR); |
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if ((int)predictions[i] != labels[i]) |
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{ |
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for (int j = 0; j < img.rows; j++) |
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{ |
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for (int k = 0; k < img.cols; k++) |
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{ |
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img.at<Vec3b>(j, k)[0] = 0; |
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img.at<Vec3b>(j, k)[1] = 0; |
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} |
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} |
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} |
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vis.push_back(img); |
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} |
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mosaic(25, vis, mos); |
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} |
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static void bincount(const Mat& x, const Mat& weights, const int min_length, vector<double>& bins) |
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{ |
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double max_x_val = 0; |
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minMaxLoc(x, NULL, &max_x_val); |
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bins = vector<double>(max((int)max_x_val, min_length)); |
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for (int i = 0; i < x.rows; i++) |
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{ |
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for (int j = 0; j < x.cols; j++) |
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{ |
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bins[x.at<int>(i, j)] += weights.at<float>(i, j); |
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} |
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} |
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} |
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static void preprocess_hog(const vector<Mat>& digits, Mat& hog) |
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{ |
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int bin_n = 16; |
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int half_cell = SZ / 2; |
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double eps = 1e-7; |
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hog = Mat(Size(4 * bin_n, (int)digits.size()), CV_32F); |
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for (size_t img_index = 0; img_index < digits.size(); img_index++) |
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{ |
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Mat gx; |
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Sobel(digits[img_index], gx, CV_32F, 1, 0); |
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Mat gy; |
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Sobel(digits[img_index], gy, CV_32F, 0, 1); |
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Mat mag; |
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Mat ang; |
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cartToPolar(gx, gy, mag, ang); |
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Mat bin(ang.size(), CV_32S); |
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for (int i = 0; i < ang.rows; i++) |
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{ |
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for (int j = 0; j < ang.cols; j++) |
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{ |
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bin.at<int>(i, j) = (int)(bin_n * ang.at<float>(i, j) / (2 * CV_PI)); |
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} |
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} |
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Mat bin_cells[] = { |
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bin(Rect(0, 0, half_cell, half_cell)), |
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bin(Rect(half_cell, 0, half_cell, half_cell)), |
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bin(Rect(0, half_cell, half_cell, half_cell)), |
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bin(Rect(half_cell, half_cell, half_cell, half_cell)) |
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}; |
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Mat mag_cells[] = { |
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mag(Rect(0, 0, half_cell, half_cell)), |
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mag(Rect(half_cell, 0, half_cell, half_cell)), |
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mag(Rect(0, half_cell, half_cell, half_cell)), |
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mag(Rect(half_cell, half_cell, half_cell, half_cell)) |
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}; |
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vector<double> hist; |
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hist.reserve(4 * bin_n); |
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for (int i = 0; i < 4; i++) |
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{ |
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vector<double> partial_hist; |
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bincount(bin_cells[i], mag_cells[i], bin_n, partial_hist); |
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hist.insert(hist.end(), partial_hist.begin(), partial_hist.end()); |
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} |
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// transform to Hellinger kernel
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double sum = 0; |
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for (size_t i = 0; i < hist.size(); i++) |
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{ |
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sum += hist[i]; |
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} |
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for (size_t i = 0; i < hist.size(); i++) |
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{ |
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hist[i] /= sum + eps; |
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hist[i] = sqrt(hist[i]); |
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} |
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double hist_norm = norm(hist); |
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for (size_t i = 0; i < hist.size(); i++) |
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{ |
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hog.at<float>((int)img_index, (int)i) = (float)(hist[i] / (hist_norm + eps)); |
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} |
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} |
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} |
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static void shuffle(vector<Mat>& digits, vector<int>& labels) |
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{ |
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vector<int> shuffled_indexes(digits.size()); |
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for (size_t i = 0; i < digits.size(); i++) |
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{ |
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shuffled_indexes[i] = (int)i; |
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} |
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randShuffle(shuffled_indexes); |
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vector<Mat> shuffled_digits(digits.size()); |
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vector<int> shuffled_labels(labels.size()); |
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for (size_t i = 0; i < shuffled_indexes.size(); i++) |
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{ |
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shuffled_digits[shuffled_indexes[i]] = digits[i]; |
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shuffled_labels[shuffled_indexes[i]] = labels[i]; |
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} |
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digits = shuffled_digits; |
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labels = shuffled_labels; |
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} |
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int main() |
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{ |
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help(); |
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vector<Mat> digits; |
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vector<int> labels; |
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load_digits(DIGITS_FN, digits, labels); |
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cout << "preprocessing..." << endl; |
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// shuffle digits
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shuffle(digits, labels); |
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vector<Mat> digits2; |
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for (size_t i = 0; i < digits.size(); i++) |
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{ |
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Mat deskewed_digit; |
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deskew(digits[i], deskewed_digit); |
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digits2.push_back(deskewed_digit); |
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} |
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Mat samples; |
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preprocess_hog(digits2, samples); |
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int train_n = (int)(0.9 * samples.rows); |
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Mat test_set; |
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vector<Mat> digits_test(digits2.begin() + train_n, digits2.end()); |
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mosaic(25, digits_test, test_set); |
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imshow("test set", test_set); |
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Mat samples_train = samples(Rect(0, 0, samples.cols, train_n)); |
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Mat samples_test = samples(Rect(0, train_n, samples.cols, samples.rows - train_n)); |
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vector<int> labels_train(labels.begin(), labels.begin() + train_n); |
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vector<int> labels_test(labels.begin() + train_n, labels.end()); |
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Ptr<ml::KNearest> k_nearest; |
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Ptr<ml::SVM> svm; |
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vector<float> predictions; |
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Mat vis; |
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cout << "training KNearest..." << endl; |
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k_nearest = ml::KNearest::create(); |
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k_nearest->train(samples_train, ml::ROW_SAMPLE, labels_train); |
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// predict digits with KNearest
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k_nearest->findNearest(samples_test, 4, predictions); |
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evaluate_model(predictions, digits_test, labels_test, vis); |
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imshow("KNearest test", vis); |
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k_nearest.release(); |
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cout << "training SVM..." << endl; |
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svm = ml::SVM::create(); |
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svm->setGamma(5.383); |
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svm->setC(2.67); |
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svm->setKernel(ml::SVM::RBF); |
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svm->setType(ml::SVM::C_SVC); |
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svm->train(samples_train, ml::ROW_SAMPLE, labels_train); |
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// predict digits with SVM
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svm->predict(samples_test, predictions); |
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evaluate_model(predictions, digits_test, labels_test, vis); |
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imshow("SVM test", vis); |
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cout << "Saving SVM as \"digits_svm.yml\"..." << endl; |
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svm->save("digits_svm.yml"); |
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svm.release(); |
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waitKey(); |
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return 0; |
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} |
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