#!/usr/bin/env python ''' SVM and KNearest digit recognition. Sample loads a dataset of handwritten digits from '../data/digits.png'. Then it trains a SVM and KNearest classifiers on it and evaluates their accuracy. Following preprocessing is applied to the dataset: - Moment-based image deskew (see deskew()) - Digit images are split into 4 10x10 cells and 16-bin histogram of oriented gradients is computed for each cell - Transform histograms to space with Hellinger metric (see [1] (RootSIFT)) [1] R. Arandjelovic, A. Zisserman "Three things everyone should know to improve object retrieval" http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf ''' # Python 2/3 compatibility from __future__ import print_function # built-in modules from multiprocessing.pool import ThreadPool import cv2 import numpy as np from numpy.linalg import norm SZ = 20 # size of each digit is SZ x SZ CLASS_N = 10 DIGITS_FN = '../../../samples/data/digits.png' def split2d(img, cell_size, flatten=True): h, w = img.shape[:2] sx, sy = cell_size cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)] cells = np.array(cells) if flatten: cells = cells.reshape(-1, sy, sx) return cells def load_digits(fn): digits_img = cv2.imread(fn, 0) digits = split2d(digits_img, (SZ, SZ)) labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N) return digits, labels def deskew(img): m = cv2.moments(img) if abs(m['mu02']) < 1e-2: return img.copy() skew = m['mu11']/m['mu02'] M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]]) img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) return img class StatModel(object): def load(self, fn): self.model.load(fn) # Known bug: https://github.com/Itseez/opencv/issues/4969 def save(self, fn): self.model.save(fn) class KNearest(StatModel): def __init__(self, k = 3): self.k = k self.model = cv2.ml.KNearest_create() def train(self, samples, responses): self.model.train(samples, cv2.ml.ROW_SAMPLE, responses) def predict(self, samples): retval, results, neigh_resp, dists = self.model.findNearest(samples, self.k) return results.ravel() class SVM(StatModel): def __init__(self, C = 1, gamma = 0.5): self.model = cv2.ml.SVM_create() self.model.setGamma(gamma) self.model.setC(C) self.model.setKernel(cv2.ml.SVM_RBF) self.model.setType(cv2.ml.SVM_C_SVC) def train(self, samples, responses): self.model.train(samples, cv2.ml.ROW_SAMPLE, responses) def predict(self, samples): return self.model.predict(samples)[1].ravel() def evaluate_model(model, digits, samples, labels): resp = model.predict(samples) err = (labels != resp).mean() confusion = np.zeros((10, 10), np.int32) for i, j in zip(labels, resp): confusion[i, j] += 1 return err, confusion def preprocess_simple(digits): return np.float32(digits).reshape(-1, SZ*SZ) / 255.0 def preprocess_hog(digits): samples = [] for img in digits: gx = cv2.Sobel(img, cv2.CV_32F, 1, 0) gy = cv2.Sobel(img, cv2.CV_32F, 0, 1) mag, ang = cv2.cartToPolar(gx, gy) bin_n = 16 bin = np.int32(bin_n*ang/(2*np.pi)) bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:] mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:] hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)] hist = np.hstack(hists) # transform to Hellinger kernel eps = 1e-7 hist /= hist.sum() + eps hist = np.sqrt(hist) hist /= norm(hist) + eps samples.append(hist) return np.float32(samples) from tests_common import NewOpenCVTests class digits_test(NewOpenCVTests): def test_digits(self): digits, labels = load_digits(DIGITS_FN) # shuffle digits rand = np.random.RandomState(321) shuffle = rand.permutation(len(digits)) digits, labels = digits[shuffle], labels[shuffle] digits2 = list(map(deskew, digits)) samples = preprocess_hog(digits2) train_n = int(0.9*len(samples)) digits_train, digits_test = np.split(digits2, [train_n]) samples_train, samples_test = np.split(samples, [train_n]) labels_train, labels_test = np.split(labels, [train_n]) errors = list() confusionMatrixes = list() model = KNearest(k=4) model.train(samples_train, labels_train) error, confusion = evaluate_model(model, digits_test, samples_test, labels_test) errors.append(error) confusionMatrixes.append(confusion) model = SVM(C=2.67, gamma=5.383) model.train(samples_train, labels_train) error, confusion = evaluate_model(model, digits_test, samples_test, labels_test) errors.append(error) confusionMatrixes.append(confusion) eps = 0.001 normEps = len(samples_test) * 0.02 confusionKNN = [[45, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 57, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 59, 1, 0, 0, 0, 0, 1, 0], [ 0, 0, 0, 43, 0, 0, 0, 1, 0, 0], [ 0, 0, 0, 0, 38, 0, 2, 0, 0, 0], [ 0, 0, 0, 2, 0, 48, 0, 0, 1, 0], [ 0, 1, 0, 0, 0, 0, 51, 0, 0, 0], [ 0, 0, 1, 0, 0, 0, 0, 54, 0, 0], [ 0, 0, 0, 0, 0, 1, 0, 0, 46, 0], [ 1, 1, 0, 1, 1, 0, 0, 0, 2, 42]] confusionSVM = [[45, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 57, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 59, 2, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 43, 0, 0, 0, 1, 0, 0], [ 0, 0, 0, 0, 40, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 50, 0, 0, 0, 0], [ 0, 0, 0, 0, 1, 0, 51, 0, 0, 0], [ 0, 0, 1, 0, 0, 0, 0, 54, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 47, 0], [ 0, 1, 0, 1, 0, 0, 0, 0, 1, 45]] self.assertLess(cv2.norm(confusionMatrixes[0] - confusionKNN, cv2.NORM_L1), normEps) self.assertLess(cv2.norm(confusionMatrixes[1] - confusionSVM, cv2.NORM_L1), normEps) self.assertLess(errors[0] - 0.034, eps) self.assertLess(errors[1] - 0.018, eps)