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