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#!/usr/bin/env python
'''
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from '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
Usage:
digits.py
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
# built-in modules
from multiprocessing.pool import ThreadPool
from numpy.linalg import norm
# local modules
from common import clock, mosaic
SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
DIGITS_FN = '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):
fn = cv.samples.findFile(fn)
print('loading "%s" ...' % fn)
digits_img = cv.imread(fn, cv.IMREAD_GRAYSCALE)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
def deskew(img):
m = cv.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 = cv.warpAffine(img, M, (SZ, SZ), flags=cv.WARP_INVERSE_MAP | cv.INTER_LINEAR)
return img
class KNearest(object):
def __init__(self, k = 3):
self.k = k
self.model = cv.ml.KNearest_create()
def train(self, samples, responses):
self.model.train(samples, cv.ml.ROW_SAMPLE, responses)
def predict(self, samples):
_retval, results, _neigh_resp, _dists = self.model.findNearest(samples, self.k)
return results.ravel()
def load(self, fn):
self.model = cv.ml.KNearest_load(fn)
def save(self, fn):
self.model.save(fn)
class SVM(object):
def __init__(self, C = 1, gamma = 0.5):
self.model = cv.ml.SVM_create()
self.model.setGamma(gamma)
self.model.setC(C)
self.model.setKernel(cv.ml.SVM_RBF)
self.model.setType(cv.ml.SVM_C_SVC)
def train(self, samples, responses):
self.model.train(samples, cv.ml.ROW_SAMPLE, responses)
def predict(self, samples):
return self.model.predict(samples)[1].ravel()
def load(self, fn):
self.model = cv.ml.SVM_load(fn)
def save(self, fn):
self.model.save(fn)
def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print('error: %.2f %%' % (err*100))
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, int(j)] += 1
print('confusion matrix:')
print(confusion)
print()
vis = []
for img, flag in zip(digits, resp == labels):
img = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
return mosaic(25, vis)
def preprocess_simple(digits):
return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv.Sobel(img, cv.CV_32F, 1, 0)
gy = cv.Sobel(img, cv.CV_32F, 0, 1)
mag, ang = cv.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)
if __name__ == '__main__':
print(__doc__)
digits, labels = load_digits(DIGITS_FN)
print('preprocessing...')
# 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))
cv.imshow('test set', mosaic(25, digits[train_n:]))
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])
print('training KNearest...')
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv.imshow('KNearest test', vis)
print('training SVM...')
model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv.imshow('SVM test', vis)
print('saving SVM as "digits_svm.dat"...')
model.save('digits_svm.dat')
cv.waitKey(0)
cv.destroyAllWindows()
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