HoG and Hellinger-metric preprocess for digit recognition
line breaks in fitline.py description
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Alexander Mordvintsev
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@ -3,8 +3,19 @@ SVN and KNearest digit recognition.
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Sample loads a dataset of handwritten digits from 'digits.png'.
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Then it trains a SVN and KNearest classifiers on it and evaluates
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their accuracy. Moment-based image deskew is used to improve
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the recognition accuracy.
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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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Usage:
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digits.py
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@ -14,17 +25,25 @@ import numpy as np
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import cv2
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from multiprocessing.pool import ThreadPool
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from common import clock, mosaic
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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 = '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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print 'loading "%s" ...' % fn
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digits_img = cv2.imread(fn, 0)
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h, w = digits_img.shape
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digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
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digits = np.array(digits).reshape(-1, SZ, SZ)
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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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@ -92,6 +111,31 @@ def evaluate_model(model, digits, samples, labels):
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vis.append(img)
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return mosaic(25, vis)
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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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if __name__ == '__main__':
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print __doc__
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@ -100,13 +144,13 @@ if __name__ == '__main__':
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print 'preprocessing...'
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# shuffle digits
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rand = np.random.RandomState(12345)
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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 = map(deskew, digits)
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samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
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samples = preprocess_hog(digits2)
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train_n = int(0.9*len(samples))
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cv2.imshow('test set', mosaic(25, digits[train_n:]))
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digits_train, digits_test = np.split(digits2, [train_n])
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@ -115,13 +159,13 @@ if __name__ == '__main__':
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print 'training KNearest...'
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model = KNearest(k=1)
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model = KNearest(k=4)
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model.train(samples_train, labels_train)
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vis = evaluate_model(model, digits_test, samples_test, labels_test)
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cv2.imshow('KNearest test', vis)
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print 'training SVM...'
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model = SVM(C=4.66, gamma=0.08)
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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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vis = evaluate_model(model, digits_test, samples_test, labels_test)
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cv2.imshow('SVM test', vis)
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@ -76,7 +76,7 @@ class App(object):
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shuffle = np.random.permutation(len(digits))
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digits, labels = digits[shuffle], labels[shuffle]
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digits2 = map(deskew, digits)
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samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
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samples = preprocess_hog(digits2)
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return samples, labels
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def get_dataset(self):
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@ -95,8 +95,8 @@ class App(object):
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return ires
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def adjust_SVM(self):
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Cs = np.logspace(0, 5, 10, base=2)
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gammas = np.logspace(-7, -2, 10, base=2)
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Cs = np.logspace(0, 10, 15, base=2)
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gammas = np.logspace(-7, 4, 15, base=2)
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scores = np.zeros((len(Cs), len(gammas)))
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scores[:] = np.nan
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@ -114,6 +114,9 @@ class App(object):
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print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
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print scores
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print 'writing score table to "svm_scores.npz"'
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np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
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i, j = np.unravel_index(scores.argmin(), scores.shape)
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best_params = dict(C = Cs[i], gamma=gammas[j])
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print 'best params:', best_params
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@ -142,7 +145,6 @@ if __name__ == '__main__':
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print __doc__
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args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
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args = dict(args)
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args.setdefault('--model', 'svm')
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@ -1,10 +1,10 @@
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import numpy as np
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import cv2
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import digits
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import os
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import video
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from common import mosaic
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from digits import *
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def main():
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@ -15,11 +15,9 @@ def main():
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print '"%s" not found, run digits.py first' % classifier_fn
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return
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model = digits.SVM()
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model = SVM()
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model.load('digits_svm.dat')
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SZ = 20
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while True:
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ret, frame = cap.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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@ -55,13 +53,12 @@ def main():
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A[:,:2] = np.eye(2)*s
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A[:,2] = t
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sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
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sub1 = digits.deskew(sub1)
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sub1 = deskew(sub1)
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if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
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frame[y:,x+w:][:SZ, :SZ] = sub1[...,np.newaxis]
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sample = np.float32(sub1).reshape(1,SZ*SZ) / 255.0
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sample = preprocess_hog([sub1])
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digit = model.predict(sample)[0]
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cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
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@ -2,14 +2,16 @@
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Robust line fitting.
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==================
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Example of using cv2.fitLine function for fitting line to points in presence of outliers.
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Example of using cv2.fitLine function for fitting line
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to points in presence of outliers.
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Usage
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-----
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fitline.py
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Switch through different M-estimator functions and see, how well the robust functions
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fit the line even in case of ~50% of outliers.
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Switch through different M-estimator functions and see,
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how well the robust functions fit the line even
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in case of ~50% of outliers.
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Keys
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----
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