hog_svm.py

import cv2
import numpy as np

SZ = 20
bin_n = 16

affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR


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 = affine_flags)
	return img

def hog(img):
	gx = cv2.Sobel(img, cv2.CV_32F,1, 0)
	gy = cv2.Sobel(img, cv2.CV_32F,0, 1)
	mag, ang = cv2.cartToPolar(gx,gy)
	bins = np.int32(bin_n*ang/(2*np.pi))


	bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
	mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]

	hist1 = [np.bincount(b.ravel(),m.ravel(),bin_n) for b,m in zip(bin_cells,mag_cells)]

	hist = np.hstack(hist1)

	return hist

img = cv2.imread('digits.png',0)

#20x20 from 1000x2000
cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]
train_cells = [column[:50] for column in cells]
test_cells = [column[50:] for column in cells]

deskew_img = [list(map(deskew,img)) for img in train_cells]
hog_img = [list(map(hog,img)) for img in deskew_img]
train_data = np.float32(hog_img).reshape(-1,64)
responses = np.repeat(np.arange(10),250).reshape(2500,1)


svm = cv2.ml.SVM_create()
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.setType(cv2.ml.SVM_C_SVC)
svm.setC(2.67)
svm.setGamma(5.383)
svm.train(train_data, cv2.ml.ROW_SAMPLE, responses)
svm.save('svm_data.dat')

# Test Data
deskewed = [map(deskew,row) for row in test_cells]
hogdata = [map(hog,row) for row in deskewed]
testData = np.float32(hogdata).reshape(-1,bin_n*4)
result = svm.predict(testData)[1]
mask = result==responses
correct = np.count_nonzero(mask)
print correct*100.0/result.size