import cv2
import numpy as np
SZ = 20
bin_n = 16  # Number of bins

svm_params = dict(kernel_type = cv2.SVM_)

svm_params = dict(kernel_type=cv2.SVM_LINEAR,
                  svm_type=cv2.SVM_C_SVC,
                  C=2.67, gamma=5.383)

affine_flags = cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR

img=cv2.imread("u39.png")
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))  # quantizing binvalues in (0...16)
    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:]
    hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
    hist = np.hstack(hists)  # hist is a 64 bit vector
    return hist


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

cells = [np.hsplit(row, 100) for row in np.vsplit(img, 50)]

# First half is trainData, remaining is testData
train_cells = [i[:50] for i in cells]
test_cells = [i[50:] for i in cells]

######     Now training      ########################

deskewed = [map(deskew, row) for row in train_cells]
hogdata = [map(hog, row) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1, 64)
responses = np.float32(np.repeat(np.arange(10), 250)[:, np.newaxis])

svm = cv2.SVM()
svm.train(trainData, responses, params=svm_params)
svm.save('svm_data.dat')

######     Now testing      ########################

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_all(testData)

#######   Check Accuracy   ########################
mask = result == responses
correct = np.count_nonzero(mask)
print correct * 100.0 / result.size