def recognition_digit_image(fname, digit = 100):
im = cv2.imread(fname)
im = img.change_size_with_size(im, 28, 28)
im = img.change_grayscale(im)
im = 255. - im
input_data = im
input_data = input_data.astype(np.float64)
input_data = im / im.max()
tmp_data = np.reshape(input_data, (28*28, 1))
neuro_obj.test(tmp_data, teach_data)
output = neuro_obj.get_output()
if digit >=0 and digit <= 9:
if neuro_obj.get_max_output_index() == digit : print "judged(success):", neuro_obj.get_max_output_index()
else : print "judged(miss) :", neuro_obj.get_max_output_index()
def recognition_digit_image(fname, digit = 100):
im = cv2.imread(fname)
im = img.change_size_with_size(im, 28, 28)
im = img.change_grayscale(im)
im = 255 - im
input_data = im
input_data = input_data.astype(np.float64)
input_data = im / im.max()
input_data = np.reshape(input_data, (1, 28*28))
neuro_obj.test(input_data, teach_data)
output = neuro_obj.get_output()
if digit >=0 and digit <= 9:
print "judged:", neuro_obj.get_max_output_index(),
print ", target order:", np.where(np.fliplr(np.argsort(output)) == digit)[1] + 1,
print ", order array:", np.fliplr(np.argsort(output))
else:
print "judged:", neuro_obj.get_max_output_index(),
print ", order array:", np.fliplr(np.argsort(output))
cv2.imshow("input_data", im)
cv2.waitKey(0)
cv2.destroyAllWindows()
