dataset.py

# import the necessary packages
import numpy as np
import mahotas
import imutils
import cv2
def load_digits(datasetPath):
	# build the dataset and then split it into data and labels
	data = np.genfromtxt(datasetPath, delimiter = ",", dtype = "uint8")
	target = data[:, 0]
	data = data[:, 1:].reshape(data.shape[0], 28, 28)
	# return a tuple of the data and targets
	return (data, target)

def deskew(image, width):
	# grab the width and height of the image and compute its moments
	(h, w) = image.shape[:2]
	moments = cv2.moments(image)
	# deskew the image by applying an affine transformation
	skew = moments["mu11"] / moments["mu02"]
	M = np.float32([
	[1, skew, -0.5 * w * skew],
	[0, 1, 0]])
	image = cv2.warpAffine(image, M, (w, h), flags = cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
	# resize the image to have a constant width
	image = imutils.resize(image, width = width)
	# return the deskewed image
	return image

def center_extent(image, size):
	# grab the extent width and height
	(eW, eH) = size
	# handle when the width is greater than the height
	if (image.shape[1] > image.shape[0]):
		image = imutils.resize(image, width = eW)
	# otherwise, the height is greater than the width
	else:
		image = imutils.resize(image, height = eH)
	# allocate memory for the extent of the image and grab it
	extent = np.zeros((eH, eW), dtype = "uint8")
	offsetX = int((eW - image.shape[1]) / 2)
	offsetY = int((eH - image.shape[0]) / 2)
	extent[ offsetY:offsetY + image.shape[0], offsetX:offsetX + image.shape[1] ] = image
	# compute the center of mass of the image and then
	# move the center of mass to the center of the image
	(cY, cX) = np.round(mahotas.center_of_mass(extent)).astype("int32")
	(dX, dY) = ((size[0] / 2) - cX, (size[1] / 2) - cY)
	M = np.float32([[1, 0, dX], [0, 1, dY]])
	extent = cv2.warpAffine(extent, M, size)
	# return the extent of the image
	return extent