OpenCV 3 Computer Vision with Python Cookbook

How to do it...

1. Import the modules:

import cv2import numpy as np

2. Specify a few constants:

CELL_SIZE = 20     # Digit image size. NCLASSES = 10      # Number of digits.TRAIN_RATIO = 0.8  # Part of all samples used for training.

3. Read the digits image and prepare the labels:

digits_img = cv2.imread('../data/digits.png', 0)digits = [np.hsplit(r, digits_img.shape[1] // CELL_SIZE)           for r in np.vsplit(digits_img, digits_img.shape[0] // CELL_SIZE)]digits = np.array(digits).reshape(-1, CELL_SIZE, CELL_SIZE)nsamples = digits.shape[0]labels = np.repeat(np.arange(NCLASSES), nsamples // NCLASSES)

4. Perform geometric normalization, compute image moments, and align each sample:

for i in range(nsamples):    m = cv2.moments(digits[i])    if m['mu02'] > 1e-3: s = m['mu11'] ...