q0_simple_e13b_display.py

#!/usr/bin/python
'''simple_e13b_display.py displays a plot of the characters in the E13B font

See the explanation of the E13B character set in ocr_utils.load_E13B. 

Created on Jun 20, 2016

from Python Machine Learning by Sebastian Raschka under the following license

The MIT License (MIT)

Copyright (c) 2015, 2016 SEBASTIAN RASCHKA (mail@sebastianraschka.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

@author: richard lyman

'''
import ocr_utils
import numpy as np
 
#############################################################################
# read images and scatter plot

# retrieve 400 sets of target numbers and column sums
#    y: the ascii characters 48 and 49 ('0', '1')
#    X: the sum of the vertical pixels in the rows in horizontal columns 9 and 17
ascii_characters_to_train = (48,49)
columnsXY = (9,17)       
y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train=ascii_characters_to_train , columns=columnsXY,nChars=256) 

# put the ASCII equivalent of the unique characters in y into the legend of the plot
legend=[]
for ys in np.unique(y):
    legend.append('{} \'{}\''.format(ys, chr(ys)))
           
ocr_utils.scatter_plot(X=X, 
                  y=y,
                  legend_entries=legend,
                  axis_labels = ['column {} sum'.format(columnsXY[i]) for i in range(len(columnsXY))], 
                  title='E13B sum of columns')

#############################################################################
# read and show character images for '0', and '1'
# select the digits in columnsXY in the E13B font

fd = {'m_label': ascii_characters_to_train, 'font': 'E13B'}

# output only the character label and the image
fl = ['m_label','image'] 

# read the complete image (20x20) = 400 pixels for each character
ds = ocr_utils.read_data(input_filters_dict=fd, output_feature_list=fl, dtype=np.int32)   
y,X = ds.train.features

# change to a 2D shape 
X=np.reshape(X,(X.shape[0],ds.train.num_rows, ds.train.num_columns))
ocr_utils.montage(X,title='some E13B Characters')



print ('\n########################### No Errors ####################################')