#!/usr/bin/env python
# This script can be used to see how bitmaps will be preprocessed before
# subsetting the CASIA HWDB1.1 data set
import sys
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import utils
if len(sys.argv) != 2:
print 'Usage: %s gnt_dirpath' % sys.argv[0]
exit()
gnt_dirpath = sys.argv[1]
for i, (bitmap,
tagcode) in enumerate(utils.read_gnt_in_directory(gnt_dirpath)):
print utils.tagcode_to_unicode(tagcode).encode(
'utf-8') # wrong terminal encoding = garbage
proc_bitmap = utils.normalize_bitmap(bitmap)
proc_bitmap = utils.preprocess_bitmap(proc_bitmap)
plt.subplot(121)
plt.imshow(bitmap, cmap=cm.Greys_r)
plt.subplot(122)
plt.imshow(np.squeeze(proc_bitmap, axis=0), cmap=cm.Greys_r)
plt.show()
def predict(self, data, num_predictions=5, verbose=0, plot_cam=False):
'''
Returns the prediction for an input image/images
'''
imgs = data[0]
prepr_imgs = data[1]
num_segments = len(prepr_imgs)
pY = self.model.predict(x=prepr_imgs, verbose=0)
labels = pY.argsort(axis=1)[:, -num_predictions:][:, ::-1]
assert num_segments == len(labels)
if plot_cam:
heat_maps = []
Wgwap = self.model.get_layer(
'global_weighted_average_pooling2d_1').get_weights()[0]
activation_layer = self.model.get_layer('activation_14')
model_trunc = Model(inputs=self.model.input,
outputs=activation_layer.output)
# get the feature map weights -
# 'GWOAP - FC-3755' weight matrix:
final_dense = self.model.get_layer('dense_1')
W = final_dense.get_weights()[0] # 448 x 3755
# get the feature maps:
fmaps = model_trunc.predict(
x=prepr_imgs) # num_segments x 6 x 6 x 448
fmaps *= Wgwap
for i in range(num_segments):
# get the last weight matrix' column for the predicted class:
w = W[:, labels[i, 0]] # (448 x 1)
# get the heat_map - class activation map -
# calculate the dot-product between fmaps and w:
heat_map = fmaps[i].dot(w) # (6 x 6)
# resize and save the heat_map:
heat_map = sp.misc.imresize(heat_map,
size=(96, 96),
interp='bilinear',
mode='F')
heat_maps.append(heat_map)
chars_candidates = np.chararray((num_predictions, num_segments),
unicode=True)
scores = np.empty((num_predictions, num_segments))
if verbose != 0:
print('\nMelnyk-Net response: ')
# NOTE: in the following piece of code i - columns, j - rows:
for i in range(num_segments):
predictions = self.label2tagcode[labels[i]].ravel().astype(
np.uint16)
j = 0
for prediction, p in zip(predictions, pY[i, labels[i]].ravel()):
prediction = tagcode_to_unicode(prediction)[0]
chars_candidates[j, i] = prediction
scores[j, i] = np.round(p, 3)
if verbose != 0:
if j % num_predictions == 0:
print()
print('Dear User, I am %.3f %s sure it\'s %s ' %
(p * 100.0, '%', prediction))
j += 1
pltz = PyplotZ()
pltz.enable_chinese()
if plot_cam:
plt.subplot(1, 2, 1)
plt.imshow(imgs[i], cmap='gray')
pltz.title('prediction: ' + tagcode_to_unicode(predictions[0])[0],
fontsize=25,
y=1.03)
pltz.automate_font_size(scale=1.2)
if plot_cam:
plt.subplot(1, 2, 2)
plt.imshow(prepr_imgs[i].reshape(96, 96),
cmap='gray',
alpha=0.7)
plt.imshow(heat_maps[i], cmap='jet', alpha=0.6)
plt.title('class activation map', size=22, y=1.03)
plt.show()
return chars_candidates, scores
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This script counts the characters of the CASIA HWDB1.1 data set
import sys
from collections import Counter, defaultdict
import utils
if len(sys.argv) != 3:
print('Usage: %s trn_dirpath tst_dirpath' % sys.argv[0])
exit()
trn_dirpath = sys.argv[1]
tst_dirpath = sys.argv[2]
frequencies = defaultdict(Counter)
for bitmap, tagcode in utils.read_gnt_in_directory(trn_dirpath):
tagcode_unicode = utils.tagcode_to_unicode(tagcode)
frequencies[tagcode_unicode].update(trn=1)
for bitmap, tagcode in utils.read_gnt_in_directory(tst_dirpath):
tagcode_unicode = utils.tagcode_to_unicode(tagcode)
frequencies[tagcode_unicode].update(tst=1)
with open('frequencies.txt', 'w') as f:
for k, v in sorted(frequencies.items(),
key=lambda k_v: k_v[1]['trn'],
reverse=True):
f.write('%s: %d, %d\n' % (k.encode('utf-8'), v['trn'], v['tst']))
#!/usr/bin/env python2
# This script can be used to see how bitmaps will be preprocessed before
# subsetting the CASIA HWDB1.1 data set
import sys
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import utils
if len(sys.argv) != 2:
print 'Usage: %s gnt_dirpath' % sys.argv[0]
exit()
gnt_dirpath = sys.argv[1]
for i, (bitmap, tagcode) in enumerate(utils.read_gnt_in_directory(gnt_dirpath)):
print utils.tagcode_to_unicode(tagcode).encode('utf-8') # wrong terminal encoding = garbage
proc_bitmap = utils.normalize_bitmap(bitmap)
proc_bitmap = utils.preprocess_bitmap(proc_bitmap)
plt.subplot(121)
plt.imshow(bitmap, cmap=cm.Greys_r)
plt.subplot(122)
plt.imshow(np.squeeze(proc_bitmap, axis=0), cmap=cm.Greys_r)
plt.show()
# -*- coding: utf-8 -*-
# This script can be used to see how bitmaps will be preprocessed before
# subsetting the CASIA HWDB1.1 data set
import sys
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import utils
if len(sys.argv) != 2:
print('Usage: %s gnt_dirpath' % sys.argv[0])
exit()
gnt_dirpath = sys.argv[1]
for i, (bitmap,
tagcode) in enumerate(utils.read_gnt_in_directory(gnt_dirpath)):
print(
utils.tagcode_to_unicode(tagcode)) # wrong terminal encoding = garbage
proc_bitmap = utils.normalize_bitmap(bitmap)
proc_bitmap = utils.preprocess_bitmap(proc_bitmap)
plt.subplot(121)
plt.imshow(bitmap, cmap=cm.Greys_r)
plt.subplot(122)
plt.imshow(np.squeeze(proc_bitmap, axis=0), cmap=cm.Greys_r)
plt.show()
#!/usr/bin/env python2
# This script counts the characters of the CASIA HWDB1.1 data set
import sys
from collections import Counter, defaultdict
import utils
if len(sys.argv) != 3:
print 'Usage: %s trn_dirpath tst_dirpath' % sys.argv[0]
exit()
trn_dirpath = sys.argv[1]
tst_dirpath = sys.argv[2]
frequencies = defaultdict(Counter)
for bitmap, tagcode in utils.read_gnt_in_directory(trn_dirpath):
tagcode_unicode = utils.tagcode_to_unicode(tagcode)
frequencies[tagcode_unicode].update(trn=1)
for bitmap, tagcode in utils.read_gnt_in_directory(tst_dirpath):
tagcode_unicode = utils.tagcode_to_unicode(tagcode)
frequencies[tagcode_unicode].update(tst=1)
with open('frequencies.txt', 'w') as f:
for k, v in sorted(frequencies.iteritems(), key=lambda (k, v): v['trn'], reverse=True):
f.write('%s: %d, %d\n' % (k.encode('utf-8'), v['trn'], v['tst']))
