def main(which, debug=True):
with(open('settings.yml')) as f:
settings = yaml.load(f)
clf = pickle.load(open('classifier.pickle'))
img = capture_image()
prediction, binarized_img, chars = predict(clf, img=img,
boundaries=settings['crop'][which])
if debug:
pp.gray()
pp.subplot(2,1,1)
pp.title('Prediction: %s' % prediction, fontsize='xx-large')
pp.imshow(binarized_img)
for i in range(6):
pp.subplot(2,6,7+i)
pp.imshow(chars[i])
pp.show()
print prediction
settings = yaml.load(f)
class ValidationError(Exception):
pass
def validate_user(username, password):
if settings["users"][username] != password:
raise ValidationError()
@app.route("/<int:token_id>", methods=["POST"])
def index(token_id):
try:
username = request.values["username"]
password = request.values["password"]
validate_user(username, password)
boundaries = settings["crop"][token_id]
except KeyError, ValidationError:
abort(401)
img = capture_image()
prediction, binarized_img, chars = predict(clf, img=img, boundaries=boundaries)
return jsonify({"prediction": prediction})
if __name__ == "__main__":
app.run(debug=True)
def interactive_benchmark(clf, raw_images_glob, save_to):
with(open('settings.yml')) as f:
settings = yaml.load(f)
# interactive plotting
pp.ion()
pp.figure()
pp.gray()
if not os.path.exists(save_to):
print "CREATING DIRECTORY", save_to
os.makedirs(save_to)
instructions = '[Enter] to accept, [q] to quit, or key the correct answer'
print instructions
lines = []
raw_images_fn = glob.glob(raw_images_glob)
training_images = set(yaml.load(open('training/training_set.yml')).keys())
for fn in raw_images_fn:
if os.path.basename(fn) in training_images:
print 'Already trained on %s. Skipping' % fn
continue
prediction, binarized_img, chars = predict(clf, img=fn,
boundaries=settings['crop'][0])
# plot the binarize_img across the top panel
pp.clf()
pp.subplot(2,1,1)
pp.imshow(binarized_img)
# plot the 6 chars (separated) across the bottom panel
for i in range(6):
pp.subplot(2,6,7+i)
pp.imshow(chars[i])
title = ' '.join(prediction[:3]) + ' ' + ' '.join(prediction[3:])
pp.suptitle(title, fontsize='xx-large')
pp.draw()
ask_again = True
while ask_again:
response = raw_input('[Enter/q/key]: ')
ask_again = False # default
if response == '':
line = '%s: "%s"' % (os.path.basename(fn), prediction)
print line
lines.append(line)
io.imsave(os.path.join(save_to, os.path.basename(fn)),
1.0*binarized_img)
elif re.match('\d{6}', response):
line = '%s: "%s"' % (os.path.basename(fn), response)
print 'CORRECTED', line
lines.append(line)
io.imsave(os.path.join(save_to, os.path.basename(fn)),
1.0*binarized_img)
elif response == 'q':
print 'Quitting...\n'
print ('Instructions: Move the files from %s into the training '
'directory, and add the lines below to the file'
'training_set.yml:\n' % save_to)
print os.linesep.join(lines)
return
else:
print instructions
ask_again = True
