from create_characters import load_test_set
from create_classifier import load_classifier
if len(argv) < 3:
print 'Usage: python %s NEIGHBOURS BLUR_SCALE [ C GAMMA ]' % argv[0]
exit(1)
neighbours = int(argv[1])
blur_scale = float(argv[2])
# Load classifier
if len(argv) > 4:
c = float(argv[3])
gamma = float(argv[4])
classifier = load_classifier(neighbours, blur_scale, c=c, gamma=gamma, \
verbose=1)
else:
classifier = load_classifier(neighbours, blur_scale, verbose=1)
# Load test set
test_set = load_test_set(neighbours, blur_scale, verbose=1)
# Classify each character in the test set, remembering all faulty
# classified characters
l = len(test_set)
matches = 0
classified = []
for i, char in enumerate(test_set):
prediction = classifier.classify(char, char.value)
for image in sorted(listdir(IMAGES_FOLDER + value)):
f = IMAGES_FOLDER + value + "/" + image
image = GrayscaleImage(f)
char = Character(value, [], image)
chars.append(char)
i += 1
if i == count:
br = True
break
if br:
break
# Load classifier (run create_classifier.py first)
classifier = load_classifier(neighbours, blur_scale, verbose=1)
# Measure the time it takes to recognize <count> characters
start = time()
for char in chars:
# Normalize the character image
char.image = NormalizedCharacterImage(image, blur=blur_scale, height=42)
# Create the image's feature vector
char.get_single_cell_feature_vector(neighbours)
# Feed the feature vector to the classifier
classifier.classify(char)
elapsed = time() - start
