Exemplo n.º 1
0
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)
Exemplo n.º 2
0
    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