def process_and_save_images(source_dir, dest_dir):
for number, name, image in iterate_images_folder(source_dir):
processed = binarize_image(image)
save_dir = root(dest_dir, number)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
processed.save(root(save_dir, name))
def get_average_images(dirname):
dirname = root(dirname)
averages = {}
for number, name, image in iterate_images_folder(dirname):
averages[number] = image
return averages
def execute_test_set(trainning_patterns_dir, test_set_dir):
averages = get_average_images(trainning_patterns_dir)
confusion = numpy.zeros(shape=(10,10))
for test_number, name, image in iterate_images_folder(test_set_dir):
recognized = recognize_number_in_image(image, averages)
confusion[int(test_number)][int(recognized)] += 1
probabilities = create_probabilities_matrix(confusion)
return confusion, probabilities
def process_and_save_averages(source_dir, dest_dir):
images_dict = {}
for number, name, image in iterate_images_folder(source_dir):
if not number in images_dict:
images_dict[number] = []
images_dict[number].append(image)
averages = calculate_image_averages(images_dict)
for key in averages:
save_dir = root(dest_dir, key)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
averages[key].save(root(save_dir, key + '.jpg'))
def execute_test_set(trainning_patterns_dir, test_set_dir):
averages = get_average_images(trainning_patterns_dir)
results = {}
for test_number, name, image in iterate_images_folder(test_set_dir):
recognized = recognize_number_in_image(image, averages)
if not test_number in results:
results[test_number] = {}
results[test_number]['success'] = 0
results[test_number]['error'] = 0
if recognized == test_number:
results[test_number]['success'] += 1
else:
results[test_number]['error'] += 1
return results
