def _load_iam_paragraphs(): print('Loading IAM paragraph crops and ground truth from image files...') images = [] gt_images = [] ids = [] for filename in CROPS_DIRNAME.glob('*.jpg'): id_ = filename.stem image = util.read_image(filename, grayscale=True) image = 1. - image / 255 gt_filename = GT_DIRNAME / f'{id_}.png' gt_image = util.read_image(gt_filename, grayscale=True) images.append(image) gt_images.append(gt_image) ids.append(id_) images = np.array(images).astype(np.float32) gt_images = util.to_categorical(np.array(gt_images), 3).astype(np.uint8) return images, gt_images, np.array(ids)
def y_test(self): return to_categorical(self.y_test_int, self.num_classes)
def y_train(self): return to_categorical(self.y_train_int, self.num_classes)
def convert_strings_to_categorical_labels(labels, mapping): return np.array([ to_categorical([mapping[c] for c in label], num_classes=len(mapping)) for label in labels ])
def y_test(self): """Return y_test""" return util.to_categorical(self.y_test_int, self.num_classes)
def y_train(self): """Return y_train""" return util.to_categorical(self.y_train_int, self.num_classes)