diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py index 8960354..b14df5e 100644 --- a/sbb_binarize/sbb_binarize.py +++ b/sbb_binarize/sbb_binarize.py @@ -1,42 +1,43 @@ """ Tool to load model and binarize a given image. """ - +import argparse import sys -from glob import glob from os import environ, devnull -from os.path import join -from warnings import catch_warnings, simplefilter +from pathlib import Path +from typing import Union -import numpy as np -from PIL import Image import cv2 +import numpy as np + environ['TF_CPP_MIN_LOG_LEVEL'] = '3' stderr = sys.stderr sys.stderr = open(devnull, 'w') import tensorflow as tf from tensorflow.keras.models import load_model from tensorflow.python.keras import backend as tensorflow_backend -sys.stderr = stderr +sys.stderr = stderr import logging + def resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) + class SbbBinarizer: - def __init__(self, model_dir, logger=None): - self.model_dir = model_dir + def __init__(self, model_dir: Union[str, Path], logger=None): + model_dir = Path(model_dir) self.log = logger if logger else logging.getLogger('SbbBinarizer') self.start_new_session() - self.model_files = glob('%s/*.h5' % self.model_dir) + self.model_files = list([str(p.absolute()) for p in model_dir.rglob("*.h5")]) if not self.model_files: - raise ValueError(f"No models found in {self.model_dir}") - + raise ValueError(f"No models found in {str(model_dir)}") + self.models = [] for model_file in self.model_files: self.models.append(self.load_model(model_file)) @@ -53,54 +54,51 @@ class SbbBinarizer: self.session.close() del self.session - def load_model(self, model_name): - model = load_model(model_name, compile=False) - model_height = model.layers[len(model.layers)-1].output_shape[1] - model_width = model.layers[len(model.layers)-1].output_shape[2] - n_classes = model.layers[len(model.layers)-1].output_shape[3] + def load_model(self, model_path: str): + model = load_model(model_path, compile=False) + model_height = model.layers[len(model.layers) - 1].output_shape[1] + model_width = model.layers[len(model.layers) - 1].output_shape[2] + n_classes = model.layers[len(model.layers) - 1].output_shape[3] return model, model_height, model_width, n_classes def predict(self, model_in, img, use_patches): tensorflow_backend.set_session(self.session) model, model_height, model_width, n_classes = model_in - + img_org_h = img.shape[0] img_org_w = img.shape[1] - + if img.shape[0] < model_height and img.shape[1] >= model_width: - img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] )) - - index_start_h = int( abs( img.shape[0] - model_height) /2.) + img_padded = np.zeros((model_height, img.shape[1], img.shape[2])) + + index_start_h = int(abs(img.shape[0] - model_height) / 2.) index_start_w = 0 - - img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:] - + + img_padded[index_start_h: index_start_h + img.shape[0], :, :] = img[:, :, :] + elif img.shape[0] >= model_height and img.shape[1] < model_width: - img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] )) - - index_start_h = 0 - index_start_w = int( abs( img.shape[1] - model_width) /2.) - - img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:] - - + img_padded = np.zeros((img.shape[0], model_width, img.shape[2])) + + index_start_h = 0 + index_start_w = int(abs(img.shape[1] - model_width) / 2.) + + img_padded[:, index_start_w: index_start_w + img.shape[1], :] = img[:, :, :] + + elif img.shape[0] < model_height and img.shape[1] < model_width: - img_padded = np.zeros(( model_height, model_width, img.shape[2] )) - - index_start_h = int( abs( img.shape[0] - model_height) /2.) - index_start_w = int( abs( img.shape[1] - model_width) /2.) - - img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:] - + img_padded = np.zeros((model_height, model_width, img.shape[2])) + + index_start_h = int(abs(img.shape[0] - model_height) / 2.) + index_start_w = int(abs(img.shape[1] - model_width) / 2.) + + img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :] + else: index_start_h = 0 - index_start_w = 0 + index_start_w = 0 img_padded = np.copy(img) - - + img = np.copy(img_padded) - - if use_patches: @@ -109,7 +107,6 @@ class SbbBinarizer: width_mid = model_width - 2 * margin height_mid = model_height - 2 * margin - img = img / float(255.0) img_h = img.shape[0] @@ -169,49 +166,49 @@ class SbbBinarizer: mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color - elif i == nxf-1 and j == nyf-1: + elif i == nxf - 1 and j == nyf - 1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color - elif i == 0 and j == nyf-1: + elif i == 0 and j == nyf - 1: seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color - elif i == nxf-1 and j == 0: + elif i == nxf - 1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color - elif i == 0 and j != 0 and j != nyf-1: + elif i == 0 and j != 0 and j != nyf - 1: seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color - elif i == nxf-1 and j != 0 and j != nyf-1: + elif i == nxf - 1 and j != 0 and j != nyf - 1: seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color - elif i != 0 and i != nxf-1 and j == 0: + elif i != 0 and i != nxf - 1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color - elif i != 0 and i != nxf-1 and j == nyf-1: + elif i != 0 and i != nxf - 1 and j == nyf - 1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin] @@ -224,10 +221,8 @@ class SbbBinarizer: mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color - - - - prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:] + + prediction_true = prediction_true[index_start_h: index_start_h + img_org_h, index_start_w: index_start_w + img_org_w, :] prediction_true = prediction_true.astype(np.uint8) else: @@ -242,17 +237,16 @@ class SbbBinarizer: seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = prediction_true.astype(np.uint8) - return prediction_true[:,:,0] + return prediction_true[:, :, 0] def run(self, image=None, image_path=None, save=None, use_patches=False): - if (image is not None and image_path is not None) or \ - (image is None and image_path is None): + if (image is not None and image_path is not None) or (image is None and image_path is None): raise ValueError("Must pass either a opencv2 image or an image_path") if image_path is not None: image = cv2.imread(image_path) img_last = 0 for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): - self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) + self.log.info(f"Predicting with model {model_file} [{n + 1}/{len(self.model_files)}]") res = self.predict(model, image, use_patches) @@ -272,5 +266,7 @@ class SbbBinarizer: img_last[:, :][img_last[:, :] > 0] = 255 img_last = (img_last[:, :] == 0) * 255 if save: + # Create the output directory (and if necessary it's parents) if it doesn't exist already + Path(save).parent.mkdir(parents=True, exist_ok=True) cv2.imwrite(save, img_last) return img_last