diff --git a/requirements.txt b/requirements.txt index a847f92..1f6e5c8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,4 @@ setuptools >= 41 opencv-python-headless ocrd >= 2.22.3 tensorflow >= 2.4.0 +mpire \ No newline at end of file diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py index b14df5e..a7a5dd3 100644 --- a/sbb_binarize/sbb_binarize.py +++ b/sbb_binarize/sbb_binarize.py @@ -1,272 +1,168 @@ -""" -Tool to load model and binarize a given image. -""" import argparse -import sys -from os import environ, devnull +import gc +import itertools +import math +import os from pathlib import Path -from typing import Union +from typing import Union, List, Any 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 - -import logging - - -def resize_image(img_in, input_height, input_width): - return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) +from mpire import WorkerPool +from mpire.utils import make_single_arguments +from tensorflow.python.keras.saving.save import load_model class SbbBinarizer: - - def __init__(self, model_dir: Union[str, Path], logger=None): + def __init__(self) -> None: + super().__init__() + self.model: Any = None + self.model_height: int = 0 + self.model_width: int = 0 + self.n_classes: int = 0 + + def load_model(self, model_dir: Union[str, Path]): model_dir = Path(model_dir) - self.log = logger if logger else logging.getLogger('SbbBinarizer') - - self.start_new_session() - - 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 {str(model_dir)}") - - self.models = [] - for model_file in self.model_files: - self.models.append(self.load_model(model_file)) - - def start_new_session(self): - config = tf.compat.v1.ConfigProto() - config.gpu_options.allow_growth = True - - self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() - tensorflow_backend.set_session(self.session) - - def end_session(self): - tensorflow_backend.clear_session() - self.session.close() - del self.session - - 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.) - index_start_w = 0 - - 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[:, :, :] - - - 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[:, :, :] - - else: - index_start_h = 0 - index_start_w = 0 - img_padded = np.copy(img) - - img = np.copy(img_padded) - - if use_patches: - - margin = int(0.1 * model_width) - - width_mid = model_width - 2 * margin - height_mid = model_height - 2 * margin - - img = img / float(255.0) - - img_h = img.shape[0] - img_w = img.shape[1] - - prediction_true = np.zeros((img_h, img_w, 3)) - mask_true = np.zeros((img_h, img_w)) - nxf = img_w / float(width_mid) - nyf = img_h / float(height_mid) - - if nxf > int(nxf): - nxf = int(nxf) + 1 - else: - nxf = int(nxf) - - if nyf > int(nyf): - nyf = int(nyf) + 1 - else: - nyf = int(nyf) - - for i in range(nxf): - for j in range(nyf): - - if i == 0: - index_x_d = i * width_mid - index_x_u = index_x_d + model_width - elif i > 0: - index_x_d = i * width_mid - index_x_u = index_x_d + model_width - - if j == 0: - index_y_d = j * height_mid - index_y_u = index_y_d + model_height - elif j > 0: - index_y_d = j * height_mid - index_y_u = index_y_d + model_height - - if index_x_u > img_w: - index_x_u = img_w - index_x_d = img_w - model_width - if index_y_u > img_h: - index_y_u = img_h - index_y_d = img_h - model_height - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - - label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) - - seg = np.argmax(label_p_pred, axis=3)[0] - - seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) - - if i == 0 and j == 0: - seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] - seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin] - - 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: - 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: - 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: - 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: - 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: - 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: - 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: - 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] - - mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg - prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color - - else: - seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] - seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin] - - 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.astype(np.uint8) - - else: - img_h_page = img.shape[0] - img_w_page = img.shape[1] - img = img / float(255.0) - img = resize_image(img, model_height, model_width) - - label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) - - seg = np.argmax(label_p_pred, axis=3)[0] - 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] - - 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): - 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(f"Predicting with model {model_file} [{n + 1}/{len(self.model_files)}]") - - res = self.predict(model, image, use_patches) - - img_fin = np.zeros((res.shape[0], res.shape[1], 3)) - res[:, :][res[:, :] == 0] = 2 - res = res - 1 - res = res * 255 - img_fin[:, :, 0] = res - img_fin[:, :, 1] = res - img_fin[:, :, 2] = res - - img_fin = img_fin.astype(np.uint8) - img_fin = (res[:, :] == 0) * 255 - img_last = img_last + img_fin - - kernel = np.ones((5, 5), np.uint8) - 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 + self.model = load_model(str(model_dir.absolute()), compile=False) + self.model_height = self.model.layers[len(self.model.layers) - 1].output_shape[1] + self.model_width = self.model.layers[len(self.model.layers) - 1].output_shape[2] + self.n_classes = self.model.layers[len(self.model.layers) - 1].output_shape[3] + + def binarize_image(self, image_path: Path, save_path: Path): + if not image_path.exists(): + raise ValueError(f"Image not found: {str(image_path)}") + + # Most operations are expecting BGR as this is the standard way how CV2 reads images + # noinspection PyUnresolvedReferences + img = cv2.imread(str(image_path)) + original_image_height, original_image_width, image_channels = img.shape + + # Padded images must be multiples of model size + padded_image_height = math.ceil(original_image_height / self.model_height) * self.model_height + padded_image_width = math.ceil(original_image_width / self.model_width) * self.model_width + padded_image = np.zeros((padded_image_height, padded_image_width, image_channels)) + padded_image[0:original_image_height, 0:original_image_width, :] = img[:, :, :] + + image_batch = np.expand_dims(padded_image, 0) # To create the batch information + patches = tf.image.extract_patches( + images=image_batch, + sizes=[1, self.model_height, self.model_width, 1], + strides=[1, self.model_height, self.model_width, 1], + rates=[1, 1, 1, 1], + padding='SAME' + ) + + number_of_horizontal_patches = patches.shape[1] + number_of_vertical_patches = patches.shape[2] + total_number_of_patches = number_of_horizontal_patches * number_of_vertical_patches + target_shape = (total_number_of_patches, self.model_height, self.model_width, image_channels) + # Squeeze all image patches (n, m, width, height, channels) into a single big batch (b, width, height, channels) + image_patches = tf.reshape(patches, target_shape) + # Normalize the image to values between 0.0 - 1.0 + image_patches = image_patches / float(255.0) + + predicted_patches = self.model.predict(image_patches) + # We have to manually call garbage collection and clear_session here to avoid memory leaks. + # Taken from https://medium.com/dive-into-ml-ai/dealing-with-memory-leak-issue-in-keras-model-training-e703907a6501 + gc.collect() + tf.keras.backend.clear_session() + + binary_patches = np.invert(np.argmax(predicted_patches, axis=3).astype(bool)).astype(np.uint8) * 255 + full_image_with_padding = self._patches_to_image( + binary_patches, + padded_image_height, + padded_image_width, + self.model_height, + self.model_width + ) + full_image = full_image_with_padding[0:original_image_height, 0:original_image_width] + Path(save_path).parent.mkdir(parents=True, exist_ok=True) + # noinspection PyUnresolvedReferences + cv2.imwrite(str(save_path), full_image) + + def _patches_to_image( + self, + patches: np.ndarray, + image_height: int, + image_width: int, + patch_height: int, + patch_width: int + ): + height = math.ceil(image_height / patch_height) * patch_height + width = math.ceil(image_width / patch_width) * patch_width + + image_reshaped = np.reshape( + np.squeeze(patches), + [height // patch_height, width // patch_width, patch_height, patch_width] + ) + image_transposed = np.transpose(a=image_reshaped, axes=[0, 2, 1, 3]) + image_resized = np.reshape(image_transposed, [height, width]) + return image_resized + + +def split_list_into_worker_batches(files: List[Any], number_of_workers: int) -> List[List[Any]]: + """ Splits any given list into batches for the specified number of workers and returns a list of lists. """ + batches = [] + batch_size = math.ceil(len(files) / number_of_workers) + batch_start = 0 + for i in range(1, number_of_workers + 1): + batch_end = i * batch_size + file_batch_to_delete = files[batch_start: batch_end] + batches.append(file_batch_to_delete) + batch_start = batch_end + return batches + + +def batch_predict(input_data): + model_dir, input_images, output_images, worker_number = input_data + print(f"Setting visible cuda devices to {str(worker_number)}") + os.environ["CUDA_VISIBLE_DEVICES"] = str(worker_number) + + binarizer = SbbBinarizer() + binarizer.load_model(model_dir) + + for image_path, output_path in zip(input_images, output_images): + binarizer.binarize_image(image_path=image_path, save_path=output_path) + print(f"Binarized {image_path}") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-m', '--model_dir', default="model_2021_03_09", help="Path to the directory where the TF model resides or path to an h5 file.") + parser.add_argument('-i', '--input-path', required=True) + parser.add_argument('-o', '--output-path', required=True) + args = parser.parse_args() + + input_path = Path(args.input_path) + output_path = Path(args.output_path) + model_directory = args.model_dir + + if input_path.is_dir(): + print(f"Enumerating all PNG files in {str(input_path)}") + all_input_images = list(input_path.rglob("*.png")) + print(f"Filtering images that have already been binarized in {str(output_path)}") + input_images = [i for i in all_input_images if not (output_path / (i.relative_to(input_path))).exists()] + output_images = [output_path / (i.relative_to(input_path)) for i in input_images] + input_images = [i for i in input_images] + + print(f"Starting binarization of {len(input_images)} images") + + number_of_gpus = len(tf.config.list_physical_devices('GPU')) + number_of_workers = max(1, number_of_gpus) + image_batches = split_list_into_worker_batches(input_images, number_of_workers) + output_batches = split_list_into_worker_batches(output_images, number_of_workers) + + with WorkerPool(n_jobs=number_of_workers, start_method='spawn') as pool: + model_dirs = itertools.repeat(model_directory, len(image_batches)) + input_data = zip(model_dirs, image_batches, output_batches, range(number_of_workers)) + contents = pool.map_unordered( + batch_predict, + make_single_arguments(input_data), + iterable_len=number_of_workers, + progress_bar=False + ) + else: + binarizer = SbbBinarizer() + binarizer.load_model(model_directory) + binarizer.binarize_image(image_path=input_path, save_path=output_path)