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https://github.com/qurator-spk/sbb_binarization.git
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Merge pull request #1 from bertsky/batch-prediction
fixup for batch prediction PR
This commit is contained in:
Alexander Pacha
GitHub
commit
3ade5eccba
3 changed files with 52 additions and 30 deletions
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@ -15,4 +15,4 @@ from .sbb_binarize import SbbBinarizer
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def main(model_dir, input_image, output_image):
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binarizer = SbbBinarizer()
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binarizer.load_model(model_dir)
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binarizer.binarize_image(image_path=input_image, save_path=output_image)
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binarizer.binarize_image_file(image_path=input_image, save_path=output_image)
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@ -69,7 +69,8 @@ class SbbBinarizeProcessor(Processor):
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raise FileNotFoundError("Does not exist or is not a directory: %s" % model_path)
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# resolve relative path via OCR-D ResourceManager
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model_path = self.resolve_resource(str(model_path))
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self.binarizer = SbbBinarizer(model_dir=model_path, logger=LOG)
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self.binarizer = SbbBinarizer()
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self.binarizer.load_model(model_path)
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def process(self):
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"""
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@ -110,7 +111,7 @@ class SbbBinarizeProcessor(Processor):
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if oplevel == 'page':
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LOG.info("Binarizing on 'page' level in page '%s'", page_id)
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bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image)))
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bin_image = cv2pil(self.binarizer.binarize_image(pil2cv(page_image)))
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# update METS (add the image file):
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bin_image_path = self.workspace.save_image_file(bin_image,
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file_id + '.IMG-BIN',
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@ -124,7 +125,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text/table regions", page_id)
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for region in regions:
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region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized')
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region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image)))
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region_image_bin = cv2pil(self.binarizer.binarize_image(image=pil2cv(region_image)))
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region_image_bin_path = self.workspace.save_image_file(
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region_image_bin,
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"%s_%s.IMG-BIN" % (file_id, region.id),
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@ -139,7 +140,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text lines", page_id)
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for region_id, line in region_line_tuples:
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line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
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line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image)))
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line_image_bin = cv2pil(self.binarizer.binarize_image(image=pil2cv(line_image)))
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line_image_bin_path = self.workspace.save_image_file(
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line_image_bin,
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"%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),
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@ -3,51 +3,74 @@ import gc
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import itertools
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import math
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import os
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import sys
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from pathlib import Path
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from typing import Union, List, Any
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from typing import Union, List, Tuple, Any
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import cv2
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import numpy as np
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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stderr = sys.stderr
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sys.stderr = open(os.devnull, 'w')
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import tensorflow as tf
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from tensorflow.python.keras.saving.save import load_model
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sys.stderr = stderr
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from mpire import WorkerPool
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from mpire.utils import make_single_arguments
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from tensorflow.python.keras.saving.save import load_model
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class SbbBinarizer:
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def __init__(self) -> None:
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super().__init__()
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self.model: Any = None
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self.model_height: int = 0
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self.model_width: int = 0
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self.n_classes: int = 0
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self.models: List[Tuple[Any, int, int, int]] = []
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def load_model(self, model_dir: Union[str, Path]):
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model_dir = Path(model_dir)
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self.model = load_model(str(model_dir.absolute()), compile=False)
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self.model_height = self.model.layers[len(self.model.layers) - 1].output_shape[1]
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self.model_width = self.model.layers[len(self.model.layers) - 1].output_shape[2]
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self.n_classes = self.model.layers[len(self.model.layers) - 1].output_shape[3]
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model_paths = list(model_dir.glob('*.h5')) or list(model_dir.glob('*/'))
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for path in model_paths:
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model = load_model(str(path.absolute()), compile=False)
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height = model.layers[len(model.layers) - 1].output_shape[1]
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width = model.layers[len(model.layers) - 1].output_shape[2]
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classes = model.layers[len(model.layers) - 1].output_shape[3]
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self.models.append((model, height, width, classes))
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def binarize_image(self, image_path: Path, save_path: Path):
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def binarize_image_file(self, image_path: Path, save_path: Path):
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if not image_path.exists():
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raise ValueError(f"Image not found: {str(image_path)}")
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# noinspection PyUnresolvedReferences
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img = cv2.imread(str(image_path))
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full_image = self.binarize_image(img)
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Path(save_path).parent.mkdir(parents=True, exist_ok=True)
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# noinspection PyUnresolvedReferences
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cv2.imwrite(str(save_path), full_image)
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def binarize_image(self, img: np.ndarray) -> np.ndarray:
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img_last = False
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for model, model_height, model_width, _ in self.models:
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img_res = self.binarize_image_by_model(img, model, model_height, model_width)
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img_last = img_last + (img_res == 0)
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img_last = (~img_last).astype(np.uint8) * 255
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return img_last
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def binarize_image_by_model(self, img: np.ndarray, model: Any, model_height: int, model_width: int) -> np.ndarray:
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# Padded images must be multiples of model size
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original_image_height, original_image_width, image_channels = img.shape
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# Padded images must be multiples of model size
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padded_image_height = math.ceil(original_image_height / self.model_height) * self.model_height
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padded_image_width = math.ceil(original_image_width / self.model_width) * self.model_width
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padded_image_height = math.ceil(original_image_height / model_height) * model_height
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padded_image_width = math.ceil(original_image_width / model_width) * model_width
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padded_image = np.zeros((padded_image_height, padded_image_width, image_channels))
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padded_image[0:original_image_height, 0:original_image_width, :] = img[:, :, :]
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image_batch = np.expand_dims(padded_image, 0) # Create the batch dimension
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patches = tf.image.extract_patches(
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images=image_batch,
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sizes=[1, self.model_height, self.model_width, 1],
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strides=[1, self.model_height, self.model_width, 1],
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sizes=[1, model_height, model_width, 1],
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strides=[1, model_height, model_width, 1],
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rates=[1, 1, 1, 1],
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padding='SAME'
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)
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@ -55,17 +78,17 @@ class SbbBinarizer:
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number_of_horizontal_patches = patches.shape[1]
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number_of_vertical_patches = patches.shape[2]
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total_number_of_patches = number_of_horizontal_patches * number_of_vertical_patches
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target_shape = (total_number_of_patches, self.model_height, self.model_width, image_channels)
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target_shape = (total_number_of_patches, model_height, model_width, image_channels)
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# Squeeze all image patches (n, m, width, height, channels) into a single big batch (b, width, height, channels)
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image_patches = tf.reshape(patches, target_shape)
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# Normalize the image to values between 0.0 - 1.0
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image_patches = image_patches / float(255.0)
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predicted_patches = self.model.predict(image_patches)
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predicted_patches = model.predict(image_patches, verbose=0)
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# We have to manually call garbage collection and clear_session here to avoid memory leaks.
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# Taken from https://medium.com/dive-into-ml-ai/dealing-with-memory-leak-issue-in-keras-model-training-e703907a6501
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gc.collect()
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tf.keras.backend.clear_session()
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#gc.collect()
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#tf.keras.backend.clear_session()
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# The result is a white-on-black image that needs to be inverted to be displayed as black-on-white image
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# We do this by converting the binary values to a boolean numpy-array and then inverting the values
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@ -76,13 +99,11 @@ class SbbBinarizer:
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grayscale_patches,
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padded_image_height,
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padded_image_width,
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self.model_height,
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self.model_width
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model_height,
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model_width
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)
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full_image = full_image_with_padding[0:original_image_height, 0:original_image_width]
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Path(save_path).parent.mkdir(parents=True, exist_ok=True)
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# noinspection PyUnresolvedReferences
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cv2.imwrite(str(save_path), full_image)
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return full_image
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def _patches_to_image(self, patches: np.ndarray, image_height: int, image_width: int, patch_height: int, patch_width: int):
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height = math.ceil(image_height / patch_height) * patch_height
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