diff --git a/sbb_binarize/cli.py b/sbb_binarize/cli.py index ad1850e..e0c4d1a 100644 --- a/sbb_binarize/cli.py +++ b/sbb_binarize/cli.py @@ -15,4 +15,4 @@ from .sbb_binarize import SbbBinarizer def main(model_dir, input_image, output_image): binarizer = SbbBinarizer() binarizer.load_model(model_dir) - binarizer.binarize_image(image_path=input_image, save_path=output_image) + binarizer.binarize_image_file(image_path=input_image, save_path=output_image) diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py index fa42223..4d12e19 100644 --- a/sbb_binarize/sbb_binarize.py +++ b/sbb_binarize/sbb_binarize.py @@ -5,7 +5,7 @@ import math import os import sys from pathlib import Path -from typing import Union, List, Any +from typing import Union, List, Tuple, Any import cv2 import numpy as np @@ -24,37 +24,53 @@ from mpire.utils import make_single_arguments class SbbBinarizer: 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 + self.models: List[Tuple[Any, int, int, int]] = [] def load_model(self, model_dir: Union[str, Path]): model_dir = Path(model_dir) - 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): + model_paths = list(model_dir.glob('*.h5')) or list(model_dir.glob('*/')) + for path in model_paths: + model = load_model(str(path.absolute()), compile=False) + height = model.layers[len(model.layers) - 1].output_shape[1] + width = model.layers[len(model.layers) - 1].output_shape[2] + classes = model.layers[len(model.layers) - 1].output_shape[3] + self.models.append((model, height, width, classes)) + + def binarize_image_file(self, image_path: Path, save_path: Path): if not image_path.exists(): raise ValueError(f"Image not found: {str(image_path)}") # noinspection PyUnresolvedReferences img = cv2.imread(str(image_path)) - original_image_height, original_image_width, image_channels = img.shape + full_image = self.binarize_image(img) + + Path(save_path).parent.mkdir(parents=True, exist_ok=True) + # noinspection PyUnresolvedReferences + cv2.imwrite(str(save_path), full_image) + + def binarize_image(self, img: np.ndarray) -> np.ndarray: + img_last = False + for model, model_height, model_width, _ in self.models: + img_res = self.binarize_image_by_model(img, model, model_height, model_width) + img_last = img_last + (img_res == 0) + img_last = (~img_last).astype(np.uint8) * 255 + return img_last + + def binarize_image_by_model(self, img: np.ndarray, model: Any, model_height: int, model_width: int) -> np.ndarray: # 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 + original_image_height, original_image_width, image_channels = img.shape + + padded_image_height = math.ceil(original_image_height / model_height) * model_height + padded_image_width = math.ceil(original_image_width / model_width) * 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) # Create the batch dimension 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], + sizes=[1, model_height, model_width, 1], + strides=[1, model_height, model_width, 1], rates=[1, 1, 1, 1], padding='SAME' ) @@ -62,13 +78,13 @@ class SbbBinarizer: 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) + target_shape = (total_number_of_patches, model_height, 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) + predicted_patches = model.predict(image_patches, verbose=0) # 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() @@ -83,13 +99,11 @@ class SbbBinarizer: grayscale_patches, padded_image_height, padded_image_width, - self.model_height, - self.model_width + model_height, + 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) + return 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