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Diffstat (limited to 'src/text_recognizer/paragraph_text_recognizer.py')
| -rw-r--r-- | src/text_recognizer/paragraph_text_recognizer.py | 153 |
1 files changed, 153 insertions, 0 deletions
diff --git a/src/text_recognizer/paragraph_text_recognizer.py b/src/text_recognizer/paragraph_text_recognizer.py new file mode 100644 index 0000000..aa39662 --- /dev/null +++ b/src/text_recognizer/paragraph_text_recognizer.py @@ -0,0 +1,153 @@ +"""Full model. + +Takes an image and returns the text in the image, by first segmenting the image with a LineDetector, then extracting the +each crop of the image corresponding to line regions, and feeding them to a LinePredictor model that outputs the text +in each region. +""" +from typing import Dict, List, Tuple, Union + +import cv2 +import numpy as np +import torch + +from text_recognizer.models import SegmentationModel, TransformerModel +from text_recognizer.util import read_image + + +class ParagraphTextRecognizor: + """Given an image of a single handwritten character, recognizes it.""" + + def __init__(self, line_predictor_args: Dict, line_detector_args: Dict) -> None: + self._line_predictor = TransformerModel(**line_predictor_args) + self._line_detector = SegmentationModel(**line_detector_args) + self._line_detector.eval() + self._line_predictor.eval() + + def predict(self, image_or_filename: Union[str, np.ndarray]) -> Tuple: + """Takes an image and returns all text within it.""" + image = ( + read_image(image_or_filename) + if isinstance(image_or_filename, str) + else image_or_filename + ) + + line_region_crops = self._get_line_region_crops(image) + processed_line_region_crops = [ + self._process_image_for_line_predictor(image=crop) + for crop in line_region_crops + ] + line_region_strings = [ + self.line_predictor_model.predict_on_image(crop)[0] + for crop in processed_line_region_crops + ] + + return " ".join(line_region_strings), line_region_crops + + def _get_line_region_crops( + self, image: np.ndarray, min_crop_len_factor: float = 0.02 + ) -> List[np.ndarray]: + """Returns all the crops of text lines in a square image.""" + processed_image, scale_down_factor = self._process_image_for_line_detector( + image + ) + line_segmentation = self._line_detector.predict_on_image(processed_image) + bounding_boxes = _find_line_bounding_boxes(line_segmentation) + + bounding_boxes = (bounding_boxes * scale_down_factor).astype(int) + + min_crop_len = int(min_crop_len_factor * min(image.shape[0], image.shape[1])) + line_region_crops = [ + image[y : y + h, x : x + w] + for x, y, w, h in bounding_boxes + if w >= min_crop_len and h >= min_crop_len + ] + return line_region_crops + + def _process_image_for_line_detector( + self, image: np.ndarray + ) -> Tuple[np.ndarray, float]: + """Convert uint8 image to float image with black background with shape self._line_detector.image_shape.""" + resized_image, scale_down_factor = _resize_image_for_line_detector( + image=image, max_shape=self._line_detector.image_shape + ) + resized_image = (1.0 - resized_image / 255).astype("float32") + return resized_image, scale_down_factor + + def _process_image_for_line_predictor(self, image: np.ndarray) -> np.ndarray: + """Preprocessing of image before feeding it to the LinePrediction model. + + Convert uint8 image to float image with black background with shape + self._line_predictor.image_shape while maintaining the image aspect ratio. + + Args: + image (np.ndarray): Crop of text line. + + Returns: + np.ndarray: Processed crop for feeding line predictor. + """ + expected_shape = self._line_detector.image_shape + scale_factor = (np.array(expected_shape) / np.array(image.shape)).min() + scaled_image = cv2.resize( + image, + dsize=None, + fx=scale_factor, + fy=scale_factor, + interpolation=cv2.INTER_AREA, + ) + + pad_with = ( + (0, expected_shape[0] - scaled_image.shape[0]), + (0, expected_shape[1] - scaled_image.shape[1]), + ) + + padded_image = np.pad( + scaled_image, pad_with=pad_with, mode="constant", constant_values=255 + ) + return 1 - padded_image / 255 + + +def _find_line_bounding_boxes(line_segmentation: np.ndarray) -> np.ndarray: + """Given a line segmentation, find bounding boxes for connected-component regions corresponding to non-0 labels.""" + + def _find_line_bounding_boxes_in_channel( + line_segmentation_channel: np.ndarray, + ) -> np.ndarray: + line_segmentation_image = cv2.dilate( + line_segmentation_channel, kernel=np.ones((3, 3)), iterations=1 + ) + line_activation_image = (line_segmentation_image * 255).astype("uint8") + line_activation_image = cv2.threshold( + line_activation_image, 0.5, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU + )[1] + + bounding_cnts, _ = cv2.findContours( + line_segmentation_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + ) + return np.array([cv2.boundingRect(cnt) for cnt in bounding_cnts]) + + bounding_boxes = np.concatenate( + [ + _find_line_bounding_boxes_in_channel(line_segmentation[:, :, i]) + for i in [1, 2] + ], + axis=0, + ) + + return bounding_boxes[np.argsort(bounding_boxes[:, 1])] + + +def _resize_image_for_line_detector( + image: np.ndarray, max_shape: Tuple[int, int] +) -> Tuple[np.ndarray, float]: + """Resize the image to less than the max_shape while maintaining the aspect ratio.""" + scale_down_factor = max(np.ndarray(image.shape) / np.ndarray(max_shape)) + if scale_down_factor == 1: + return image.copy(), scale_down_factor + resize_image = cv2.resize( + image, + dsize=None, + fx=1 / scale_down_factor, + fy=1 / scale_down_factor, + interpolation=cv2.INTER_AREA, + ) + return resize_image, scale_down_factor |