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-rw-r--r--text_recognizer/paragraph_text_recognizer.py153
1 files changed, 153 insertions, 0 deletions
diff --git a/text_recognizer/paragraph_text_recognizer.py b/text_recognizer/paragraph_text_recognizer.py
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+++ b/text_recognizer/paragraph_text_recognizer.py
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+"""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