Removing legacy code

1 files changed, 0 insertions, 153 deletions

diff --git a/text_recognizer/paragraph_text_recognizer.py b/text_recognizer/paragraph_text_recognizer.py
deleted file mode 100644
index aa39662..0000000
--- a/text_recognizer/paragraph_text_recognizer.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""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