Delete src/utils.py

src/utils.py

@@ -1,81 +0,0 @@
-# Necessary imports
-import re
-import supervision as sv
-from PIL import Image
-
-
-# Text cleaning function
-def clean_text(text):
-    """
-    Cleans the given text by removing unwanted tokens, extra spaces,
-    and ensures proper spacing between words and after punctuation marks.
-
-    Args:
-        text (str): The input text to be cleaned.
-
-    Returns:
-        str: The cleaned and properly formatted text.
-    """
-    # Remove unwanted tokens
-    text = text.replace("<pad>", "").replace("</s>", "").strip()
-
-    # Split the text into lines and clean each line
-    lines = text.split("\n")
-    cleaned_lines = [line.strip() for line in lines if line.strip()]
-
-    # Join the cleaned lines into a single string with a space between each line
-    cleaned_text = " ".join(cleaned_lines)
-
-    # Ensure proper spacing using regex
-    cleaned_text = re.sub(
-        r"\s+", " ", cleaned_text
-    )  # Replace multiple spaces with a single space
-    cleaned_text = re.sub(
-        r"(?<=[.,!?])(?=[^\s])", r" ", cleaned_text
-    )  # Add space after punctuation if not followed by a space
-    cleaned_text = re.sub(
-        r"(?<=[a-z])(?=[A-Z])", r" ", cleaned_text
-    )  # Add space between joined words where a lowercase letter is followed by an uppercase letter
-    cleaned_text = re.sub(
-        r"(\w)([A-Z][a-z])", r"\1 \2", cleaned_text
-    )  # Add space between camel case words
-
-    # Return the cleaned text
-    return cleaned_text
-
-
-# Draw OCR bounding boxes with enhanced visual elements
-def draw_ocr_bboxes(image: Image, detections: sv.Detections) -> Image:
-    """
-    Draws bounding boxes and labels on the input image based on the OCR detections.
-
-    Args:
-        - image (PIL.Image): The input image on which to draw the bounding boxes and labels.
-        - detections (sv.Detections): The OCR detections containing the bounding box coordinates and labels.
-
-    Returns:
-        PIL.Image: The annotated image with bounding boxes and labels.
-    """
-    # Copy the input image to avoid modifying the original image
-    annotated_image = image.copy()
-
-    # Calculate the optimal line thickness and text scale based on the image resolution
-    thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)
-    text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
-
-    # Initialize the bounding box and label annotators
-    box_annotator = sv.BoxAnnotator(
-        color_lookup=sv.ColorLookup.INDEX, thickness=thickness
-    )
-    label_annotator = sv.LabelAnnotator(
-        color_lookup=sv.ColorLookup.INDEX,
-        text_scale=text_scale,
-        text_thickness=thickness,
-    )
-
-    # Annotate the image with bounding boxes and labels
-    annotated_image = box_annotator.annotate(annotated_image, detections)
-    annotated_image = label_annotator.annotate(annotated_image, detections)
-
-    # Return the annotated image
-    return annotated_image