I have images of different sizes but around 100×2000-800×8000 & 2000×100 in files: .jpg and corresponding files .txt with normalized bounding boxes (YOLOv8 format)

I want to resize images to height= 4096 or width=4096 depending on orientation but keep the aspect ration.
Later I want to paste them in middle in white image. And this part of the code works like charm.

from PIL import Image, ImageDraw
import os
import shutil
import numpy as np

class ImageProcessor:
    def __init__(self, image: Image.Image) -> None:
        self.image = image 

    def resize_to_square(self, width: int = 4096, height: int = 4096) -> Image.Image:
        '''
        Resize the image to a square with the specified dimensions.
        The image is centered on a white background.
        '''
        # Calculate the new size while maintaining aspect ratio
        aspect_ratio = self.image.width / self.image.height
        if self.image.width > self.image.height:
            new_width = width
            new_height = int(width / aspect_ratio)
        else:
            new_height = height
            new_width = int(height * aspect_ratio)
        
        # Resize the image
        resized_image = self.image.resize((new_width, new_height), Image.LANCZOS)

        # Create a new white image with the specified dimensions
        white_image = Image.new('RGB', (width, height), (255, 255, 255))

        # Calculate the position to place the resized image in the middle
        x = (width - new_width) // 2
        y = (height - new_height) // 2

        # Paste the resized image onto the white image
        white_image.paste(resized_image, (x, y))

        return white_image, x, y, new_width, new_height

The second part doesn’t work. I’m trying to format normalized bounding box to match the new image.

My tries:

class LabelProcessor:
    def __init__(self, label_path: str) -> None:
        self.label = label_path
        self.bboxes = self._read_bounding_boxes()
    
    def _read_bounding_boxes(self):
        with open(self.label, 'r') as f:
            lines = f.readlines()
        boxes = []
        for line in lines:
            parts = line.strip().split()
            class_id = int(parts[0])
            x_center = float(parts[1])
            y_center = float(parts[2])
            width = float(parts[3])
            height = float(parts[4])
            boxes.append((class_id, x_center, y_center, width, height))
        return boxes
    
    def adjust_bounding_boxes(self, orig_width, orig_height, new_x, new_y, new_width, new_height, target_width=4096, target_height=4096):
        adjusted_boxes = []
        for class_id, x_center, y_center, width, height in self.bboxes:
            # Convert to original pixel coordinates
            x_center_pix = x_center * orig_width
            y_center_pix = y_center * orig_height
            width_pix = width * orig_width
            height_pix = height * orig_height

            # Adjust coordinates to the new image size
            new_x_center_pix = x_center_pix + new_x
            new_y_center_pix = y_center_pix + new_y

            # Convert back to normalized coordinates
            new_x_center = new_x_center_pix / target_width
            new_y_center = new_y_center_pix / target_height
            new_width = width_pix / target_width
            new_height = height_pix / target_height

            adjusted_boxes.append((class_id, new_x_center, new_y_center, new_width, new_height))
        
        self.bboxes = adjusted_boxes

The original Bboxes:

3 0.19407051282051282 0.9483531651594479 0.3711217948717949 0.023960019038553073
0 0.4331410256410256 0.20447881960970968 0.787275641025641 0.050556877677296524
2 0.26346153846153847 0.8097858162779629 0.5161217948717949 0.016049500237981913
1 0.44384615384615383 0.06699190861494526 0.8877243589743591 0.008643503093764875
1 0.44144230769230763 0.05762970014278915 0.8828525641025641 0.007201332698714898
1 0.4450641025641026 0.04844359828653023 0.8804487179487179 0.009005235602094241
1 0.44144230769230763 0.04051880057115659 0.8828525641025641 0.007563065207044265
1 0.43900641025641024 0.032774869109947646 0.8683012820512821 0.007924797715373631

The original file and result: