scripts/build_features.py

import os
import json
import numpy as np
from PIL import Image
from pathlib import Path
import shutil
from PIL import Image, ImageFont, ImageDraw
import cv2 as cv
import os
import shutil
from pathlib import Path
import os
import json

def get_data_and_annots():
    """
    Load image file paths and their corresponding annotations from the PubLayNet dataset.

    Returns:
        images (dict): A dictionary where keys are image IDs and values are dictionaries
        data (dict): The entire JSON data loaded from the annotations file

    """
    images = {}
    with open('data/raw/label/publaynet/train.json') as t:
        data = json.load(t)

    for train_images in os.walk('data/raw/train/publaynet/train'):
        train_imgs = train_images[2]

    for image in data['images']:
        if image['file_name'] in train_imgs:
            images[image['id']] = {'file_name': "data/raw/train/publaynet/train/" + image['file_name'], 'annotations': []}
        # if len(images) == 10000:
        #   break

    for ann in data['annotations']:
        if ann['image_id'] in images.keys():
            images[ann['image_id']]['annotations'].append(ann)
    
    return images, data



def write_file(image_id, inside, filename, content, check_set):
    """
    Writes content to a file

    Inputs:
        image_id (str): The ID of the image.
        inside (bool): Flag to determine if content should be appended or overwritten.
        filename (str): The path to the file.
        content (str): The content to write to the file.
        check_set (set): A set to keep track of image IDs
    Returns:
    
    
    """
    if inside:
        with open(filename, "a") as file:
            file.write("\n")
            file.write(content)
    else:
        check_set.add(image_id)
        with open(filename, "w") as file:
            file.write(content)

def get_bb_shape(bboxe, img):
    """
    Calculates the shape of the bounding box in the image.

    Inputs:
        bboxe (list): Bounding box coordinates [x, y, width, height].
        img (numpy.ndarray): The image array.

    Returns:
        tuple: The shape (height, width) of the bounding box
        
    """
    tleft = (bboxe[0], bboxe[1])
    tright = (bboxe[0] + bboxe[2], bboxe[1])
    bleft = (bboxe[0], bboxe[1] + bboxe[3])
    bright = (bboxe[0] + bboxe[2], bboxe[1] + bboxe[3])

    top_left_x = min([tleft[0], tright[0], bleft[0], bright[0]])
    top_left_y = min([tleft[1], tright[1], bleft[1], bright[1]])
    bot_right_x = max([tleft[0], tright[0], bleft[0], bright[0]])
    bot_right_y = max([tleft[1], tright[1], bleft[1], bright[1]])

    image = img[int(top_left_y):int(bot_right_y) + 1, int(top_left_x):int(bot_right_x) + 1]

    return image.shape[:2]

def coco_to_yolo(x1, y1, w, h, image_w, image_h):
    """
    Converts COCO format bounding box to YOLO format.

    Inputs:
        x1 (float): Top-left x coordinate.
        y1 (float): Top-left y coordinate.
        w (float): Width of the bounding box.
        h (float): Height of the bounding box.
        image_w (int): Width of the image.
        image_h (int): Height of the image.

    Returns:
        list: YOLO format bounding box [x_center, y_center, width, height]
        
    """
    return [((2 * x1 + w) / (2 * image_w)), ((2 * y1 + h) / (2 * image_h)), w / image_w, h / image_h]

def create_directory(path):
    """
    Creates a directory, deleting it first if it already exists.

    Inputs:
        path (str): The path to the directory
        
    """
    dirpath = Path(path)
    if dirpath.exists() and dirpath.is_dir():
        shutil.rmtree(dirpath)
    os.mkdir(dirpath)

def generate_yolo_labels(images):
    """
    Generates YOLO format labels from the given images and annotations.

    Inputs:
        images (dict): Dictionary containing image data and annotations
        
    """
    check_set = set()
    
    create_directory(os.getcwd() + '/data/processed/yolo')
    
    for key in images:
        image_id = ','.join(map(str, [image_id['image_id'] for image_id in images[key]['annotations']]))
        category_id = ''.join(map(str, [cat_id['category_id'] - 1 for cat_id in images[key]['annotations']]))
        bbox = [bbox['bbox'] for bbox in images[key]['annotations']]
        image_path = images[key]['file_name']
        filename = os.getcwd() + '/data/processed/yolo/' + image_path.split('/')[-1].split(".")[0] + '.txt'

        for index, b in enumerate(bbox):
            bbox = [b[0], b[1], b[2], b[3]]
            shape = get_bb_shape(bbox, cv.imread(image_path))
            yolo_bbox = coco_to_yolo(bbox[0], bbox[1], shape[1], shape[0], cv.imread(image_path).shape[1], cv.imread(image_path).shape[0])
            content = category_id[index] + ' ' + str(yolo_bbox[0]) + ' ' + str(yolo_bbox[1]) + ' ' + str(yolo_bbox[2]) + ' ' + str(yolo_bbox[3])

            if image_id in check_set:
                write_file(image_id, True, filename, content, check_set)
            else:
                write_file(image_id, False, filename, content, check_set)

def delete_additional_images(old_train_path, temp_images_path, yolo_path):
    """
    Delete additional images that don't have corresponding YOLO labels.

    This function moves images from the old training path to a temporary path
    if they have corresponding YOLO label files.

    Inputs:
        old_train_path (str): Path to the original training images.
        temp_images_path (str): Path to store the temporary images.
        yolo_path (str): Path to the YOLO label files.

    Returns:
        
    """
    train = next(os.walk(old_train_path), (None, None, []))[2] 
    label = next(os.walk(yolo_path), (None, None, []))[2]
    
    dirpath = Path(temp_images_path) 
    if dirpath.exists() and dirpath.is_dir():
        shutil.rmtree(dirpath)
    os.mkdir(dirpath)

    for img in train:
        splited = img.split(".")[0]
        txt = f"{splited}.txt"
        if txt in label:
            shutil.move(f"{old_train_path}/{img}", f"{temp_images_path}/{img}")

def split_data(temp_images_path):
    """
    Split the dataset into training and validation sets.

    This function splits the images in the temporary path into training (90%)
    and validation (10%) sets, and moves them to their respective directories.

    Inputs:
        temp_images_path (str): Path to the temporary images.

    Returns:
        list: List of validation image names without file extensions
        
    """
    image = next(os.walk(temp_images_path), (None, None, []))[2]
    train = image[int(len(image) * .1) : int(len(image) * .90)]
    validation = list(set(image) - set(train))
    
    create_directory(os.getcwd() + '/data/processed/training')
    create_directory(os.getcwd() + '/data/processed/validation')
    create_directory(os.getcwd() + '/data/processed/training/images/')
    create_directory(os.getcwd() + '/data/processed/validation/images/')

    for train_img in train:
        shutil.move(f'{temp_images_path}/{train_img}', os.getcwd() + '/data/processed/training/images/')

    for valid_img in validation:
        shutil.move(f'{temp_images_path}/{valid_img}', os.getcwd() + '/data/processed/validation/images/')
    
    validation_without_ext = [i.split('.')[0] for i in validation]
    return validation_without_ext

def create_directory(path):
    """
    Create a new directory, removing it first if it already exists.

    Inputs:
        path (str): Path to the directory to be created.

    Returns:
        
    """
    dirpath = Path(path)
    if dirpath.exists() and dirpath.is_dir():
        shutil.rmtree(dirpath)
    os.mkdir(dirpath)

def get_labels(yolo_path, valid_without_extension):
    """
    Move YOLO label files to their respective training and validation directories.

    Inputs:
        yolo_path (str): Path to the YOLO label files.
        valid_without_extension (list): List of validation image names without file extensions.

    Returns:
        
    """
    create_directory(os.getcwd() + '/data/processed/training/labels')
    create_directory(os.getcwd() + '/data/processed/validation/labels')
    
    label = next(os.walk(yolo_path), (None, None, []))[2]
    for lab in label:
        split = lab.split(".")[0]
        if split in valid_without_extension:
            shutil.move(f"{yolo_path}/{lab}", os.getcwd() + f'/data/processed/validation/labels/{lab}')
        else:
            shutil.move(f"{yolo_path}/{lab}", os.getcwd() + f'/data/processed/training/labels/{lab}')

def final_preparation(old_train_path, temp_images_path, yolo_path):
    """
    Perform final preparation steps for the dataset.

    This function orchestrates the entire data preparation process, including
    deleting additional images, splitting the data, and organizing labels.

    Inputs:
        old_train_path (str): Path to the original training images.
        temp_images_path (str): Path to store the temporary images.
        yolo_path (str): Path to the YOLO label files.

    Returns:
        
    """
    delete_additional_images(old_train_path, temp_images_path, yolo_path)
    valid_without_extension = split_data(temp_images_path)
    
    dirpath = Path(temp_images_path) 
    if dirpath.exists() and dirpath.is_dir():
        shutil.rmtree(dirpath)
    
    return get_labels(yolo_path, valid_without_extension)

def annotate_tables(directory):
    """
    Annotate and crop tables from images based on YOLO labels.

    This function processes images in the given directory, reads corresponding
    YOLO labels, crops table regions, and saves them as separate images.

    Inputs:
        directory (str): Path to the directory containing images to be processed.

    Returns:
        
    """
    dirpath = Path(os.getcwd() + f'/data/processed/tables') 
    if dirpath.exists() and dirpath.is_dir():
        shutil.rmtree(dirpath)
    os.mkdir(dirpath)
    
    for filename in os.listdir(directory):
        file_path = os.path.join(directory, filename)
        
        if os.path.isfile(file_path):
            img_name = filename.split('.')[0]
            print(f'\f {img_name}')
            if os.path.isfile(os.getcwd() + f'/data/processed/training/images/{img_name}.jpg'):
                with open(os.getcwd() + f'/data/processed/training/labels/{img_name}.txt', 'r') as f:
                    results = f.read()
                original_image = Image.open(os.getcwd() + f'/data/processed/training/images/{img_name}.jpg')
                
            elif os.path.isfile(os.getcwd() + f'/data/processed/validation/images/{img_name}.jpg'):
                with open(os.getcwd() + f'/data/processed/validation/labels/{img_name}.txt', 'r') as f:
                    results = f.read()
                original_image = Image.open(os.getcwd() + f'/data/processed/validation/images/{img_name}.jpg')
            
            for r in results:
                boxes = r.boxes 
                
                for box in boxes:
                    if box.cls == 3:  
                        x1, y1, x2, y2 = box.xyxy[0]
                        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                        table_image = original_image.crop((x1, y1, x2, y2))
                        table_image.show()
                        table_image.save(os.getcwd() + f'/data/processed/tables/{img_name}.jpg')
                        break
                break


if __name__ == '__main__':

    images,data = get_data_and_annots()
    generate_labels = generate_yolo_labels(images)
    finalPrep = final_preparation(os.path.join(os.getcwd() + r'\data\raw\train\publaynet\train'),os.path.join(os.getcwd() + r"\data\processed\images"), os.getcwd() + '/data/processed/yolo',images)
    annotate_tables(os.getcwd() + '/data/processed/hand_labeled_tables/hand_labeled_tables')