PaddleX/paddlex/inference/pipelines/table_recognition/table_recognition_post_processing_v2.py

# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math

import numpy as np

from ..components import convert_points_to_boxes
from ..layout_parsing.utils import get_sub_regions_ocr_res
from ..ocr.result import OCRResult
from .result import SingleTableRecognitionResult


def get_ori_image_coordinate(x: int, y: int, box_list: list) -> list:
    """
    get the original coordinate from Cropped image to Original image.
    Args:
        x (int): x coordinate of cropped image
        y (int): y coordinate of cropped image
        box_list (list): list of table bounding boxes, eg. [[x1, y1, x2, y2, x3, y3, x4, y4]]
    Returns:
        list: list of original coordinates, eg. [[x1, y1, x2, y2, x3, y3, x4, y4]]
    """
    if not box_list:
        return box_list
    offset = np.array([x, y] * 4)
    box_list = np.array(box_list)
    if box_list.shape[-1] == 2:
        offset = offset.reshape(4, 2)
    ori_box_list = offset + box_list
    return ori_box_list


def convert_table_structure_pred_bbox(
    cell_points_list: list, crop_start_point: list, img_shape: tuple
) -> None:
    """
    Convert the predicted table structure bounding boxes to the original image coordinate system.

    Args:
        cell_points_list (list):  Bounding boxes ('bbox').
        crop_start_point (list): A list of two integers representing the starting point (x, y) of the cropped image region.
        img_shape (tuple): A tuple of two integers representing the shape (height, width) of the original image.

    Returns:
        cell_points_list (list):  Bounding boxes ('bbox').
    """

    ori_cell_points_list = get_ori_image_coordinate(
        crop_start_point[0], crop_start_point[1], cell_points_list
    )
    ori_cell_points_list = np.reshape(ori_cell_points_list, (-1, 4, 2))
    cell_box_list = convert_points_to_boxes(ori_cell_points_list)

    img_height, img_width = img_shape
    cell_box_list = np.clip(
        cell_box_list, 0, [img_width, img_height, img_width, img_height]
    )
    return cell_box_list


def distance(box_1: list, box_2: list) -> float:
    """
    compute the distance between two boxes

    Args:
        box_1 (list): first rectangle box,eg.(x1, y1, x2, y2)
        box_2 (list): second rectangle box,eg.(x1, y1, x2, y2)

    Returns:
        float: the distance between two boxes
    """
    x1, y1, x2, y2 = box_1
    x3, y3, x4, y4 = box_2
    center1_x = (x1 + x2) / 2
    center1_y = (y1 + y2) / 2
    center2_x = (x3 + x4) / 2
    center2_y = (y3 + y4) / 2
    dis = math.sqrt((center2_x - center1_x) ** 2 + (center2_y - center1_y) ** 2)
    dis_2 = abs(x3 - x1) + abs(y3 - y1)
    dis_3 = abs(x4 - x2) + abs(y4 - y2)
    return dis + min(dis_2, dis_3)


def compute_iou(rec1: list, rec2: list) -> float:
    """
    computing IoU
    Args:
        rec1 (list): (x1, y1, x2, y2)
        rec2 (list): (x1, y1, x2, y2)
    Returns:
        float: Intersection over Union
    """
    # computing area of each rectangles
    S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
    S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])

    # computing the sum_area
    sum_area = S_rec1 + S_rec2

    # find the each edge of intersect rectangle
    left_line = max(rec1[0], rec2[0])
    right_line = min(rec1[2], rec2[2])
    top_line = max(rec1[1], rec2[1])
    bottom_line = min(rec1[3], rec2[3])

    # judge if there is an intersect
    if left_line >= right_line or top_line >= bottom_line:
        return 0.0
    else:
        intersect = (right_line - left_line) * (bottom_line - top_line)
        return (intersect / (sum_area - intersect)) * 1.0


def compute_inter(rec1, rec2):
    """
    computing intersection over rec2_area
    Args:
        rec1 (list): (x1, y1, x2, y2)
        rec2 (list): (x1, y1, x2, y2)
    Returns:
        float: Intersection over rec2_area
    """
    x1_1, y1_1, x2_1, y2_1 = map(float, rec1)
    x1_2, y1_2, x2_2, y2_2 = map(float, rec2)
    x_left = max(x1_1, x1_2)
    y_top = max(y1_1, y1_2)
    x_right = min(x2_1, x2_2)
    y_bottom = min(y2_1, y2_2)
    inter_width = max(0, x_right - x_left)
    inter_height = max(0, y_bottom - y_top)
    inter_area = inter_width * inter_height
    rec2_area = (x2_2 - x1_2) * (y2_2 - y1_2)
    if rec2_area == 0:
        return 0
    iou = inter_area / rec2_area
    return iou


def match_table_and_ocr(cell_box_list, ocr_dt_boxes, table_cells_flag, row_start_index):
    """
    match table and ocr

    Args:
        cell_box_list (list): bbox for table cell, 2 points, [left, top, right, bottom]
        ocr_dt_boxes (list): bbox for ocr, 2 points, [left, top, right, bottom]

    Returns:
        dict: matched dict, key is table index, value is ocr index
    """
    all_matched = []
    for k in range(len(table_cells_flag) - 1):
        matched = {}
        for i, table_box in enumerate(
            cell_box_list[table_cells_flag[k] : table_cells_flag[k + 1]]
        ):
            if len(table_box) == 8:
                table_box = [
                    np.min(table_box[0::2]),
                    np.min(table_box[1::2]),
                    np.max(table_box[0::2]),
                    np.max(table_box[1::2]),
                ]
            for j, ocr_box in enumerate(np.array(ocr_dt_boxes)):
                if compute_inter(table_box, ocr_box) > 0.7:
                    if i not in matched.keys():
                        matched[i] = [j]
                    else:
                        matched[i].append(j)
        real_len = max(matched.keys()) + 1 if len(matched) != 0 else 0
        if table_cells_flag[k + 1] < row_start_index[k + 1]:
            for s in range(row_start_index[k + 1] - table_cells_flag[k + 1]):
                matched[real_len + s] = []
        elif table_cells_flag[k + 1] > row_start_index[k + 1]:
            for s in range(table_cells_flag[k + 1] - row_start_index[k + 1]):
                matched[real_len - 1].append(matched[real_len + s])
        all_matched.append(matched)
    return all_matched


def get_html_result(
    all_matched_index: dict, ocr_contents: dict, pred_structures: list, table_cells_flag
) -> str:
    """
    Generates HTML content based on the matched index, OCR contents, and predicted structures.

    Args:
        matched_index (dict): A dictionary containing matched indices.
        ocr_contents (dict): A dictionary of OCR contents.
        pred_structures (list): A list of predicted HTML structures.

    Returns:
        str: Generated HTML content as a string.
    """
    pred_html = []
    td_index = 0
    td_count = 0
    matched_list_index = 0
    head_structure = pred_structures[0:3]
    html = "".join(head_structure)
    table_structure = pred_structures[3:-3]
    for tag in table_structure:
        matched_index = all_matched_index[matched_list_index]
        if "</td>" in tag:
            if "<td></td>" == tag:
                pred_html.extend("<td>")
            if td_index in matched_index.keys():
                if len(matched_index[td_index]) == 0:
                    continue
                b_with = False
                if (
                    "<b>" in ocr_contents[matched_index[td_index][0]]
                    and len(matched_index[td_index]) > 1
                ):
                    b_with = True
                    pred_html.extend("<b>")
                for i, td_index_index in enumerate(matched_index[td_index]):
                    content = ocr_contents[td_index_index]
                    if len(matched_index[td_index]) > 1:
                        if len(content) == 0:
                            continue
                        if content[0] == " ":
                            content = content[1:]
                        if "<b>" in content:
                            content = content[3:]
                        if "</b>" in content:
                            content = content[:-4]
                        if len(content) == 0:
                            continue
                        if i != len(matched_index[td_index]) - 1 and " " != content[-1]:
                            content += " "
                    pred_html.extend(content)
                if b_with:
                    pred_html.extend("</b>")
            if "<td></td>" == tag:
                pred_html.append("</td>")
            else:
                pred_html.append(tag)
            td_index += 1
            td_count += 1
            if (
                td_count >= table_cells_flag[matched_list_index + 1]
                and matched_list_index < len(all_matched_index) - 1
            ):
                matched_list_index += 1
                td_index = 0
        else:
            pred_html.append(tag)
    html += "".join(pred_html)
    end_structure = pred_structures[-3:]
    html += "".join(end_structure)
    return html


def sort_table_cells_boxes(boxes):
    """
    Sort the input list of bounding boxes.

    Args:
        boxes (list of lists): The input list of bounding boxes, where each bounding box is formatted as [x1, y1, x2, y2].

    Returns:
        sorted_boxes (list of lists): The list of bounding boxes sorted.
    """

    boxes_sorted_by_y = sorted(boxes, key=lambda box: box[1])
    rows = []
    current_row = []
    current_y = None
    tolerance = 10
    for box in boxes_sorted_by_y:
        x1, y1, x2, y2 = box
        if current_y is None:
            current_row.append(box)
            current_y = y1
        else:
            if abs(y1 - current_y) <= tolerance:
                current_row.append(box)
            else:
                current_row.sort(key=lambda x: x[0])
                rows.append(current_row)
                current_row = [box]
                current_y = y1
    if current_row:
        current_row.sort(key=lambda x: x[0])
        rows.append(current_row)
    sorted_boxes = []
    flag = [0]
    for i in range(len(rows)):
        sorted_boxes.extend(rows[i])
        if i < len(rows):
            flag.append(flag[i] + len(rows[i]))
    return sorted_boxes, flag


def convert_to_four_point_coordinates(boxes):
    """
    Convert bounding boxes from [x1, y1, x2, y2] format to
    [x1, y1, x2, y1, x2, y2, x1, y2] format.

    Parameters:
    - boxes: A list of bounding boxes, each defined as a list of integers
             in the format [x1, y1, x2, y2].

    Returns:
    - A list of bounding boxes, each converted to the format
      [x1, y1, x2, y1, x2, y2, x1, y2].
    """
    # Initialize an empty list to store the converted bounding boxes
    converted_boxes = []

    # Loop over each box in the input list
    for box in boxes:
        x1, y1, x2, y2 = box

        # Define the four corner points
        top_left = (x1, y1)
        top_right = (x2, y1)
        bottom_right = (x2, y2)
        bottom_left = (x1, y2)

        # Create a new list for the converted box
        converted_box = [
            top_left[0],
            top_left[1],  # Top-left corner
            top_right[0],
            top_right[1],  # Top-right corner
            bottom_right[0],
            bottom_right[1],  # Bottom-right corner
            bottom_left[0],
            bottom_left[1],  # Bottom-left corner
        ]

        # Append the converted box to the list
        converted_boxes.append(converted_box)

    return converted_boxes


def find_row_start_index(html_list):
    """
    find the index of the first cell in each row

    Args:
        html_list (list): list for html results

    Returns:
        row_start_indices (list): list for the index of the first cell in each row
    """
    # Initialize an empty list to store the indices of row start positions
    row_start_indices = []
    # Variable to track the current index in the flattened HTML content
    current_index = 0
    # Flag to check if we are inside a table row
    inside_row = False
    # Iterate through the HTML tags
    for keyword in html_list:
        # If a new row starts, set the inside_row flag to True
        if keyword == "<tr>":
            inside_row = True
        # If we encounter a closing row tag, set the inside_row flag to False
        elif keyword == "</tr>":
            inside_row = False
        # If we encounter a cell and we are inside a row
        elif (keyword == "<td></td>" or keyword == "</td>") and inside_row:
            # Append the current index as the starting index of the row
            row_start_indices.append(current_index)
            # Set the flag to ensure we only record the first cell of the current row
            inside_row = False
        # Increment the current index if we encounter a cell regardless of being inside a row or not
        if keyword == "<td></td>" or keyword == "</td>":
            current_index += 1
    # Return the computed starting indices of each row
    return row_start_indices


def map_and_get_max(table_cells_flag, row_start_index):
    """
    Retrieve table recognition result from cropped image info, table structure prediction, and overall OCR result.

    Args:
        table_cells_flag (list): List of the flags representing the end of each row of the table cells detection results.
        row_start_index (list): List of the flags representing the end of each row of the table structure predicted results.

    Returns:
        max_values: List of the process results.
    """

    max_values = []
    i = 0
    max_value = None
    for j in range(len(row_start_index)):
        while i < len(table_cells_flag) and table_cells_flag[i] <= row_start_index[j]:
            if max_value is None or table_cells_flag[i] > max_value:
                max_value = table_cells_flag[i]
            i += 1
        max_values.append(max_value if max_value is not None else row_start_index[j])
    return max_values


def get_table_recognition_res(
    table_box: list,
    table_structure_result: list,
    table_cells_result: list,
    overall_ocr_res: OCRResult,
    table_ocr_pred: dict,
    cells_texts_list: list,
    use_table_cells_ocr_results: bool,
    use_table_cells_split_ocr: bool,
) -> SingleTableRecognitionResult:
    """
    Retrieve table recognition result from cropped image info, table structure prediction, and overall OCR result.

    Args:
        table_box (list): Information about the location of cropped image, including the bounding box.
        table_structure_result (list): Predicted table structure.
        table_cells_result (list): Predicted table cells.
        overall_ocr_res (OCRResult): Overall OCR result from the input image.
        table_ocr_pred (dict): Table OCR result from the input image.
        cells_texts_list (list): OCR results with cells.
        use_table_cells_ocr_results (bool): whether to use OCR results with cells.

    Returns:
        SingleTableRecognitionResult: An object containing the single table recognition result.
    """

    table_cells_result = convert_to_four_point_coordinates(table_cells_result)
    table_box = np.array([table_box])

    if not (use_table_cells_ocr_results == True and use_table_cells_split_ocr == True):
        table_ocr_pred = get_sub_regions_ocr_res(overall_ocr_res, table_box)

    crop_start_point = [table_box[0][0], table_box[0][1]]
    img_shape = overall_ocr_res["doc_preprocessor_res"]["output_img"].shape[0:2]

    if len(table_cells_result) == 0 or len(table_ocr_pred["rec_boxes"]) == 0:
        pred_html = " ".join(table_structure_result)
        if len(table_cells_result) != 0:
            table_cells_result = convert_table_structure_pred_bbox(
                table_cells_result, crop_start_point, img_shape
            )
        single_img_res = {
            "cell_box_list": table_cells_result,
            "table_ocr_pred": table_ocr_pred,
            "pred_html": pred_html,
        }
        return SingleTableRecognitionResult(single_img_res)

    table_cells_result = convert_table_structure_pred_bbox(
        table_cells_result, crop_start_point, img_shape
    )

    if use_table_cells_ocr_results == True and use_table_cells_split_ocr == False:
        ocr_dt_boxes = table_cells_result
        ocr_texts_res = cells_texts_list
    else:
        ocr_dt_boxes = table_ocr_pred["rec_boxes"]
        ocr_texts_res = table_ocr_pred["rec_texts"]

    table_cells_result, table_cells_flag = sort_table_cells_boxes(table_cells_result)
    row_start_index = find_row_start_index(table_structure_result)
    table_cells_flag = map_and_get_max(table_cells_flag, row_start_index)
    table_cells_flag.append(len(table_cells_result))
    row_start_index.append(len(table_cells_result))
    matched_index = match_table_and_ocr(
        table_cells_result, ocr_dt_boxes, table_cells_flag, table_cells_flag
    )
    pred_html = get_html_result(
        matched_index, ocr_texts_res, table_structure_result, row_start_index
    )

    single_img_res = {
        "cell_box_list": table_cells_result,
        "table_ocr_pred": table_ocr_pred,
        "pred_html": pred_html,
    }

    return SingleTableRecognitionResult(single_img_res)