PaddleX/paddlex/inference/pipelines_new/layout_parsing/utils.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

__all__ = [
    "convert_points_to_boxes",
    "get_sub_regions_ocr_res"
]

import numpy as np
import copy

def convert_points_to_boxes(dt_polys):
    if len(dt_polys) > 0:
        dt_polys_tmp = dt_polys.copy()
        dt_polys_tmp = np.array(dt_polys_tmp)
        boxes_left = np.min(dt_polys_tmp[:, :, 0], axis=1)
        boxes_right = np.max(dt_polys_tmp[:, :, 0], axis=1)
        boxes_top = np.min(dt_polys_tmp[:, :, 1], axis=1)
        boxes_bottom = np.max(dt_polys_tmp[:, :, 1], axis=1)
        dt_boxes = np.array([boxes_left, boxes_top, boxes_right, boxes_bottom])
        dt_boxes = dt_boxes.T
    else:
        dt_boxes = np.array([])
    return dt_boxes

def get_overlap_boxes_idx(src_boxes, ref_boxes):
    '''get overlap boxes idx''' 
    match_idx_list = []
    src_boxes_num = len(src_boxes)
    if src_boxes_num > 0 and len(ref_boxes) > 0:
        for rno in range(len(ref_boxes)):
            ref_box = ref_boxes[rno]
            x1 = np.maximum(ref_box[0], src_boxes[:, 0])
            y1 = np.maximum(ref_box[1], src_boxes[:, 1])
            x2 = np.minimum(ref_box[2], src_boxes[:, 2])
            y2 = np.minimum(ref_box[3], src_boxes[:, 3])
            pub_w = x2 - x1
            pub_h = y2 - y1
            match_idx = np.where((pub_w > 3) & (pub_h > 3))[0]
            match_idx_list.extend(match_idx)                                   
    return match_idx_list

def get_sub_regions_ocr_res(overall_ocr_res, object_boxes, flag_within=True):
    """
    :param flag_within: True (within the object regions), False (outside the object regions)
    :return:
    """

    sub_regions_ocr_res = copy.deepcopy(overall_ocr_res)
    sub_regions_ocr_res['input_img'] = overall_ocr_res['input_img']
    sub_regions_ocr_res['img_id'] = -1
    sub_regions_ocr_res['dt_polys'] = []
    sub_regions_ocr_res['rec_text'] = []
    sub_regions_ocr_res['rec_score'] = []
    sub_regions_ocr_res['dt_boxes'] = []

    overall_text_boxes = overall_ocr_res['dt_boxes']
    match_idx_list = get_overlap_boxes_idx(overall_text_boxes, object_boxes)
    match_idx_list = list(set(match_idx_list))
    for box_no in range(len(overall_text_boxes)):
        if flag_within:
            if box_no in match_idx_list:
                flag_match = True
            else:
                flag_match = False
        else:
            if box_no not in match_idx_list:
                flag_match = True
            else:
                flag_match = False
        if flag_match:
            sub_regions_ocr_res['dt_polys'].append(overall_ocr_res['dt_polys'][box_no])
            sub_regions_ocr_res['rec_text'].append(overall_ocr_res['rec_text'][box_no])
            sub_regions_ocr_res['rec_score'].append(overall_ocr_res['rec_score'][box_no])
            sub_regions_ocr_res['dt_boxes'].append(overall_ocr_res['dt_boxes'][box_no])
    return sub_regions_ocr_res