OpenOCR/tools/utils/e2e_utils/pgnet_pp_utils.py

import torch
import os
import sys

__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, ".."))
from extract_textpoint_slow import *
from extract_textpoint_fast import generate_pivot_list_fast, restore_poly


class PGNet_PostProcess(object):
    # two different post-process
    def __init__(
            self,
            character_dict_path,
            valid_set,
            score_thresh,
            outs_dict,
            shape_list,
            point_gather_mode=None, ):
        self.Lexicon_Table = get_dict(character_dict_path)
        self.valid_set = valid_set
        self.score_thresh = score_thresh
        self.outs_dict = outs_dict
        self.shape_list = shape_list
        self.point_gather_mode = point_gather_mode

    def pg_postprocess_fast(self):
        p_score = self.outs_dict["f_score"]
        p_border = self.outs_dict["f_border"]
        p_char = self.outs_dict["f_char"]
        p_direction = self.outs_dict["f_direction"]
        if isinstance(p_score, torch.Tensor):
            p_score = p_score[0].numpy()
            p_border = p_border[0].numpy()
            p_direction = p_direction[0].numpy()
            p_char = p_char[0].numpy()
        else:
            p_score = p_score[0]
            p_border = p_border[0]
            p_direction = p_direction[0]
            p_char = p_char[0]

        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
        instance_yxs_list, seq_strs = generate_pivot_list_fast(
            p_score,
            p_char,
            p_direction,
            self.Lexicon_Table,
            score_thresh=self.score_thresh,
            point_gather_mode=self.point_gather_mode, )
        poly_list, keep_str_list = restore_poly(
            instance_yxs_list,
            seq_strs,
            p_border,
            ratio_w,
            ratio_h,
            src_w,
            src_h,
            self.valid_set, )
        data = {
            "points": poly_list,
            "texts": keep_str_list,
        }
        return data

    def pg_postprocess_slow(self):
        p_score = self.outs_dict["f_score"]
        p_border = self.outs_dict["f_border"]
        p_char = self.outs_dict["f_char"]
        p_direction = self.outs_dict["f_direction"]
        if isinstance(p_score, torch.Tensor):
            p_score = p_score[0].numpy()
            p_border = p_border[0].numpy()
            p_direction = p_direction[0].numpy()
            p_char = p_char[0].numpy()
        else:
            p_score = p_score[0]
            p_border = p_border[0]
            p_direction = p_direction[0]
            p_char = p_char[0]
        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
        is_curved = self.valid_set == "totaltext"
        char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
            p_score,
            p_char,
            p_direction,
            score_thresh=self.score_thresh,
            is_backbone=True,
            is_curved=is_curved, )
        seq_strs = []
        for char_idx_set in char_seq_idx_set:
            pr_str = "".join([self.Lexicon_Table[pos] for pos in char_idx_set])
            seq_strs.append(pr_str)
        poly_list = []
        keep_str_list = []
        all_point_list = []
        all_point_pair_list = []
        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
            if len(yx_center_line) == 1:
                yx_center_line.append(yx_center_line[-1])

            offset_expand = 1.0
            if self.valid_set == "totaltext":
                offset_expand = 1.2

            point_pair_list = []
            for batch_id, y, x in yx_center_line:
                offset = p_border[:, y, x].reshape(2, 2)
                if offset_expand != 1.0:
                    offset_length = np.linalg.norm(
                        offset, axis=1, keepdims=True)
                    expand_length = np.clip(
                        offset_length * (offset_expand - 1),
                        a_min=0.5,
                        a_max=3.0)
                    offset_detal = offset / offset_length * expand_length
                    offset = offset + offset_detal
                ori_yx = np.array([y, x], dtype=np.float32)
                point_pair = ((ori_yx + offset)[:, ::-1] * 4.0 /
                              np.array([ratio_w, ratio_h]).reshape(-1, 2))
                point_pair_list.append(point_pair)

                all_point_list.append([
                    int(round(x * 4.0 / ratio_w)),
                    int(round(y * 4.0 / ratio_h))
                ])
                all_point_pair_list.append(point_pair.round().astype(np.int32)
                                           .tolist())

            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
            detected_poly = expand_poly_along_width(
                detected_poly, shrink_ratio_of_width=0.2)
            detected_poly[:, 0] = np.clip(
                detected_poly[:, 0], a_min=0, a_max=src_w)
            detected_poly[:, 1] = np.clip(
                detected_poly[:, 1], a_min=0, a_max=src_h)

            if len(keep_str) < 2:
                continue

            keep_str_list.append(keep_str)
            detected_poly = np.round(detected_poly).astype("int32")
            if self.valid_set == "partvgg":
                middle_point = len(detected_poly) // 2
                detected_poly = detected_poly[
                    [0, middle_point - 1, middle_point, -1], :]
                poly_list.append(detected_poly)
            elif self.valid_set == "totaltext":
                poly_list.append(detected_poly)
            else:
                print("--> Not supported format.")
                exit(-1)
        data = {
            "points": poly_list,
            "texts": keep_str_list,
        }
        return data