PaddleX/paddlex/inference/models/text_recognition/processors.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.


import os
import os.path as osp
from typing import List

import re
import numpy as np
from PIL import Image
import cv2
import math
import json
import tempfile
from tokenizers import Tokenizer as TokenizerFast

from ....utils import logging
from ...utils.benchmark import benchmark


@benchmark.timeit
class OCRReisizeNormImg:
    """for ocr image resize and normalization"""

    def __init__(self, rec_image_shape=[3, 48, 320]):
        super().__init__()
        self.rec_image_shape = rec_image_shape

    def resize_norm_img(self, img, max_wh_ratio):
        """resize and normalize the img"""
        imgC, imgH, imgW = self.rec_image_shape
        assert imgC == img.shape[2]
        imgW = int((imgH * max_wh_ratio))

        h, w = img.shape[:2]
        ratio = w / float(h)
        if math.ceil(imgH * ratio) > imgW:
            resized_w = imgW
        else:
            resized_w = int(math.ceil(imgH * ratio))
        resized_image = cv2.resize(img, (resized_w, imgH))
        resized_image = resized_image.astype("float32")
        resized_image = resized_image.transpose((2, 0, 1)) / 255
        resized_image -= 0.5
        resized_image /= 0.5
        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
        padding_im[:, :, 0:resized_w] = resized_image
        return padding_im

    def __call__(self, imgs):
        """apply"""
        return [self.resize(img) for img in imgs]

    def resize(self, img):
        imgC, imgH, imgW = self.rec_image_shape
        max_wh_ratio = imgW / imgH
        h, w = img.shape[:2]
        wh_ratio = w * 1.0 / h
        max_wh_ratio = max(max_wh_ratio, wh_ratio)
        img = self.resize_norm_img(img, max_wh_ratio)
        return img


@benchmark.timeit
class BaseRecLabelDecode:
    """Convert between text-label and text-index"""

    def __init__(self, character_str=None, use_space_char=True):
        super().__init__()
        self.reverse = False
        character_list = (
            list(character_str)
            if character_str is not None
            else list("0123456789abcdefghijklmnopqrstuvwxyz")
        )
        if use_space_char:
            character_list.append(" ")

        character_list = self.add_special_char(character_list)
        self.dict = {}
        for i, char in enumerate(character_list):
            self.dict[char] = i
        self.character = character_list

    def pred_reverse(self, pred):
        """pred_reverse"""
        pred_re = []
        c_current = ""
        for c in pred:
            if not bool(re.search("[a-zA-Z0-9 :*./%+-]", c)):
                if c_current != "":
                    pred_re.append(c_current)
                pred_re.append(c)
                c_current = ""
            else:
                c_current += c
        if c_current != "":
            pred_re.append(c_current)

        return "".join(pred_re[::-1])

    def add_special_char(self, character_list):
        """add_special_char"""
        return character_list

    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
        """convert text-index into text-label."""
        result_list = []
        ignored_tokens = self.get_ignored_tokens()
        batch_size = len(text_index)
        for batch_idx in range(batch_size):
            selection = np.ones(len(text_index[batch_idx]), dtype=bool)
            if is_remove_duplicate:
                selection[1:] = text_index[batch_idx][1:] != text_index[batch_idx][:-1]
            for ignored_token in ignored_tokens:
                selection &= text_index[batch_idx] != ignored_token

            char_list = [
                self.character[text_id] for text_id in text_index[batch_idx][selection]
            ]
            if text_prob is not None:
                conf_list = text_prob[batch_idx][selection]
            else:
                conf_list = [1] * len(selection)
            if len(conf_list) == 0:
                conf_list = [0]

            text = "".join(char_list)

            if self.reverse:  # for arabic rec
                text = self.pred_reverse(text)

            result_list.append((text, np.mean(conf_list).tolist()))
        return result_list

    def get_ignored_tokens(self):
        """get_ignored_tokens"""
        return [0]  # for ctc blank

    def __call__(self, pred):
        """apply"""
        preds = np.array(pred)
        if isinstance(preds, tuple) or isinstance(preds, list):
            preds = preds[-1]
        preds_idx = preds.argmax(axis=-1)
        preds_prob = preds.max(axis=-1)
        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
        texts = []
        scores = []
        for t in text:
            texts.append(t[0])
            scores.append(t[1])
        return texts, scores


@benchmark.timeit
class CTCLabelDecode(BaseRecLabelDecode):
    """Convert between text-label and text-index"""

    def __init__(self, character_list=None, use_space_char=True):
        super().__init__(character_list, use_space_char=use_space_char)

    def __call__(self, pred):
        """apply"""
        preds = np.array(pred[0])
        preds_idx = preds.argmax(axis=-1)
        preds_prob = preds.max(axis=-1)
        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
        texts = []
        scores = []
        for t in text:
            texts.append(t[0])
            scores.append(t[1])
        return texts, scores

    def add_special_char(self, character_list):
        """add_special_char"""
        character_list = ["blank"] + character_list
        return character_list


@benchmark.timeit
class ToBatch:
    """A class for batching and padding images to a uniform width."""

    def __pad_imgs(self, imgs: List[np.ndarray]) -> List[np.ndarray]:
        """Pad images to the maximum width in the batch.

        Args:
            imgs (list of np.ndarrays): List of images to pad.

        Returns:
            list of np.ndarrays: List of padded images.
        """
        max_width = max(img.shape[2] for img in imgs)
        padded_imgs = []
        for img in imgs:
            _, height, width = img.shape
            pad_width = max_width - width
            padded_img = np.pad(
                img,
                ((0, 0), (0, 0), (0, pad_width)),
                mode="constant",
                constant_values=0,
            )
            padded_imgs.append(padded_img)
        return padded_imgs

    def __call__(self, imgs: List[np.ndarray]) -> List[np.ndarray]:
        """Call method to pad images and stack them into a batch.

        Args:
            imgs (list of np.ndarrays): List of images to process.

        Returns:
            list of np.ndarrays: List containing a stacked tensor of the padded images.
        """
        imgs = self.__pad_imgs(imgs)
        return [np.stack(imgs, axis=0).astype(dtype=np.float32, copy=False)]