AIStoryBoard/python/PaddleOCR-main/tools/infer/predict_rec.py

# Copyright (c) 2020 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 os
import sys
from PIL import Image

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))

os.environ["FLAGS_allocator_strategy"] = "auto_growth"

import cv2
import numpy as np
import math
import time
import traceback
import paddle

import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read

logger = get_logger()


class TextRecognizer(object):
    def __init__(self, args, logger=None):
        if os.path.exists(f"{args.rec_model_dir}/inference.yml"):
            model_config = utility.load_config(f"{args.rec_model_dir}/inference.yml")
            model_name = model_config.get("Global", {}).get("model_name", "")
            if model_name and model_name not in [
                "PP-OCRv5_mobile_rec",
                "PP-OCRv5_server_rec",
                "korean_PP-OCRv5_mobile_rec",
                "eslav_PP-OCRv5_mobile_rec",
                "latin_PP-OCRv5_mobile_rec",
                "en_PP-OCRv5_mobile_rec",
                "th_PP-OCRv5_mobile_rec",
                "el_PP-OCRv5_mobile_rec",
            ]:
                raise ValueError(
                    f"{model_name} is not supported. Please check if the model is supported by the PaddleOCR wheel."
                )

            if args.rec_char_dict_path == "./ppocr/utils/ppocr_keys_v1.txt":
                rec_char_list = model_config.get("PostProcess", {}).get(
                    "character_dict", []
                )
                if rec_char_list:
                    new_rec_char_dict_path = f"{args.rec_model_dir}/ppocr_keys.txt"
                    with open(new_rec_char_dict_path, "w", encoding="utf-8") as f:
                        f.writelines([char + "\n" for char in rec_char_list])
                    args.rec_char_dict_path = new_rec_char_dict_path

        if logger is None:
            logger = get_logger()
        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
        self.rec_batch_num = args.rec_batch_num
        self.rec_algorithm = args.rec_algorithm
        postprocess_params = {
            "name": "CTCLabelDecode",
            "character_dict_path": args.rec_char_dict_path,
            "use_space_char": args.use_space_char,
        }
        if self.rec_algorithm == "SRN":
            postprocess_params = {
                "name": "SRNLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "RARE":
            postprocess_params = {
                "name": "AttnLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "NRTR":
            postprocess_params = {
                "name": "NRTRLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "SAR":
            postprocess_params = {
                "name": "SARLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "VisionLAN":
            postprocess_params = {
                "name": "VLLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
                "max_text_length": args.max_text_length,
            }
        elif self.rec_algorithm == "ViTSTR":
            postprocess_params = {
                "name": "ViTSTRLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "ABINet":
            postprocess_params = {
                "name": "ABINetLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "SPIN":
            postprocess_params = {
                "name": "SPINLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "RobustScanner":
            postprocess_params = {
                "name": "SARLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
                "rm_symbol": True,
            }
        elif self.rec_algorithm == "RFL":
            postprocess_params = {
                "name": "RFLLabelDecode",
                "character_dict_path": None,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "SATRN":
            postprocess_params = {
                "name": "SATRNLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
                "rm_symbol": True,
            }
        elif self.rec_algorithm in ["CPPD", "CPPDPadding"]:
            postprocess_params = {
                "name": "CPPDLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
                "rm_symbol": True,
            }
        elif self.rec_algorithm == "PREN":
            postprocess_params = {"name": "PRENLabelDecode"}
        elif self.rec_algorithm == "CAN":
            self.inverse = args.rec_image_inverse
            postprocess_params = {
                "name": "CANLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        elif self.rec_algorithm == "LaTeXOCR":
            postprocess_params = {
                "name": "LaTeXOCRDecode",
                "rec_char_dict_path": args.rec_char_dict_path,
            }
        elif self.rec_algorithm == "ParseQ":
            postprocess_params = {
                "name": "ParseQLabelDecode",
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char,
            }
        self.postprocess_op = build_post_process(postprocess_params)
        self.postprocess_params = postprocess_params
        (
            self.predictor,
            self.input_tensor,
            self.output_tensors,
            self.config,
        ) = utility.create_predictor(args, "rec", logger)
        self.benchmark = args.benchmark
        self.use_onnx = args.use_onnx
        if args.benchmark:
            import auto_log

            pid = os.getpid()
            gpu_id = utility.get_infer_gpuid()
            self.autolog = auto_log.AutoLogger(
                model_name="rec",
                model_precision=args.precision,
                batch_size=args.rec_batch_num,
                data_shape="dynamic",
                save_path=None,  # not used if logger is not None
                inference_config=self.config,
                pids=pid,
                process_name=None,
                gpu_ids=gpu_id if args.use_gpu else None,
                time_keys=["preprocess_time", "inference_time", "postprocess_time"],
                warmup=0,
                logger=logger,
            )
        self.return_word_box = args.return_word_box

    def resize_norm_img(self, img, max_wh_ratio):
        imgC, imgH, imgW = self.rec_image_shape
        if self.rec_algorithm == "NRTR" or self.rec_algorithm == "ViTSTR":
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            # return padding_im
            image_pil = Image.fromarray(np.uint8(img))
            if self.rec_algorithm == "ViTSTR":
                img = image_pil.resize([imgW, imgH], Image.BICUBIC)
            else:
                img = image_pil.resize([imgW, imgH], Image.Resampling.LANCZOS)
            img = np.array(img)
            norm_img = np.expand_dims(img, -1)
            norm_img = norm_img.transpose((2, 0, 1))
            if self.rec_algorithm == "ViTSTR":
                norm_img = norm_img.astype(np.float32) / 255.0
            else:
                norm_img = norm_img.astype(np.float32) / 128.0 - 1.0
            return norm_img
        elif self.rec_algorithm == "RFL":
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
            resized_image = resized_image.astype("float32")
            resized_image = resized_image / 255
            resized_image = resized_image[np.newaxis, :]
            resized_image -= 0.5
            resized_image /= 0.5
            return resized_image

        assert imgC == img.shape[2]
        imgW = int((imgH * max_wh_ratio))
        if self.use_onnx:
            w = self.input_tensor.shape[3:][0]
            if isinstance(w, str):
                pass
            elif w is not None and w > 0:
                imgW = w
        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))
        if self.rec_algorithm == "RARE":
            if resized_w > self.rec_image_shape[2]:
                resized_w = self.rec_image_shape[2]
            imgW = self.rec_image_shape[2]
        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 resize_norm_img_vl(self, img, image_shape):
        imgC, imgH, imgW = image_shape
        img = img[:, :, ::-1]  # bgr2rgb
        resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
        resized_image = resized_image.astype("float32")
        resized_image = resized_image.transpose((2, 0, 1)) / 255
        return resized_image

    def resize_norm_img_srn(self, img, image_shape):
        imgC, imgH, imgW = image_shape

        img_black = np.zeros((imgH, imgW))
        im_hei = img.shape[0]
        im_wid = img.shape[1]

        if im_wid <= im_hei * 1:
            img_new = cv2.resize(img, (imgH * 1, imgH))
        elif im_wid <= im_hei * 2:
            img_new = cv2.resize(img, (imgH * 2, imgH))
        elif im_wid <= im_hei * 3:
            img_new = cv2.resize(img, (imgH * 3, imgH))
        else:
            img_new = cv2.resize(img, (imgW, imgH))

        img_np = np.asarray(img_new)
        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
        img_black[:, 0 : img_np.shape[1]] = img_np
        img_black = img_black[:, :, np.newaxis]

        row, col, c = img_black.shape
        c = 1

        return np.reshape(img_black, (c, row, col)).astype(np.float32)

    def srn_other_inputs(self, image_shape, num_heads, max_text_length):
        imgC, imgH, imgW = image_shape
        feature_dim = int((imgH / 8) * (imgW / 8))

        encoder_word_pos = (
            np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64")
        )
        gsrm_word_pos = (
            np.array(range(0, max_text_length))
            .reshape((max_text_length, 1))
            .astype("int64")
        )

        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
            [-1, 1, max_text_length, max_text_length]
        )
        gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype(
            "float32"
        ) * [-1e9]

        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
            [-1, 1, max_text_length, max_text_length]
        )
        gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype(
            "float32"
        ) * [-1e9]

        encoder_word_pos = encoder_word_pos[np.newaxis, :]
        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]

        return [
            encoder_word_pos,
            gsrm_word_pos,
            gsrm_slf_attn_bias1,
            gsrm_slf_attn_bias2,
        ]

    def process_image_srn(self, img, image_shape, num_heads, max_text_length):
        norm_img = self.resize_norm_img_srn(img, image_shape)
        norm_img = norm_img[np.newaxis, :]

        [
            encoder_word_pos,
            gsrm_word_pos,
            gsrm_slf_attn_bias1,
            gsrm_slf_attn_bias2,
        ] = self.srn_other_inputs(image_shape, num_heads, max_text_length)

        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
        encoder_word_pos = encoder_word_pos.astype(np.int64)
        gsrm_word_pos = gsrm_word_pos.astype(np.int64)

        return (
            norm_img,
            encoder_word_pos,
            gsrm_word_pos,
            gsrm_slf_attn_bias1,
            gsrm_slf_attn_bias2,
        )

    def resize_norm_img_sar(self, img, image_shape, width_downsample_ratio=0.25):
        imgC, imgH, imgW_min, imgW_max = image_shape
        h = img.shape[0]
        w = img.shape[1]
        valid_ratio = 1.0
        # make sure new_width is an integral multiple of width_divisor.
        width_divisor = int(1 / width_downsample_ratio)
        # resize
        ratio = w / float(h)
        resize_w = math.ceil(imgH * ratio)
        if resize_w % width_divisor != 0:
            resize_w = round(resize_w / width_divisor) * width_divisor
        if imgW_min is not None:
            resize_w = max(imgW_min, resize_w)
        if imgW_max is not None:
            valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
            resize_w = min(imgW_max, resize_w)
        resized_image = cv2.resize(img, (resize_w, imgH))
        resized_image = resized_image.astype("float32")
        # norm
        if image_shape[0] == 1:
            resized_image = resized_image / 255
            resized_image = resized_image[np.newaxis, :]
        else:
            resized_image = resized_image.transpose((2, 0, 1)) / 255
        resized_image -= 0.5
        resized_image /= 0.5
        resize_shape = resized_image.shape
        padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
        padding_im[:, :, 0:resize_w] = resized_image
        pad_shape = padding_im.shape

        return padding_im, resize_shape, pad_shape, valid_ratio

    def resize_norm_img_spin(self, img):
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # return padding_im
        img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
        img = np.array(img, np.float32)
        img = np.expand_dims(img, -1)
        img = img.transpose((2, 0, 1))
        mean = [127.5]
        std = [127.5]
        mean = np.array(mean, dtype=np.float32)
        std = np.array(std, dtype=np.float32)
        mean = np.float32(mean.reshape(1, -1))
        stdinv = 1 / np.float32(std.reshape(1, -1))
        img -= mean
        img *= stdinv
        return img

    def resize_norm_img_svtr(self, img, image_shape):
        imgC, imgH, imgW = image_shape
        max_wh_ratio = imgW * 1.0 / imgH
        h, w = img.shape[0], img.shape[1]
        ratio = w * 1.0 / h
        max_wh_ratio = min(max(max_wh_ratio, ratio), max_wh_ratio)
        imgW = int(imgH * max_wh_ratio)
        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 resize_norm_img_cppd_padding(
        self, img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR
    ):
        imgC, imgH, imgW = image_shape
        h = img.shape[0]
        w = img.shape[1]
        if not padding:
            resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation)
            resized_w = imgW
        else:
            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")
        if image_shape[0] == 1:
            resized_image = resized_image / 255
            resized_image = resized_image[np.newaxis, :]
        else:
            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 resize_norm_img_abinet(self, img, image_shape):
        imgC, imgH, imgW = image_shape

        resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
        resized_image = resized_image.astype("float32")
        resized_image = resized_image / 255.0

        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        resized_image = (resized_image - mean[None, None, ...]) / std[None, None, ...]
        resized_image = resized_image.transpose((2, 0, 1))
        resized_image = resized_image.astype("float32")

        return resized_image

    def norm_img_can(self, img, image_shape):
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # CAN only predict gray scale image

        if self.inverse:
            img = 255 - img

        if self.rec_image_shape[0] == 1:
            h, w = img.shape
            _, imgH, imgW = self.rec_image_shape
            if h < imgH or w < imgW:
                padding_h = max(imgH - h, 0)
                padding_w = max(imgW - w, 0)
                img_padded = np.pad(
                    img,
                    ((0, padding_h), (0, padding_w)),
                    "constant",
                    constant_values=(255),
                )
                img = img_padded

        img = np.expand_dims(img, 0) / 255.0  # h,w,c -> c,h,w
        img = img.astype("float32")

        return img

    def pad_(self, img, divable=32):
        threshold = 128
        data = np.array(img.convert("LA"))
        if data[..., -1].var() == 0:
            data = (data[..., 0]).astype(np.uint8)
        else:
            data = (255 - data[..., -1]).astype(np.uint8)
        data = (data - data.min()) / (data.max() - data.min()) * 255
        if data.mean() > threshold:
            # To invert the text to white
            gray = 255 * (data < threshold).astype(np.uint8)
        else:
            gray = 255 * (data > threshold).astype(np.uint8)
            data = 255 - data

        coords = cv2.findNonZero(gray)  # Find all non-zero points (text)
        a, b, w, h = cv2.boundingRect(coords)  # Find minimum spanning bounding box
        rect = data[b : b + h, a : a + w]
        im = Image.fromarray(rect).convert("L")
        dims = []
        for x in [w, h]:
            div, mod = divmod(x, divable)
            dims.append(divable * (div + (1 if mod > 0 else 0)))
        padded = Image.new("L", dims, 255)
        padded.paste(im, (0, 0, im.size[0], im.size[1]))
        return padded

    def minmax_size_(
        self,
        img,
        max_dimensions,
        min_dimensions,
    ):
        if max_dimensions is not None:
            ratios = [a / b for a, b in zip(img.size, max_dimensions)]
            if any([r > 1 for r in ratios]):
                size = np.array(img.size) // max(ratios)
                img = img.resize(tuple(size.astype(int)), Image.BILINEAR)
        if min_dimensions is not None:
            # hypothesis: there is a dim in img smaller than min_dimensions, and return a proper dim >= min_dimensions
            padded_size = [
                max(img_dim, min_dim)
                for img_dim, min_dim in zip(img.size, min_dimensions)
            ]
            if padded_size != list(img.size):  # assert hypothesis
                padded_im = Image.new("L", padded_size, 255)
                padded_im.paste(img, img.getbbox())
                img = padded_im
        return img

    def norm_img_latexocr(self, img):
        # CAN only predict gray scale image
        shape = (1, 1, 3)
        mean = [0.7931, 0.7931, 0.7931]
        std = [0.1738, 0.1738, 0.1738]
        scale = np.float32(1.0 / 255.0)
        min_dimensions = [32, 32]
        max_dimensions = [672, 192]
        mean = np.array(mean).reshape(shape).astype("float32")
        std = np.array(std).reshape(shape).astype("float32")

        im_h, im_w = img.shape[:2]
        if (
            min_dimensions[0] <= im_w <= max_dimensions[0]
            and min_dimensions[1] <= im_h <= max_dimensions[1]
        ):
            pass
        else:
            img = Image.fromarray(np.uint8(img))
            img = self.minmax_size_(self.pad_(img), max_dimensions, min_dimensions)
            img = np.array(img)
            im_h, im_w = img.shape[:2]
            img = np.dstack([img, img, img])
        img = (img.astype("float32") * scale - mean) / std
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        divide_h = math.ceil(im_h / 16) * 16
        divide_w = math.ceil(im_w / 16) * 16
        img = np.pad(
            img, ((0, divide_h - im_h), (0, divide_w - im_w)), constant_values=(1, 1)
        )
        img = img[:, :, np.newaxis].transpose(2, 0, 1)
        img = img.astype("float32")
        return img

    def __call__(self, img_list):
        img_num = len(img_list)
        # Calculate the aspect ratio of all text bars
        width_list = []
        for img in img_list:
            width_list.append(img.shape[1] / float(img.shape[0]))
        # Sorting can speed up the recognition process
        indices = np.argsort(np.array(width_list))
        rec_res = [["", 0.0]] * img_num
        batch_num = self.rec_batch_num
        st = time.time()
        if self.benchmark:
            self.autolog.times.start()
        for beg_img_no in range(0, img_num, batch_num):
            end_img_no = min(img_num, beg_img_no + batch_num)
            norm_img_batch = []
            if self.rec_algorithm == "SRN":
                encoder_word_pos_list = []
                gsrm_word_pos_list = []
                gsrm_slf_attn_bias1_list = []
                gsrm_slf_attn_bias2_list = []
            if self.rec_algorithm == "SAR":
                valid_ratios = []
            imgC, imgH, imgW = self.rec_image_shape[:3]
            max_wh_ratio = imgW / imgH
            wh_ratio_list = []
            for ino in range(beg_img_no, end_img_no):
                h, w = img_list[indices[ino]].shape[0:2]
                wh_ratio = w * 1.0 / h
                max_wh_ratio = max(max_wh_ratio, wh_ratio)
                wh_ratio_list.append(wh_ratio)
            for ino in range(beg_img_no, end_img_no):
                if self.rec_algorithm == "SAR":
                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
                        img_list[indices[ino]], self.rec_image_shape
                    )
                    norm_img = norm_img[np.newaxis, :]
                    valid_ratio = np.expand_dims(valid_ratio, axis=0)
                    valid_ratios.append(valid_ratio)
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm == "SRN":
                    norm_img = self.process_image_srn(
                        img_list[indices[ino]], self.rec_image_shape, 8, 25
                    )
                    encoder_word_pos_list.append(norm_img[1])
                    gsrm_word_pos_list.append(norm_img[2])
                    gsrm_slf_attn_bias1_list.append(norm_img[3])
                    gsrm_slf_attn_bias2_list.append(norm_img[4])
                    norm_img_batch.append(norm_img[0])
                elif self.rec_algorithm in ["SVTR", "SATRN", "ParseQ", "CPPD"]:
                    norm_img = self.resize_norm_img_svtr(
                        img_list[indices[ino]], self.rec_image_shape
                    )
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm in ["CPPDPadding"]:
                    norm_img = self.resize_norm_img_cppd_padding(
                        img_list[indices[ino]], self.rec_image_shape
                    )
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm in ["VisionLAN", "PREN"]:
                    norm_img = self.resize_norm_img_vl(
                        img_list[indices[ino]], self.rec_image_shape
                    )
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm == "SPIN":
                    norm_img = self.resize_norm_img_spin(img_list[indices[ino]])
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm == "ABINet":
                    norm_img = self.resize_norm_img_abinet(
                        img_list[indices[ino]], self.rec_image_shape
                    )
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                elif self.rec_algorithm == "RobustScanner":
                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
                        img_list[indices[ino]],
                        self.rec_image_shape,
                        width_downsample_ratio=0.25,
                    )
                    norm_img = norm_img[np.newaxis, :]
                    valid_ratio = np.expand_dims(valid_ratio, axis=0)
                    valid_ratios = []
                    valid_ratios.append(valid_ratio)
                    norm_img_batch.append(norm_img)
                    word_positions_list = []
                    word_positions = np.array(range(0, 40)).astype("int64")
                    word_positions = np.expand_dims(word_positions, axis=0)
                    word_positions_list.append(word_positions)
                elif self.rec_algorithm == "CAN":
                    norm_img = self.norm_img_can(img_list[indices[ino]], max_wh_ratio)
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                    norm_image_mask = np.ones(norm_img.shape, dtype="float32")
                    word_label = np.ones([1, 36], dtype="int64")
                    norm_img_mask_batch = []
                    word_label_list = []
                    norm_img_mask_batch.append(norm_image_mask)
                    word_label_list.append(word_label)
                elif self.rec_algorithm == "LaTeXOCR":
                    norm_img = self.norm_img_latexocr(img_list[indices[ino]])
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
                else:
                    norm_img = self.resize_norm_img(
                        img_list[indices[ino]], max_wh_ratio
                    )
                    norm_img = norm_img[np.newaxis, :]
                    norm_img_batch.append(norm_img)
            norm_img_batch = np.concatenate(norm_img_batch)
            norm_img_batch = norm_img_batch.copy()
            if self.benchmark:
                self.autolog.times.stamp()

            if self.rec_algorithm == "SRN":
                encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
                gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
                gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list)
                gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list)

                inputs = [
                    norm_img_batch,
                    encoder_word_pos_list,
                    gsrm_word_pos_list,
                    gsrm_slf_attn_bias1_list,
                    gsrm_slf_attn_bias2_list,
                ]
                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = {"predict": outputs[2]}
                else:
                    input_names = self.predictor.get_input_names()
                    for i in range(len(input_names)):
                        input_tensor = self.predictor.get_input_handle(input_names[i])
                        input_tensor.copy_from_cpu(inputs[i])
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    preds = {"predict": outputs[2]}
            elif self.rec_algorithm == "SAR":
                valid_ratios = np.concatenate(valid_ratios)
                inputs = [
                    norm_img_batch,
                    np.array([valid_ratios], dtype=np.float32).T,
                ]
                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = outputs[0]
                else:
                    input_names = self.predictor.get_input_names()
                    for i in range(len(input_names)):
                        input_tensor = self.predictor.get_input_handle(input_names[i])
                        input_tensor.copy_from_cpu(inputs[i])
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    preds = outputs[0]
            elif self.rec_algorithm == "RobustScanner":
                valid_ratios = np.concatenate(valid_ratios)
                word_positions_list = np.concatenate(word_positions_list)
                inputs = [norm_img_batch, valid_ratios, word_positions_list]

                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = outputs[0]
                else:
                    input_names = self.predictor.get_input_names()
                    for i in range(len(input_names)):
                        input_tensor = self.predictor.get_input_handle(input_names[i])
                        input_tensor.copy_from_cpu(inputs[i])
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    preds = outputs[0]
            elif self.rec_algorithm == "CAN":
                norm_img_mask_batch = np.concatenate(norm_img_mask_batch)
                word_label_list = np.concatenate(word_label_list)
                inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]
                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = outputs
                else:
                    input_names = self.predictor.get_input_names()
                    input_tensor = []
                    for i in range(len(input_names)):
                        input_tensor_i = self.predictor.get_input_handle(input_names[i])
                        input_tensor_i.copy_from_cpu(inputs[i])
                        input_tensor.append(input_tensor_i)
                    self.input_tensor = input_tensor
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    preds = outputs
            elif self.rec_algorithm == "LaTeXOCR":
                inputs = [norm_img_batch]
                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = outputs
                else:
                    input_names = self.predictor.get_input_names()
                    input_tensor = []
                    for i in range(len(input_names)):
                        input_tensor_i = self.predictor.get_input_handle(input_names[i])
                        input_tensor_i.copy_from_cpu(inputs[i])
                        input_tensor.append(input_tensor_i)
                    self.input_tensor = input_tensor
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    preds = outputs
            else:
                if self.use_onnx:
                    input_dict = {}
                    input_dict[self.input_tensor.name] = norm_img_batch
                    outputs = self.predictor.run(self.output_tensors, input_dict)
                    preds = outputs[0]
                else:
                    self.input_tensor.copy_from_cpu(norm_img_batch)
                    self.predictor.run()
                    outputs = []
                    for output_tensor in self.output_tensors:
                        output = output_tensor.copy_to_cpu()
                        outputs.append(output)
                    if self.benchmark:
                        self.autolog.times.stamp()
                    if len(outputs) != 1:
                        preds = outputs
                    else:
                        preds = outputs[0]
            if self.postprocess_params["name"] == "CTCLabelDecode":
                rec_result = self.postprocess_op(
                    preds,
                    return_word_box=self.return_word_box,
                    wh_ratio_list=wh_ratio_list,
                    max_wh_ratio=max_wh_ratio,
                )
            elif self.postprocess_params["name"] == "LaTeXOCRDecode":
                preds = [p.reshape([-1]) for p in preds]
                rec_result = self.postprocess_op(preds)
            else:
                rec_result = self.postprocess_op(preds)
            for rno in range(len(rec_result)):
                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
            if self.benchmark:
                self.autolog.times.end(stamp=True)
        return rec_res, time.time() - st


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    valid_image_file_list = []
    img_list = []

    # logger
    log_file = args.save_log_path
    if os.path.isdir(args.save_log_path) or (
        not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/")
    ):
        log_file = os.path.join(log_file, "benchmark_recognition.log")
    logger = get_logger(log_file=log_file)

    # create text recognizer
    text_recognizer = TextRecognizer(args)

    logger.info(
        "In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
        "if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
    )

    # warmup 2 times
    if args.warmup:
        img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
        for i in range(2):
            res = text_recognizer([img] * int(args.rec_batch_num))

    for image_file in image_file_list:
        img, flag, _ = check_and_read(image_file)
        if not flag:
            img = cv2.imread(image_file)
        if img is None:
            logger.info("error in loading image:{}".format(image_file))
            continue
        valid_image_file_list.append(image_file)
        img_list.append(img)
    try:
        rec_res, _ = text_recognizer(img_list)

    except Exception as E:
        logger.info(traceback.format_exc())
        logger.info(E)
        exit()
    for ino in range(len(img_list)):
        logger.info(
            "Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino])
        )
    if args.benchmark:
        text_recognizer.autolog.report()


if __name__ == "__main__":
    main(utility.parse_args())