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 utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import get_image_file_list
import cv2
import copy
import numpy as np
import math
import time
from ppocr.utils.character import CharacterOps


class TextRecognizer(object):
    def __init__(self, args):
        self.predictor, self.input_tensor, self.output_tensors =\
            utility.create_predictor(args, mode="rec")
        image_shape = [int(v) for v in args.rec_image_shape.split(",")]
        self.rec_image_shape = image_shape
        self.character_type = args.rec_char_type
        char_ops_params = {}
        char_ops_params["character_type"] = args.rec_char_type
        char_ops_params["character_dict_path"] = args.rec_char_dict_path
        char_ops_params['loss_type'] = 'ctc'
        self.char_ops = CharacterOps(char_ops_params)

    def resize_norm_img(self, img, max_wh_ratio):
        imgC, imgH, imgW = self.rec_image_shape
        if self.character_type == "ch":
            imgW = int(32 * max_wh_ratio)
        h = img.shape[0]
        w = img.shape[1]
        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, img_list):
        img_num = len(img_list)
        batch_num = 30
        rec_res = []
        predict_time = 0
        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 = []
            max_wh_ratio = 0
            for ino in range(beg_img_no, end_img_no):
                h, w = img_list[ino].shape[0:2]
                wh_ratio = w * 1.0 / h
                max_wh_ratio = max(max_wh_ratio, wh_ratio)
            for ino in range(beg_img_no, end_img_no):
                norm_img = self.resize_norm_img(img_list[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()
            starttime = time.time()
            self.input_tensor.copy_from_cpu(norm_img_batch)
            self.predictor.zero_copy_run()
            rec_idx_batch = self.output_tensors[0].copy_to_cpu()
            rec_idx_lod = self.output_tensors[0].lod()[0]
            predict_batch = self.output_tensors[1].copy_to_cpu()
            predict_lod = self.output_tensors[1].lod()[0]
            elapse = time.time() - starttime
            predict_time += elapse
            starttime = time.time()
            for rno in range(len(rec_idx_lod) - 1):
                beg = rec_idx_lod[rno]
                end = rec_idx_lod[rno + 1]
                rec_idx_tmp = rec_idx_batch[beg:end, 0]
                preds_text = self.char_ops.decode(rec_idx_tmp)
                beg = predict_lod[rno]
                end = predict_lod[rno + 1]
                probs = predict_batch[beg:end, :]
                ind = np.argmax(probs, axis=1)
                blank = probs.shape[1]
                valid_ind = np.where(ind != (blank - 1))[0]
                score = np.mean(probs[valid_ind, ind[valid_ind]])
                rec_res.append([preds_text, score])
        return rec_res, predict_time


if __name__ == "__main__":
    args = utility.parse_args()
    image_file_list = get_image_file_list(args.image_dir)
    text_recognizer = TextRecognizer(args)
    valid_image_file_list = []
    img_list = []
    for image_file in image_file_list:
        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)
    rec_res, predict_time = text_recognizer(img_list)
    rec_res, predict_time = text_recognizer(img_list)
    for ino in range(len(img_list)):
        print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
    print("Total predict time for %d images:%.3f" %
          (len(img_list), predict_time))