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
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))

import tools.infer.utility as utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
import cv2
import copy
import numpy as np
import math
import time
import paddle.fluid as fluid
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")
        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
        self.character_type = args.rec_char_type
        self.rec_batch_num = args.rec_batch_num
        self.rec_algorithm = args.rec_algorithm
        self.text_len = args.max_text_length
        char_ops_params = {
            "character_type": args.rec_char_type,
            "character_dict_path": args.rec_char_dict_path,
            "use_space_char": args.use_space_char,
            "max_text_length": args.max_text_length
        }
        if self.rec_algorithm in ["CRNN", "Rosetta", "STAR-Net"]:
            char_ops_params['loss_type'] = 'ctc'
            self.loss_type = 'ctc'
        elif self.rec_algorithm == "RARE":
            char_ops_params['loss_type'] = 'attention'
            self.loss_type = 'attention'
        elif self.rec_algorithm == "SRN":
            char_ops_params['loss_type'] = 'srn'
            self.loss_type = 'srn'
        self.char_ops = CharacterOps(char_ops_params)

    def resize_norm_img(self, img, max_wh_ratio):
        imgC, imgH, imgW = self.rec_image_shape
        assert imgC == img.shape[2]
        if self.character_type == "ch":
            imgW = int((32 * 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 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,
                         char_num):

        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,
                          char_ops=None):
        norm_img = self.resize_norm_img_srn(img, image_shape)
        norm_img = norm_img[np.newaxis, :]
        char_num = char_ops.get_char_num()

        [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, char_num)

        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)

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

    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 = []
        rec_res = [['', 0.0]] * img_num
        batch_num = self.rec_batch_num
        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]
                h, w = img_list[indices[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):
                if self.loss_type != "srn":
                    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)
                else:
                    norm_img = self.process_image_srn(img_list[indices[ino]],
                                                      self.rec_image_shape, 8,
                                                      25, self.char_ops)
                    encoder_word_pos_list = []
                    gsrm_word_pos_list = []
                    gsrm_slf_attn_bias1_list = []
                    gsrm_slf_attn_bias2_list = []
                    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])

            norm_img_batch = np.concatenate(norm_img_batch, axis=0)
            norm_img_batch = norm_img_batch.copy()

            if self.loss_type == "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)
                starttime = time.time()

                norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
                encoder_word_pos_list = fluid.core.PaddleTensor(
                    encoder_word_pos_list)
                gsrm_word_pos_list = fluid.core.PaddleTensor(gsrm_word_pos_list)
                gsrm_slf_attn_bias1_list = fluid.core.PaddleTensor(
                    gsrm_slf_attn_bias1_list)
                gsrm_slf_attn_bias2_list = fluid.core.PaddleTensor(
                    gsrm_slf_attn_bias2_list)

                inputs = [
                    norm_img_batch, encoder_word_pos_list,
                    gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list,
                    gsrm_word_pos_list
                ]

                self.predictor.run(inputs)
            else:
                self.input_tensor.copy_from_cpu(norm_img_batch)
                self.predictor.zero_copy_run()

            if self.loss_type == "ctc":
                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
                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]
                    if len(valid_ind) == 0:
                        continue
                    score = np.mean(probs[valid_ind, ind[valid_ind]])
                    # rec_res.append([preds_text, score])
                    rec_res[indices[beg_img_no + rno]] = [preds_text, score]
            elif self.loss_type == 'srn':
                rec_idx_batch = self.output_tensors[0].copy_to_cpu()
                probs = self.output_tensors[1].copy_to_cpu()
                char_num = self.char_ops.get_char_num()
                preds = rec_idx_batch.reshape(-1)
                elapse = time.time() - starttime
                predict_time += elapse
                total_preds = preds.copy()
                for ino in range(int(len(rec_idx_batch) / self.text_len)):
                    preds = total_preds[ino * self.text_len:(ino + 1) *
                                        self.text_len]
                    ind = np.argmax(probs, axis=1)
                    valid_ind = np.where(preds != int(char_num - 1))[0]
                    if len(valid_ind) == 0:
                        continue
                    score = np.mean(probs[valid_ind, ind[valid_ind]])
                    preds = preds[:valid_ind[-1] + 1]
                    preds_text = self.char_ops.decode(preds)

                    rec_res[indices[beg_img_no + ino]] = [preds_text, score]
            else:
                rec_idx_batch = self.output_tensors[0].copy_to_cpu()
                predict_batch = self.output_tensors[1].copy_to_cpu()
                elapse = time.time() - starttime
                predict_time += elapse
                for rno in range(len(rec_idx_batch)):
                    end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
                    if len(end_pos) <= 1:
                        preds = rec_idx_batch[rno, 1:]
                        score = np.mean(predict_batch[rno, 1:])
                    else:
                        preds = rec_idx_batch[rno, 1:end_pos[1]]
                        score = np.mean(predict_batch[rno, 1:end_pos[1]])
                    preds_text = self.char_ops.decode(preds)
                    # rec_res.append([preds_text, score])
                    rec_res[indices[beg_img_no + rno]] = [preds_text, score]

        return rec_res, predict_time


def main(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, flag = check_and_read_gif(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, predict_time = text_recognizer(img_list)
    except Exception as e:
        print(e)
        logger.info(
            "ERROR!!!! \n"
            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
            "If your model has tps module:  "
            "TPS does not support variable shape.\n"
            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
        exit()
    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))


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