module.py

# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
sys.path.insert(0, ".")
import copy

from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo, runnable, serving
import cv2
import paddlehub as hub

from tools.infer.utility import base64_to_cv2
from tools.infer.predict_cls import TextClassifier
from tools.infer.utility import parse_args


@moduleinfo(
    name="ocr_cls",
    version="1.0.0",
    summary="ocr recognition service",
    author="paddle-dev",
    author_email="paddle-dev@baidu.com",
    type="cv/text_recognition")
class OCRCls(hub.Module):
    def _initialize(self, use_gpu=False, enable_mkldnn=False):
        """
        initialize with the necessary elements
        """
        cfg = self.merge_configs()

        cfg.use_gpu = use_gpu
        if use_gpu:
            try:
                _places = os.environ["CUDA_VISIBLE_DEVICES"]
                int(_places[0])
                print("use gpu: ", use_gpu)
                print("CUDA_VISIBLE_DEVICES: ", _places)
                cfg.gpu_mem = 8000
            except:
                raise RuntimeError(
                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
                )
        cfg.ir_optim = True
        cfg.enable_mkldnn = enable_mkldnn

        self.text_classifier = TextClassifier(cfg)

    def merge_configs(self, ):
        # deafult cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()

        update_cfg_map = vars(read_params())

        for key in update_cfg_map:
            cfg.__setattr__(key, update_cfg_map[key])

        sys.argv = copy.deepcopy(backup_argv)
        return cfg

    def read_images(self, paths=[]):
        images = []
        for img_path in paths:
            assert os.path.isfile(
                img_path), "The {} isn't a valid file.".format(img_path)
            img = cv2.imread(img_path)
            if img is None:
                logger.info("error in loading image:{}".format(img_path))
                continue
            images.append(img)
        return images

    def predict(self, images=[], paths=[]):
        """
        Get the text angle in the predicted images.
        Args:
            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
            paths (list[str]): The paths of images. If paths not images
        Returns:
            res (list): The result of text detection box and save path of images.
        """

        if images != [] and isinstance(images, list) and paths == []:
            predicted_data = images
        elif images == [] and isinstance(paths, list) and paths != []:
            predicted_data = self.read_images(paths)
        else:
            raise TypeError("The input data is inconsistent with expectations.")

        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."

        img_list = []
        for img in predicted_data:
            if img is None:
                continue
            img_list.append(img)

        rec_res_final = []
        try:
            img_list, cls_res, predict_time = self.text_classifier(img_list)
            for dno in range(len(cls_res)):
                angle, score = cls_res[dno]
                rec_res_final.append({
                    'angle': angle,
                    'confidence': float(score),
                })
        except Exception as e:
            print(e)
            return [[]]

        return [rec_res_final]

    @serving
    def serving_method(self, images, **kwargs):
        """
        Run as a service.
        """
        images_decode = [base64_to_cv2(image) for image in images]
        results = self.predict(images_decode, **kwargs)
        return results


if __name__ == '__main__':
    ocr = OCRCls()
    image_path = [
        './doc/imgs_words/ch/word_1.jpg',
        './doc/imgs_words/ch/word_2.jpg',
        './doc/imgs_words/ch/word_3.jpg',
    ]
    res = ocr.predict(paths=image_path)
    print(res)