138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
# -*- coding:utf-8 -*-
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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sys.path.insert(0, ".")
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import copy
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from paddlehub.common.logger import logger
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from paddlehub.module.module import moduleinfo, runnable, serving
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import cv2
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import paddlehub as hub
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from tools.infer.utility import base64_to_cv2
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from tools.infer.predict_cls import TextClassifier
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from tools.infer.utility import parse_args
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from deploy.hubserving.ocr_cls.params import read_params
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@moduleinfo(
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name="ocr_cls",
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version="1.0.0",
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summary="ocr recognition service",
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author="paddle-dev",
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author_email="paddle-dev@baidu.com",
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type="cv/text_recognition")
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class OCRCls(hub.Module):
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def _initialize(self, use_gpu=False, enable_mkldnn=False):
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"""
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initialize with the necessary elements
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"""
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cfg = self.merge_configs()
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cfg.use_gpu = use_gpu
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if use_gpu:
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try:
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_places = os.environ["CUDA_VISIBLE_DEVICES"]
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int(_places[0])
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print("use gpu: ", use_gpu)
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print("CUDA_VISIBLE_DEVICES: ", _places)
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cfg.gpu_mem = 8000
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except:
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raise RuntimeError(
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"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."
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)
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cfg.ir_optim = True
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cfg.enable_mkldnn = enable_mkldnn
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self.text_classifier = TextClassifier(cfg)
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def merge_configs(self, ):
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# deafult cfg
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backup_argv = copy.deepcopy(sys.argv)
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sys.argv = sys.argv[:1]
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cfg = parse_args()
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update_cfg_map = vars(read_params())
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for key in update_cfg_map:
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cfg.__setattr__(key, update_cfg_map[key])
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sys.argv = copy.deepcopy(backup_argv)
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return cfg
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def read_images(self, paths=[]):
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images = []
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for img_path in paths:
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assert os.path.isfile(
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img_path), "The {} isn't a valid file.".format(img_path)
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img = cv2.imread(img_path)
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if img is None:
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logger.info("error in loading image:{}".format(img_path))
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continue
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images.append(img)
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return images
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def predict(self, images=[], paths=[]):
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"""
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Get the text angle in the predicted images.
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Args:
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images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
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paths (list[str]): The paths of images. If paths not images
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Returns:
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res (list): The result of text detection box and save path of images.
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"""
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if images != [] and isinstance(images, list) and paths == []:
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predicted_data = images
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elif images == [] and isinstance(paths, list) and paths != []:
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predicted_data = self.read_images(paths)
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else:
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raise TypeError("The input data is inconsistent with expectations.")
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assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
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img_list = []
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for img in predicted_data:
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if img is None:
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continue
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img_list.append(img)
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rec_res_final = []
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try:
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img_list, cls_res, predict_time = self.text_classifier(img_list)
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for dno in range(len(cls_res)):
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angle, score = cls_res[dno]
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rec_res_final.append({
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'angle': angle,
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'confidence': float(score),
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})
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except Exception as e:
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print(e)
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return [[]]
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return [rec_res_final]
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@serving
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def serving_method(self, images, **kwargs):
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"""
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Run as a service.
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"""
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images_decode = [base64_to_cv2(image) for image in images]
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results = self.predict(images_decode, **kwargs)
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return results
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if __name__ == '__main__':
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ocr = OCRCls()
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image_path = [
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'./doc/imgs_words/ch/word_1.jpg',
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'./doc/imgs_words/ch/word_2.jpg',
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'./doc/imgs_words/ch/word_3.jpg',
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]
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res = ocr.predict(paths=image_path)
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print(res)
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