318 lines
13 KiB
Python
318 lines
13 KiB
Python
# 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(__file__)
|
||
sys.path.append(os.path.join(__dir__, ''))
|
||
|
||
import cv2
|
||
import numpy as np
|
||
from pathlib import Path
|
||
import tarfile
|
||
import requests
|
||
from tqdm import tqdm
|
||
|
||
from tools.infer import predict_system
|
||
from ppocr.utils.utility import initial_logger
|
||
|
||
logger = initial_logger()
|
||
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
|
||
|
||
__all__ = ['PaddleOCR']
|
||
|
||
model_urls = {
|
||
'det':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar',
|
||
'rec': {
|
||
'ch': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
|
||
},
|
||
'en': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/ic15_dict.txt'
|
||
},
|
||
'french': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/dict/french_dict.txt'
|
||
},
|
||
'german': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/dict/german_dict.txt'
|
||
},
|
||
'korean': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/dict/korean_dict.txt'
|
||
},
|
||
'japan': {
|
||
'url':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar',
|
||
'dict_path': './ppocr/utils/dict/japan_dict.txt'
|
||
}
|
||
},
|
||
'cls':
|
||
'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar'
|
||
}
|
||
|
||
SUPPORT_DET_MODEL = ['DB']
|
||
SUPPORT_REC_MODEL = ['CRNN']
|
||
BASE_DIR = os.path.expanduser("~/.paddleocr/")
|
||
|
||
|
||
def download_with_progressbar(url, save_path):
|
||
response = requests.get(url, stream=True)
|
||
total_size_in_bytes = int(response.headers.get('content-length', 0))
|
||
block_size = 1024 # 1 Kibibyte
|
||
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
|
||
with open(save_path, 'wb') as file:
|
||
for data in response.iter_content(block_size):
|
||
progress_bar.update(len(data))
|
||
file.write(data)
|
||
progress_bar.close()
|
||
if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
|
||
logger.error("Something went wrong while downloading models")
|
||
sys.exit(0)
|
||
|
||
|
||
def maybe_download(model_storage_directory, url):
|
||
# using custom model
|
||
if not os.path.exists(os.path.join(
|
||
model_storage_directory, 'model')) or not os.path.exists(
|
||
os.path.join(model_storage_directory, 'params')):
|
||
tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
|
||
print('download {} to {}'.format(url, tmp_path))
|
||
os.makedirs(model_storage_directory, exist_ok=True)
|
||
download_with_progressbar(url, tmp_path)
|
||
with tarfile.open(tmp_path, 'r') as tarObj:
|
||
for member in tarObj.getmembers():
|
||
if "model" in member.name:
|
||
filename = 'model'
|
||
elif "params" in member.name:
|
||
filename = 'params'
|
||
else:
|
||
continue
|
||
file = tarObj.extractfile(member)
|
||
with open(
|
||
os.path.join(model_storage_directory, filename),
|
||
'wb') as f:
|
||
f.write(file.read())
|
||
os.remove(tmp_path)
|
||
|
||
|
||
|
||
def parse_args(mMain=True,add_help=True):
|
||
|
||
import argparse
|
||
|
||
def str2bool(v):
|
||
return v.lower() in ("true", "t", "1")
|
||
|
||
if mMain:
|
||
parser = argparse.ArgumentParser(add_help=add_help)
|
||
# params for prediction engine
|
||
parser.add_argument("--use_gpu", type=str2bool, default=True)
|
||
parser.add_argument("--ir_optim", type=str2bool, default=True)
|
||
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
|
||
parser.add_argument("--gpu_mem", type=int, default=8000)
|
||
|
||
# params for text detector
|
||
parser.add_argument("--image_dir", type=str)
|
||
parser.add_argument("--det_algorithm", type=str, default='DB')
|
||
parser.add_argument("--det_model_dir", type=str, default=None)
|
||
parser.add_argument("--det_max_side_len", type=float, default=960)
|
||
|
||
# DB parmas
|
||
parser.add_argument("--det_db_thresh", type=float, default=0.3)
|
||
parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
|
||
parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
|
||
|
||
# EAST parmas
|
||
parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
|
||
parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
|
||
parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
|
||
|
||
# params for text recognizer
|
||
parser.add_argument("--rec_algorithm", type=str, default='CRNN')
|
||
parser.add_argument("--rec_model_dir", type=str, default=None)
|
||
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
|
||
parser.add_argument("--rec_char_type", type=str, default='ch')
|
||
parser.add_argument("--rec_batch_num", type=int, default=30)
|
||
parser.add_argument("--max_text_length", type=int, default=25)
|
||
parser.add_argument("--rec_char_dict_path", type=str, default=None)
|
||
parser.add_argument("--use_space_char", type=bool, default=True)
|
||
|
||
# params for text classifier
|
||
parser.add_argument("--cls_model_dir", type=str, default=None)
|
||
parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
|
||
parser.add_argument("--label_list", type=list, default=['0', '180'])
|
||
parser.add_argument("--cls_batch_num", type=int, default=30)
|
||
parser.add_argument("--cls_thresh", type=float, default=0.9)
|
||
|
||
parser.add_argument("--enable_mkldnn", type=bool, default=False)
|
||
parser.add_argument("--use_zero_copy_run", type=bool, default=False)
|
||
parser.add_argument("--use_pdserving", type=str2bool, default=False)
|
||
|
||
parser.add_argument("--lang", type=str, default='ch')
|
||
parser.add_argument("--det", type=str2bool, default=True)
|
||
parser.add_argument("--rec", type=str2bool, default=True)
|
||
parser.add_argument("--use_angle_cls", type=str2bool, default=True)
|
||
return parser.parse_args()
|
||
else:
|
||
return argparse.Namespace( use_gpu=True,
|
||
ir_optim=True,
|
||
use_tensorrt=False,
|
||
gpu_mem=8000,
|
||
image_dir='',
|
||
det_algorithm='DB',
|
||
det_model_dir=None,
|
||
det_max_side_len=960,
|
||
det_db_thresh=0.3,
|
||
det_db_box_thresh=0.5,
|
||
det_db_unclip_ratio=2.0,
|
||
det_east_score_thresh=0.8,
|
||
det_east_cover_thresh=0.1,
|
||
det_east_nms_thresh=0.2,
|
||
rec_algorithm='CRNN',
|
||
rec_model_dir=None,
|
||
rec_image_shape="3, 32, 320",
|
||
rec_char_type='ch',
|
||
rec_batch_num=30,
|
||
max_text_length=25,
|
||
rec_char_dict_path=None,
|
||
use_space_char=True,
|
||
cls_model_dir=None,
|
||
cls_image_shape="3, 48, 192",
|
||
label_list=['0', '180'],
|
||
cls_batch_num=30,
|
||
cls_thresh=0.9,
|
||
enable_mkldnn=False,
|
||
use_zero_copy_run=False,
|
||
use_pdserving=False,
|
||
lang='ch',
|
||
det=True,
|
||
rec=True,
|
||
use_angle_cls=True
|
||
)
|
||
|
||
|
||
|
||
class PaddleOCR(predict_system.TextSystem):
|
||
def __init__(self, **kwargs):
|
||
"""
|
||
paddleocr package
|
||
args:
|
||
**kwargs: other params show in paddleocr --help
|
||
"""
|
||
postprocess_params = parse_args(mMain=False, add_help=False)
|
||
postprocess_params.__dict__.update(**kwargs)
|
||
self.use_angle_cls = postprocess_params.use_angle_cls
|
||
lang = postprocess_params.lang
|
||
assert lang in model_urls[
|
||
'rec'], 'param lang must in {}, but got {}'.format(
|
||
model_urls['rec'].keys(), lang)
|
||
if postprocess_params.rec_char_dict_path is None:
|
||
postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
|
||
'dict_path']
|
||
|
||
# init model dir
|
||
if postprocess_params.det_model_dir is None:
|
||
postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
|
||
if postprocess_params.rec_model_dir is None:
|
||
postprocess_params.rec_model_dir = os.path.join(
|
||
BASE_DIR, 'rec/{}'.format(lang))
|
||
if postprocess_params.cls_model_dir is None:
|
||
postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
|
||
print(postprocess_params)
|
||
# download model
|
||
maybe_download(postprocess_params.det_model_dir, model_urls['det'])
|
||
maybe_download(postprocess_params.rec_model_dir,
|
||
model_urls['rec'][lang]['url'])
|
||
maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
|
||
|
||
if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
|
||
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
|
||
sys.exit(0)
|
||
if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
|
||
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
|
||
sys.exit(0)
|
||
|
||
postprocess_params.rec_char_dict_path = Path(
|
||
__file__).parent / postprocess_params.rec_char_dict_path
|
||
|
||
# init det_model and rec_model
|
||
super().__init__(postprocess_params)
|
||
|
||
def ocr(self, img, det=True, rec=True, cls=False):
|
||
"""
|
||
ocr with paddleocr
|
||
args：
|
||
img: img for ocr, support ndarray, img_path and list or ndarray
|
||
det: use text detection or not, if false, only rec will be exec. default is True
|
||
rec: use text recognition or not, if false, only det will be exec. default is True
|
||
"""
|
||
assert isinstance(img, (np.ndarray, list, str))
|
||
self.use_angle_cls = cls
|
||
if isinstance(img, str):
|
||
image_file = img
|
||
img, flag = check_and_read_gif(image_file)
|
||
if not flag:
|
||
img = cv2.imread(image_file)
|
||
if img is None:
|
||
logger.error("error in loading image:{}".format(image_file))
|
||
return None
|
||
if det and rec:
|
||
dt_boxes, rec_res = self.__call__(img)
|
||
return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
|
||
elif det and not rec:
|
||
dt_boxes, elapse = self.text_detector(img)
|
||
if dt_boxes is None:
|
||
return None
|
||
return [box.tolist() for box in dt_boxes]
|
||
else:
|
||
if not isinstance(img, list):
|
||
img = [img]
|
||
if self.use_angle_cls:
|
||
img, cls_res, elapse = self.text_classifier(img)
|
||
if not rec:
|
||
return cls_res
|
||
rec_res, elapse = self.text_recognizer(img)
|
||
return rec_res
|
||
|
||
|
||
def main():
|
||
# for com
|
||
args = parse_args(mMain=True)
|
||
image_file_list = get_image_file_list(args.image_dir)
|
||
if len(image_file_list) == 0:
|
||
logger.error('no images find in {}'.format(args.image_dir))
|
||
return
|
||
|
||
ocr_engine = PaddleOCR(**(args.__dict__))
|
||
for img_path in image_file_list:
|
||
print(img_path)
|
||
result = ocr_engine.ocr(img_path,
|
||
det=args.det,
|
||
rec=args.rec,
|
||
cls=args.use_angle_cls)
|
||
if result is not None:
|
||
for line in result:
|
||
print(line)
|