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paddleocr.py
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# 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
import importlib
__dir__ = os.path.dirname(__file__)
import paddle
from paddle.utils import try_import
sys.path.append(os.path.join(__dir__, ""))
import cv2
import logging
import numpy as np
from pathlib import Path
import base64
from io import BytesIO
import pprint
from PIL import Image
from tools.infer import predict_system
def _import_file(module_name, file_path, make_importable=False):
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
if make_importable:
sys.modules[module_name] = module
return module
tools = _import_file(
"tools", os.path.join(__dir__, "tools/__init__.py"), make_importable=True
)
ppocr = importlib.import_module("ppocr", "paddleocr")
ppstructure = importlib.import_module("ppstructure", "paddleocr")
from ppocr.utils.logging import get_logger
logger = get_logger()
from ppocr.utils.utility import (
check_and_read,
get_image_file_list,
alpha_to_color,
binarize_img,
)
from ppocr.utils.network import (
maybe_download,
download_with_progressbar,
is_link,
confirm_model_dir_url,
)
from tools.infer.utility import draw_ocr, str2bool, check_gpu
from ppstructure.utility import init_args, draw_structure_result
from ppstructure.predict_system import StructureSystem, save_structure_res, to_excel
logger = get_logger()
__all__ = [
"PaddleOCR",
"PPStructure",
"draw_ocr",
"draw_structure_result",
"save_structure_res",
"download_with_progressbar",
"to_excel",
]
SUPPORT_DET_MODEL = ["DB"]
SUPPORT_REC_MODEL = ["CRNN", "SVTR_LCNet"]
BASE_DIR = os.path.expanduser("~/.paddleocr/")
DEFAULT_OCR_MODEL_VERSION = "PP-OCRv4"
SUPPORT_OCR_MODEL_VERSION = ["PP-OCR", "PP-OCRv2", "PP-OCRv3", "PP-OCRv4"]
DEFAULT_STRUCTURE_MODEL_VERSION = "PP-StructureV2"
SUPPORT_STRUCTURE_MODEL_VERSION = ["PP-Structure", "PP-StructureV2"]
MODEL_URLS = {
"OCR": {
"PP-OCRv4": {
"det": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar",
},
"ml": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar"
},
},
"rec": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/ppocr_keys_v1.txt",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/english/en_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/en_dict.txt",
},
"korean": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/korean_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/korean_dict.txt",
},
"japan": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/japan_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/japan_dict.txt",
},
"chinese_cht": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/chinese_cht_dict.txt",
},
"ta": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/ta_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ta_dict.txt",
},
"te": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/te_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/te_dict.txt",
},
"ka": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/ka_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ka_dict.txt",
},
"latin": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/latin_dict.txt",
},
"arabic": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/arabic_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/arabic_dict.txt",
},
"cyrillic": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/cyrillic_dict.txt",
},
"devanagari": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/devanagari_PP-OCRv4_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/devanagari_dict.txt",
},
},
"cls": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar",
}
},
},
"PP-OCRv3": {
"det": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar",
},
"ml": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar"
},
},
"rec": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/ppocr_keys_v1.txt",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/en_dict.txt",
},
"korean": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/korean_dict.txt",
},
"japan": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/japan_dict.txt",
},
"chinese_cht": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/chinese_cht_dict.txt",
},
"ta": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ta_dict.txt",
},
"te": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/te_dict.txt",
},
"ka": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ka_dict.txt",
},
"latin": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/latin_dict.txt",
},
"arabic": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/arabic_dict.txt",
},
"cyrillic": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/cyrillic_dict.txt",
},
"devanagari": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/devanagari_dict.txt",
},
},
"cls": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar",
}
},
},
"PP-OCRv2": {
"det": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar",
},
},
"rec": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar",
"dict_path": "./ppocr/utils/ppocr_keys_v1.txt",
}
},
"cls": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar",
}
},
},
"PP-OCR": {
"det": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar",
},
"structure": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar"
},
},
"rec": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/ppocr_keys_v1.txt",
},
"en": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/en_dict.txt",
},
"french": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/french_dict.txt",
},
"german": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/german_dict.txt",
},
"korean": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/korean_dict.txt",
},
"japan": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/japan_dict.txt",
},
"chinese_cht": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/chinese_cht_dict.txt",
},
"ta": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ta_dict.txt",
},
"te": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/te_dict.txt",
},
"ka": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/ka_dict.txt",
},
"latin": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/latin_dict.txt",
},
"arabic": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/arabic_dict.txt",
},
"cyrillic": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/cyrillic_dict.txt",
},
"devanagari": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar",
"dict_path": "./ppocr/utils/dict/devanagari_dict.txt",
},
"structure": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar",
"dict_path": "ppocr/utils/dict/table_dict.txt",
},
},
"cls": {
"ch": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar",
}
},
},
},
"STRUCTURE": {
"PP-Structure": {
"table": {
"en": {
"url": "https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar",
"dict_path": "ppocr/utils/dict/table_structure_dict.txt",
}
}
},
"PP-StructureV2": {
"table": {
"en": {
"url": "https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar",
"dict_path": "ppocr/utils/dict/table_structure_dict.txt",
},
"ch": {
"url": "https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar",
"dict_path": "ppocr/utils/dict/table_structure_dict_ch.txt",
},
},
"layout": {
"en": {
"url": "https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar",
"dict_path": "ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt",
},
"ch": {
"url": "https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar",
"dict_path": "ppocr/utils/dict/layout_dict/layout_cdla_dict.txt",
},
},
},
},
}
def parse_args(mMain=True):
import argparse
parser = init_args()
parser.add_help = mMain
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("--type", type=str, default="ocr")
parser.add_argument("--savefile", type=str2bool, default=False)
parser.add_argument(
"--ocr_version",
type=str,
choices=SUPPORT_OCR_MODEL_VERSION,
default="PP-OCRv4",
help="OCR Model version, the current model support list is as follows: "
"1. PP-OCRv4/v3 Support Chinese and English detection and recognition model, and direction classifier model"
"2. PP-OCRv2 Support Chinese detection and recognition model. "
"3. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.",
)
parser.add_argument(
"--structure_version",
type=str,
choices=SUPPORT_STRUCTURE_MODEL_VERSION,
default="PP-StructureV2",
help="Model version, the current model support list is as follows:"
" 1. PP-Structure Support en table structure model."
" 2. PP-StructureV2 Support ch and en table structure model.",
)
for action in parser._actions:
if action.dest in [
"rec_char_dict_path",
"table_char_dict_path",
"layout_dict_path",
]:
action.default = None
if mMain:
return parser.parse_args()
else:
inference_args_dict = {}
for action in parser._actions:
inference_args_dict[action.dest] = action.default
return argparse.Namespace(**inference_args_dict)
def parse_lang(lang):
latin_lang = [
"af",
"az",
"bs",
"cs",
"cy",
"da",
"de",
"es",
"et",
"fr",
"ga",
"hr",
"hu",
"id",
"is",
"it",
"ku",
"la",
"lt",
"lv",
"mi",
"ms",
"mt",
"nl",
"no",
"oc",
"pi",
"pl",
"pt",
"ro",
"rs_latin",
"sk",
"sl",
"sq",
"sv",
"sw",
"tl",
"tr",
"uz",
"vi",
"french",
"german",
]
arabic_lang = ["ar", "fa", "ug", "ur"]
cyrillic_lang = [
"ru",
"rs_cyrillic",
"be",
"bg",
"uk",
"mn",
"abq",
"ady",
"kbd",
"ava",
"dar",
"inh",
"che",
"lbe",
"lez",
"tab",
]
devanagari_lang = [
"hi",
"mr",
"ne",
"bh",
"mai",
"ang",
"bho",
"mah",
"sck",
"new",
"gom",
"sa",
"bgc",
]
if lang in latin_lang:
lang = "latin"
elif lang in arabic_lang:
lang = "arabic"
elif lang in cyrillic_lang:
lang = "cyrillic"
elif lang in devanagari_lang:
lang = "devanagari"
assert (
lang in MODEL_URLS["OCR"][DEFAULT_OCR_MODEL_VERSION]["rec"]
), "param lang must in {}, but got {}".format(
MODEL_URLS["OCR"][DEFAULT_OCR_MODEL_VERSION]["rec"].keys(), lang
)
if lang == "ch":
det_lang = "ch"
elif lang == "structure":
det_lang = "structure"
elif lang in ["en", "latin"]:
det_lang = "en"
else:
det_lang = "ml"
return lang, det_lang
def get_model_config(type, version, model_type, lang):
if type == "OCR":
DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION
elif type == "STRUCTURE":
DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION
else:
raise NotImplementedError
model_urls = MODEL_URLS[type]
if version not in model_urls:
version = DEFAULT_MODEL_VERSION
if model_type not in model_urls[version]:
if model_type in model_urls[DEFAULT_MODEL_VERSION]:
version = DEFAULT_MODEL_VERSION
else:
logger.error(
"{} models is not support, we only support {}".format(
model_type, model_urls[DEFAULT_MODEL_VERSION].keys()
)
)
sys.exit(-1)
if lang not in model_urls[version][model_type]:
if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]:
version = DEFAULT_MODEL_VERSION
else:
logger.error(
"lang {} is not support, we only support {} for {} models".format(
lang,
model_urls[DEFAULT_MODEL_VERSION][model_type].keys(),
model_type,
)
)
sys.exit(-1)
return model_urls[version][model_type][lang]
def img_decode(content: bytes):
np_arr = np.frombuffer(content, dtype=np.uint8)
return cv2.imdecode(np_arr, cv2.IMREAD_UNCHANGED)
def check_img(img, alpha_color=(255, 255, 255)):
"""
Check the image data. If it is another type of image file, try to decode it into a numpy array.
The inference network requires three-channel images, So the following channel conversions are done
single channel image: Gray to RGB R←Y,G←Y,B←Y
four channel image: alpha_to_color
args:
img: image data
file format: jpg, png and other image formats that opencv can decode, as well as gif and pdf formats
storage type: binary image, net image file, local image file
alpha_color: Background color in images in RGBA format
return: numpy.array (h, w, 3) or list (p, h, w, 3) (p: page of pdf), boolean, boolean
"""
flag_gif, flag_pdf = False, False
if isinstance(img, bytes):
img = img_decode(img)
if isinstance(img, str):
# download net image
if is_link(img):
download_with_progressbar(img, "tmp.jpg")
img = "tmp.jpg"
image_file = img
img, flag_gif, flag_pdf = check_and_read(image_file)
if not flag_gif and not flag_pdf:
with open(image_file, "rb") as f:
img_str = f.read()
img = img_decode(img_str)
if img is None:
try:
buf = BytesIO()
image = BytesIO(img_str)
im = Image.open(image)
rgb = im.convert("RGB")
rgb.save(buf, "jpeg")
buf.seek(0)
image_bytes = buf.read()
data_base64 = str(base64.b64encode(image_bytes), encoding="utf-8")
image_decode = base64.b64decode(data_base64)
img_array = np.frombuffer(image_decode, np.uint8)
img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
except:
logger.error("error in loading image:{}".format(image_file))
return None, flag_gif, flag_pdf
if img is None:
logger.error("error in loading image:{}".format(image_file))
return None, flag_gif, flag_pdf
# single channel image array.shape:h,w
if isinstance(img, np.ndarray) and len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
# four channel image array.shape:h,w,c
if isinstance(img, np.ndarray) and len(img.shape) == 3 and img.shape[2] == 4:
img = alpha_to_color(img, alpha_color)
return img, flag_gif, flag_pdf
class PaddleOCR(predict_system.TextSystem):
def __init__(self, **kwargs):
"""
paddleocr package
args:
**kwargs: other params show in paddleocr --help
"""
params = parse_args(mMain=False)
params.__dict__.update(**kwargs)
assert (
params.ocr_version in SUPPORT_OCR_MODEL_VERSION
), "ocr_version must in {}, but get {}".format(
SUPPORT_OCR_MODEL_VERSION, params.ocr_version
)
params.use_gpu = check_gpu(params.use_gpu)
if not params.show_log:
logger.setLevel(logging.INFO)
self.use_angle_cls = params.use_angle_cls
lang, det_lang = parse_lang(params.lang)
# init model dir
det_model_config = get_model_config("OCR", params.ocr_version, "det", det_lang)
params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir,
os.path.join(BASE_DIR, "whl", "det", det_lang),
det_model_config["url"],
)
rec_model_config = get_model_config("OCR", params.ocr_version, "rec", lang)
params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir,
os.path.join(BASE_DIR, "whl", "rec", lang),
rec_model_config["url"],
)
cls_model_config = get_model_config("OCR", params.ocr_version, "cls", "ch")
params.cls_model_dir, cls_url = confirm_model_dir_url(
params.cls_model_dir,
os.path.join(BASE_DIR, "whl", "cls"),
cls_model_config["url"],
)
if params.ocr_version in ["PP-OCRv3", "PP-OCRv4"]:
params.rec_image_shape = "3, 48, 320"
else:
params.rec_image_shape = "3, 32, 320"
# download model if using paddle infer
if not params.use_onnx:
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.cls_model_dir, cls_url)
if params.det_algorithm not in SUPPORT_DET_MODEL:
logger.error("det_algorithm must in {}".format(SUPPORT_DET_MODEL))
sys.exit(0)
if params.rec_algorithm not in SUPPORT_REC_MODEL:
logger.error("rec_algorithm must in {}".format(SUPPORT_REC_MODEL))
sys.exit(0)
if params.rec_char_dict_path is None:
params.rec_char_dict_path = str(
Path(__file__).parent / rec_model_config["dict_path"]
)
logger.debug(params)
# init det_model and rec_model
super().__init__(params)
self.page_num = params.page_num
def ocr(
self,
img,
det=True,
rec=True,
cls=True,
bin=False,
inv=False,
alpha_color=(255, 255, 255),
slice={},
):
"""
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
cls: use angle classifier or not. Default is True. If True, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
bin: binarize image to black and white. Default is False.
inv: invert image colors. Default is False.
alpha_color: set RGB color Tuple for transparent parts replacement. Default is pure white.
slice: use sliding window inference for large images, det and rec must be True. Requires int values for slice["horizontal_stride"], slice["vertical_stride"], slice["merge_x_thres"], slice["merge_y_thres] (See doc/doc_en/slice_en.md). Default is {}.
"""
assert isinstance(img, (np.ndarray, list, str, bytes))
if isinstance(img, list) and det == True:
logger.error("When input a list of images, det must be false")
exit(0)
if cls == True and self.use_angle_cls == False:
logger.warning(
"Since the angle classifier is not initialized, it will not be used during the forward process"
)
img, flag_gif, flag_pdf = check_img(img, alpha_color)
# for infer pdf file
if isinstance(img, list) and flag_pdf:
if self.page_num > len(img) or self.page_num == 0:
imgs = img
else:
imgs = img[: self.page_num]
else:
imgs = [img]
def preprocess_image(_image):
_image = alpha_to_color(_image, alpha_color)
if inv:
_image = cv2.bitwise_not(_image)
if bin:
_image = binarize_img(_image)
return _image
if det and rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, rec_res, _ = self.__call__(img, cls, slice)
if not dt_boxes and not rec_res:
ocr_res.append(None)
continue
tmp_res = [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
ocr_res.append(tmp_res)
return ocr_res
elif det and not rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, elapse = self.text_detector(img)
if dt_boxes.size == 0:
ocr_res.append(None)
continue
tmp_res = [box.tolist() for box in dt_boxes]
ocr_res.append(tmp_res)
return ocr_res
else:
ocr_res = []
cls_res = []
for idx, img in enumerate(imgs):
if not isinstance(img, list):
img = preprocess_image(img)
img = [img]
if self.use_angle_cls and cls:
img, cls_res_tmp, elapse = self.text_classifier(img)
if not rec:
cls_res.append(cls_res_tmp)
rec_res, elapse = self.text_recognizer(img)
ocr_res.append(rec_res)
if not rec:
return cls_res
return ocr_res
class PPStructure(StructureSystem):
def __init__(self, **kwargs):
params = parse_args(mMain=False)
params.__dict__.update(**kwargs)
assert (
params.structure_version in SUPPORT_STRUCTURE_MODEL_VERSION
), "structure_version must in {}, but get {}".format(
SUPPORT_STRUCTURE_MODEL_VERSION, params.structure_version
)
params.use_gpu = check_gpu(params.use_gpu)
params.mode = "structure"
if not params.show_log:
logger.setLevel(logging.INFO)
lang, det_lang = parse_lang(params.lang)
if lang == "ch":
table_lang = "ch"
else:
table_lang = "en"
if params.structure_version == "PP-Structure":
params.merge_no_span_structure = False
# init model dir
det_model_config = get_model_config("OCR", params.ocr_version, "det", det_lang)
params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir,
os.path.join(BASE_DIR, "whl", "det", det_lang),
det_model_config["url"],
)
rec_model_config = get_model_config("OCR", params.ocr_version, "rec", lang)
params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir,
os.path.join(BASE_DIR, "whl", "rec", lang),
rec_model_config["url"],
)
table_model_config = get_model_config(
"STRUCTURE", params.structure_version, "table", table_lang
)
params.table_model_dir, table_url = confirm_model_dir_url(
params.table_model_dir,
os.path.join(BASE_DIR, "whl", "table"),
table_model_config["url"],
)
layout_model_config = get_model_config(
"STRUCTURE", params.structure_version, "layout", lang
)
params.layout_model_dir, layout_url = confirm_model_dir_url(
params.layout_model_dir,
os.path.join(BASE_DIR, "whl", "layout"),
layout_model_config["url"],
)
# download model
if not params.use_onnx:
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.table_model_dir, table_url)
maybe_download(params.layout_model_dir, layout_url)
if params.rec_char_dict_path is None:
params.rec_char_dict_path = str(
Path(__file__).parent / rec_model_config["dict_path"]
)
if params.table_char_dict_path is None:
params.table_char_dict_path = str(
Path(__file__).parent / table_model_config["dict_path"]
)
if params.layout_dict_path is None:
params.layout_dict_path = str(
Path(__file__).parent / layout_model_config["dict_path"]
)
logger.debug(params)
super().__init__(params)
def __call__(
self,
img,
return_ocr_result_in_table=False,
img_idx=0,
alpha_color=(255, 255, 255),
):
img, flag_gif, flag_pdf = check_img(img, alpha_color)
if isinstance(img, list) and flag_pdf:
res_list = []
for index, pdf_img in enumerate(img):
logger.info("processing {}/{} page:".format(index + 1, len(img)))
res, _ = super().__call__(
pdf_img, return_ocr_result_in_table, img_idx=index
)
res_list.append(res)
return res_list
res, _ = super().__call__(img, return_ocr_result_in_table, img_idx=img_idx)
return res
def main():
# for cmd
args = parse_args(mMain=True)
image_dir = args.image_dir
if is_link(image_dir):
download_with_progressbar(image_dir, "tmp.jpg")
image_file_list = ["tmp.jpg"]
else:
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
if args.type == "ocr":
engine = PaddleOCR(**(args.__dict__))
elif args.type == "structure":
engine = PPStructure(**(args.__dict__))
else:
raise NotImplementedError
for img_path in image_file_list:
img_name = os.path.basename(img_path).split(".")[0]
logger.info("{}{}{}".format("*" * 10, img_path, "*" * 10))
if args.type == "ocr":
result = engine.ocr(
img_path,
det=args.det,
rec=args.rec,
cls=args.use_angle_cls,
bin=args.binarize,
inv=args.invert,
alpha_color=args.alphacolor,
)
if result is not None:
lines = []
for res in result:
for line in res:
logger.info(line)
lines.append(pprint.pformat(line) + "\n")
if args.savefile:
if os.path.exists(args.output) is False:
os.mkdir(args.output)
outfile = args.output + "/" + img_name + ".txt"
with open(outfile, "w", encoding="utf-8") as f:
f.writelines(lines)
elif args.type == "structure":
img, flag_gif, flag_pdf = check_and_read(img_path)
if not flag_gif and not flag_pdf:
img = cv2.imread(img_path)
if args.recovery and args.use_pdf2docx_api and flag_pdf:
try_import("pdf2docx")
from pdf2docx.converter import Converter
docx_file = os.path.join(args.output, "{}.docx".format(img_name))
cv = Converter(img_path)
cv.convert(docx_file)
cv.close()
logger.info("docx save to {}".format(docx_file))
continue
if not flag_pdf:
if img is None:
logger.error("error in loading image:{}".format(img_path))
continue
img_paths = [[img_path, img]]
else:
img_paths = []
for index, pdf_img in enumerate(img):
os.makedirs(os.path.join(args.output, img_name), exist_ok=True)
pdf_img_path = os.path.join(
args.output, img_name, img_name + "_" + str(index) + ".jpg"
)
cv2.imwrite(pdf_img_path, pdf_img)
img_paths.append([pdf_img_path, pdf_img])
all_res = []
for index, (new_img_path, img) in enumerate(img_paths):
logger.info("processing {}/{} page:".format(index + 1, len(img_paths)))
result = engine(img, img_idx=index)
save_structure_res(result, args.output, img_name, index)
if args.recovery and result != []:
from copy import deepcopy
from ppstructure.recovery.recovery_to_doc import sorted_layout_boxes
h, w, _ = img.shape
result_cp = deepcopy(result)
result_sorted = sorted_layout_boxes(result_cp, w)
all_res += result_sorted
if args.recovery and all_res != []:
try:
from ppstructure.recovery.recovery_to_doc import convert_info_docx
convert_info_docx(img, all_res, args.output, img_name)
except Exception as ex:
logger.error(
"error in layout recovery image:{}, err msg: {}".format(
img_name, ex
)
)
continue
for item in all_res:
item.pop("img")
item.pop("res")
logger.info(item)
logger.info("result save to {}".format(args.output))