paddle ocr v4 2.6.1实战笔记

效果图：

安装

模型权重是自动下载，如果提前下载会报错。

识别orc，并opencv可视化结果，支持中文可视化

官方原版预测可视化：

效果图：

安装

安装2.5.2识别结果为空

pip install paddlepaddle-gpu==2.6.1

模型权重是自动下载，如果提前下载会报错。

测试代码：


import os
import time
from paddleocr import PaddleOCR

filepath = r"weights/123.jpg"

ocr_model = PaddleOCR(use_angle_cls=True, lang="ch", use_gpu=True, show_log=1,
                det_db_box_thresh=0.1, use_dilation=True,
                det_model_dir='weight/ch_PP-OCRv4_det_server_infer.tar',
                cls_model_dir='weight/ch_ppocr_mobile_v2.0_cls_infer.tar',
                rec_model_dir='weight/ch_PP-OCRv4_rec_server_infer.tar')

t1 = time.time()
for i in range(1):
    result = ocr_model.ocr(img=filepath, det=True, rec=True, cls=True)[0]
t2 = time.time()
print((t2-t1) / 10)

for res_str in result:
    print(res_str)

识别orc，并opencv可视化结果，支持中文可视化

import codecs
import os
import time

import cv2
import numpy as np
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw

from paddleocr import PaddleOCR

filepath = r"weights/124.jpg"

ocr_model = PaddleOCR(use_angle_cls=True, lang="ch", use_gpu=True, show_log=1,
                det_db_box_thresh=0.1, use_dilation=True,
                det_model_dir='weight/ch_PP-OCRv4_det_server_infer.tar',
                cls_model_dir='weight/ch_ppocr_mobile_v2.0_cls_infer.tar',
                rec_model_dir='weight/ch_PP-OCRv4_rec_server_infer.tar')

t1 = time.time()
for i in range(1):
    result = ocr_model.ocr(img=filepath, det=True, rec=True, cls=True)[0]
t2 = time.time()
print((t2-t1) / 10)

font_path = 'simhei.ttf'  # 需要替换为你的中文字体路径
font = ImageFont.truetype(font_path, 24)
def cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30):
    img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(img)
    draw.text(position, text, textColor, font=font)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

image=cv2.imread(filepath)

ocr_index=0
for res_str in result:
    if res_str[0][0][0]>36 and res_str[0][2][0]<84:
        print(ocr_index,res_str)
        points=res_str[0]
        text = res_str[1][0]
        points = np.array(points, dtype=np.int32).reshape((-1, 1, 2))
        cv2.polylines(image, [points], isClosed=True, color=(255, 0, 0), thickness=2)
        text_position = (int(points[0][0][0]), int(points[0][0][1] + 20))  # 微调文本位置

        # cv2.putText(image, '中文文本', (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3)
        image= cv2AddChineseText(image, text, text_position, textColor=(0, 255, 0), textSize=30)
        print(ocr_index)
    if res_str[0][0][0]>346 and res_str[0][2][0]<391:
        print(ocr_index,res_str)
        points=res_str[0]
        text = res_str[1][0]
        points = np.array(points, dtype=np.int32).reshape((-1, 1, 2))
        cv2.polylines(image, [points], isClosed=True, color=(255, 0, 0), thickness=2)
        text_position = (int(points[0][0][0]), int(points[0][0][1] + 20))  # 微调文本位置

        # cv2.putText(image, '中文文本', (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3)
        image= cv2AddChineseText(image, text, text_position, textColor=(0, 255, 0), textSize=30)
    if res_str[0][0][0]>658 and res_str[0][2][0]<705:
        print(ocr_index,res_str)
        points=res_str[0]
        text=res_str[1][0]
        points=np.array(points,dtype=np.int32).reshape((-1, 1, 2))
        cv2.polylines(image, [points], isClosed=True, color=(255, 0, 0), thickness=2)
        text_position = (int(points[0][0][0]), int(points[0][0][1] + 20))  # 微调文本位置
        image= cv2AddChineseText(image, text, text_position, textColor=(0, 255, 0), textSize=30)

cv2.imshow('Image with Rectangle and Text', image)
cv2.waitKey(0)

官方原版预测可视化：

# 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
from PIL import Image, ImageFont, ImageDraw
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"]
VERSION = "2.8.0"
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), ):
        """
        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.
        """
        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)
                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 cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30):
    img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(img)
    draw.text(position, text, textColor, font=font)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

if __name__ == '__main__':

    font_path = 'simhei.ttf'  # 需要替换为你的中文字体路径
    font = ImageFont.truetype(font_path, 24)


    # for cmd
    args = parse_args(mMain=True)
    image_dir = args.image_dir
    image_file_list=['weights/123.jpg']
    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":
            image=cv2.imread(img_path)
            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 idx in range(len(result)):
                    res = result[idx]
                    for line in res:

                        points = line[0]
                        text = line[1][0]
                        points = np.array(points, dtype=np.int32).reshape((-1, 1, 2))
                        cv2.polylines(image, [points], isClosed=True, color=(255, 0, 0), thickness=2)
                        text_position = (int(points[0][0][0]), int(points[0][0][1] + 20))  # 微调文本位置

                        # cv2.putText(image, '中文文本', (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3)
                        image = cv2AddChineseText(image, text, text_position, textColor=(0, 255, 0), textSize=30)

                        logger.info(line)
                        val = "["
                        for box in line[0]:
                            val += str(box[0]) + "," + str(box[1]) + ","

                        val = val[:-1]
                        val += "]," + line[1][0] + "," + str(line[1][1]) + "\n"
                        lines.append(val)
                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 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)))
                new_img_name = os.path.basename(new_img_path).split(".")[0]
                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))

        cv2.imshow('image', image)
        cv2.waitKey(0)