276 lines
9.2 KiB
Python
276 lines
9.2 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import ast
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import PIL
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from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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from tools.infer.utility import (
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draw_ocr_box_txt,
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str2bool,
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str2int_tuple,
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init_args as infer_args,
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)
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import math
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def init_args():
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parser = infer_args()
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# params for output
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parser.add_argument("--output", type=str, default="./output")
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# params for table structure
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parser.add_argument("--table_max_len", type=int, default=488)
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parser.add_argument("--table_algorithm", type=str, default="TableAttn")
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parser.add_argument("--table_model_dir", type=str)
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parser.add_argument("--merge_no_span_structure", type=str2bool, default=True)
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parser.add_argument(
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"--table_char_dict_path",
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type=str,
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default="../ppocr/utils/dict/table_structure_dict_ch.txt",
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)
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# params for layout
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parser.add_argument("--layout_model_dir", type=str)
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parser.add_argument(
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"--layout_dict_path",
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type=str,
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default="../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt",
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)
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parser.add_argument(
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"--layout_score_threshold", type=float, default=0.5, help="Threshold of score."
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)
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parser.add_argument(
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"--layout_nms_threshold", type=float, default=0.5, help="Threshold of nms."
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)
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# params for kie
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parser.add_argument("--kie_algorithm", type=str, default="LayoutXLM")
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parser.add_argument("--ser_model_dir", type=str)
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parser.add_argument("--re_model_dir", type=str)
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parser.add_argument("--use_visual_backbone", type=str2bool, default=True)
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parser.add_argument(
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"--ser_dict_path", type=str, default="../train_data/XFUND/class_list_xfun.txt"
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)
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# need to be None or tb-yx
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parser.add_argument("--ocr_order_method", type=str, default=None)
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# params for inference
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parser.add_argument(
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"--mode",
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type=str,
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choices=["structure", "kie"],
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default="structure",
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help="structure and kie is supported",
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)
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parser.add_argument(
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"--image_orientation",
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type=bool,
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default=False,
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help="Whether to enable image orientation recognition",
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)
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parser.add_argument(
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"--layout",
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type=str2bool,
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default=True,
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help="Whether to enable layout analysis",
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)
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parser.add_argument(
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"--table",
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type=str2bool,
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default=True,
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help="In the forward, whether the table area uses table recognition",
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)
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parser.add_argument(
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"--ocr",
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type=str2bool,
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default=True,
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help="In the forward, whether the non-table area is recognition by ocr",
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)
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# param for recovery
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parser.add_argument(
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"--recovery",
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type=str2bool,
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default=False,
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help="Whether to enable layout of recovery",
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)
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parser.add_argument(
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"--use_pdf2docx_api",
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type=str2bool,
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default=False,
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help="Whether to use pdf2docx api",
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)
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parser.add_argument(
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"--invert",
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type=str2bool,
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default=False,
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help="Whether to invert image before processing",
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)
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parser.add_argument(
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"--binarize",
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type=str2bool,
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default=False,
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help="Whether to threshold binarize image before processing",
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)
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parser.add_argument(
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"--alphacolor",
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type=str2int_tuple,
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default=(255, 255, 255),
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help="Replacement color for the alpha channel, if the latter is present; R,G,B integers",
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)
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return parser
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def parse_args():
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parser = init_args()
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return parser.parse_args()
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def draw_structure_result(image, result, font_path):
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if isinstance(image, np.ndarray):
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image = Image.fromarray(image)
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boxes, txts, scores = [], [], []
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img_layout = image.copy()
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draw_layout = ImageDraw.Draw(img_layout)
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text_color = (255, 255, 255)
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text_background_color = (80, 127, 255)
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catid2color = {}
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font_size = 15
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font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
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for region in result:
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if region["type"] not in catid2color:
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box_color = (
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random.randint(0, 255),
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random.randint(0, 255),
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random.randint(0, 255),
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)
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catid2color[region["type"]] = box_color
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else:
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box_color = catid2color[region["type"]]
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box_layout = region["bbox"]
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draw_layout.rectangle(
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[(box_layout[0], box_layout[1]), (box_layout[2], box_layout[3])],
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outline=box_color,
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width=3,
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)
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if int(PIL.__version__.split(".")[0]) < 10:
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text_w, text_h = font.getsize(region["type"])
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else:
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left, top, right, bottom = font.getbbox(region["type"])
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text_w, text_h = right - left, bottom - top
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draw_layout.rectangle(
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[
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(box_layout[0], box_layout[1]),
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(box_layout[0] + text_w, box_layout[1] + text_h),
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],
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fill=text_background_color,
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)
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draw_layout.text(
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(box_layout[0], box_layout[1]), region["type"], fill=text_color, font=font
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)
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if region["type"] == "table":
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pass
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else:
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for text_result in region["res"]:
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boxes.append(np.array(text_result["text_region"]))
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txts.append(text_result["text"])
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scores.append(text_result["confidence"])
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if "text_word_region" in text_result:
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for word_region in text_result["text_word_region"]:
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char_box = word_region
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box_height = int(
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math.sqrt(
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(char_box[0][0] - char_box[3][0]) ** 2
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+ (char_box[0][1] - char_box[3][1]) ** 2
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)
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)
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box_width = int(
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math.sqrt(
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(char_box[0][0] - char_box[1][0]) ** 2
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+ (char_box[0][1] - char_box[1][1]) ** 2
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)
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)
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if box_height == 0 or box_width == 0:
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continue
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boxes.append(word_region)
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txts.append("")
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scores.append(1.0)
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im_show = draw_ocr_box_txt(
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img_layout, boxes, txts, scores, font_path=font_path, drop_score=0
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)
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return im_show
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def cal_ocr_word_box(rec_str, box, rec_word_info):
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"""Calculate the detection frame for each word based on the results of recognition and detection of ocr"""
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col_num, word_list, word_col_list, state_list = rec_word_info
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box = box.tolist()
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bbox_x_start = box[0][0]
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bbox_x_end = box[1][0]
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bbox_y_start = box[0][1]
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bbox_y_end = box[2][1]
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cell_width = (bbox_x_end - bbox_x_start) / col_num
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word_box_list = []
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word_box_content_list = []
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cn_width_list = []
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cn_col_list = []
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for word, word_col, state in zip(word_list, word_col_list, state_list):
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if state == "cn":
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if len(word_col) != 1:
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char_seq_length = (word_col[-1] - word_col[0] + 1) * cell_width
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char_width = char_seq_length / (len(word_col) - 1)
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cn_width_list.append(char_width)
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cn_col_list += word_col
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word_box_content_list += word
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else:
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cell_x_start = bbox_x_start + int(word_col[0] * cell_width)
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cell_x_end = bbox_x_start + int((word_col[-1] + 1) * cell_width)
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cell = (
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(cell_x_start, bbox_y_start),
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(cell_x_end, bbox_y_start),
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(cell_x_end, bbox_y_end),
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(cell_x_start, bbox_y_end),
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)
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word_box_list.append(cell)
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word_box_content_list.append("".join(word))
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if len(cn_col_list) != 0:
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if len(cn_width_list) != 0:
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avg_char_width = np.mean(cn_width_list)
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else:
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avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_str)
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for center_idx in cn_col_list:
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center_x = (center_idx + 0.5) * cell_width
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cell_x_start = max(int(center_x - avg_char_width / 2), 0) + bbox_x_start
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cell_x_end = (
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min(int(center_x + avg_char_width / 2), bbox_x_end - bbox_x_start)
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+ bbox_x_start
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)
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cell = (
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(cell_x_start, bbox_y_start),
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(cell_x_end, bbox_y_start),
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(cell_x_end, bbox_y_end),
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(cell_x_start, bbox_y_end),
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)
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word_box_list.append(cell)
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return word_box_content_list, word_box_list
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