chore: import upstream snapshot with attribution

ppstructure/predict_system.py

@@ -0,0 +1,415 @@
+# 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 subprocess
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../")))
+
+os.environ["FLAGS_allocator_strategy"] = "auto_growth"
+import cv2
+import json
+import numpy as np
+import time
+import logging
+from copy import deepcopy
+
+from paddle.utils import try_import
+from ppocr.utils.utility import get_image_file_list, check_and_read
+from ppocr.utils.logging import get_logger
+from ppocr.utils.visual import draw_ser_results, draw_re_results
+from tools.infer.predict_system import TextSystem
+from tools.infer.predict_rec import TextRecognizer
+from ppstructure.layout.predict_layout import LayoutPredictor
+from ppstructure.table.predict_table import TableSystem, to_excel
+from ppstructure.utility import parse_args, draw_structure_result, cal_ocr_word_box
+
+logger = get_logger()
+
+
+class StructureSystem(object):
+    def __init__(self, args):
+        self.mode = args.mode
+        self.recovery = args.recovery
+
+        self.image_orientation_predictor = None
+        if args.image_orientation:
+            import paddleclas
+
+            self.image_orientation_predictor = paddleclas.PaddleClas(
+                model_name="text_image_orientation"
+            )
+
+        if self.mode == "structure":
+            if not args.show_log:
+                logger.setLevel(logging.INFO)
+            if args.layout == False and args.ocr == True:
+                args.ocr = False
+                logger.warning(
+                    "When args.layout is false, args.ocr is automatically set to false"
+                )
+            # init model
+            self.layout_predictor = None
+            self.text_system = None
+            self.table_system = None
+            self.formula_system = None
+            if args.layout:
+                self.layout_predictor = LayoutPredictor(args)
+                if args.ocr:
+                    self.text_system = TextSystem(args)
+            if args.table:
+                if self.text_system is not None:
+                    self.table_system = TableSystem(
+                        args,
+                        self.text_system.text_detector,
+                        self.text_system.text_recognizer,
+                    )
+                else:
+                    self.table_system = TableSystem(args)
+            if args.formula:
+                args_formula = deepcopy(args)
+                args_formula.rec_algorithm = args.formula_algorithm
+                args_formula.rec_model_dir = args.formula_model_dir
+                args_formula.rec_char_dict_path = args.formula_char_dict_path
+                args_formula.rec_batch_num = args.formula_batch_num
+                self.formula_system = TextRecognizer(args_formula)
+
+        elif self.mode == "kie":
+            from ppstructure.kie.predict_kie_token_ser_re import SerRePredictor
+
+            self.kie_predictor = SerRePredictor(args)
+
+        self.return_word_box = args.return_word_box
+
+    def __call__(self, img, return_ocr_result_in_table=False, img_idx=0):
+        time_dict = {
+            "image_orientation": 0,
+            "layout": 0,
+            "table": 0,
+            "table_match": 0,
+            "formula": 0,
+            "det": 0,
+            "rec": 0,
+            "kie": 0,
+            "all": 0,
+        }
+        start = time.time()
+
+        if self.image_orientation_predictor is not None:
+            tic = time.time()
+            cls_result = self.image_orientation_predictor.predict(input_data=img)
+            cls_res = next(cls_result)
+            angle = cls_res[0]["label_names"][0]
+            cv_rotate_code = {
+                "90": cv2.ROTATE_90_COUNTERCLOCKWISE,
+                "180": cv2.ROTATE_180,
+                "270": cv2.ROTATE_90_CLOCKWISE,
+            }
+            if angle in cv_rotate_code:
+                img = cv2.rotate(img, cv_rotate_code[angle])
+            toc = time.time()
+            time_dict["image_orientation"] = toc - tic
+
+        if self.mode == "structure":
+            ori_im = img.copy()
+            if self.layout_predictor is not None:
+                layout_res, elapse = self.layout_predictor(img)
+                time_dict["layout"] += elapse
+            else:
+                h, w = ori_im.shape[:2]
+                layout_res = [dict(bbox=None, label="table", score=0.0)]
+
+            # As reported in issues such as #10270 and #11665, the old
+            # implementation, which recognizes texts from the layout regions,
+            # has problems with OCR recognition accuracy.
+            #
+            # To enhance the OCR recognition accuracy, we implement a patch fix
+            # that first use text_system to detect and recognize all text information
+            # and then filter out relevant texts according to the layout regions.
+            text_res = None
+            if self.text_system is not None:
+                text_res, ocr_time_dict = self._predict_text(img)
+                time_dict["det"] += ocr_time_dict["det"]
+                time_dict["rec"] += ocr_time_dict["rec"]
+
+            res_list = []
+            for region in layout_res:
+                res = ""
+                if region["bbox"] is not None:
+                    x1, y1, x2, y2 = region["bbox"]
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                    roi_img = ori_im[y1:y2, x1:x2, :]
+                else:
+                    x1, y1, x2, y2 = 0, 0, w, h
+                    roi_img = ori_im
+                bbox = [x1, y1, x2, y2]
+
+                if region["label"] == "table":
+                    if self.table_system is not None:
+                        res, table_time_dict = self.table_system(
+                            roi_img, return_ocr_result_in_table
+                        )
+                        time_dict["table"] += table_time_dict["table"]
+                        time_dict["table_match"] += table_time_dict["match"]
+                        time_dict["det"] += table_time_dict["det"]
+                        time_dict["rec"] += table_time_dict["rec"]
+
+                elif region["label"] == "equation" and self.formula_system is not None:
+                    latex_res, formula_time = self.formula_system([roi_img])
+                    time_dict["formula"] += formula_time
+                    res = {"latex": latex_res[0]}
+
+                else:
+                    if text_res is not None:
+                        # Filter the text results whose regions intersect with the current layout bbox.
+                        res = self._filter_text_res(text_res, bbox)
+
+                res_list.append(
+                    {
+                        "type": region["label"].lower(),
+                        "bbox": bbox,
+                        "img": roi_img,
+                        "res": res,
+                        "img_idx": img_idx,
+                        "score": region["score"],
+                    }
+                )
+
+            end = time.time()
+            time_dict["all"] = end - start
+            return res_list, time_dict
+
+        elif self.mode == "kie":
+            re_res, elapse = self.kie_predictor(img)
+            time_dict["kie"] = elapse
+            time_dict["all"] = elapse
+            return re_res[0], time_dict
+
+        return None, None
+
+    def _predict_text(self, img):
+        filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(img)
+
+        # remove style char,
+        # when using the recognition model trained on the PubtabNet dataset,
+        # it will recognize the text format in the table, such as <b>
+        style_token = [
+            "<strike>",
+            "<strike>",
+            "<sup>",
+            "</sub>",
+            "<b>",
+            "</b>",
+            "<sub>",
+            "</sup>",
+            "<overline>",
+            "</overline>",
+            "<underline>",
+            "</underline>",
+            "<i>",
+            "</i>",
+        ]
+        res = []
+        for box, rec_res in zip(filter_boxes, filter_rec_res):
+            rec_str, rec_conf = rec_res[0], rec_res[1]
+            for token in style_token:
+                if token in rec_str:
+                    rec_str = rec_str.replace(token, "")
+            if self.return_word_box:
+                word_box_content_list, word_box_list = cal_ocr_word_box(
+                    rec_str, box, rec_res[2]
+                )
+                res.append(
+                    {
+                        "text": rec_str,
+                        "confidence": float(rec_conf),
+                        "text_region": box.tolist(),
+                        "text_word": word_box_content_list,
+                        "text_word_region": word_box_list,
+                    }
+                )
+            else:
+                res.append(
+                    {
+                        "text": rec_str,
+                        "confidence": float(rec_conf),
+                        "text_region": box.tolist(),
+                    }
+                )
+        return res, ocr_time_dict
+
+    def _filter_text_res(self, text_res, bbox):
+        res = []
+        for r in text_res:
+            box = r["text_region"]
+            rect = box[0][0], box[0][1], box[2][0], box[2][1]
+            if self._has_intersection(bbox, rect):
+                res.append(r)
+        return res
+
+    def _has_intersection(self, rect1, rect2):
+        x_min1, y_min1, x_max1, y_max1 = rect1
+        x_min2, y_min2, x_max2, y_max2 = rect2
+        if x_min1 > x_max2 or x_max1 < x_min2:
+            return False
+        if y_min1 > y_max2 or y_max1 < y_min2:
+            return False
+        return True
+
+
+def save_structure_res(res, save_folder, img_name, img_idx=0):
+    excel_save_folder = os.path.join(save_folder, img_name)
+    os.makedirs(excel_save_folder, exist_ok=True)
+    res_cp = deepcopy(res)
+    # save res
+    with open(
+        os.path.join(excel_save_folder, "res_{}.txt".format(img_idx)),
+        "w",
+        encoding="utf8",
+    ) as f:
+        for region in res_cp:
+            roi_img = region.pop("img")
+            f.write("{}\n".format(json.dumps(region)))
+
+            if (
+                region["type"].lower() == "table"
+                and len(region["res"]) > 0
+                and "html" in region["res"]
+            ):
+                excel_path = os.path.join(
+                    excel_save_folder, "{}_{}.xlsx".format(region["bbox"], img_idx)
+                )
+                to_excel(region["res"]["html"], excel_path)
+            elif region["type"].lower() == "figure":
+                img_path = os.path.join(
+                    excel_save_folder, "{}_{}.jpg".format(region["bbox"], img_idx)
+                )
+                cv2.imwrite(img_path, roi_img)
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list
+    image_file_list = image_file_list[args.process_id :: args.total_process_num]
+
+    if not args.use_pdf2docx_api:
+        structure_sys = StructureSystem(args)
+        save_folder = os.path.join(args.output, structure_sys.mode)
+        os.makedirs(save_folder, exist_ok=True)
+    img_num = len(image_file_list)
+
+    for i, image_file in enumerate(image_file_list):
+        logger.info("[{}/{}] {}".format(i, img_num, image_file))
+        img, flag_gif, flag_pdf = check_and_read(image_file)
+        img_name = os.path.basename(image_file).split(".")[0]
+
+        if args.recovery and args.use_pdf2docx_api and flag_pdf:
+            try_import("pdf2docx")
+            from pdf2docx.converter import Converter
+
+            os.makedirs(args.output, exist_ok=True)
+            docx_file = os.path.join(args.output, "{}_api.docx".format(img_name))
+            cv = Converter(image_file)
+            cv.convert(docx_file)
+            cv.close()
+            logger.info("docx save to {}".format(docx_file))
+            continue
+
+        if not flag_gif and not flag_pdf:
+            img = cv2.imread(image_file)
+
+        if not flag_pdf:
+            if img is None:
+                logger.error("error in loading image:{}".format(image_file))
+                continue
+            imgs = [img]
+        else:
+            imgs = img
+
+        all_res = []
+        for index, img in enumerate(imgs):
+            res, time_dict = structure_sys(img, img_idx=index)
+            img_save_path = os.path.join(
+                save_folder, img_name, "show_{}.jpg".format(index)
+            )
+            os.makedirs(os.path.join(save_folder, img_name), exist_ok=True)
+            if structure_sys.mode == "structure" and res != []:
+                draw_img = draw_structure_result(img, res, args.vis_font_path)
+                save_structure_res(res, save_folder, img_name, index)
+            elif structure_sys.mode == "kie":
+                if structure_sys.kie_predictor.predictor is not None:
+                    draw_img = draw_re_results(img, res, font_path=args.vis_font_path)
+                else:
+                    draw_img = draw_ser_results(img, res, font_path=args.vis_font_path)
+
+                with open(
+                    os.path.join(save_folder, img_name, "res_{}_kie.txt".format(index)),
+                    "w",
+                    encoding="utf8",
+                ) as f:
+                    res_str = "{}\t{}\n".format(
+                        image_file, json.dumps({"ocr_info": res}, ensure_ascii=False)
+                    )
+                    f.write(res_str)
+            if res != []:
+                cv2.imwrite(img_save_path, draw_img)
+                logger.info("result save to {}".format(img_save_path))
+            if args.recovery and res != []:
+                from ppstructure.recovery.recovery_to_doc import (
+                    sorted_layout_boxes,
+                    convert_info_docx,
+                )
+                from ppstructure.recovery.recovery_to_markdown import (
+                    convert_info_markdown,
+                )
+
+                h, w, _ = img.shape
+                res = sorted_layout_boxes(res, w)
+                all_res += res
+
+        if args.recovery and all_res != []:
+            try:
+                convert_info_docx(img, all_res, save_folder, img_name)
+                if args.recovery_to_markdown:
+                    convert_info_markdown(all_res, save_folder, img_name)
+            except Exception as ex:
+                logger.error(
+                    "error in layout recovery image:{}, err msg: {}".format(
+                        image_file, ex
+                    )
+                )
+                continue
+        logger.info("Predict time : {:.3f}s".format(time_dict["all"]))
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    if args.use_mp:
+        p_list = []
+        total_process_num = args.total_process_num
+        for process_id in range(total_process_num):
+            cmd = (
+                [sys.executable, "-u"]
+                + sys.argv
+                + ["--process_id={}".format(process_id), "--use_mp={}".format(False)]
+            )
+            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+            p_list.append(p)
+        for p in p_list:
+            p.wait()
+    else:
+        main(args)