FORMAT_CONVERSION_MAXCOMPUTE/format_convert/convert_image.py

# encoding=utf8
import copy
import inspect
import io
import logging
import os
import sys
import time
import requests
import numpy as np
from PIL import Image
sys.path.append(os.path.dirname(__file__) + "/../")
from pdfminer.layout import LTLine
import traceback
import cv2
from isr.pre_process import count_red_pixel
from format_convert.utils import judge_error_code, add_div, LineTable, get_table_html, get_logger, log, \
    memory_decorator, pil_resize, np2bytes, ocr_cant_read
from format_convert.convert_need_interface import from_otr_interface, from_ocr_interface, from_gpu_interface_redis, \
    from_idc_interface, from_isr_interface
from format_convert.table_correct import get_rotated_image


def image_process(image_np, image_path, is_from_pdf=False, is_from_docx=False, use_ocr=True):
    from format_convert.convert_tree import _Table, _Sentence

    def get_cluster(t_list, b_list, axis):
        zip_list = list(zip(t_list, b_list))
        if len(zip_list) == 0:
            return t_list, b_list
        if len(zip_list[0]) > 0:
            zip_list.sort(key=lambda x: x[1][axis][1])
        cluster_list = []
        margin = 5
        for text, bbox in zip_list:
            _find = 0
            for cluster in cluster_list:
                if abs(cluster[1] - bbox[axis][1]) <= margin:
                    cluster[0].append([text, bbox])
                    cluster[1] = bbox[axis][1]
                    _find = 1
                    break
            if not _find:
                cluster_list.append([[[text, bbox]], bbox[axis][1]])

        new_text_list = []
        new_bbox_list = []
        for cluster in cluster_list:
            # print("=============convert_image")
            # print("cluster_list", cluster)
            center_y = 0
            for text, bbox in cluster[0]:
                center_y += bbox[axis][1]
            center_y = int(center_y / len(cluster[0]))
            for text, bbox in cluster[0]:
                bbox[axis][1] = center_y
                new_text_list.append(text)
                new_bbox_list.append(bbox)
            # print("cluster_list", cluster)
        return new_text_list, new_bbox_list

    def merge_textbox(textbox_list, in_objs):
        delete_obj = []
        threshold = 5
        textbox_list.sort(key=lambda x:x.bbox[0])
        for k in range(len(textbox_list)):
            tb1 = textbox_list[k]
            if tb1 not in in_objs and tb1 not in delete_obj:
                for m in range(k+1, len(textbox_list)):
                    tb2 = textbox_list[m]
                    if tb2 in in_objs:
                        continue
                    if abs(tb1.bbox[1]-tb2.bbox[1]) <= threshold \
                            and abs(tb1.bbox[3]-tb2.bbox[3]) <= threshold:
                        if tb1.bbox[0] <= tb2.bbox[0]:
                            tb1.text = tb1.text + tb2.text
                        else:
                            tb1.text = tb2.text + tb1.text
                        tb1.bbox[0] = min(tb1.bbox[0], tb2.bbox[0])
                        tb1.bbox[2] = max(tb1.bbox[2], tb2.bbox[2])
                        delete_obj.append(tb2)
        for _obj in delete_obj:
            if _obj in textbox_list:
                textbox_list.remove(_obj)
        return textbox_list

    def idc_process(_image_np):
        # 图片倾斜校正，写入原来的图片路径
        # print("image_process", image_path)
        # g_r_i = get_rotated_image(_image_np, image_path)
        # if judge_error_code(g_r_i):
        #     if is_from_docx:
        #         return []
        #     else:
        #         return g_r_i
        # _image_np = cv2.imread(image_path)
        # if _image_np is None:
        #     return []
        # return _image_np

        # if _image_np is None:
        #     return []

        # idc模型实现图片倾斜校正
        h, w = get_best_predict_size2(_image_np, 1080)
        image_resize = pil_resize(_image_np, h, w)
        # image_resize_path = image_path.split(".")[0] + "_resize_idc." + image_path.split(".")[-1]
        # cv2.imwrite(image_resize_path, image_resize)

        # with open(image_resize_path, "rb") as f:
        #     image_bytes = f.read()
        image_bytes = np2bytes(image_resize)
        angle = from_idc_interface(image_bytes)
        if judge_error_code(angle):
            if is_from_docx:
                return []
            else:
                return angle
        # 根据角度旋转
        image_pil = Image.fromarray(_image_np)
        _image_np = np.array(image_pil.rotate(angle, expand=1))
        # 写入
        # idc_path = image_path.split(".")[0] + "_idc." + image_path.split(".")[-1]
        # cv2.imwrite(idc_path, image_np)
        return _image_np

    def isr_process(_image_np):
        image_np_copy = copy.deepcopy(_image_np)
        # isr模型去除印章
        _isr_time = time.time()
        if count_red_pixel(_image_np):
            # 红色像素达到一定值才过模型
            image_bytes = np2bytes(_image_np)
            _image_np = from_isr_interface(image_bytes)
            if judge_error_code(_image_np):
                if is_from_docx:
                    return []
                else:
                    return _image_np
            # [1]代表检测不到印章，直接返回
            if isinstance(_image_np, list) and _image_np == [1]:
                log("no seals detected!")
                _image_np = image_np_copy
        log("isr total time "+str(time.time()-_isr_time))
        return _image_np

    def ocr_process(_image_np, _threshold=1024):
        # ocr图片过大内存溢出，需resize
        log("ocr_process image shape " + str(_image_np.shape))
        # 大图按比例缩小，小图维持不变；若统一拉伸成固定大小如1024会爆显存
        if _image_np.shape[0] >= _threshold or _image_np.shape[1] >= _threshold:
            best_h, best_w = get_best_predict_size2(_image_np, 1024)
            _image_np = pil_resize(_image_np, best_h, best_w)
            log("ocr_process image resize " + str(_image_np.shape))
        # 调用ocr模型接口
        image_bytes = np2bytes(_image_np)
        text_list, bbox_list = from_ocr_interface(image_bytes, is_table=True)
        if judge_error_code(text_list):
            return text_list, text_list

        for i in range(len(bbox_list)):
            point = bbox_list[i]
            bbox_list[i] = [[int(point[0][0]), int(point[0][1])],
                            [int(point[1][0]), int(point[1][1])],
                            [int(point[2][0]), int(point[2][1])],
                            [int(point[3][0]), int(point[3][1])]]
        return text_list, bbox_list

    def otr_process(_image_np):
        # otr模型识别表格，需要图片resize成模型所需大小, 写入另一个路径
        best_h, best_w = get_best_predict_size(_image_np)
        image_resize = pil_resize(_image_np, best_h, best_w)
        # image_resize_path = image_path.split(".")[0] + "_resize_otr." + image_path.split(".")[-1]
        # cv2.imwrite(image_resize_path, image_resize)

        # 调用otr模型接口
        # with open(image_resize_path, "rb") as f:
        #     image_bytes = f.read()
        image_bytes = np2bytes(image_resize)
        list_line = from_otr_interface(image_bytes, is_from_pdf)
        if judge_error_code(list_line):
            if is_from_docx:
                return []
            else:
                return list_line

        # otr resize后得到的bbox根据比例还原
        start_time = time.time()
        ratio = (_image_np.shape[0]/best_h, _image_np.shape[1]/best_w)
        for i in range(len(list_line)):
            point = list_line[i]
            list_line[i] = [int(point[0]*ratio[1]), int(point[1]*ratio[0]),
                            int(point[2]*ratio[1]), int(point[3]*ratio[0])]
        log("otr resize bbox recover " + str(time.time()-start_time))
        return list_line

    def table_process(list_line, text_list, bbox_list):
        # 调用现成方法形成表格
        try:
            from format_convert.convert_tree import TableLine
            list_lines = []
            for line in list_line:
                list_lines.append(LTLine(1, (line[0], line[1]), (line[2], line[3])))
            from format_convert.convert_tree import TextBox
            list_text_boxes = []
            for i in range(len(bbox_list)):
                bbox = bbox_list[i]
                b_text = text_list[i]
                list_text_boxes.append(TextBox([bbox[0][0], bbox[0][1],
                                                bbox[2][0], bbox[2][1]], b_text))

            # for _textbox in list_text_boxes:
            #     print("==",_textbox.get_text())
            lt = LineTable()
            tables, obj_in_table, _ = lt.recognize_table(list_text_boxes, list_lines, False)

            # 合并同一行textbox
            list_text_boxes = merge_textbox(list_text_boxes, obj_in_table)
            return list_text_boxes, tables, obj_in_table
        except:
            traceback.print_exc()
            return [-8], [-8], [-8]

    log("into image_preprocess")
    try:
        if image_np is None:
            return []
        if image_np.shape[0] <= 20 or image_np.shape[1] <= 20:
            return []

        # 判断是否需要长图分割
        slice_flag = need_image_slice(image_np)
        log("need_image_slice " + str(slice_flag) + " " + str(image_np.shape))
        idc_flag = False
        image_np_list = [image_np]
        if slice_flag:
            # 方向分类
            image_np = idc_process(image_np)
            idc_flag = True
            if isinstance(image_np, list):
                return image_np

            # 再判断
            if need_image_slice(image_np):
                # 长图分割
                image_np_list = image_slice_new(image_np)
        if len(image_np_list) < 1:
            return [-12]

        all_obj_list = []
        _add_y = 0
        for image_np in image_np_list:
            print("sub image shape", image_np.shape)
            # 整体分辨率限制
            threshold = 2000
            if image_np.shape[0] > threshold or image_np.shape[1] > threshold:
                h, w = get_best_predict_size2(image_np, threshold=threshold)
                log("global image resize " + str(image_np.shape[:2]) + " -> " + str(h) + "," + str(w))
                image_np = pil_resize(image_np, h, w)

            # 印章去除
            image_np = isr_process(image_np)
            if isinstance(image_np, list):
                return image_np

            # 文字识别
            text_list, box_list = ocr_process(image_np)
            if judge_error_code(text_list):
                return text_list

            # 判断ocr识别是否正确
            if ocr_cant_read(text_list, box_list) and not idc_flag:
                # 方向分类
                image_np = idc_process(image_np)
                # cv2.imshow("idc_process", image_np)
                # cv2.waitKey(0)
                if isinstance(image_np, list):
                    return image_np

                # 文字识别
                text_list1, box_list_1 = ocr_process(image_np)
                if judge_error_code(text_list1):
                    return text_list1

                # 比较字数
                # print("ocr process", len("".join(text_list)), len("".join(text_list1)))
                if len("".join(text_list)) < len("".join(text_list1)):
                    text_list = text_list1
                    box_list = box_list_1

            # 表格识别
            line_list = otr_process(image_np)
            if judge_error_code(line_list):
                return line_list

            # 表格生成
            text_box_list, table_list, obj_in_table_list = table_process(line_list, text_list, box_list)
            if judge_error_code(table_list):
                return table_list

            # 对象生成
            obj_list = []
            for table in table_list:
                obj_list.append(_Table(table["table"], table["bbox"]))
            for text_box in text_box_list:
                if text_box not in obj_in_table_list:
                    obj_list.append(_Sentence(text_box.get_text(), text_box.bbox))

            # 修正y
            if len(image_np_list) > 1:
                list_y = []
                for obj in obj_list:
                    obj.y += _add_y
                    list_y.append(obj.y)
                if len(list_y) > 0:
                    _add_y = max(list_y)

            # 合并
            all_obj_list += obj_list

        return all_obj_list

    except Exception as e:
        log("image_preprocess error")
        traceback.print_exc()
        return [-1]


@memory_decorator
def picture2text(path, html=False):
    log("into picture2text")
    try:
        # 判断图片中表格
        img = cv2.imread(path)
        if img is None:
            return [-3]

        text = image_process(img, path)
        if judge_error_code(text):
            return text

        if html:
            text = add_div(text)
        return [text]
    except Exception as e:
        log("picture2text error!")
        print("picture2text", traceback.print_exc())
        return [-1]


def get_best_predict_size(image_np, times=64):
    sizes = []
    for i in range(1, 100):
        if i*times <= 1300:
            sizes.append(i*times)
    sizes.sort(key=lambda x: x, reverse=True)

    min_len = 10000
    best_height = sizes[0]
    for height in sizes:
        if abs(image_np.shape[0] - height) < min_len:
            min_len = abs(image_np.shape[0] - height)
            best_height = height

    min_len = 10000
    best_width = sizes[0]
    for width in sizes:
        if abs(image_np.shape[1] - width) < min_len:
            min_len = abs(image_np.shape[1] - width)
            best_width = width

    return best_height, best_width


def get_best_predict_size2(image_np, threshold=3000):
    h, w = image_np.shape[:2]
    scale = threshold / max(h, w)
    h = int(h * scale)
    w = int(w * scale)
    return h, w


def image_slice(image_np):
    """
    slice the image if the height is to large
    :return:
    """
    _sum = np.average(image_np, axis=1)
    list_white_line = []
    list_ave = list(_sum)
    for _i in range(len(list_ave)):
        if (list_ave[_i] > 250).all():
            list_white_line.append(_i)
    set_white_line = set(list_white_line)
    width = image_np.shape[1]
    height = image_np.shape[0]
    list_images = []
    _begin = 0
    _end = 0
    while 1:
        if _end > height:
            break
        _end += width
        while 1:
            if _begin in set_white_line:
                break
            if _begin > height:
                break
            _begin += 1
        _image = image_np[_begin:_end, ...]
        list_images.append(_image)
        _begin = _end
    log("image_slice into %d parts" % (len(list_images)))
    return list_images


def image_slice_new(image_np):
    """
    长图分割

    :return:
    """
    height, width = image_np.shape[:2]
    image_origin = copy.deepcopy(image_np)

    # 去除黑边
    image_np = remove_black_border(image_np)

    # 1.  转化成灰度图
    image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)

    # 2. 二值化
    ret, binary = cv2.threshold(image_np, 125, 255, cv2.THRESH_BINARY_INV)

    # 3. 膨胀和腐蚀操作的核函数
    kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

    # 4. 膨胀一次，让轮廓突出
    dilation = cv2.dilate(binary, kernal, iterations=1)
    # dilation = np.add(np.int0(np.full(dilation.shape, 255)), -1 * np.int0(dilation))
    # dilation = np.uint8(dilation)
    # cv2.namedWindow("dilation", 0)
    # cv2.resizeWindow("dilation", 1000, 800)
    # cv2.imshow("dilation", dilation)
    # cv2.waitKey(0)
    # cv2.imwrite("error.jpg", dilation)

    # 按行求平均
    width_avg = np.average(np.float32(dilation), axis=1)
    zero_index = np.where(width_avg == 0.)[0]
    # print(height, width)
    # print(width_avg)
    # print(width_avg.shape)
    # print(zero_index)
    # print(zero_index.shape)
    # zero_index.sort(key=lambda x: x)

    # 截取范围内寻找分割点
    max_distance = int(width / 2)
    image_list = []
    last_h = 0
    for i in range(height // width + 1):
        h = last_h + width

        # 前后的分割点
        zero_h_after = zero_index[np.where(zero_index >= h)]
        zero_h_before = zero_index[np.where(zero_index <= h)]
        # print("last_h, h", last_h, h)
        # print("last_h, h", last_h, h)
        # print(zero_index.shape)
        # print("zero_h_after.shape", zero_h_after.shape)
        if zero_h_after.shape[0] == 0:
            # 最后一截
            last_image = image_origin[last_h:, :, :]
            if last_image.shape[0] <= max_distance:
                image_list[-1] = np.concatenate([image_list[-1], last_image], axis=0)
            else:
                image_list.append(last_image)
            break

        # 分割点距离不能太远
        cut_h = zero_h_after.tolist()[0]
        if abs(h - cut_h) <= max_distance:
            image_list.append(image_origin[last_h:cut_h, :, :])
            last_h = cut_h
        # 后面找不到往前找
        else:
            cut_h = zero_h_before.tolist()[-1]
            if abs(cut_h - h) <= max_distance:
                image_list.append(image_origin[last_h:cut_h, :, :])
                last_h = cut_h

    # i = 0
    # for im in image_list:
    #     print(im.shape)
    #     cv2.imwrite("error" + str(i) + ".jpg", im)
    #     i += 1
    #     cv2.namedWindow("im", 0)
    #     cv2.resizeWindow("im", 1000, 800)
    #     cv2.imshow("im", im)
    #     cv2.waitKey(0)

    log("image_slice into %d parts" % (len(image_list)))
    return image_list


def need_image_slice(image_np):
    h, w = image_np.shape[:2]
    # if h > 3000 and w < 2000:
    #     return True
    if 2. <= h / w and w >= 100:
        return True
    return False


def remove_black_border(img_np):
    try:
        # 阈值
        threshold = 100

        # 转换为灰度图像
        gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)

        # 获取图片尺寸
        h, w = gray.shape[:2]

        # 无法区分黑色区域超过一半的情况
        rowc = gray[:, int(1/2*w)]
        colc = gray[int(1/2*h), :]

        rowflag = np.argwhere(rowc > threshold)
        colflag = np.argwhere(colc > threshold)

        left, bottom, right, top = rowflag[0, 0], colflag[-1, 0], rowflag[-1, 0], colflag[0, 0]

        # cv2.imshow('remove_black_border', img_np[left:right, top:bottom, :])
        # cv2.waitKey()
        return img_np[left:right, top:bottom, :]
    except:
        return img_np


class ImageConvert:
    def __init__(self, path, unique_type_dir):
        from format_convert.convert_tree import _Document
        self._doc = _Document(path)
        self.path = path
        self.unique_type_dir = unique_type_dir

    def init_package(self):
        # 各个包初始化
        try:
            with open(self.path, "rb") as f:
                self.image = f.read()
        except:
            log("cannot open image!")
            traceback.print_exc()
            self._doc.error_code = [-3]

    def convert(self):
        from format_convert.convert_tree import _Page, _Image
        self.init_package()
        if self._doc.error_code is not None:
            return

        _page = _Page(None, 0)
        _image = _Image(self.image, self.path)
        _page.add_child(_image)
        self._doc.add_child(_page)

    def get_html(self):
        try:
            self.convert()
        except:
            traceback.print_exc()
            self._doc.error_code = [-1]
        if self._doc.error_code is not None:
            return self._doc.error_code
        return self._doc.get_html()


def image_process_old(image_np, image_path, is_from_pdf=False, is_from_docx=False, use_ocr=True):
    from format_convert.convert_tree import _Table, _Sentence

    def get_cluster(t_list, b_list, axis):
        zip_list = list(zip(t_list, b_list))
        if len(zip_list) == 0:
            return t_list, b_list
        if len(zip_list[0]) > 0:
            zip_list.sort(key=lambda x: x[1][axis][1])
        cluster_list = []
        margin = 5
        for text, bbox in zip_list:
            _find = 0
            for cluster in cluster_list:
                if abs(cluster[1] - bbox[axis][1]) <= margin:
                    cluster[0].append([text, bbox])
                    cluster[1] = bbox[axis][1]
                    _find = 1
                    break
            if not _find:
                cluster_list.append([[[text, bbox]], bbox[axis][1]])

        new_text_list = []
        new_bbox_list = []
        for cluster in cluster_list:
            # print("=============convert_image")
            # print("cluster_list", cluster)
            center_y = 0
            for text, bbox in cluster[0]:
                center_y += bbox[axis][1]
            center_y = int(center_y / len(cluster[0]))
            for text, bbox in cluster[0]:
                bbox[axis][1] = center_y
                new_text_list.append(text)
                new_bbox_list.append(bbox)
            # print("cluster_list", cluster)
        return new_text_list, new_bbox_list

    def merge_textbox(textbox_list, in_objs):
        delete_obj = []
        threshold = 5
        textbox_list.sort(key=lambda x:x.bbox[0])
        for k in range(len(textbox_list)):
            tb1 = textbox_list[k]
            if tb1 not in in_objs and tb1 not in delete_obj:
                for m in range(k+1, len(textbox_list)):
                    tb2 = textbox_list[m]
                    if tb2 in in_objs:
                        continue
                    if abs(tb1.bbox[1]-tb2.bbox[1]) <= threshold \
                            and abs(tb1.bbox[3]-tb2.bbox[3]) <= threshold:
                        if tb1.bbox[0] <= tb2.bbox[0]:
                            tb1.text = tb1.text + tb2.text
                        else:
                            tb1.text = tb2.text + tb1.text
                        tb1.bbox[0] = min(tb1.bbox[0], tb2.bbox[0])
                        tb1.bbox[2] = max(tb1.bbox[2], tb2.bbox[2])
                        delete_obj.append(tb2)
        for _obj in delete_obj:
            if _obj in textbox_list:
                textbox_list.remove(_obj)
        return textbox_list

    log("into image_preprocess")
    try:
        if image_np is None:
            return []

        # 整体分辨率限制
        if image_np.shape[0] > 2000 or image_np.shape[1] > 2000:
            h, w = get_best_predict_size2(image_np, threshold=2000)
            log("global image resize " + str(image_np.shape[:2]) + " -> " + str(h) + "," + str(w))
            image_np = pil_resize(image_np, h, w)

        # 图片倾斜校正，写入原来的图片路径
        # print("image_process", image_path)
        g_r_i = get_rotated_image(image_np, image_path)
        if judge_error_code(g_r_i):
            if is_from_docx:
                return []
            else:
                return g_r_i
        image_np = cv2.imread(image_path)
        image_np_copy = copy.deepcopy(image_np)
        if image_np is None:
            return []

        # if image_np is None:
        #     return []
        #
        # # idc模型实现图片倾斜校正
        # image_resize = pil_resize(image_np, 640, 640)
        # image_resize_path = image_path.split(".")[0] + "_resize_idc." + image_path.split(".")[-1]
        # cv2.imwrite(image_resize_path, image_resize)
        #
        # with open(image_resize_path, "rb") as f:
        #     image_bytes = f.read()
        # angle = from_idc_interface(image_bytes)
        # if judge_error_code(angle):
        #     if is_from_docx:
        #         return []
        #     else:
        #         return angle
        # # 根据角度旋转
        # image_pil = Image.fromarray(image_np)
        # image_np = np.array(image_pil.rotate(angle, expand=1))
        # # 写入
        # idc_path = image_path.split(".")[0] + "_idc." + image_path.split(".")[-1]
        # cv2.imwrite(idc_path, image_np)

        # isr模型去除印章
        _isr_time = time.time()
        if count_red_pixel(image_np):
            # 红色像素达到一定值才过模型
            with open(image_path, "rb") as f:
                image_bytes = f.read()
            image_np = from_isr_interface(image_bytes)
            if judge_error_code(image_np):
                if is_from_docx:
                    return []
                else:
                    return image_np
            # [1]代表检测不到印章，直接返回
            if isinstance(image_np, list) and image_np == [1]:
                log("no seals detected!")
                image_np = image_np_copy
            else:
                isr_path = image_path.split(".")[0] + "_isr." + image_path.split(".")[-1]
                cv2.imwrite(isr_path, image_np)
        log("isr total time "+str(time.time()-_isr_time))

        # otr模型识别表格，需要图片resize成模型所需大小, 写入另一个路径
        best_h, best_w = get_best_predict_size(image_np)
        # image_resize = cv2.resize(image_np, (best_w, best_h), interpolation=cv2.INTER_AREA)
        image_resize = pil_resize(image_np, best_h, best_w)
        image_resize_path = image_path.split(".")[0] + "_resize_otr." + image_path.split(".")[-1]
        cv2.imwrite(image_resize_path, image_resize)

        # 调用otr模型接口
        with open(image_resize_path, "rb") as f:
            image_bytes = f.read()
        list_line = from_otr_interface(image_bytes, is_from_pdf)
        if judge_error_code(list_line):
            return list_line

        # # 预处理
        # if is_from_pdf:
        #     prob = 0.2
        # else:
        #     prob = 0.5
        # with open(image_resize_path, "rb") as f:
        #     image_bytes = f.read()
        # img_new, inputs = table_preprocess(image_bytes, prob)
        # if type(img_new) is list and judge_error_code(img_new):
        #     return img_new
        # log("img_new.shape " + str(img_new.shape))
        #
        # # 调用模型运行接口
        # _dict = {"inputs": inputs, "md5": _global.get("md5")}
        # result = from_gpu_interface(_dict, model_type="otr", predictor_type="")
        # if judge_error_code(result):
        #     logging.error("from_gpu_interface failed! " + str(result))
        #     raise requests.exceptions.RequestException
        #
        # pred = result.get("preds")
        # gpu_time = result.get("gpu_time")
        # log("otr model predict time " + str(gpu_time))
        #
        # # # 解压numpy
        # # decompressed_array = io.BytesIO()
        # # decompressed_array.write(pred)
        # # decompressed_array.seek(0)
        # # pred = np.load(decompressed_array, allow_pickle=True)['arr_0']
        # # log("inputs.shape" + str(pred.shape))
        #

        # 调用gpu共享内存处理
        # _dict = {"inputs": inputs, "md5": _global.get("md5")}
        # result = from_gpu_share_memory(_dict, model_type="otr", predictor_type="")
        # if judge_error_code(result):
        #     logging.error("from_gpu_interface failed! " + str(result))
        #     raise requests.exceptions.RequestException
        #
        # pred = result.get("preds")
        # gpu_time = result.get("gpu_time")
        # log("otr model predict time " + str(gpu_time))
        #
        # # 后处理
        # list_line = table_postprocess(img_new, pred, prob)
        # log("len(list_line) " + str(len(list_line)))
        # if judge_error_code(list_line):
        #     return list_line

        # otr resize后得到的bbox根据比例还原
        start_time = time.time()
        ratio = (image_np.shape[0]/best_h, image_np.shape[1]/best_w)
        for i in range(len(list_line)):
            point = list_line[i]
            list_line[i] = [int(point[0]*ratio[1]), int(point[1]*ratio[0]),
                            int(point[2]*ratio[1]), int(point[3]*ratio[0])]
        log("otr resize bbox recover " + str(time.time()-start_time))

        # ocr图片过大内存溢出，需resize
        start_time = time.time()
        threshold = 3000
        ocr_resize_flag = 0
        if image_np.shape[0] >= threshold or image_np.shape[1] >= threshold:
            ocr_resize_flag = 1
            best_h, best_w = get_best_predict_size2(image_np, threshold)
            # image_resize = cv2.resize(image_np, (best_w, best_h), interpolation=cv2.INTER_AREA)
            image_resize = pil_resize(image_np, best_h, best_w)
            log("ocr_process image resize " + str(image_resize.shape))
            image_resize_path = image_path.split(".")[0] + "_resize_ocr." + image_path.split(".")[-1]
            cv2.imwrite(image_resize_path, image_resize)
        log("ocr resize before " + str(time.time()-start_time))

        # 调用ocr模型接口
        with open(image_resize_path, "rb") as f:
            image_bytes = f.read()
        text_list, bbox_list = from_ocr_interface(image_bytes, is_table=True)
        if judge_error_code(text_list):
            return text_list

        # # PaddleOCR内部包括预处理，调用模型运行接口，后处理
        # paddle_ocr = PaddleOCR(use_angle_cls=True, lang="ch")
        # results = paddle_ocr.ocr(image_resize, det=True, rec=True, cls=True)
        # # 循环每张图片识别结果
        # text_list = []
        # bbox_list = []
        # for line in results:
        #     # print("ocr_interface line", line)
        #     text_list.append(line[-1][0])
        #     bbox_list.append(line[0])
        # if len(text_list) == 0:
        #     return []

        # ocr resize后的bbox还原
        if ocr_resize_flag:
            ratio = (image_np.shape[0]/best_h, image_np.shape[1]/best_w)
        else:
            ratio = (1, 1)
        for i in range(len(bbox_list)):
            point = bbox_list[i]
            bbox_list[i] = [[int(point[0][0]*ratio[1]), int(point[0][1]*ratio[0])],
                            [int(point[1][0]*ratio[1]), int(point[1][1]*ratio[0])],
                            [int(point[2][0]*ratio[1]), int(point[2][1]*ratio[0])],
                            [int(point[3][0]*ratio[1]), int(point[3][1]*ratio[0])]]

        # 调用现成方法形成表格
        try:
            from format_convert.convert_tree import TableLine
            list_lines = []
            for line in list_line:
                list_lines.append(LTLine(1, (line[0], line[1]), (line[2], line[3])))
            from format_convert.convert_tree import TextBox
            list_text_boxes = []
            for i in range(len(bbox_list)):
                bbox = bbox_list[i]
                b_text = text_list[i]
                list_text_boxes.append(TextBox([bbox[0][0], bbox[0][1],
                                                bbox[2][0], bbox[2][1]], b_text))

            # for _textbox in list_text_boxes:
            #     print("==",_textbox.get_text())
            lt = LineTable()
            tables, obj_in_table, _ = lt.recognize_table(list_text_boxes, list_lines, False)

            # 合并同一行textbox
            list_text_boxes = merge_textbox(list_text_boxes, obj_in_table)

            obj_list = []
            for table in tables:
                obj_list.append(_Table(table["table"], table["bbox"]))
            for text_box in list_text_boxes:
                if text_box not in obj_in_table:
                    obj_list.append(_Sentence(text_box.get_text(), text_box.bbox))
            return obj_list
        except:
            traceback.print_exc()
            return [-8]

    except Exception as e:
        log("image_preprocess error")
        traceback.print_exc()
        return [-1]


if __name__ == "__main__":
    image_slice_new(cv2.imread("C:/Users/Administrator/Desktop/test_image/error23.png"))