FORMAT_CONVERSION_MAXCOMPUTE/otr/table_line.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 9 23:11:51 2020
table line detect
@author: chineseocr
"""
import copy
import logging
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D
from tensorflow.keras.layers import LeakyReLU
from otr.utils import letterbox_image, get_table_line, adjust_lines, line_to_line, draw_boxes
import numpy as np
import cv2
import time
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')


def dice_coef(y_true, y_pred, smooth=1e-5):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)


def dice_coef_loss():
    def dice_coef_loss_fixed(y_true, y_pred):
        return -dice_coef(y_true, y_pred)
    return dice_coef_loss_fixed


def focal_loss(gamma=3., alpha=.5):
    # 3 0.85 2000e          acc-0.6 p-0.99 r-0.99 val_acc-0.56 val_p-0.86 val_r-0.95
    # 2 0.85 double_gpu     acc-
    # 3 0.25 gpu 50e        acc-0.5 p-0.99 r-0.99 val_acc-0.45 val_p-0.96 val_r-0.88
    # 2 0.25 gpu            acc-
    # 3 0.5  double_gpu     acc-0.6 p-0.99 r-0.99 val_acc-0.60 val_p-0.93 val_r-0.93
    def focal_loss_fixed(y_true, y_pred):
        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
        return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(K.epsilon()+pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0 + K.epsilon()))
    return focal_loss_fixed


def table_net(input_shape=(1152, 896, 3), num_classes=1):
    inputs = Input(shape=input_shape)
    # 512
    use_bias = False
    down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs)
    down0a = BatchNormalization()(down0a)
    down0a = LeakyReLU(alpha=0.1)(down0a)
    down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0a)
    down0a = BatchNormalization()(down0a)
    down0a = LeakyReLU(alpha=0.1)(down0a)
    down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a)

    # 256
    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0a_pool)
    down0 = BatchNormalization()(down0)
    
    down0 = LeakyReLU(alpha=0.1)(down0)
    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
    down0 = BatchNormalization()(down0)
    down0 = LeakyReLU(alpha=0.1)(down0)
    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
    # 128

    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.1)(down1)
    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
    # 64

    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.1)(down2)
    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
    # 32

    down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down3)
    down3 = BatchNormalization()(down3)
    down3 = LeakyReLU(alpha=0.1)(down3)
    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
    # 16

    down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down3_pool)
    down4 = BatchNormalization()(down4)
    down4 = LeakyReLU(alpha=0.1)(down4)
    down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down4)
    down4 = BatchNormalization()(down4)
    down4 = LeakyReLU(alpha=0.1)(down4)
    down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
    # 8

    center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(down4_pool)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.1)(center)
    center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(center)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.1)(center)
    # center

    up4 = UpSampling2D((2, 2))(center)
    up4 = concatenate([down4, up4], axis=3)
    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
    up4 = BatchNormalization()(up4)
    up4 = LeakyReLU(alpha=0.1)(up4)
    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
    up4 = BatchNormalization()(up4)
    up4 = LeakyReLU(alpha=0.1)(up4)
    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
    up4 = BatchNormalization()(up4)
    up4 = LeakyReLU(alpha=0.1)(up4)
    # 16

    up3 = UpSampling2D((2, 2))(up4)
    up3 = concatenate([down3, up3], axis=3)
    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
    up3 = BatchNormalization()(up3)
    up3 = LeakyReLU(alpha=0.1)(up3)
    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
    up3 = BatchNormalization()(up3)
    up3 = LeakyReLU(alpha=0.1)(up3)
    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
    up3 = BatchNormalization()(up3)
    up3 = LeakyReLU(alpha=0.1)(up3)
    # 32

    up2 = UpSampling2D((2, 2))(up3)
    up2 = concatenate([down2, up2], axis=3)
    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.1)(up2)
    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.1)(up2)
    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.1)(up2)
    # 64

    up1 = UpSampling2D((2, 2))(up2)
    up1 = concatenate([down1, up1], axis=3)
    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.1)(up1)
    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.1)(up1)
    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.1)(up1)
    # 128

    up0 = UpSampling2D((2, 2))(up1)
    up0 = concatenate([down0, up0], axis=3)
    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.1)(up0)
    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.1)(up0)
    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.1)(up0)
    # 256

    up0a = UpSampling2D((2, 2))(up0)
    up0a = concatenate([down0a, up0a], axis=3)
    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = LeakyReLU(alpha=0.1)(up0a)
    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = LeakyReLU(alpha=0.1)(up0a)
    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
    up0a = BatchNormalization()(up0a)
    up0a = LeakyReLU(alpha=0.1)(up0a)
    # 512
    classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0a)
    model = Model(inputs=inputs, outputs=classify)
    return model


model = table_net((None, None, 3), 2)


def draw_pixel(pred, prob=0.2):
    import matplotlib.pyplot as plt
    _array = []
    for _h in range(len(pred)):
        _line = []
        for _w in range(len(pred[_h])):
            _prob = pred[_h][_w]
            if _prob[0]>prob:
                _line.append((0,255,255))
            elif _prob[1]>prob:
                _line.append((255,255,0))
            else:
                _line.append((255,255,255))
        _array.append(_line)
    plt.imshow(np.array(_array))
    plt.show()


def points2lines(pred, sourceP_LB=True, prob=0.2, line_width=7, padding=3, min_len=10,
                 cell_width=13):
    def inBbox(bbox,point,line_width):
        x,y = point
        if x>=bbox[0]-line_width and x<=bbox[2]+line_width and y>=bbox[1]-line_width and y<=bbox[3]+line_width:
            return True,[min(x,bbox[0]),min(y,bbox[1]),max(x,bbox[2]),max(y,bbox[3])]
        return False,None
    _time = time.time()
    height = len(pred)
    width = len(pred[0])

    clust_horizontal = []
    clust_vertical = []
    h_index = -1
    _step = line_width
    _sum = list(np.sum(np.array((pred[...,1]>prob)).astype(int),axis=0))
    _last = False
    _current = False
    while 1:
        h_index += 2
        if h_index>=height:
            break
        w_index = -1
        if sourceP_LB:
            h_i = height-1-h_index
        else:
            h_i = h_index
        while 1:
            w_index += 2
            if w_index>=width:
                break
            if _sum[w_index]<min_len:
                continue
            _h,_v = pred[h_index][w_index]

            if _v>prob:

                _find  = False
                _point = (w_index,h_i)
                for l_h_i in range(len(clust_vertical)):
                    l_h = clust_vertical[len(clust_vertical)-l_h_i-1]
                    bbox = l_h.get("bbox")
                    b_in,_bbox = inBbox(bbox,_point,line_width)
                    if b_in:
                        _find = True
                        l_h.get("points").append(_point)
                        l_h["bbox"] = _bbox
                        break
                if not _find:
                    clust_vertical.append({"points":[_point],"bbox":[w_index,h_i,w_index,h_i]})

    w_index = -1
    _sum = list(np.sum(np.array((pred[...,0]>prob)).astype(int),axis=1))
    while 1:
        w_index += 2
        if w_index>=width:
            break
        h_index = -1
        while 1:
            h_index += 2
            if h_index>=height:
                break
            if _sum[h_index]<min_len:
                continue

            if sourceP_LB:
                h_i = height-1-h_index
            else:
                h_i = h_index

            _h,_v = pred[h_index][w_index]

            if _h>prob:
                _find  = False
                _point = (w_index,h_i)
                for l_h_i in range(len(clust_horizontal)):
                    l_h = clust_horizontal[len(clust_horizontal)-l_h_i-1]
                    bbox = l_h.get("bbox")
                    b_in,_bbox = inBbox(bbox,_point,line_width)
                    if b_in:
                        _find = True
                        l_h.get("points").append(_point)
                        l_h["bbox"] = _bbox
                        break
                if not _find:
                    clust_horizontal.append({"points":[_point],"bbox":[w_index,h_i,w_index,h_i]})

    tmp_vertical = []
    for _dict in clust_vertical:
        _bbox = _dict.get("bbox")
        if _bbox[2]-_bbox[0]>=min_len or _bbox[3]-_bbox[1]>=min_len:
            tmp_vertical.append([(_bbox[0]+_bbox[2])/2,_bbox[1]-padding,(_bbox[0]+_bbox[2])/2,_bbox[3]+padding])
    tmp_horizontal = []
    for _dict in clust_horizontal:
        _bbox = _dict.get("bbox")
        if _bbox[2]-_bbox[0]>=min_len or _bbox[3]-_bbox[1]>=min_len:
            tmp_horizontal.append([_bbox[0]-padding,(_bbox[1]+_bbox[3])/2,_bbox[2]+padding,(_bbox[1]+_bbox[3])/2])

    #merge lines
    tmp_vertical.sort(key=lambda x:x[3],reverse=True)
    tmp_horizontal.sort(key=lambda x:x[0])

    pop_index = []
    final_vertical = []
    for _line in tmp_vertical:
        _find = False
        x0,y0,x1,y1 = _line
        for _line2 in final_vertical:
            x2,y2,x3,y3 = _line2
            if abs(x0-x2)<line_width and abs(y0-y3)<cell_width or abs(y1-y2)<cell_width:
                _find = True
                final_vertical.append([x0,min(y0,y2),x1,max(y1,y3)])
                break
        if not _find:
            final_vertical.append(_line)

    final_horizontal = []
    for _line in tmp_horizontal:
        _find = False
        x0,y0,x1,y1 = _line
        for _line2 in final_horizontal:
            x2,y2,x3,y3 = _line2
            if abs(y0-y2)<line_width and abs(x0-x3)<cell_width or abs(x1-x2)<cell_width:
                _find = True
                final_horizontal.append([min(x0,x2),y0,max(x1,x3),y1])
                break
        if not _find:
            final_horizontal.append(_line)

    list_line = []
    for _line in final_vertical:
        list_line.append(_line)
    for _line in final_horizontal:
        list_line.append(_line)

    logging.info("points2lines cost %.2fs"%(time.time()-_time))

    # import matplotlib.pyplot as plt
    # plt.figure()
    # for _line in list_line:
    #     x0,y0,x1,y1 = _line
    #     plt.plot([x0,x1],[y0,y1])
    # for _line in list_line:
    #     x0,y0,x1,y1 = _line.bbox
    #     plt.plot([x0,x1],[y0,y1])
    # for point in list_crosspoints:
    #     plt.scatter(point.get("point")[0],point.get("point")[1])
    # plt.show()

    return list_line


def get_line_from_binary_image(image_np, point_value=1, axis=0):
    """
    根据像素点的变化，将像素点为特定值的转化为line，即找出端点坐标。
    需要二值化的图。
    仅支持竖线横线。

    :param image_np: numpy格式 image
    :param point_value: 像素点的特定值
    :param axis: 是否是行，否则为列
    :return: line list
    """
    def get_axis_points(_list, axis=0):
        _list.sort(key=lambda x: (x[1-axis], x[axis]))

        standard_axis = points[axis][1-axis]
        axis_points = []
        sub_points = []
        for p in _list:
            if p[1-axis] == standard_axis:
                sub_points.append(p)
            else:
                standard_axis = p[1-axis]
                if sub_points:
                    axis_points.append(sub_points)
                sub_points = []
        # 最后一行/列
        if sub_points:
            axis_points.append(sub_points)
        return axis_points

    def get_axis_lines(_list, axis=0):
        # 逐行/列判断，一行/列可能多条横线/竖线
        points_lines = []
        for axis_list in _list:
            sub_line = [axis_list[0]]
            for p in axis_list:
                # 设置基准点
                standard_p = sub_line[-1]

                # 判断连续
                if p[axis] - standard_p[axis] == 1:
                    sub_line.append(p)
                else:
                    points_lines.append(sub_line)
                    sub_line = [p]
            # 最后一行/列
            if sub_line:
                points_lines.append(sub_line)

        # 许多点组成的line转为两点line
        lines = []
        for line in points_lines:
            line.sort(key=lambda x: (x[axis], x[1-axis]))
            lines.append([line[0][0], line[0][1], line[-1][0], line[-1][1]])
        return lines

    # 取值大于point_value的点的坐标
    ys, xs = np.where(image_np >= point_value)
    points = [[xs[i], ys[i]] for i in range(len(xs))]

    # 提出所有相同x或相同y的点
    # 提取行/列
    axis_points = get_axis_points(points, axis)

    # 提取每行/列的横线/竖线
    axis_lines = get_axis_lines(axis_points, axis)
    # print("axis_lines", axis_lines)

    return axis_lines


def table_line(img, model, size=(512, 1024), hprob=0.5, vprob=0.5, row=50, col=30, alph=15):
    sizew, sizeh = size
    img_new = cv2.resize(img, (sizew, sizeh), interpolation=cv2.INTER_AREA)

    pred = model.predict(np.array([img_new]))
    pred = pred[0]

    # 横线预测结果
    # row_pred = pred[..., 0] > hprob
    # row_pred = row_pred.astype(np.uint8)
    # # 竖线预测结果
    # col_pred = pred[..., 1] > vprob
    # col_pred = col_pred.astype(np.uint8)
    # # 打印模型输出
    # cv2.imshow("predict", (col_pred+row_pred)*255)
    # cv2.waitKey(0)

    _time = time.time()
    list_line = points2lines(pred, False)

    # 分成横竖线
    list_rows = []
    list_cols = []
    for line in list_line:
        if line[0] == line[2]:
            list_cols.append(line)
        elif line[1] == line[3]:
            list_rows.append(line)

    # 删掉贴着边框的line
    temp_list = []
    threshold = 5
    for line in list_rows:
        if line[1]-0 <= threshold or size[1]-line[1] <= threshold:
            continue
        # 内部排序
        if line[0] > line[2]:
            line = [line[2], line[3], line[0], line[1]]
        temp_list.append(line)
    list_rows = temp_list
    temp_list = []
    for line in list_cols:
        if line[0]-0 <= threshold or size[0]-line[0] <= threshold:
            continue
        # 内部排序
        if line[1] > line[3]:
            line = [line[2], line[3], line[0], line[1]]
        temp_list.append(line)
    list_cols = temp_list
    if not list_rows or not list_cols:
        return []

    # 合并错开线
    list_rows = merge_line(list_rows, axis=0)
    list_cols = merge_line(list_cols, axis=1)

    # 计算交点、分割线
    cross_points = get_points(list_rows, list_cols, (img_new.shape[0], img_new.shape[1]))
    if not cross_points:
        return []

    # 清掉外围的没用的线
    list_rows, list_cols = delete_outline(list_rows, list_cols, cross_points)

    # 多个表格分割线
    split_lines, split_y = get_split_line(cross_points, list_cols, img_new)

    # 修复边框
    new_rows, new_cols, long_rows, long_cols = fix_outline(img_new, list_rows, list_cols, cross_points,
                                                           split_y)
    # 如有补线
    if new_rows or new_cols:
        # 连接至补线的延长线
        if long_rows:
            list_rows = long_rows
        if long_cols:
            list_cols = long_cols
        # 新的补线
        if new_rows:
            list_rows += new_rows
        if new_cols:
            list_cols += new_cols

        # 修复边框后重新计算交点、分割线
        cross_points = get_points(list_rows, list_cols, (img_new.shape[0], img_new.shape[1]))
        split_lines, split_y = get_split_line(cross_points, list_cols, img_new)

        # 修复内部缺线
        cross_points = fix_inner(list_rows, list_cols, cross_points, split_y)
        if not cross_points:
            return []

    # 修复表格4个角
    list_rows, list_cols = fix_corner(list_rows, list_cols, split_y)

    list_line = list_rows + list_cols
    return list_line


def table_line2(img, model, size=(512, 1024), hprob=0.5, vprob=0.5, row=50, col=30, alph=15):
    sizew, sizeh = size
    # [..., ::-1] 最后一维内部反向输出
    # inputBlob, fx, fy = letterbox_image(img[..., ::-1], (sizew, sizeh))
    # pred = model.predict(np.array([np.array(inputBlob)]))
    # pred = model.predict(np.array([np.array(inputBlob)/255.0]))
    img_new = cv2.resize(img, (sizew, sizeh), interpolation=cv2.INTER_AREA)
    # logging.info("into table_line 1")

    pred = model.predict(np.array([img_new]))
    # logging.info("into table_line 2")
    pred = pred[0]

    draw_pixel(pred)
    _time = time.time()
    points2lines(pred)
    logging.info("points2lines takes %ds"%(time.time()-_time))

    vpred = pred[..., 1] > vprob   # 横线
    hpred = pred[..., 0] > hprob   # 竖线
    vpred = vpred.astype(int)
    hpred = hpred.astype(int)

    # print("vpred shape", vpred)
    # print("hpred shape", hpred)

    colboxes = get_table_line(vpred, axis=1, lineW=col)
    rowboxes = get_table_line(hpred, axis=0, lineW=row)
    # logging.info("into table_line 3")

    # if len(rowboxes) > 0:
    #     rowboxes = np.array(rowboxes)
    #     rowboxes[:, [0, 2]] = rowboxes[:, [0, 2]]/fx
    #     rowboxes[:, [1, 3]] = rowboxes[:, [1, 3]]/fy
    #     rowboxes = rowboxes.tolist()
    # if len(colboxes) > 0:
    #     colboxes = np.array(colboxes)
    #     colboxes[:, [0, 2]] = colboxes[:, [0, 2]]/fx
    #     colboxes[:, [1, 3]] = colboxes[:, [1, 3]]/fy
    #     colboxes = colboxes.tolist()

    nrow = len(rowboxes)
    ncol = len(colboxes)
    for i in range(nrow):
        for j in range(ncol):
            rowboxes[i] = line_to_line(rowboxes[i], colboxes[j], 10)
            colboxes[j] = line_to_line(colboxes[j], rowboxes[i], 10)
    # logging.info("into table_line 4")

    # 删掉贴着边框的line
    temp_list = []
    threshold = 5
    for line in rowboxes:
        if line[1]-0 <= threshold or size[1]-line[1] <= threshold:
            continue
        # 内部排序
        if line[0] > line[2]:
            line = [line[2], line[3], line[0], line[1]]
        temp_list.append(line)
    rowboxes = temp_list

    temp_list = []
    for line in colboxes:
        if line[0]-0 <= threshold or size[0]-line[0] <= threshold:
            continue
        # 内部排序
        if line[1] > line[3]:
            line = [line[2], line[3], line[0], line[1]]
        temp_list.append(line)
    colboxes = temp_list

    return rowboxes, colboxes, img_new


def get_outline(points, image_np):
    # 取出x, y的最大值最小值
    x_min = points[0][0]
    x_max = points[-1][0]
    points.sort(key=lambda x: (x[1], x[0]))
    y_min = points[0][1]
    y_max = points[-1][1]

    # 创建空图
    # outline_img = np.zeros(image_size, np.uint8)
    outline_img = np.copy(image_np)
    cv2.rectangle(outline_img, (x_min-5, y_min-5), (x_max+5, y_max+5), (0, 0, 0), 2)
    # cv2.imshow("outline_img", outline_img)
    # cv2.waitKey(0)
    return outline_img


def get_split_line(points, col_lines, image_np):
    # print("get_split_line", image_np.shape)
    points.sort(key=lambda x: (x[1], x[0]))
    # 遍历y坐标，并判断y坐标与上一个y坐标是否存在连接线
    i = 0
    split_line_y = []
    for point in points:
        # 从已分开的线下面开始判断
        if split_line_y:
            if point[1] <= split_line_y[-1] + 5:
                last_y = point[1]
                continue
            if last_y <= split_line_y[-1] + 5:
                last_y = point[1]
                continue

        if i == 0:
            last_y = point[1]
            i += 1
            continue

        current_line = (last_y, point[1])
        split_flag = 1
        for col in col_lines:
            # 只要找到一条col包含就不是分割线
            if current_line[0] >= col[1]-3 and current_line[1] <= col[3]+3:
                split_flag = 0
                # print("img", img.shape)
                # print("col", col)
                # print("current_line", current_line)
                break

        if split_flag:
            split_line_y.append(current_line[0]+5)
            split_line_y.append(current_line[1]-5)

        last_y = point[1]

    # 加上收尾分割线
    points.sort(key=lambda x: (x[1], x[0]))
    y_min = points[0][1]
    y_max = points[-1][1]
    # print("加上收尾分割线", y_min, y_max)
    if y_min-5 < 0:
        split_line_y.append(0)
    else:
        split_line_y.append(y_min-5)
    if y_max+5 > image_np.shape[0]:
        split_line_y.append(image_np.shape[0])
    else:
        split_line_y.append(y_max+5)
    split_line_y = list(set(split_line_y))

    # 剔除两条相隔太近分割线
    temp_split_line_y = []
    split_line_y.sort(key=lambda x: x)
    last_y = -20
    for y in split_line_y:
        # print(y)
        if y - last_y >= 20:
            # print(y, last_y)
            temp_split_line_y.append(y)
            last_y = y
    split_line_y = temp_split_line_y

    # print("split_line_y", split_line_y)

    # 生成分割线
    split_line = []
    last_y = 0
    for y in split_line_y:
        # if y - last_y <= 15:
        #     continue
        split_line.append([(0, y), (image_np.shape[1], y)])
        last_y = y
    split_line.append([(0, 0), (image_np.shape[1], 0)])
    split_line.append([(0, image_np.shape[0]), (image_np.shape[1], image_np.shape[0])])
    split_line.sort(key=lambda x: x[0][1])
    # print("split_line", split_line)

    # 画图画线
    # split_line_img = np.copy(image_np)
    # for y in split_line_y:
    #     cv2.line(split_line_img, (0, y), (image_np.shape[1], y), (0, 0, 0), 1)
    # cv2.imshow("split_line_img", split_line_img)
    # cv2.waitKey(0)

    return split_line, split_line_y


def get_points(row_lines, col_lines, image_size):
    # 创建空图
    row_img = np.zeros(image_size, np.uint8)
    col_img = np.zeros(image_size, np.uint8)

    # 画线
    thresh = 3
    for row in row_lines:
        cv2.line(row_img, (int(row[0]-thresh), int(row[1])), (int(row[2]+thresh), int(row[3])), (255, 255, 255), 1)
    for col in col_lines:
        cv2.line(col_img, (int(col[0]), int(col[1]-thresh)), (int(col[2]), int(col[3]+thresh)), (255, 255, 255), 1)

    # 求出交点
    point_img = np.bitwise_and(row_img, col_img)
    # cv2.imshow("point_img", np.bitwise_not(point_img))
    # cv2.waitKey(0)

    # 识别黑白图中的白色交叉点，将横纵坐标取出
    ys, xs = np.where(point_img > 0)
    points = []
    for i in range(len(xs)):
        points.append((xs[i], ys[i]))
    points.sort(key=lambda x: (x[0], x[1]))
    return points


def get_minAreaRect(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.bitwise_not(gray)
    thresh = cv2.threshold(gray, 0, 255,
                           cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    coords = np.column_stack(np.where(thresh > 0))
    return cv2.minAreaRect(coords)


def get_contours(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

    contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(image, contours, -1, (0, 0, 255), 3)

    cv2.imshow("get contours", image)
    cv2.waitKey(0)


def merge_line(lines, axis, threshold=5):
    """
    解决模型预测一条直线错开成多条直线，合并成一条直线

    :param lines: 线条列表
    :param axis: 0:横线 1:竖线
    :param threshold: 两条线间像素差阈值
    :return: 合并后的线条列表
    """
    # 任意一条line获取该合并的line，横线往下找，竖线往右找
    lines.sort(key=lambda x: (x[axis], x[1-axis]))
    merged_lines = []
    used_lines = []
    for line1 in lines:
        if line1 in used_lines:
            continue

        merged_line = [line1]
        used_lines.append(line1)
        for line2 in lines:
            if line2 in used_lines:
                continue

            if line1[1-axis]-threshold <= line2[1-axis] <= line1[1-axis]+threshold:
                # 计算基准长度
                min_axis = 10000
                max_axis = 0
                for line3 in merged_line:
                    if line3[axis] < min_axis:
                        min_axis = line3[axis]
                    if line3[axis+2] > max_axis:
                        max_axis = line3[axis+2]
                # 判断两条线有无交集
                if min_axis <= line2[axis] <= max_axis \
                        or min_axis <= line2[axis+2] <= max_axis:
                    merged_line.append(line2)
                    used_lines.append(line2)
        if merged_line:
            merged_lines.append(merged_line)

    # 合并line
    result_lines = []
    for merged_line in merged_lines:
        # 获取line宽的平均值
        axis_average = 0
        for line in merged_line:
            axis_average += line[1-axis]
        axis_average = int(axis_average/len(merged_line))

        # 获取最长line两端
        merged_line.sort(key=lambda x: (x[axis]))
        axis_start = merged_line[0][axis]
        merged_line.sort(key=lambda x: (x[axis+2]))
        axis_end = merged_line[-1][axis+2]

        if axis:
            result_lines.append([axis_average, axis_start, axis_average, axis_end])
        else:
            result_lines.append([axis_start, axis_average, axis_end, axis_average])
    return result_lines


def fix_inner2(row_points, col_points, row_lines, col_lines, threshold=3):
    for i in range(len(row_points)):
        row = row_points[i]
        row.sort(key=lambda x: (x[1], x[0]))
        for j in range(len(row)):
            # 当前点
            point = row[j]

            # 获取当前点在所在行的下个点
            if j >= len(row) - 1:
                next_row_point = []
            else:
                next_row_point = row[j+1]

            if next_row_point:
                for k in range(len(row_lines)):
                    line = row_lines[k]
                    if line[1] - threshold <= point[1] <= line[1] + threshold:
                        if not line[0] <= point[0] <= next_row_point[0] <= line[2]:
                            if point[0] <= line[2] < next_row_point[0]:
                                if line[2] - point[0] >= 1/3 * (next_row_point[0] - point[0]):
                                    row_lines[k][2] = next_row_point[0]
                            if point[0] < line[0] <= next_row_point[0]:
                                if next_row_point[0] - line[0] >= 1/3 * (next_row_point[0] - point[0]):
                                    row_lines[k][0] = point[0]

            # 获取当前点所在列的下个点
            next_col_point = []
            for col in col_points:
                if point in col:
                    col.sort(key=lambda x: (x[0], x[1]))
                    if col.index(point) < len(col) - 1:
                        next_col_point = col[col.index(point)+1]
                        break

            # 获取当前点的对角线点，通过该列下个点所在行的下个点获得
            next_row_next_col_point = []
            if next_col_point:
                for row2 in row_points:
                    if next_col_point in row2:
                        row2.sort(key=lambda x: (x[1], x[0]))
                        if row2.index(next_col_point) < len(row2) - 1:
                            next_row_next_col_point = row2[row2.index(next_col_point)+1]
                            break

                # 有该列下一点但没有该列下一点所在行的下个点
                if not next_row_next_col_point:
                    # 如果有该行下个点
                    if next_row_point:
                        next_row_next_col_point = [next_row_point[0], next_col_point[1]]

            if next_col_point:
                for k in range(len(col_lines)):
                    line = col_lines[k]
                    if line[0] - threshold <= point[0] <= line[0] + threshold:
                        if not line[1] <= point[1] <= next_col_point[1] <= line[3]:
                            if point[1] <= line[3] < next_col_point[1]:
                                if line[3] - point[1] >= 1/3 * (next_col_point[1] - point[1]):
                                    col_lines[k][3] = next_col_point[1]
                            if point[1] < line[1] <= next_col_point[1]:
                                if next_col_point[1] - line[1] >= 1/3 * (next_col_point[1] - point[1]):
                                    col_lines[k][1] = point[1]

            if next_row_next_col_point:
                for k in range(len(col_lines)):
                    line = col_lines[k]
                    if line[0] - threshold <= next_row_next_col_point[0] <= line[0] + threshold:
                        if not line[1] <= point[1] <= next_row_next_col_point[1] <= line[3]:
                            if point[1] < line[1] <= next_row_next_col_point[1]:
                                if next_row_next_col_point[1] - line[1] >= 1/3 * (next_row_next_col_point[1] - point[1]):
                                    col_lines[k][1] = point[1]

    return row_lines, col_lines


def fix_inner(row_lines, col_lines, points, split_y):
    def fix(fix_lines, assist_lines, split_points, axis):
        new_points = []
        for line1 in fix_lines:
            min_assist_line = [[], []]
            min_distance = [1000, 1000]
            if_find = [0, 0]

            # 获取fix_line中的所有col point，里面可能不包括两个顶点，col point是交点，顶点可能不是交点
            fix_line_points = []
            for point in split_points:
                if abs(point[1-axis] - line1[1-axis]) <= 2:
                    if line1[axis] <= point[axis] <= line1[axis+2]:
                        fix_line_points.append(point)

            # 找出离两个顶点最近的assist_line, 并且assist_line与fix_line不相交
            line1_point = [line1[:2], line1[2:]]
            for i in range(2):
                point = line1_point[i]
                for line2 in assist_lines:
                    if not if_find[i] and abs(point[axis] - line2[axis]) <= 2:
                        if line1[1-axis] <= point[1-axis] <= line2[1-axis+2]:
                            # print("line1, match line2", line1, line2)
                            if_find[i] = 1
                            break
                    else:
                        if abs(point[axis] - line2[axis]) < min_distance[i] and line2[1-axis] <= point[1-axis] <= line2[1-axis+2]:
                            if line1[axis] <= line2[axis] <= line1[axis+2]:
                                continue
                            min_distance[i] = abs(line1[axis] - line2[axis])
                            min_assist_line[i] = line2

            # 找出离assist_line最近的交点
            # 顶点到交点的距离(多出来的线)需大于assist_line到交点的距离(bbox的边)的1/3
            min_distance = [1000, 1000]
            min_col_point = [[], []]
            for i in range(2):
                # print("顶点", i, line1_point[i])
                if not if_find[i]:
                    if min_assist_line[i]:
                        for point in fix_line_points:
                            if abs(point[axis] - min_assist_line[i][axis]) < min_distance[i]:
                                min_distance[i] = abs(point[axis] - min_assist_line[i][axis])
                                min_col_point[i] = point

                        if min_col_point[i]:
                            if abs(min_col_point[i][axis] - line1_point[i][axis]) >= abs(min_col_point[i][axis] - min_assist_line[i][axis])/3:
                                add_point = (line1_point[i][1-axis], min_assist_line[i][axis])
                                # print("fix_inner add point", add_point)
                                # print("line1, line2", line1, min_assist_line[i])
                                new_points.append(add_point)

        return new_points

    new_points = []
    for i in range(1, len(split_y)):
        last_y = split_y[i-1]
        y = split_y[i]

        # 先对点线进行分区
        split_row_lines = []
        split_col_lines = []
        split_points = []
        for row in row_lines:
            if last_y <= row[1] <= y:
                split_row_lines.append(row)
        for col in col_lines:
            if last_y <= col[1] <= y:
                split_col_lines.append(col)
        for point in points:
            if last_y <= point[1] <= y:
                split_points.append(point)

        new_points += fix(split_col_lines, split_row_lines, split_points, axis=1)
        new_points += fix(split_row_lines, split_col_lines, split_points, axis=0)

        # 找出所有col的顶点不在row上的、row的顶点不在col上的

        # for col in split_col_lines:
        #     print("*"*30)
        #
        #     # 获取该line中的所有point
        #     col_points = []
        #     for point in split_points:
        #         if abs(point[0] - col[0]) <= 2:
        #             if col[1] <= point[1] <= col[3]:
        #                 col_points.append(point)
        #
        #     # 比较顶点
        #     min_row_1 = []
        #     min_row_2 = []
        #     min_distance_1 = 1000
        #     min_distance_2 = 1000
        #     if_find_1 = 0
        #     if_find_2 = 0
        #     for row in split_row_lines:
        #         # 第一个顶点
        #         if not if_find_1 and abs(col[1] - row[1]) <= 2:
        #             if row[0] <= col[0] <= row[2]:
        #                 print("col, match row", col, row)
        #                 if_find_1 = 1
        #                 break
        #         else:
        #             if abs(col[1] - row[1]) < min_distance_1 and row[0] <= col[0] <= row[2]:
        #                 if col[1] <= row[1] <= col[3]:
        #                     continue
        #                 min_distance_1 = abs(col[1] - row[1])
        #                 min_row_1 = row
        #
        #         # 第二个顶点
        #         if not if_find_2 and abs(col[3] - row[1]) <= 2:
        #             if row[0] <= col[2] <= row[2]:
        #                 if_find_2 = 1
        #                 break
        #         else:
        #             if abs(col[3] - row[1]) < min_distance_2 and row[0] <= col[2] <= row[2]:
        #                 min_distance_2 = abs(col[3] - row[1])
        #                 min_row_2 = row
        #
        #     if not if_find_1:
        #         print("col", col)
        #         print("min_row_1", min_row_1)
        #         if min_row_1:
        #             min_distance_1 = 1000
        #             min_col_point = []
        #             for point in col_points:
        #                 if abs(point[1] - min_row_1[1]) < min_distance_1:
        #                     min_distance_1 = abs(point[1] - min_row_1[1])
        #                     min_col_point = point
        #
        #             if abs(min_col_point[1] - col[1]) >= abs(min_col_point[1] - min_row_1[1])/3:
        #
        #                 add_point = (col[0], min_row_1[1])
        #                 print("fix_inner add point", add_point)
        #                 new_points.append(add_point)
        #             else:
        #                 print("distance too long", min_col_point, min_row_1)
        #                 print(abs(min_col_point[1] - col[1]), abs(min_col_point[1] - min_row_1[1])/3)

    return points+new_points


def fix_corner(row_lines, col_lines, split_y):
    new_row_lines = []
    new_col_lines = []
    last_y = split_y[0]
    for y in split_y:
        if y == last_y:
            continue

        split_row_lines = []
        split_col_lines = []
        for row in row_lines:
            if last_y <= row[1] <= y or last_y <= row[3] <= y:
                split_row_lines.append(row)
        for col in col_lines:
            if last_y <= col[1] <= y or last_y <= col[3] <= y:
                split_col_lines.append(col)

        if not split_row_lines or not split_col_lines:
            last_y = y
            continue

        split_row_lines.sort(key=lambda x: (x[1], x[0]))
        split_col_lines.sort(key=lambda x: (x[0], x[1]))

        up_line = split_row_lines[0]
        bottom_line = split_row_lines[-1]
        left_line = split_col_lines[0]
        right_line = split_col_lines[-1]

        # 左上角
        if up_line[0:2] != left_line[0:2]:
            # print("up_line, left_line", up_line, left_line)
            add_corner = [left_line[0], up_line[1]]
            split_row_lines[0][0] = add_corner[0]
            split_col_lines[0][1] = add_corner[1]

        # 右上角
        if up_line[2:] != right_line[:2]:
            # print("up_line, right_line", up_line, right_line)
            add_corner = [right_line[0], up_line[1]]
            split_row_lines[0][2] = add_corner[0]
            split_col_lines[-1][1] = add_corner[1]

        new_row_lines = new_row_lines + split_row_lines
        new_col_lines = new_col_lines + split_col_lines
        last_y = y

    return new_row_lines, new_col_lines


def delete_outline(row_lines, col_lines, points):
    row_lines.sort(key=lambda x: (x[1], x[0]))
    col_lines.sort(key=lambda x: (x[0], x[1]))
    line = [row_lines[0], row_lines[-1], col_lines[0], col_lines[-1]]

    threshold = 2
    point_cnt = [0, 0, 0, 0]
    for point in points:
        for i in range(4):
            if i < 2:
                if line[i][1]-threshold <= point[1] <= line[i][1]+threshold:
                    if line[i][0] <= point[0] <= line[i][2]:
                        point_cnt[i] += 1
            else:
                if line[i][0]-threshold <= point[0] <= line[i][0]+threshold:
                    if line[i][1] <= point[1] <= line[i][3]:
                        point_cnt[i] += 1
        # if line[0][1]-threshold <= point[1] <= line[0][1]+threshold:
        #     if line[0][0] <= point[0] <= line[0][2]:
        #         point_cnt[0] += 1
        # elif line[1][1]-threshold <= point[1] <= line[1][1]+threshold:
        #     if line[1][0] <= point[0] <= line[1][2]:
        #         point_cnt[1] += 1
        # elif line[2][0]-threshold <= point[0] <= line[2][0]+threshold:
        #     if line[2][1] <= point[1] <= line[2][3]:
        #         point_cnt[2] += 1
        # elif line[3][0]-threshold <= point[0] <= line[3][0]+threshold:
        #     if line[3][1] <= point[1] <= line[3][3]:
        #         point_cnt[3] += 1

    # 轮廓line至少包含3个交点
    for i in range(4):
        if point_cnt[i] < 3:
            if i < 2:
                if line[i] in row_lines:
                    row_lines.remove(line[i])
            else:
                if line[i] in col_lines:
                    col_lines.remove(line[i])
    return row_lines, col_lines


def fix_outline(image, row_lines, col_lines, points, split_y):
    print("split_y", split_y)
    # 分割线纵坐标
    if len(split_y) < 2:
        return [], [], [], []
    # elif len(split_y) == 2:
    #     split_y = [2000., 2000., 2000., 2000.]

    split_y.sort(key=lambda x: x)
    new_split_y = []
    for i in range(1, len(split_y), 2):
        new_split_y.append(int((split_y[i]+split_y[i-1])/2))

    # # 查看是否正确输出区域分割线
    # for line in split_y:
    #     cv2.line(image, (0, int(line)), (int(image.shape[1]), int(line)), (0, 0, 255), 2)
    #     cv2.imshow("split_y", image)
    #     cv2.waitKey(0)

    # 预测线根据分割线纵坐标分为多个分割区域
    # row_lines.sort(key=lambda x: (x[3], x[2], x[1], x[0]))
    # col_lines.sort(key=lambda x: (x[3], x[2], x[1], x[0]))
    # points.sort(key=lambda x: (x[1], x[0]))
    # row_count = 0
    # col_count = 0
    # point_count = 0
    split_row_list = []
    split_col_list = []
    split_point_list = []

    # for i in range(1, len(split_y)):
    #     y = split_y[i]
    #     last_y = split_y[i-1]
    #     row_lines = row_lines[row_count:]
    #     col_lines = col_lines[col_count:]
    #     points = points[point_count:]
    #     row_count = 0
    #     col_count = 0
    #     point_count = 0
    #
    #     if not row_lines:
    #         split_row_list.append([])
    #     for row in row_lines:
    #         if last_y <= row[3] <= y:
    #             row_count += 1
    #         else:
    #             split_row_list.append(row_lines[:row_count])
    #             break
    #         if row_count == len(row_lines):
    #             split_row_list.append(row_lines[:row_count])
    #             break
    #
    #     if not col_lines:
    #         split_col_list.append([])
    #
    #     for col in col_lines:
    #         # if last_y <= col[3] <= y:
    #         if col[1] <= last_y <= y <= col[3] or last_y <= col[3] <= y:
    #         # if last_y <= col[1] <= y or last_y <= col[3] <= y:
    #             col_count += 1
    #         else:
    #             split_col_list.append(col_lines[:col_count])
    #             break
    #         if col_count == len(col_lines):
    #             split_col_list.append(col_lines[:col_count])
    #             break
    #
    #     if not points:
    #         split_point_list.append([])
    #     for point in points:
    #         if last_y <= point[1] <= y:
    #             point_count += 1
    #         else:
    #             split_point_list.append(points[:point_count])
    #             break
    #         if point_count == len(points):
    #             split_point_list.append(points[:point_count])
    #             break
    #
    # # print("len(split_row_list)", len(split_row_list))
    # # print("len(split_col_list)", len(split_col_list))
    # if row_count < len(row_lines) - 1 and col_count < len(col_lines) - 1:
    #     row_lines = row_lines[row_count:]
    #     split_row_list.append(row_lines)
    #     col_lines = col_lines[col_count:]
    #     split_col_list.append(col_lines)
    #
    # if point_count < len(points) - 1:
    #     points = points[point_count:len(points)]
    #     split_point_list.append(points)

    for i in range(1, len(split_y)):
        y = split_y[i]
        last_y = split_y[i-1]

        split_row = []
        for row in row_lines:
            if last_y <= row[3] <= y:
                split_row.append(row)
        split_row_list.append(split_row)

        split_col = []
        for col in col_lines:
            if last_y <= col[1] <= y or last_y <= col[3] <= y or col[1] < last_y < y < col[3]:
                split_col.append(col)
        split_col_list.append(split_col)

        split_point = []
        for point in points:
            if last_y <= point[1] <= y:
                split_point.append(point)
        split_point_list.append(split_point)

    # 预测线取上下左右4个边(会有超出表格部分) [(), ()]
    area_row_line = []
    area_col_line = []
    for area in split_row_list:
        if not area:
            area_row_line.append([])
            continue
        area.sort(key=lambda x: (x[1], x[0]))
        up_line = area[0]
        bottom_line = area[-1]
        area_row_line.append([up_line, bottom_line])
    for area in split_col_list:
        if not area:
            area_col_line.append([])
            continue
        area.sort(key=lambda x: x[0])
        left_line = area[0]
        right_line = area[-1]
        area_col_line.append([left_line, right_line])

    # 线交点根据分割线纵坐标分为多个分割区域
    # points.sort(key=lambda x: (x[1], x[0]))
    # point_count = 0
    # split_point_list = []
    # for y in new_split_y:
    #     points = points[point_count:len(points)]
    #     point_count = 0
    #     for point in points:
    #         if point[1] <= y:
    #             point_count += 1
    #         else:
    #             split_point_list.append(points[:point_count])
    #             break
    #         if point_count == len(points):
    #             split_point_list.append(points[:point_count])
    #             break
    # if point_count < len(points) - 1:
    #     points = points[point_count:len(points)]
    #     split_point_list.append(points)
    # print("len(split_point_list)", len(split_point_list))

    # 取每个分割区域的4条线(无超出表格部分)
    area_row_line2 = []
    area_col_line2 = []
    for area in split_point_list:
        if not area:
            area_row_line2.append([])
            area_col_line2.append([])
            continue
        area.sort(key=lambda x: (x[0], x[1]))
        left_up = area[0]
        right_bottom = area[-1]

        up_line = [left_up[0], left_up[1], right_bottom[0], left_up[1]]
        bottom_line = [left_up[0], right_bottom[1], right_bottom[0], right_bottom[1]]
        left_line = [left_up[0], left_up[1], left_up[0], right_bottom[1]]
        right_line = [right_bottom[0], left_up[1], right_bottom[0], right_bottom[1]]
        area_row_line2.append([up_line, bottom_line])
        area_col_line2.append([left_line, right_line])

    # 判断超出部分的长度，超出一定长度就补线
    new_row_lines = []
    new_col_lines = []
    longer_row_lines = []
    longer_col_lines = []
    all_longer_row_lines = []
    all_longer_col_lines = []
    # print("split_y", split_y)
    # print("split_row_list", split_row_list, len(split_row_list))
    # print("split_row_list", split_col_list, len(split_col_list))
    # print("area_row_line", area_row_line, len(area_row_line))
    # print("area_col_line", area_col_line, len(area_col_line))
    for i in range(len(area_row_line)):
        if not area_row_line[i] or not area_col_line[i]:
            continue

        up_line = area_row_line[i][0]
        up_line2 = area_row_line2[i][0]
        bottom_line = area_row_line[i][1]
        bottom_line2 = area_row_line2[i][1]
        left_line = area_col_line[i][0]
        left_line2 = area_col_line2[i][0]
        right_line = area_col_line[i][1]
        right_line2 = area_col_line2[i][1]

        # 计算单格高度宽度
        if len(split_row_list[i]) > 1:
            height_dict = {}
            for j in range(len(split_row_list[i])):
                if j + 1 > len(split_row_list[i]) - 1:
                    break
                height = abs(int(split_row_list[i][j][3] - split_row_list[i][j+1][3]))
                if height in height_dict.keys():
                    height_dict[height] = height_dict[height] + 1
                else:
                    height_dict[height] = 1
            height_list = [[x, height_dict[x]] for x in height_dict.keys()]
            height_list.sort(key=lambda x: (x[1], -x[0]), reverse=True)
            # print("height_list", height_list)
            box_height = height_list[0][0]
        else:
            box_height = 10

        if len(split_col_list[i]) > 1:
            box_width = abs(split_col_list[i][1][2] - split_col_list[i][0][2])
        else:
            box_width = 10
        print("box_height", box_height, "box_width", box_width)

        # cv2.line(image, (int(up_line[0]), int(up_line[1])),
        #          (int(up_line[2]), int(up_line[3])),
        #          (255, 255, 0), 2)
        # cv2.line(image, (int(right_line[0]), int(right_line[1])),
        #          (int(right_line[2]), int(right_line[3])),
        #          (0, 255, 255), 2)
        # cv2.imshow("right_line", image)
        # cv2.waitKey(0)

        # 补左右两条竖线超出来的线的row
        if (up_line[1] - left_line[1] >= 10 and up_line[1] - right_line[1] >= 2) or \
                (up_line[1] - left_line[1] >= 2 and up_line[1] - right_line[1] >= 10):

            if up_line[1] - left_line[1] >= up_line[1] - right_line[1]:
                new_row_lines.append([left_line[0], left_line[1], right_line[0], left_line[1]])
                new_col_y = left_line[1]
                # 补了row，要将其他短的col连到row上
                for j in range(len(split_col_list[i])):
                    col = split_col_list[i][j]
                    # 且距离不能相差大于一格
                    # print("abs(new_col_y - col[1])", abs(new_col_y - col[1]))
                    if abs(new_col_y - col[1]) <= box_height:
                        split_col_list[i][j][1] = min([new_col_y, col[1]])
                        longer_col_lines.append([col[0], min([new_col_y, col[1]]), col[2], col[3]])

            else:
                new_row_lines.append([left_line[0], right_line[1], right_line[0], right_line[1]])
                new_col_y = right_line[1]
                # 补了row，要将其他短的col连到row上
                for j in range(len(split_col_list[i])):
                    # 需判断该线在这个区域中
                    # if up_line2[1]-3 <= col[1] <= col[3] <= bottom_line2[1]+3:
                    col = split_col_list[i][j]
                    # 且距离不能相差太大
                    # print("abs(new_col_y - col[1])", abs(new_col_y - col[1]))
                    if abs(new_col_y - col[1]) <= box_height:
                        split_col_list[i][j][1] = min([new_col_y, col[1]])

        if (left_line[3] - bottom_line[3] >= 10 and right_line[3] - bottom_line[3] >= 2) or \
                (left_line[3] - bottom_line[3] >= 2 and right_line[3] - bottom_line[3] >= 10):

            if left_line[3] - bottom_line[3] >= right_line[3] - bottom_line[3]:
                new_row_lines.append([left_line[2], left_line[3], right_line[2], left_line[3]])
                new_col_y = left_line[3]
                # 补了row，要将其他短的col连到row上
                for j in range(len(split_col_list[i])):
                    col = split_col_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_col_y - col[3]) <= box_height:
                        split_col_list[i][j][3] = max([new_col_y, col[3]])

            else:
                new_row_lines.append([left_line[2], right_line[3], right_line[2], right_line[3]])
                new_col_y = right_line[3]
                # 补了row，要将其他短的col连到row上
                for j in range(len(split_col_list[i])):
                    col = split_col_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_col_y - col[3]) <= box_height:
                        split_col_list[i][j][3] = max([new_col_y, col[3]])

        # 补上下两条横线超出来的线的col
        if (left_line[0] - up_line[0] >= 10 and left_line[0] - bottom_line[0] >= 2) or \
                (left_line[0] - up_line[0] >= 2 and left_line[0] - bottom_line[0] >= 10):
            if left_line[0] - up_line[0] >= left_line[0] - bottom_line[0]:
                new_col_lines.append([up_line[0], up_line[1], up_line[0], bottom_line[1]])
                new_row_x = up_line[0]
                # 补了col，要将其他短的row连到col上
                for j in range(len(split_row_list[i])):
                    row = split_row_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_row_x - row[0]) <= box_width:
                        split_row_list[i][j][0] = min([new_row_x, row[0]])
            else:
                new_col_lines.append([bottom_line[0], up_line[1], bottom_line[0], bottom_line[1]])
                new_row_x = bottom_line[0]
                # 补了col，要将其他短的row连到col上
                for j in range(len(split_row_list[i])):
                    row = split_row_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_row_x - row[0]) <= box_width:
                        split_row_list[i][j][0] = min([new_row_x, row[0]])

        if (up_line[2] - right_line[2] >= 10 and bottom_line[2] - right_line[2] >= 2) or \
                (up_line[2] - right_line[2] >= 2 and bottom_line[2] - right_line[2] >= 10):
            if up_line[2] - right_line[2] >= bottom_line[2] - right_line[2]:
                new_col_lines.append([up_line[2], up_line[3], up_line[2], bottom_line[3]])
                new_row_x = up_line[2]
                # 补了col，要将其他短的row连到col上
                for j in range(len(split_row_list[i])):
                    row = split_row_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_row_x - row[2]) <= box_width:
                        split_row_list[i][j][2] = max([new_row_x, row[2]])

            else:
                new_col_lines.append([bottom_line[2], up_line[3], bottom_line[2], bottom_line[3]])
                new_row_x = bottom_line[2]
                # 补了col，要将其他短的row连到col上
                for j in range(len(split_row_list[i])):
                    # 需判断该线在这个区域中
                    # if up_line2[1]-3 <= row[1] <= bottom_line2[1]+3:
                    row = split_row_list[i][j]
                    # 且距离不能相差太大
                    if abs(new_row_x - row[2]) <= box_width:
                        split_row_list[i][j][2] = max([new_row_x, row[2]])

        all_longer_row_lines += split_row_list[i]
        all_longer_col_lines += split_col_list[i]
        # print("all_longer_row_lines", len(all_longer_row_lines), i)
        # print("all_longer_col_lines", len(all_longer_col_lines), i)
        # print("new_row_lines", len(new_row_lines), i)
        # print("new_col_lines", len(new_col_lines), i)

        # 删除表格内部的补线
        # temp_list = []
        # for row in new_row_lines:
        #     if up_line[1]-5 <= row[1] <= bottom_line[1]+5:
        #         continue
        #     temp_list.append(row)
        # print("fix_outline", new_row_lines)
        # new_row_lines = temp_list
        # print("fix_outline", new_row_lines)
        # temp_list = []
        # for col in new_col_lines:
        #     if left_line[0]-5 <= col[0] <= right_line[0]+5:
        #         continue
        #     temp_list.append(col)
        #
        # new_col_lines = temp_list
        # print("fix_outline", new_col_lines)
        # print("fix_outline", new_row_lines)

        # 删除重复包含的补线
        # temp_list = []
        # for row in new_row_lines:
        #     if up_line[1]-5 <= row[1] <= bottom_line[1]+5:
        #         continue
        #     temp_list.append(row)
        # new_row_lines = temp_list

    # 展示上下左右边框线
    # for i in range(len(area_row_line)):
    #     print("row1", area_row_line[i])
    #     print("row2", area_row_line2[i])
    #     print("col1", area_col_line[i])
    #     print("col2", area_col_line2[i])
    #     cv2.line(image, (int(area_row_line[i][0][0]), int(area_row_line[i][0][1])),
    #              (int(area_row_line[i][0][2]), int(area_row_line[i][0][3])), (0, 255, 0), 2)
    #     cv2.line(image, (int(area_row_line2[i][1][0]), int(area_row_line2[i][1][1])),
    #              (int(area_row_line2[i][1][2]), int(area_row_line2[i][1][3])), (0, 0, 255), 2)
    #     cv2.imshow("fix_outline", image)
    #     cv2.waitKey(0)

    # 展示所有线
    # for line in all_longer_col_lines:
    #     cv2.line(image, (int(line[0]), int(line[1])),
    #              (int(line[2]), int(line[3])),
    #              (0, 255, 0), 2)
    #     cv2.imshow("fix_outline", image)
    #     cv2.waitKey(0)
    # for line in all_longer_row_lines:
    #     cv2.line(image, (int(line[0]), int(line[1])),
    #              (int(line[2]), int(line[3])),
    #              (0, 0, 255), 2)
    #     cv2.imshow("fix_outline", image)
    #     cv2.waitKey(0)

    return new_row_lines, new_col_lines, all_longer_row_lines, all_longer_col_lines


def fix_table(row_point_list, col_point_list, split_y, row_lines, col_lines):
    # 分割线纵坐标
    if len(split_y) < 2:
        return []

    # 获取bbox
    bbox = []
    # 每个点获取与其x最相近和y最相近的点
    for i in range(1, len(split_y)):

        # 循环每行
        for row in row_point_list:
            row.sort(key=lambda x: (x[0], x[1]))
            # 行不在该区域跳过
            if row[0][1] <= split_y[i-1] or row[0][1] >= split_y[i]:
                continue

            # print("len(row)", len(row))
            # print("row", row)

            # 循环行中的点
            for j in range(len(row)):
                if j == len(row) - 1:
                    break
                current_point = row[j]
                next_point_in_row_list = row[j+1:]

                # 循环这一行的下一个点
                for next_point_in_row in next_point_in_row_list:
                    # 是否在这一行点找到，找不到就这一行的下个点
                    not_found = 1
                    # 查询下个点所在列
                    next_col = []
                    for col in col_point_list:
                        col.sort(key=lambda x: (x[1], x[0]))
                        # 列不在该区域跳过
                        if col[0][1] <= split_y[i-1] or col[-1][1] >= split_y[i]:
                            continue
                        if col[0][0]-3 <= next_point_in_row[0] <= col[0][0]+3:
                            next_col = col
                            break

                    # 循环匹配当前点和下一列点
                    next_col.sort(key=lambda x: (x[1], x[0]))
                    for point1 in next_col:
                        # 同一行的就跳过
                        if current_point[1]-3 <= point1[1] <= current_point[1]+3:
                            continue
                        if point1[1] <= current_point[1]-3:
                            continue

                        # 候选bbox
                        candidate_bbox = [current_point[0], current_point[1], point1[0], point1[1]]
                        # print("candidate_bbox", candidate_bbox)

                        # 判断该bbox是否存在，判断bbox的上下两条边是否有包含在row中
                        contain_flag1 = 0
                        contain_flag2 = 0
                        for row1 in row_lines:
                            # 行不在该区域跳过
                            if row1[1] <= split_y[i-1] or row1[1] >= split_y[i]:
                                continue
                            # bbox上边框 y一样
                            if not contain_flag1:
                                if row1[1]-3 <= candidate_bbox[1] <= row1[1]+3:
                                    # 格子里的断开线段
                                    row1_break = (max([row1[0], candidate_bbox[0]]),
                                                  row1[1],
                                                  min([row1[2], candidate_bbox[2]]),
                                                  row1[3])

                                    if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                        contain_flag1 = 1

                            # bbox下边框 y一样
                            if not contain_flag2:
                                if row1[1]-3 <= candidate_bbox[3] <= row1[1]+3:
                                    # 格子里的断开线段
                                    row1_break = (max([row1[0], candidate_bbox[0]]),
                                                  row1[1],
                                                  min([row1[2], candidate_bbox[2]]),
                                                  row1[3])
                                    if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                        contain_flag2 = 1

                        # 判断该bbox是否存在，判断bbox的左右两条边是否有包含在col中
                        contain_flag3 = 0
                        contain_flag4 = 0
                        for col1 in col_lines:
                            # 列不在该区域跳过
                            if col1[1] <= split_y[i-1] or col1[3] >= split_y[i]:
                                continue
                            # bbox左边线 x一样
                            if not contain_flag3:
                                if col1[0]-3 <= candidate_bbox[0] <= col1[0]+3:
                                    # 格子里的断开线段
                                    col1_break = (col1[0],
                                                  max([col1[1], candidate_bbox[1]]),
                                                  col1[2],
                                                  min([col1[3], candidate_bbox[3]]))
                                    if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                        contain_flag3 = 1

                            # bbox右边框 x一样
                            if not contain_flag4:
                                if col1[0]-3 <= candidate_bbox[2] <= col1[0]+3:
                                    # 格子里的断开线段
                                    col1_break = (col1[0],
                                                  max([col1[1], candidate_bbox[1]]),
                                                  col1[2],
                                                  min([col1[3], candidate_bbox[3]]))
                                    if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                        contain_flag4 = 1

                        # 找到了该bbox，并且是存在的
                        if contain_flag1 and contain_flag2 and contain_flag3 and contain_flag4:
                            bbox.append([(candidate_bbox[0], candidate_bbox[1]),
                                         (candidate_bbox[2], candidate_bbox[3])])
                            not_found = 0
                            break

                    if not not_found:
                        break
    return bbox


def delete_close_points(point_list, row_point_list, col_point_list, threshold=5):
    new_point_list = []
    delete_point_list = []
    point_list.sort(key=lambda x: (x[1], x[0]))
    for i in range(len(point_list)):
        point1 = point_list[i]
        if point1 in delete_point_list:
            continue
        if i == len(point_list) - 1:
            new_point_list.append(point1)
            break
        point2 = point_list[i+1]

        # 判断坐标
        if abs(point1[0] - point2[0]) > threshold or abs(point1[1] - point2[1]) > threshold:
            new_point_list.append(point1)
        else:
            # 看两个点上的相同坐标点哪个多，就保留哪个
            count1 = 0
            count2 = 0
            for col in col_point_list:
                if point1[0] == col[0][0]:
                    count1 += len(col)
                elif point2[0] == col[0][0]:
                    count2 += len(col)
            if count1 >= count2:
                new_point_list.append(point1)
                delete_point_list.append(point2)
            else:
                new_point_list.append(point2)
                delete_point_list.append(point1)

    point_list = new_point_list
    new_point_list = []
    delete_point_list = []
    point_list.sort(key=lambda x: (x[0], x[1]))
    for i in range(len(point_list)):
        point1 = point_list[i]
        if point1 in delete_point_list:
            continue
        if i == len(point_list) - 1:
            new_point_list.append(point1)
            break
        point2 = point_list[i+1]

        # 判断坐标
        if abs(point1[0] - point2[0]) > threshold or abs(point1[1] - point2[1]) > threshold:
            new_point_list.append(point1)
        else:
            count1 = 0
            count2 = 0
            for row in row_point_list:
                if point1[0] == row[0][0]:
                    count1 += len(row)
                elif point2[0] == row[0][0]:
                    count2 += len(row)
            if count1 >= count2:
                new_point_list.append(point1)
                delete_point_list.append(point2)
            else:
                new_point_list.append(point2)
                delete_point_list.append(point1)

    return new_point_list


def get_bbox2(image_np, points):
    # # 坐标点按行分
    # row_point_list = []
    # row_point = []
    # points.sort(key=lambda x: (x[0], x[1]))
    # for p in points:
    #     if len(row_point) == 0:
    #         x = p[0]
    #     if x-5 <= p[0] <= x+5:
    #         row_point.append(p)
    #     else:
    #         row_point_list.append(row_point)
    #         row_point = []
    # # 坐标点按列分
    # col_point_list = []
    # col_point = []
    # points.sort(key=lambda x: (x[1], x[0]))
    # for p in points:
    #     if len(col_point) == 0:
    #         y = p[1]
    #     if y-5 <= p[1] <= y+5:
    #         col_point.append(p)
    #     else:
    #         col_point_list.append(col_point)
    #         col_point = []
    row_point_list = get_points_row(points)
    col_point_list = get_points_col(points)
    print("len(points)", len(points))
    for point in points:
        cv2.circle(image_np, point, 1, (0, 255, 0), 1)
    cv2.imshow("points_deleted", image_np)
    points = delete_close_points(points, row_point_list, col_point_list)
    print("len(points)", len(points))
    for point in points:
        cv2.circle(image_np, point, 1, (255, 0, 0), 3)
    cv2.imshow("points_deleted", image_np)
    cv2.waitKey(0)

    row_point_list = get_points_row(points, 5)
    col_point_list = get_points_col(points, 5)

    print("len(row_point_list)", len(row_point_list))
    for row in row_point_list:
        print("row", len(row))

    print("col_point_list", len(col_point_list))
    for col in col_point_list:
        print("col", len(col))

    bbox = []
    for i in range(len(row_point_list)):
        if i == len(row_point_list) - 1:
            break
        # 遍历每个row的point，找到其所在列的下一个点和所在行的下一个点
        current_row = row_point_list[i]
        for j in range(len(current_row)):
            current_point = current_row[j]
            if j == len(current_row) - 1:
                break
            next_row_point = current_row[j+1]
            # 找出当前点所在的col，得到该列下一个point
            current_col = col_point_list[j]
            for k in range(len(current_col)):
                if current_col[k][1] > current_point[1] + 10:
                    next_col_point = current_col[k]
                    break
            next_row = row_point_list[k]
            for k in range(len(next_row)):
                if next_row[k][0] >= next_row_point[0] + 5:
                    next_point = next_row[k]
                    break
            # 得到bbox
            bbox.append([(current_point[0], current_point[1]), (next_point[0], next_point[1])])



    # bbox = []
    # for p in points:
    #     # print("p", p)
    #     p_row = []
    #     p_col = []
    #     for row in row_point_list:
    #         if p[0] == row[0][0]:
    #             for p1 in row:
    #                 if abs(p[1]-p1[1]) <= 5:
    #                     continue
    #                 p_row.append([p1, abs(p[1]-p1[1])])
    #             p_row.sort(key=lambda x: x[1])
    #     for col in col_point_list:
    #         if p[1] == col[0][1]:
    #             for p2 in col:
    #                 if abs(p[0]-p2[0]) <= 5:
    #                     continue
    #                 p_col.append([p2, abs(p[0]-p2[0])])
    #             p_col.sort(key=lambda x: x[1])
    #     if len(p_row) == 0 or len(p_col) == 0:
    #         continue
    #     break_flag = 0
    #     for i in range(len(p_row)):
    #         for j in range(len(p_col)):
    #             # print(p_row[i][0])
    #             # print(p_col[j][0])
    #             another_point = (p_col[j][0][0], p_row[i][0][1])
    #             # print("another_point", another_point)
    #             if abs(p[0]-another_point[0]) <= 5 or abs(p[1]-another_point[1]) <= 5:
    #                 continue
    #             if p[0] >= another_point[0] or p[1] >= another_point[1]:
    #                 continue
    #             if another_point in points:
    #                 box = [p, another_point]
    #                 box.sort(key=lambda x: x[0])
    #                 if box not in bbox:
    #                     bbox.append(box)
    #                     break_flag = 1
    #                     break
    #         if break_flag:
    #             break
    #
    # # delete duplicate
    # delete_bbox = []
    # for i in range(len(bbox)):
    #     for j in range(i+1, len(bbox)):
    #         if bbox[i][0] == bbox[j][0]:
    #             if bbox[i][1][0] - bbox[j][1][0] <= 3 \
    #                     and bbox[i][1][1] - bbox[j][1][1] <= 3:
    #                 delete_bbox.append(bbox[j])
    #         if bbox[i][1] == bbox[j][1]:
    #             if bbox[i][0][0] - bbox[j][0][0] <= 3 \
    #                     and bbox[i][0][1] - bbox[j][0][1] <= 3:
    #                 delete_bbox.append(bbox[j])
    # # delete too small area
    # # for box in bbox:
    # #     if box[1][0] - box[0][0] <=
    # for d_box in delete_bbox:
    #     if d_box in bbox:
    #         bbox.remove(d_box)
    # print bbox
    bbox.sort(key=lambda x: (x[0][0], x[0][1], x[1][0], x[1][1]))
    # origin bbox
    # origin_bbox = []
    # for box in bbox:
    #     origin_bbox.append([(box[0][0], box[0][1] - 40), (box[1][0], box[1][1] - 40)])
    # for box in origin_bbox:
    #     cv2.rectangle(origin_image, box[0], box[1], (0, 0, 255), 2, 8)
    #     cv2.imshow('AlanWang', origin_image)
    #     cv2.waitKey(0)
    for box in bbox:
        cv2.rectangle(image_np, box[0], box[1], (0, 0, 255), 2, 8)
        cv2.imshow('bboxes', image_np)
        cv2.waitKey(0)
    # for point in points:
    #     print(point)
    #     cv2.circle(image_np, point, 1, (0, 0, 255), 3)
    #     cv2.imshow('points', image_np)
    #     cv2.waitKey(0)
    return bbox


def get_bbox1(image_np, points, split_y):
    # 分割线纵坐标
    # print("split_y", split_y)
    if len(split_y) < 2:
        return []

    # 计算行列，剔除相近交点
    row_point_list = get_points_row(points)
    col_point_list = get_points_col(points)
    print("len(row_point_list)", row_point_list)
    print("len(col_point_list)", len(col_point_list))
    # for point in points:
    #     cv2.circle(image_np, point, 1, (0, 255, 0), 1)
    # cv2.imshow("points", image_np)
    points = delete_close_points(points, row_point_list, col_point_list)
    # print("len(points)", len(points))
    # for point in points:
    #     cv2.circle(image_np, point, 1, (255, 0, 0), 3)
    # cv2.imshow("points_deleted", image_np)
    # cv2.waitKey(0)

    # 获取bbox
    bbox = []
    # 每个点获取与其x最相近和y最相近的点
    for i in range(1, len(split_y)):
        for point1 in points:
            if point1[1] <= split_y[i-1] or point1[1] >= split_y[i]:
                continue
            distance_x = 10000
            distance_y = 10000
            x = 0
            y = 0
            threshold = 10
            for point2 in points:
                if point2[1] <= split_y[i-1] or point2[1] >= split_y[i]:
                    continue
                # 最近 x y
                if 2 < point2[0] - point1[0] < distance_x and point2[1] - point1[1] <= threshold:
                    distance_x = point2[0] - point1[0]
                    x = point2[0]

                if 2 < point2[1] - point1[1] < distance_y and point2[0] - point1[0] <= threshold:
                    distance_y = point2[1] - point1[1]
                    y = point2[1]

            if not x or not y:
                continue

            bbox.append([(point1[0], point1[1]), (x, y)])

    # 删除包含关系bbox
    temp_list = []
    for i in range(len(bbox)):
        box1 = bbox[i]
        for j in range(len(bbox)):
            if i == j:
                continue
            box2 = bbox[j]
            contain_flag = 0
            if box2[0][0] <= box1[0][0] <= box1[1][0] <= box2[1][0] and \
                    box2[0][1] <= box1[0][1] <= box1[1][1] <= box2[1][1]:
                contain_flag = 1
                break
        temp_list.append(box1)
    bbox = temp_list

    # 展示
    for box in bbox:
        # print(box[0], box[1])
        # if abs(box[0][1] - box[1][1]) > abs(box[0][0] - box[1][0]):
        #     continue
        cv2.rectangle(image_np, box[0], box[1], (0, 0, 255), 2, 8)
        cv2.imshow('bboxes', image_np)
        cv2.waitKey(0)

    return bbox


def get_bbox0(image_np, row_point_list, col_point_list, split_y, row_lines, col_lines):
    # 分割线纵坐标
    if len(split_y) < 2:
        return []

    # 计算行列，剔除相近交点
    # row_point_list = get_points_row(points)
    # col_point_list = get_points_col(points)
    # points = delete_close_points(points, row_point_list, col_point_list)
    # row_point_list = get_points_row(points)
    # col_point_list = get_points_col(points)

    # 获取bbox
    bbox = []

    # print("get_bbox split_y", split_y)
    # 每个点获取与其x最相近和y最相近的点
    for i in range(1, len(split_y)):

        # 循环每行
        for row in row_point_list:
            row.sort(key=lambda x: (x[0], x[1]))
            # 行不在该区域跳过
            if row[0][1] <= split_y[i-1] or row[0][1] >= split_y[i]:
                continue

            # 循环行中的点
            for j in range(len(row)):
                if j == len(row) - 1:
                    break
                current_point = row[j]
                next_point_in_row = row[j+1]
                # 查询下个点所在列
                next_col = []
                for col in col_point_list:
                    col.sort(key=lambda x: (x[1], x[0]))
                    # 列不在该区域跳过
                    if col[0][1] <= split_y[i-1] or col[-1][1] >= split_y[i]:
                        continue
                    if col[0][0]-3 <= next_point_in_row[0] <= col[0][0]+3:
                        next_col = col
                        break

                # 循环匹配当前点和下一列点
                for point1 in next_col:
                    # 同一行的就跳过
                    if current_point[1]-3 <= point1[1] <= current_point[1]+3:
                        continue
                    if point1[1] <= current_point[1]-3:
                        continue

                    # 候选bbox
                    candidate_bbox = [current_point[0], current_point[1], point1[0], point1[1]]

                    # 判断该bbox是否存在，线条包含关系
                    contain_flag1 = 0
                    contain_flag2 = 0
                    for row1 in row_lines:
                        # 行不在该区域跳过
                        if row1[1] <= split_y[i-1] or row1[1] >= split_y[i]:
                            continue
                        # bbox上边框 y一样
                        if not contain_flag1:
                            if row1[1]-3 <= candidate_bbox[1] <= row1[1]+3:
                                # candidate的x1,x2需被包含在row线中
                                if row1[0]-3 <= candidate_bbox[0] <= candidate_bbox[2] <= row1[2]+3:
                                    contain_flag1 = 1
                        # bbox下边框 y一样
                        if not contain_flag2:
                            if row1[1]-3 <= candidate_bbox[3] <= row1[1]+3:
                                # candidate的x1,x2需被包含在row线中
                                if row1[0]-3 <= candidate_bbox[0] <= candidate_bbox[2] <= row1[2]+3:
                                    contain_flag2 = 1

                    # 找到了该bbox，并且是存在的
                    if contain_flag1 and contain_flag2:
                        bbox.append([(candidate_bbox[0], candidate_bbox[1]),
                                     (candidate_bbox[2], candidate_bbox[3])])
                        break
    return bbox


def get_bbox3(image_np, row_point_list, col_point_list, split_y, row_lines, col_lines):
    # 分割线纵坐标
    if len(split_y) < 2:
        return []

    # 获取bbox
    bbox = []
    # 每个点获取与其x最相近和y最相近的点
    for i in range(1, len(split_y)):

        # 循环每行
        for row in row_point_list:
            row.sort(key=lambda x: (x[0], x[1]))
            # 行不在该区域跳过
            if row[0][1] <= split_y[i-1] or row[0][1] >= split_y[i]:
                continue

            # print("len(row)", len(row))
            # print("row", row)

            # 循环行中的点
            for j in range(len(row)):
                if j == len(row) - 1:
                    break
                current_point = row[j]
                # print("current_point", current_point)
                next_point_in_row_list = row[j+1:]

                # 循环这一行的下一个点
                for next_point_in_row in next_point_in_row_list:
                    # 是否在这一行点找到，找不到就这一行的下个点
                    not_found = 1
                    # 查询下个点所在列
                    next_col = []
                    for col in col_point_list:
                        col.sort(key=lambda x: (x[1], x[0]))
                        # 列不在该区域跳过
                        if col[0][1] <= split_y[i-1] or col[-1][1] >= split_y[i]:
                            continue
                        if col[0][0]-3 <= next_point_in_row[0] <= col[0][0]+3:
                            next_col = col
                            break

                    # 循环匹配当前点和下一列点
                    next_col.sort(key=lambda x: (x[1], x[0]))
                    for point1 in next_col:
                        # 同一行的就跳过
                        if current_point[1]-3 <= point1[1] <= current_point[1]+3:
                            continue
                        if point1[1] <= current_point[1]-3:
                            continue

                        # 候选bbox
                        candidate_bbox = [current_point[0], current_point[1], point1[0], point1[1]]
                        # print("candidate_bbox", candidate_bbox)

                        # 判断该bbox是否存在，判断bbox的上下两条边是否有包含在row中
                        contain_flag1 = 0
                        contain_flag2 = 0
                        for row1 in row_lines:
                            # 行不在该区域跳过
                            if row1[1] <= split_y[i-1] or row1[1] >= split_y[i]:
                                continue
                            # bbox上边框 y一样
                            if not contain_flag1:
                                if row1[1]-3 <= candidate_bbox[1] <= row1[1]+3:
                                    # 格子里的断开线段
                                    row1_break = (max([row1[0], candidate_bbox[0]]),
                                                  row1[1],
                                                  min([row1[2], candidate_bbox[2]]),
                                                  row1[3])

                                    if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                        contain_flag1 = 1

                                    # # candidate的x1,x2需被包含在row线中
                                    # if row1[0]-3 <= candidate_bbox[0] <= candidate_bbox[2] <= row1[2]+3:
                                    #     contain_flag1 = 1
                                    #
                                    # # 判断线条有无端点在格子中
                                    # elif candidate_bbox[0] < row1[0] < candidate_bbox[2] \
                                    #         or candidate_bbox[0] < row1[2] < candidate_bbox[2]:
                                    #     # 线条会有缺一点情况，判断长度超过格子一半
                                    #     if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                    #         contain_flag1 = 1

                            # bbox下边框 y一样
                            if not contain_flag2:
                                if row1[1]-3 <= candidate_bbox[3] <= row1[1]+3:
                                    # 格子里的断开线段
                                    row1_break = (max([row1[0], candidate_bbox[0]]),
                                                  row1[1],
                                                  min([row1[2], candidate_bbox[2]]),
                                                  row1[3])
                                    if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                        contain_flag2 = 1

                                    # # candidate的x1,x2需被包含在row线中
                                    # if row1[0]-3 <= candidate_bbox[0] <= candidate_bbox[2] <= row1[2]+3:
                                    #     contain_flag2 = 1
                                    #
                                    # # 判断线条有无端点在格子中
                                    # elif candidate_bbox[0] < row1[0] < candidate_bbox[2] \
                                    #         or candidate_bbox[0] < row1[2] < candidate_bbox[2]:
                                    #     # 线条会有缺一点情况，判断长度超过格子一半
                                    #     if row1_break[2] - row1_break[0] >= (candidate_bbox[2] - candidate_bbox[0])/3:
                                    #         contain_flag2 = 1

                        # 判断该bbox是否存在，判断bbox的左右两条边是否有包含在col中
                        contain_flag3 = 0
                        contain_flag4 = 0
                        for col1 in col_lines:
                            # 列不在该区域跳过
                            if col1[1] <= split_y[i-1] or col1[3] >= split_y[i]:
                                continue
                            # bbox左边线 x一样
                            if not contain_flag3:
                                if col1[0]-3 <= candidate_bbox[0] <= col1[0]+3:
                                    # 格子里的断开线段
                                    col1_break = (col1[0],
                                                  max([col1[1], candidate_bbox[1]]),
                                                  col1[2],
                                                  min([col1[3], candidate_bbox[3]]))
                                    if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                        contain_flag3 = 1
                                    # # candidate的y1,y2需被包含在col线中
                                    # if col1[1]-3 <= candidate_bbox[1] <= candidate_bbox[3] <= col1[3]+3:
                                    #     contain_flag3 = 1
                                    #
                                    # # 判断线条有无端点在格子中
                                    # elif candidate_bbox[1] < col1[1] < candidate_bbox[3] \
                                    #         or candidate_bbox[1] < col1[3] < candidate_bbox[3]:
                                    #     # 线条会有缺一点情况，判断长度超过格子一半
                                    #     if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                    #         contain_flag3 = 1
                            # bbox右边框 x一样
                            if not contain_flag4:
                                if col1[0]-3 <= candidate_bbox[2] <= col1[0]+3:
                                    # 格子里的断开线段
                                    # col1_break = (col1[0],
                                    #               max([col1[1], candidate_bbox[1]]),
                                    #               col1[2],
                                    #               min([col1[3], candidate_bbox[3]]))
                                    # if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                    #     contain_flag4 = 1

                                    # 如果候选bbox的边的上1/3或下1/3包含在col中
                                    candidate_bbox_line1 = [candidate_bbox[1],
                                                           candidate_bbox[1] + (candidate_bbox[3]-candidate_bbox[1])/3]
                                    candidate_bbox_line2 = [candidate_bbox[3] - (candidate_bbox[3]-candidate_bbox[1])/3,
                                                            candidate_bbox[3]]

                                    if col1[1] <= candidate_bbox_line1[0] <= candidate_bbox_line1[1] <= col1[3] \
                                            or col1[1] <= candidate_bbox_line2[0] <= candidate_bbox_line2[1] <= col1[3]:
                                        # print("candidate_bbox", candidate_bbox)
                                        # print("col1", col1)
                                        contain_flag4 = 1

                                    # # candidate的y1,y2需被包含在col线中
                                    # if col1[1]-3 <= candidate_bbox[1] <= candidate_bbox[3] <= col1[3]+3:
                                    #     contain_flag4 = 1
                                    #
                                    # # 判断线条有无端点在格子中
                                    # elif candidate_bbox[1] < col1[1] < candidate_bbox[3] \
                                    #         or candidate_bbox[1] < col1[3] < candidate_bbox[3]:
                                    #     # 线条会有缺一点情况，判断长度超过格子一半
                                    #     if col1_break[3] - col1_break[1] >= (candidate_bbox[3] - candidate_bbox[1])/3:
                                    #         contain_flag4 = 1
                        # 找到了该bbox，并且是存在的
                        if contain_flag1 and contain_flag2 and contain_flag3 and contain_flag4:
                            bbox.append([(candidate_bbox[0], candidate_bbox[1]),
                                         (candidate_bbox[2], candidate_bbox[3])])
                            not_found = 0
                            # print("exist candidate_bbox", candidate_bbox)
                            # print(contain_flag1, contain_flag2, contain_flag3, contain_flag4)
                            break
                        # else:
                        #     print("candidate_bbox", candidate_bbox)
                        #     print(contain_flag1, contain_flag2, contain_flag3, contain_flag4)

                    if not not_found:
                        break
    return bbox


def get_bbox(image_np, row_point_list, col_point_list, split_y, row_lines, col_lines):
    # 分割线纵坐标
    if len(split_y) < 2:
        return []

    # 获取bbox
    bbox_list = []
    for i in range(1, len(split_y)):
        last_y = split_y[i-1]
        y = split_y[i]

        # 先对点线进行分区
        split_row_point_list = []
        split_col_point_list = []
        split_row_lines = []
        split_col_lines = []
        for row in row_point_list:
            if last_y <= row[0][1] <= y:
                row.sort(key=lambda x: (x[1], x[0]))
                split_row_point_list.append(row)
        for col in col_point_list:
            if last_y <= col[0][1] <= y:
                split_col_point_list.append(col)
        for row in row_lines:
            if last_y <= row[1] <= y:
                split_row_lines.append(row)
        for col in col_lines:
            if last_y <= col[1] <= y:
                split_col_lines.append(col)

        # 每个点获取其对角线点，以便形成bbox，按行循环
        for i in range(len(split_row_point_list)-1):
            row = split_row_point_list[i]
            # 循环该行的点
            for k in range(len(row)-1):
                point1 = row[k]
                next_point1 = row[k+1]
                # print("*"*30)
                # print("point1", point1)

                # 有三种对角线点
                # 1. 该点下一行的下一列的点
                # 2. 该点下一列的下一行的点
                # 3. 上述两个点是同一个点

                # 下一行没找到就循环后面的行
                if_find = 0
                for j in range(i+1, len(split_row_point_list)):
                    if if_find:
                        break

                    next_row = split_row_point_list[j]
                    # print("next_row", next_row)
                    # 循环下一行的点
                    for point2 in next_row:
                        if abs(point1[0] - point2[0]) <= 2:
                            continue
                        if point2[0] < point1[0]:
                            continue
                        bbox = [point1[0], point1[1], point2[0], point2[1]]

                        if abs(bbox[0] - bbox[2]) <= 10:
                            continue
                        if abs(bbox[1] - bbox[3]) <= 10:
                            continue
                        # bbox的四条边都需要验证是否在line上
                        if check_bbox(bbox, split_row_lines, split_col_lines):
                            bbox_list.append([(bbox[0], bbox[1]), (bbox[2], bbox[3])])
                            if_find = 1
                            # print("check bbox", bbox)
                            break

    return bbox_list


def check_bbox(bbox, rows, cols, threshold=5):
    def check(check_line, lines, limit_axis, axis):
        # 需检查的线的1/2段,1/3段,2/3段,1/4段,3/4段
        line_1_2 = [check_line[0], (check_line[0]+check_line[1])/2]
        line_2_2 = [(check_line[0]+check_line[1])/2, check_line[1]]
        line_1_3 = [check_line[0], check_line[0]+(check_line[1]-check_line[0])/3]
        line_2_3 = [check_line[1]-(check_line[1]-check_line[0])/3, check_line[1]]
        line_1_4 = [check_line[0], check_line[0]+(check_line[1]-check_line[0])/4]
        line_3_4 = [check_line[1]-(check_line[1]-check_line[0])/4, check_line[1]]

        # 限制row相同y,col相同x
        if_line = 0
        for line1 in lines:
            if not if_line and abs(line1[1-axis] - limit_axis) <= threshold:
                # check_line完全包含在line中
                if line1[axis] <= check_line[0] <= check_line[1] <= line1[axis+2]:
                    if_line = 1

                # check_line的1/2包含在line
                elif line1[axis] <= line_1_2[0] <= line_1_2[1] <= line1[axis+2] \
                        or line1[axis] <= line_2_2[0] <= line_2_2[1] <= line1[axis+2]:
                    if_line = 1

                # check_line两个1/3段被包含在不同line中
                elif line1[axis] <= line_1_3[0] <= line_1_3[1] <= line1[axis+2]:
                    # check_line另一边的1/4被包含
                    for line2 in lines:
                        if abs(line1[1-axis] - limit_axis) <= threshold:
                            if line2[axis] <= line_3_4[0] <= line_3_4[1] <= line2[axis+2]:
                                if_line = 1
                                break
                elif line1[axis] <= line_2_3[0] <= line_2_3[1] <= line1[axis+2]:
                    # check_line另一边的1/4被包含
                    for line2 in lines:
                        if abs(line1[1-axis] - limit_axis) <= threshold:
                            if line2[axis] <= line_1_4[0] <= line_1_4[1] <= line2[axis+2]:
                                if_line = 1
                                break
        return if_line

    up_down_line = [bbox[0], bbox[2]]
    up_y, down_y = bbox[1], bbox[3]
    left_right_line = [bbox[1], bbox[3]]
    left_x, right_x = bbox[0], bbox[2]

    # 检查bbox四条边是否存在
    if_up = check(up_down_line, rows, up_y, 0)
    if_down = check(up_down_line, rows, down_y, 0)
    if_left = check(left_right_line, cols, left_x, 1)
    if_right = check(left_right_line, cols, right_x, 1)

    # 检查bbox内部除了四条边，是否有其它line在bbox内部
    if_col = 0
    if_row = 0
    if if_up and if_down and if_left and if_right:
        for col in cols:
            if not if_col and left_x+threshold <= col[0] <= right_x-threshold:
                if col[1] <= left_right_line[0] <= left_right_line[1] <= col[3]:
                    if_col = 1
                elif left_right_line[0] <= col[1] <= left_right_line[1]:
                    if left_right_line[1] - col[1] >= (left_right_line[1] + left_right_line[0])/2:
                        if_col = 1
                elif left_right_line[0] <= col[3] <= left_right_line[1]:
                    if col[3] - left_right_line[0] >= (left_right_line[1] + left_right_line[0])/2:
                        if_col = 1

        for row in rows:
            if not if_row and up_y+threshold <= row[1] <= down_y-threshold:
                if row[0] <= up_down_line[0] <= up_down_line[1] <= row[2]:
                    if_row = 1
                elif up_down_line[0] <= row[0] <= up_down_line[1]:
                    if up_down_line[1] - row[0] >= (up_down_line[1] + up_down_line[0])/2:
                        if_row = 1
                elif up_down_line[0] <= row[2] <= up_down_line[1]:
                    if row[2] - up_down_line[0] >= (up_down_line[1] + up_down_line[0])/2:
                        if_row = 1

    if if_up and if_down and if_left and if_right and not if_col and not if_row:
        return True
    else:
        return False


def add_continue_bbox(bboxes):
    add_bbox_list = []
    bboxes.sort(key=lambda x: (x[0][0], x[0][1]))
    last_bbox = bboxes[0]
    # 先对bbox分区
    for i in range(1, len(split_y)):
        y = split_y[i]
        last_y = split_y[i-1]

        split_bbox = []
        for bbox in bboxes:
            if last_y <= bbox[1][1] <= y:
                split_bbox.append(bbox)
        split_bbox.sort

        for i in range(1, len(bboxes)):
            bbox = bboxes[i]
            if last_y <= bbox[1][1] <= y and last_y <= last_bbox[1][1] <= y:
                if abs(last_bbox[1][1] - bbox[0][1]) <= 2:
                    last_bbox = bbox
                else:
                    if last_bbox[1][1] > bbox[0][1]:
                        last_bbox = bbox
                    else:
                        add_bbox = [(last_bbox[0][0], last_bbox[1][1]),
                                    (last_bbox[1][0], bbox[0][1])]
                        add_bbox_list.append(add_bbox)
        last_y = y

    print("add_bbox_list", add_bbox_list)
    if add_bbox_list:
        bboxes = [str(x) for x in bboxes + add_bbox_list]
        bboxes = list(set(bboxes))
        bboxes = [eval(x) for x in bboxes]
        bboxes.sort(key=lambda x: (x[0][1], x[0][0]))

    return bboxes


def points_to_line(points_lines, axis):
    new_line_list = []
    for line in points_lines:
        average = 0
        _min = _min = line[0][axis]
        _max = line[-1][axis]
        for point in line:
            average += point[1-axis]
            if point[axis] < _min:
                _min = point[axis]
            if point[axis] > _max:
                _max = point[axis]
        average = int(average / len(line))

        if axis:
            new_line = [average, _min, average, _max]
        else:
            new_line = [_min, average, _max, average]
        new_line_list.append(new_line)
    return new_line_list


def get_bbox_by_contours(image_np):
    img_gray = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
    ret, img_bin = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)

    # 3.连通域分析
    img_bin, contours, hierarchy = cv2.findContours(img_bin,
                                                    cv2.RETR_LIST,
                                                    cv2.CHAIN_APPROX_SIMPLE)

    # 4.获取最小外接圆 圆心 半径
    center, radius = cv2.minEnclosingTriangle(contours[0])
    center = np.int0(center)

    # 5.绘制最小外接圆
    img_result = image_np.copy()
    cv2.circle(img_result, tuple(center), int(radius), (255, 255, 255), 2)


    # # 读入图片
    # img = image_np
    # cv2.imshow("get_bbox_by_contours ", image_np)
    # # 中值滤波，去噪
    # img = cv2.medianBlur(img, 3)
    # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # cv2.namedWindow('original', cv2.WINDOW_AUTOSIZE)
    # cv2.imshow('original', gray)
    #
    # # 阈值分割得到二值化图片
    # ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    #
    # # 膨胀操作
    # kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    # bin_clo = cv2.dilate(binary, kernel2, iterations=2)
    #
    # # 连通域分析
    # num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(bin_clo, connectivity=8)
    #
    # # 查看各个返回值
    # # 连通域数量
    # print('num_labels = ',num_labels)
    # # 连通域的信息：对应各个轮廓的x、y、width、height和面积
    # print('stats = ',stats)
    # # 连通域的中心点
    # print('centroids = ',centroids)
    # # 每一个像素的标签1、2、3.。。，同一个连通域的标签是一致的
    # print('labels = ',labels)
    #
    # # 不同的连通域赋予不同的颜色
    # output = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
    # for i in range(1, num_labels):
    #
    #     mask = labels == i
    #     output[:, :, 0][mask] = np.random.randint(0, 255)
    #     output[:, :, 1][mask] = np.random.randint(0, 255)
    #     output[:, :, 2][mask] = np.random.randint(0, 255)
    # cv2.imshow('oginal', output)
    # cv2.waitKey()
    # cv2.destroyAllWindows()


def get_points_col(points, split_y, threshold=5):
    # 坐标点按行分
    row_point_list = []
    row_point = []
    points.sort(key=lambda x: (x[0], x[1]))
    # print("get_points_col points sort", points)
    x = points[0][0]
    for i in range(1, len(split_y)):
        for p in points:
            if p[1] <= split_y[i-1] or p[1] >= split_y[i]:
                continue
            if x-threshold <= p[0] <= x+threshold:
                row_point.append(p)
            else:
                # print("row_point", row_point)
                row_point.sort(key=lambda x: (x[1], x[0]))
                if row_point:
                    row_point_list.append(row_point)
                row_point = []
                x = p[0]
                row_point.append(p)
        if row_point:
            row_point_list.append(row_point)

    return row_point_list


def get_points_row(points, split_y, threshold=5):
    # 坐标点按列分
    col_point_list = []
    col_point = []
    points.sort(key=lambda x: (x[1], x[0]))
    y = points[0][1]
    for i in range(len(split_y)):
        for p in points:
            if p[1] <= split_y[i-1] or p[1] >= split_y[i]:
                continue
            if y-threshold <= p[1] <= y+threshold:
                col_point.append(p)
            else:
                col_point.sort(key=lambda x: (x[0], x[1]))
                if col_point:
                    col_point_list.append(col_point)
                col_point = []
                y = p[1]
                col_point.append(p)
        if col_point:
            col_point_list.append(col_point)
    return col_point_list


def get_outline_point(points, split_y):
    # 分割线纵坐标
    # print("get_outline_point split_y", split_y)
    if len(split_y) < 2:
        return []

    outline_2point = []
    points.sort(key=lambda x: (x[1], x[0]))
    for i in range(1, len(split_y)):
        area_points = []
        for point in points:
            if point[1] <= split_y[i-1] or point[1] >= split_y[i]:
                continue
            area_points.append(point)

        if area_points:
            area_points.sort(key=lambda x: (x[1], x[0]))
            outline_2point.append([area_points[0], area_points[-1]])

    return outline_2point


# def merge_row(row_lines):
#     for row in row_lines:
#         for row1 in row_lines:

def get_best_predict_size(image_np):
    sizes = [1280, 1152, 1024, 896, 768, 640, 512, 384, 256, 128]

    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 choose_longer_row(lines):
    new_row = []
    jump_row = []
    for i in range(len(lines)):
        row1 = lines[i]
        jump_flag = 0
        if row1 in jump_row:
            continue
        for j in range(i+1, len(lines)):
            row2 = lines[j]
            if row2 in jump_row:
                continue
            if row2[1]-5 <= row1[1] <= row2[1]+5:
                if row1[0] <= row2[0] and row1[2] >= row2[2]:
                    new_row.append(row1)
                    jump_row.append(row1)
                    jump_row.append(row2)
                    jump_flag = 1
                    break
                elif row2[0] <= row1[0] and row2[2] >= row1[2]:
                    new_row.append(row2)
                    jump_row.append(row1)
                    jump_row.append(row2)
                    jump_flag = 1
                    break
        if not jump_flag:
            new_row.append(row1)
            jump_row.append(row1)
    return new_row


def choose_longer_col(lines):
    new_col = []
    jump_col = []
    for i in range(len(lines)):
        col1 = lines[i]
        jump_flag = 0
        if col1 in jump_col:
            continue
        for j in range(i+1, len(lines)):
            col2 = lines[j]
            if col2 in jump_col:
                continue
            if col2[0]-5 <= col1[0] <= col2[0]+5:
                if col1[1] <= col2[1] and col1[3] >= col2[3]:
                    new_col.append(col1)
                    jump_col.append(col1)
                    jump_col.append(col2)
                    jump_flag = 1
                    break
                elif col2[1] <= col1[1] and col2[3] >= col1[3]:
                    new_col.append(col2)
                    jump_col.append(col1)
                    jump_col.append(col2)
                    jump_flag = 1
                    break
        if not jump_flag:
            new_col.append(col1)
            jump_col.append(col1)
    return new_col


def delete_contain_bbox(bboxes):
    # bbox互相包含，取小的bbox
    delete_bbox = []
    for i in range(len(bboxes)):
        for j in range(i+1, len(bboxes)):
            bbox1 = bboxes[i]
            bbox2 = bboxes[j]

            # 横坐标相等情况
            if bbox1[0][0] == bbox2[0][0] and bbox1[1][0] == bbox2[1][0]:
                if bbox1[0][1] <= bbox2[0][1] <= bbox2[1][1] <= bbox1[1][1]:
                    # print("1", bbox1, bbox2)
                    delete_bbox.append(bbox1)
                elif bbox2[0][1] <= bbox1[0][1] <= bbox1[1][1] <= bbox2[1][1]:
                    # print("2", bbox1, bbox2)
                    delete_bbox.append(bbox2)

            # 纵坐标相等情况
            elif bbox1[0][1] == bbox2[0][1] and bbox1[1][1] == bbox2[1][1]:
                if bbox1[0][0] <= bbox2[0][0] <= bbox2[1][0] <= bbox1[1][0]:
                    print("3", bbox1, bbox2)
                    delete_bbox.append(bbox1)
                elif bbox2[0][0] <= bbox1[0][0] <= bbox1[1][0] <= bbox2[1][0]:
                    print("4", bbox1, bbox2)
                    delete_bbox.append(bbox2)

    print("delete_contain_bbox len(bboxes)", len(bboxes))
    print("delete_contain_bbox len(delete_bbox)", len(delete_bbox))
    for bbox in delete_bbox:
        if bbox in bboxes:
            bboxes.remove(bbox)
    print("delete_contain_bbox len(bboxes)", len(bboxes))
    return bboxes


if __name__ == '__main__':
    # p = "开标记录表3_page_0.png"
    # p = "train_data/label_1.jpg"
    # p = "test_files/train_463.jpg"
    p = "test_files/8.png"
    # p = "test_files/无边框3.jpg"
    # p = "test_files/part1.png"
    # p = "D:\\Project\\format_conversion\\appendix_test\\temp\\00e959a0bc9011ebaf5a00163e0ae709" + \
    #     "\\00e95f7cbc9011ebaf5a00163e0ae709_pdf_page0.png"
    # p = "D:\\Project\\format_conversion\\appendix_test\\temp\\00fb3e52bc7e11eb836000163e0ae709" + \
    #     "\\00fb43acbc7e11eb836000163e0ae709.png"
    # p = "test_files/table.jpg"
    # p = "data_process/create_data/0.jpg"
    # p = "../format_conversion/temp/f1fe9c4ac8e511eb81d700163e0857b6/f1fea1e0c8e511eb81d700163e0857b6.png"
    # p = "../format_conversion/1.png"
    img = cv2.imread(p)
    t = time.time()
    model.load_weights("")
    best_h, best_w = get_best_predict_size(img)
    print(img.shape)
    print((best_h, best_w))
    # row_boxes, col_boxes = table_line(img[..., ::-1], model, size=(512, 1024), hprob=0.5, vprob=0.5)
    # row_boxes, col_boxes, img = table_line(img[..., ::-1], model, size=(best_w, best_h), hprob=0.5, vprob=0.5)

    row_boxes, col_boxes, img = table_line(img, model, size=(best_w, best_h), hprob=0.5, vprob=0.5)
    print("len(row_boxes)", len(row_boxes))
    print("len(col_boxes)", col_boxes)

    # 创建空图
    test_img = np.zeros((img.shape), np.uint8)
    test_img.fill(255)
    for box in row_boxes+col_boxes:
        cv2.line(test_img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 0), 1)
    cv2.imshow("test_image", test_img)
    cv2.waitKey(0)
    cv2.imwrite("temp.jpg", test_img)

    # 计算交点、分割线
    crossover_points = get_points(row_boxes, col_boxes, (img.shape[0], img.shape[1]))
    print("len(col_boxes)", len(col_boxes))
    split_lines, split_y = get_split_line(crossover_points, col_boxes, img)
    print("split_y", split_y)

    # for point in crossover_points:
    #     cv2.circle(test_img, point, 1, (0, 255, 0), 3)
    # cv2.imshow("point image1", test_img)
    # cv2.waitKey(0)

    # 计算行列，剔除相近交点
    row_point_list = get_points_row(crossover_points, split_y, 0)
    col_point_list = get_points_col(crossover_points, split_y, 0)
    crossover_points = delete_close_points(crossover_points, row_point_list, col_point_list)
    row_point_list = get_points_row(crossover_points, split_y)
    col_point_list = get_points_col(crossover_points, split_y)

    for point in crossover_points:
        cv2.circle(test_img, point, 1, (0, 0, 255), 3)
    cv2.imshow("point image1", test_img)
    cv2.waitKey(0)

    print("len(row_boxes)", len(row_boxes))
    print("len(col_boxes)", len(col_boxes))

    # 修复边框
    new_row_boxes, new_col_boxes, long_row_boxes, long_col_boxes = \
        fix_outline(img, row_boxes, col_boxes, crossover_points, split_y)

    if new_row_boxes or new_col_boxes:
        if long_row_boxes:
            print("long_row_boxes", long_row_boxes)
            row_boxes = long_row_boxes
        if long_col_boxes:
            print("long_col_boxes", long_col_boxes)
            col_boxes = long_col_boxes

        if new_row_boxes:
            row_boxes += new_row_boxes
            print("new_row_boxes", new_row_boxes)
        if new_col_boxes:
            print("new_col_boxes", new_col_boxes)
            col_boxes += new_col_boxes

        # print("len(row_boxes)", len(row_boxes))
        # print("len(col_boxes)", len(col_boxes))
        # row_boxes += new_row_boxes
        # col_boxes += new_col_boxes

        # row_boxes = choose_longer_row(row_boxes)
        # col_boxes = choose_longer_col(col_boxes)

        # 创建空图
        test_img = np.zeros((img.shape), np.uint8)
        test_img.fill(255)
        for box in row_boxes+col_boxes:
            cv2.line(test_img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 0), 1)
        cv2.imshow("test_image2", test_img)
        cv2.waitKey(0)

        # 展示补线
        for row in new_row_boxes:
            cv2.line(test_img, (int(row[0]), int(row[1])),
                     (int(row[2]), int(row[3])), (0, 0, 255), 1)
        for col in new_col_boxes:
            cv2.line(test_img, (int(col[0]), int(col[1])),
                     (int(col[2]), int(col[3])), (0, 0, 255), 1)
        cv2.imshow("fix_outline", test_img)
        cv2.waitKey(0)
        cv2.imwrite("temp.jpg", test_img)

        # 修复边框后重新计算交点、分割线
        print("crossover_points", len(crossover_points))
        crossover_points = get_points(row_boxes, col_boxes, (img.shape[0], img.shape[1]))
        print("crossover_points new", len(crossover_points))
        split_lines, split_y = get_split_line(crossover_points, col_boxes, img)

        # 计算行列，剔除相近交点
        row_point_list = get_points_row(crossover_points, split_y, 0)
        col_point_list = get_points_col(crossover_points, split_y, 0)
        print(len(crossover_points), len(row_point_list), len(col_point_list))
        crossover_points = delete_close_points(crossover_points, row_point_list, col_point_list)
        print(len(crossover_points), len(row_point_list), len(col_point_list))
        row_point_list = get_points_row(crossover_points, split_y)
        col_point_list = get_points_col(crossover_points, split_y)

        for point in crossover_points:
            cv2.circle(test_img, point, 1, (0, 255, 0), 3)
        cv2.imshow("point image2", test_img)
        cv2.waitKey(0)

    # 获取每个表格的左上右下两个点
    outline_point = get_outline_point(crossover_points, split_y)
    # print(outline_point)
    for outline in outline_point:
        cv2.circle(test_img, outline[0], 1, (255, 0, 0), 5)
        cv2.circle(test_img, outline[1], 1, (255, 0, 0), 5)
    cv2.imshow("outline point", test_img)
    cv2.waitKey(0)

    # 获取bbox
    # get_bbox(img, crossover_points, split_y)
    # for point in crossover_points:
    #     cv2.circle(test_img, point, 1, (0, 255, 0), 3)
    # cv2.imshow("point image3", test_img)
    # cv2.waitKey(0)

    # split_y = []
    # for outline in outline_point:
    #     split_y.extend([outline[0][1]-5, outline[1][1]+5])
    print("len(row_boxes)", len(row_boxes))
    print("len(col_boxes)", len(col_boxes))
    bboxes = get_bbox(img, row_point_list, col_point_list, split_y, row_boxes, col_boxes)
    # 展示
    for box in bboxes:
        # print(box[0], box[1])
        # if abs(box[0][1] - box[1][1]) > abs(box[0][0] - box[1][0]):
        #     continue
        cv2.rectangle(test_img, box[0], box[1], (0, 0, 255), 2, 8)
        cv2.imshow('bboxes', test_img)
        cv2.waitKey(0)
    # img = draw_lines(img, row_boxes+col_boxes, color=(255, 0, 0), lineW=2)
    # img = draw_boxes(img, rowboxes+colboxes, color=(0, 0, 255))
    print(time.time()-t, len(row_boxes), len(col_boxes))
    cv2.imwrite('temp.jpg', test_img)
    # cv2.imshow('main', img)
    # cv2.waitKey(0)