import cv2
import matplotlib.pylab as plt
import numpy as np


def read_single_img(img_path):
    try:
        im = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), -1)
    except FileNotFoundError as e:
        raise e
    return im


def pre_process(image, blank_top=20, blank_bottom=-20, blur_size=5, sigma=5, debug=0):
    #   返回二值逆图
    blank_size = 20

    if image.ndim == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    elif image.ndim == 2:
        gray = image
    else:
        raise Exception('The dimension of the image should be 2 or 3!')
    #   裁边
    gray[0:blank_top, :] = 255
    gray[blank_bottom:, :] = 255
    gray[:, 0:blank_size] = 255
    gray[:, -blank_size:] = 255
    pre = 255 - gray
    pre = cv2.GaussianBlur(pre, (blur_size, blur_size), sigma)
    binary = cv2.threshold(pre, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

    if debug == 1:
        #   显示灰度图和二值图
        plt.figure(figsize=(15, 10))
        plt.subplot(211)
        plt.title('gray')
        plt.imshow(gray, cmap='gray')
        plt.subplot(212)
        plt.title('binary')
        plt.imshow(255 - binary, cmap='gray')
        plt.show()

    return binary


def pre_process_for_anchors(image, blank_top=20, blank_bottom=-20, h_ratio=(0.1, 0.9),
                            blur_size=3, sigma=5, blank_size=20, debug=0):
    #   去掉中间内容，返回上下定位点的二值逆图
    #   h_ration=0 则不裁去中间部分

    # assert(image.ndim == 2 or image.ndim == 3)
    if image.ndim == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    elif image.ndim == 2:
        gray = image.copy()
    else:
        raise Exception('The dimension of the image should be 2 or 3!')
    #   裁边
    gray[0:blank_top, :] = 255
    gray[blank_bottom:, :] = 255
    gray[:, 0:blank_size] = 255
    gray[:, -blank_size:] = 255
    #   去掉中间内容
    if h_ratio != 0:
        h0 = int(image.shape[0] * h_ratio[0])
        h1 = int(image.shape[0] * h_ratio[1])
        gray[h0:h1, :] = 255

    pre = 255 - gray
    pre = cv2.GaussianBlur(pre, (blur_size, blur_size), sigma)
    binary = cv2.threshold(pre, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

    if debug == 1:
        #   显示灰度图和二值图
        plt.figure(figsize=(15, 10))
        plt.subplot(211)
        plt.title('gray')
        plt.imshow(gray, cmap='gray')
        plt.subplot(212)
        plt.title('binary')
        plt.imshow(255 - binary, cmap='gray')
        plt.show()

    return binary


def extract_feature(binary, method=4, ker_size1=2, ker_size2=10, debug=0):
    #   对二值图进一步处理
    close_size = 3
    kernel_height = 5
    kernel_width = 1
    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (close_size, close_size))
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_height, kernel_width))

    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (ker_size1, ker_size2))
    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (ker_size2, ker_size1))

    if method == 1:
        # ret = cv2.dilate(binary, kernel)
        ret = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel, iterations=1)
        ret = cv2.morphologyEx(ret, cv2.MORPH_OPEN, vertical_kernel)
    elif method == 2:
        ret = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
        ret = cv2.morphologyEx(ret, cv2.MORPH_CLOSE, close_kernel)
    elif method == 3:
        ret = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horizontal_kernel)
        ret = cv2.morphologyEx(ret, cv2.MORPH_OPEN, vertical_kernel)
    elif method == 4:
        ret = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, close_kernel)
        ret = cv2.morphologyEx(ret, cv2.MORPH_OPEN, horizontal_kernel)
        ret = cv2.morphologyEx(ret, cv2.MORPH_OPEN, vertical_kernel)
    else:
        ret = binary
    if debug == 1:
        #   显示特征提取其后的图像
        plt.figure(figsize=(15, 10))
        plt.subplot(211)
        plt.title('before feature extraction')
        plt.imshow(255 - binary, cmap='gray')
        # plt.show()
        # plt.figure(figsize=(15, 10))
        plt.subplot(212)
        plt.title('after feature extraction')
        plt.imshow(255 - ret, cmap='gray')
        plt.show()

    return ret


def draw_contour(binary):
    (major, minor, _) = cv2.__version__.split(".")     # check cv version
    boxes = []
    contours = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours = contours[1] if major == '3' else contours[0]
    for i in range(0, len(contours)):
        xmin, ymin, w, h = cv2.boundingRect(contours[i])
        xmax = xmin + w
        ymax = ymin + h
        centroid = [xmin + w // 2, ymin + h // 2]
        boxes.append([xmin, ymin, xmax, ymax, centroid, w*h])

    return boxes


def draw_connected_component(binary):
    connectivity = 8
    boxes = []
    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=connectivity)
    for l in range(1, num_labels):
        xmin = stats[l, cv2.CC_STAT_LEFT]
        ymin = stats[l, cv2.CC_STAT_TOP]
        xmax = stats[l, cv2.CC_STAT_WIDTH] + xmin
        ymax = stats[l, cv2.CC_STAT_HEIGHT] + ymin
        area = stats[l, cv2.CC_STAT_AREA]
        boxes.append([xmin, ymin, xmax, ymax, [int(centroids[l][0]), int(centroids[l][1])], area])
    return boxes


def find_boxes(binary, method='connected', debug=0):
    #   寻找轮廓
    if method == 'contour':
        boxes = draw_contour(binary)
    elif method == 'connected':
        boxes = draw_connected_component(binary)
    else:
        raise Exception('Wrong method for finding boxes!')

    if debug == 1:
        #   显示boxes信息
        boxes.sort(key=lambda x: x[4][1])
        print('The features of the boxes after feature extractions:')
        for box in boxes:
            width = box[2] - box[0]
            height = box[3] - box[1]
            w_to_h = width / height
            area = box[-1]
            centroid = box[-2]
            area_ratio = area / (width * height)
            print('width:{}, height:{}, centroid:{}, w_to_h:{}, area:{}, area ratio:{}'.
                  format(width, height, centroid, w_to_h, area, area_ratio))
    return boxes


def find_marker_by_shape(boxes,
                         shape_para={'height': (80, 10), 'w2h': (3, 0.6), 'area': (6000, 500), 'area_ratio': 0.5},
                         debug=0):
    #   通过形状参数寻找定位点
    area_ratio_threshold = 0.96
    max_height, min_height = shape_para['height']
    max_w2h, min_w2h = shape_para['w2h']
    max_area, min_area = shape_para['area']
    min_area_ratio = shape_para['area_ratio']

    markers = []
    for box in boxes:
        w = box[2] - box[0]
        h = box[3] - box[1]

        if box[-1] >= area_ratio_threshold*w*h and min_area <= box[-1] <= max_area:
            markers.append(box)
        elif min_height <= h <= max_height and min_w2h <= w/h <= max_w2h \
                and min_area <= box[-1] <= max_area and box[-1] >= min_area_ratio*w*h:
            markers.append(box)

    if debug == 1:
        #   显示通过形状参数找到的定位点信息
        markers.sort(reverse=True, key=lambda x: x[-1])
        print('The features of the markers picking up by shapes:')
        for box in markers:
            width = box[2] - box[0]
            height = box[3] - box[1]
            w_to_h = width / height
            area = box[-1]
            centroid = box[-2]
            area_ratio = area / (width * height)
            print('width:{}, height:{}, centroid:{}, w_to_h:{}, area:{}, area ratio:{}'.
                  format(width, height, centroid, w_to_h, area, area_ratio))
    elif debug == 2:
        #   显示所有定位点信息
        print('The features of the markers without picking up by shapes:')
        for box in boxes:
            markers.append(box)
        for box in markers:
            width = box[2] - box[0]
            height = box[3] - box[1]
            w_to_h = width / height
            area = box[-1]
            centroid = box[-2]
            area_ratio = area / (width * height)
            print('width:{}, height:{}, centroid:{}, w_to_h:{}, area:{}, area ratio:{}'.
                  format(width, height, centroid, w_to_h, area, area_ratio))

    return markers


def find_box_list_by_position(box, box_list, method='h', shift_threshold=30, slope_threshold=0.2, area_threshold=0.28):
    #   根据相近原则将box加入box_list中
    if len(box_list) > 0:
        if method == 'h':   # 水平分布
            index_flag, distance = -1, shift_threshold
            for index, bl in enumerate(box_list):
                d = abs(box[4][1] - bl[-1][4][1])
                if d < distance:
                    distance = d
                    index_flag = index
            if index_flag >= 0:
                box_list[index_flag].append(box)
            else:
                box_list.append([box])
        elif method == 'v':     # 垂直分布
            index_flag, distance = -1, shift_threshold
            for index, bl in enumerate(box_list):
                d = abs(box[4][0] - bl[-1][4][0])
                if d < distance and d < abs(box[4][1] - bl[-1][4][1]) * slope_threshold:
                    distance = d
                    index_flag = index
            if index_flag >= 0:
                box_list[index_flag].append(box)
            else:
                box_list.append([box])
        elif method == 's':     # 面积相近分布
            index_flag, area_diff = -1, area_threshold
            for index, bl in enumerate(box_list):
                d = abs((box[-1] - bl[-1][-1]) / bl[-1][-1])
                if d < area_diff:
                    area_diff = d
                    index_flag = index
            if index_flag >= 0:
                box_list[index_flag].append(box)
            else:
                box_list.append([box])
    else:
        box_list.append([box])
    return box_list


def collect_markers_by_position(boxes, method='h', shift_threshold=30, slope_threshold=0.2, area_threshold=0.28, debug=0):
    #   按照相近位置排列定位点
    box_list = []
    if method == 'h':       # 按水平位置相近排列
        boxes.sort(key=lambda x: x[4][0])
        for b in boxes:
            box_list = find_box_list_by_position(b, box_list, method=method, shift_threshold=shift_threshold,
                                                 slope_threshold=slope_threshold)
        box_list.sort(key=lambda x: x[0][4][1])
    elif method == 'v':       # 按垂直位置相近排列
        boxes.sort(key=lambda x: x[4][1])
        for b in boxes:
            box_list = find_box_list_by_position(b, box_list, method=method, shift_threshold=shift_threshold,
                                                 slope_threshold=slope_threshold)
        box_list.sort(key=lambda x: x[0][4][0])
    elif method == 's':       # 按面积大小相近排列
        boxes.sort(reverse=True, key=lambda x: x[-1])
        for b in boxes:
            box_list = find_box_list_by_position(b, box_list, method=method, shift_threshold=shift_threshold,
                                                 slope_threshold=slope_threshold, area_threshold=area_threshold)
        box_list.sort(reverse=True, key=lambda x: x[0][-1])

    # if method == 'h':       # 按水平位置相近排列
    #     boxes.sort(key=lambda x: x[4][1])
    #     for b in boxes:
    #         index_flag, distance = -1, shift_threshold
    #         for index, single_list in enumerate(box_list):
    #             if abs(b[4][1] - single_list[-1][4][1]) < distance:
    #                 distance = abs(b[4][1] - single_list[-1][4][1])
    #                 index_flag = index
    #         if index_flag >= 0:
    #             box_list[index_flag].append(b)
    #         else:
    #             box_list.append([b])
    #
    # elif method == 'v':     # 按垂直位置相近排列
    #     boxes.sort(key=lambda x: x[4][0])
    #     for b in boxes:
    #         index_flag, distance = -1, shift_threshold
    #         for index, single_list in enumerate(box_list):
    #             if abs(b[4][0] - single_list[-1][4][0]) < distance:
    #                 distance = abs(b[4][0] - single_list[-1][4][0])
    #                 index_flag = index
    #         if index_flag >= 0:
    #             box_list[index_flag].append(b)
    #         else:
    #             box_list.append([b])

    if debug == 1:
        print('box list slope')
        if method == 'h':
            for box in box_list:
                if len(box) >= 2:
                    for i in range(len(box)-1):
                        slope = (box[i+1][4][1] - box[i][4][1])/(box[i+1][4][0] - box[i][4][0])
                        print(slope)
        elif method == 'v':
            for box in box_list:
                if len(box) >= 2:
                    for i in range(len(box) - 1):
                        slope = (box[i + 1][4][0] - box[i][4][0]) / (box[i + 1][4][1] - box[i][4][1])
                        print(slope)

    return box_list


def collect_markers_by_area(boxes, area_threshold=2):
    #   去除面积相差过大的定位点
    boxes.sort(reverse=True, key=lambda x: x[-1])
    # print(boxes[0])
    min_area = boxes[0][-1] / area_threshold
    boxes = [b for b in boxes if b[-1] > min_area]
    return boxes


def check_with_anchor(problem_markers, top_anchors, page_width, column_num):
    #    根据top_anchors位置去除异常markers
    min_shift = 100

    column_pos = []

    if len(top_anchors) == column_num + 1:
        column_pos.append(top_anchors[0][4][0])
        if top_anchors[1][4][0] - top_anchors[0][4][0] < top_anchors[-1][4][0] - top_anchors[-2][4][0]:
            for i in range(2, column_num+1):
                column_pos.append(top_anchors[i][4][0] - page_width)
        else:
            for i in range(1, column_num):
                column_pos.append(top_anchors[i][4][0])
        for index, markers in enumerate(problem_markers):
            remove_list = []
            for i in range(len(markers)//2):
                if abs(markers[2*i][4][0]-column_pos[index]) > min_shift or \
                        abs(markers[2*i+1][4][0]-column_pos[index]-page_width) > min_shift:
                    remove_list.extend([2*i, 2*i+1])
            problem_markers[index] = [problem_markers[index][i] for i in range(len(problem_markers[index]))
                                      if i not in remove_list]

    return problem_markers


def remove_abnormal_marker(markers, page_width, debug=0):
    #   从markers中剔除异常点
    error = 10
    max_std = 3
    min_std = 0.1
    min_area_ratio = 0.9
    min_distance = 60
    min_slope = 0.2
    distance_list = []
    remove_list = []

    for i in range(len(markers)//2-1):
        min_flag = i
        distance_flag = abs(markers[2*i+1][4][0] - markers[2*i][4][0] - page_width) + abs(markers[2*i+1][4][1] -
                                                                                          markers[2*i][4][1])
        for j in range(i+1, len(markers)//2):
            if abs(markers[2*i+1][4][0] - markers[2*j+1][4][0]) + abs(markers[2*i+1][4][1] - markers[2*j+1][4][1]) \
                    < error:
                if distance_flag < abs(markers[2*j+1][4][0] - markers[2*j][4][0] - page_width) + \
                        abs(markers[2*j+1][4][1] - markers[2*j][4][1]):
                    remove_list.extend([2*j, 2*j+1])
                else:
                    distance_flag = abs(markers[2*j+1][4][0] - markers[2*j][4][0] - page_width) + \
                                    abs(markers[2*j+1][4][1] - markers[2*j][4][1])
                    remove_list.extend([2*min_flag, 2*min_flag+1])
                    min_flag = j
    markers = [markers[i] for i in range(len(markers)) if i not in remove_list]

    remove_list = []
    if len(markers) >= 6:
        left_slope_list = np.asarray([abs((markers[2 * i][4][0] - markers[2 * i + 2][4][0])
                                          / (markers[2 * i][4][1] - markers[2 * i + 2][4][1]))
                                      for i in range(len(markers)//2-1)])
        right_slope_list = np.asarray([abs((markers[2 * i + 1][4][0] - markers[2 * i + 3][4][0]) /
                                           (markers[2 * i + 1][4][1] - markers[2 * i + 3][4][1]))
                                       for i in range(len(markers) // 2 - 1)])
        left_slope_list = left_slope_list > min_slope
        right_slope_list = right_slope_list > min_slope
        for i in range(len(left_slope_list)):
            if left_slope_list[i]:
                if i == len(left_slope_list) - 1:
                    if not left_slope_list[i-1]:
                        remove_list.extend([2*(i+1), 2*(i+1)+1])
                elif left_slope_list[i+1]:
                    remove_list.extend([2*(i+1), 2*(i+1)+1])
                elif not left_slope_list[i+1]:
                    remove_list.extend([2*i, 2*i+1])
        for i in range(len(right_slope_list)):
            if right_slope_list[i]:
                if i == len(right_slope_list) - 1:
                    if not right_slope_list[i-1]:
                        remove_list.extend([2*(i+1), 2*(i+1)+1])
                elif right_slope_list[i+1]:
                    remove_list.extend([2*(i+1), 2*(i+1)+1])
                elif not right_slope_list[i+1]:
                    remove_list.extend([2*i, 2*i+1])

        markers = [markers[i] for i in range(len(markers)) if i not in set(remove_list)]

    remove_list = []
    if len(markers) >= 2:
        left_x_list = np.asarray([markers[2*i][4][0] for i in range(len(markers)//2)])
        left_y_list = np.asarray([markers[2*i][4][1] for i in range(len(markers)//2)])
        right_x_list = np.asarray([markers[2*i+1][4][0] for i in range(len(markers)//2)])
        right_y_list = np.asarray([markers[2*i+1][4][1] for i in range(len(markers)//2)])
        distance_list = right_x_list - left_x_list
        shift_list = right_y_list - left_y_list

        left_x_mean = left_x_list.mean()
        distance_mean = distance_list.mean()
        shift_mean = shift_list.mean()
        left_x_std = left_x_list.std()
        distance_std = distance_list.std()
        shift_std = shift_list.std()
        if len(markers) >= 4:
            for i in range(len(markers)//2):
                if left_x_std > min_std and abs(left_x_list[i] - left_x_mean) / left_x_std > max_std:
                    remove_list.extend([2*i, 2*i+1])
                elif shift_std > min_std and abs(shift_list[i] - shift_mean) / shift_std > max_std:
                    remove_list.extend([2 * i, 2 * i + 1])
                elif distance_std > min_std and abs(distance_list[i] - distance_mean) / distance_std > max_std:
                    remove_list.extend([2 * i, 2 * i + 1])
        elif len(markers) == 2:
            # area_ratio_list = np.asarray([m[-1]/((m[2]-m[0])*(m[3]-m[1])) for m in markers])
            if abs(distance_list-page_width) + abs(shift_list) > min_distance:
                remove_list.extend([0, 1])
    markers = [markers[i] for i in range(len(markers)) if i not in remove_list]

    if len(markers) >= 2:
        new_page_width = markers[1][4][0] - markers[0][4][0]
    else:
        new_page_width = page_width

    if debug == 1:
        print(len(markers))
        if len(markers) >= 4:
            print('left', left_x_mean, left_x_std)
            print(left_x_list)
            for i, x in enumerate(left_x_list):
                delta = abs(x - left_x_mean) / left_x_std
                print(delta, left_y_list[i])
            print('shift', shift_mean, shift_std)
            print(shift_list)
            for i, shift in enumerate(shift_list):
                delta = abs(shift - shift_mean) / shift_std
                print(delta, left_y_list[i])
            print('distance', distance_mean, distance_std)
            print(distance_list)
            for i, distance in enumerate(distance_list):
                delta = abs(distance - distance_mean) / distance_std
                print(delta, left_y_list[i])
            print('total')
            for i in range(len(left_x_list)):
                delta = abs(left_x_list[i] - left_x_mean) / left_x_std + abs(shift_list[i] - shift_mean) / shift_std \
                        + abs(distance_list[i] - distance_mean) / distance_std
                d = abs(left_x_list[i] - left_x_mean) + abs(shift_list[i] - shift_mean) + \
                    abs(distance_list[i] - distance_mean)
                print(delta, d, left_y_list[i])
    if debug == 2:
        if len(markers) >= 2:
            print('page width', page_width, 'new page width:', new_page_width,
                  'distant difference:', abs(new_page_width - page_width) + abs(markers[1][4][1]-markers[0][4][1]))

    return markers, new_page_width


def draw_box(image, boxes, color=(0, 255, 0), debug=0):
    #   生成定位点标注框图
    for box in boxes:
        if len(box) > 0:
            cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), color, 5)

    if debug == 1:
        #   显示定位点信息
        for box in boxes:
            if len(box) == 4:
                width = box[2] - box[0]
                height = box[3] - box[1]
                w_to_h = width / height
                area = width * height
                # centroid = (box[0]+box[1])/2, (box[1]+box[3])/2
                print('width:{}, height:{}, w_to_h:{}, area:{}, top_left:{}, bottom_right:{},'.
                      format(width, height, w_to_h, area, box[0:2], box[2:4]))
            elif len(box) > 4:
                width = box[2] - box[0]
                height = box[3] - box[1]
                w_to_h = width / height
                position_ratio = box[4][0] / image.shape[1]
                area = box[-1]
                centroid = box[4]
                area_ratio = area / (width * height)
                print('width:{}, height:{}, centroid:{}, position ratio:{}, w_to_h:{}, area:{}, area ratio:{}'.
                      format(width, height, centroid, position_ratio, w_to_h, area, area_ratio))


def find_pair(marker, boxes, page_width, threshold=100):
    #   若page_width为正，在boxes中找到marker的右配对， 若page_width为负， 在在boxes中找到marker的左配对

    distance = threshold
    pair_index = -1
    for i in range(len(boxes)):
        if abs(marker[4][1] - boxes[i][4][1]) + abs(boxes[i][4][0] - marker[4][0] - page_width) <= threshold:
            if abs(marker[4][1] - boxes[i][4][1]) + abs(boxes[i][4][0] - marker[4][0] - page_width) < distance:
                distance = abs(marker[4][1] - boxes[i][4][1]) + abs(boxes[i][4][0] - marker[4][0] - page_width)
                pair_index = i
    if pair_index >= 0:
        return boxes[pair_index], pair_index, distance
    else:
        return [], pair_index, distance


def find_pair_list(marker_list, all_list, page_width, horizontal_threshold=100, debug=0):
    #   all_list 中找到与marker_list最接近的配对list
    max_count = 0
    index_flag = -1
    min_distance = horizontal_threshold
    for index, l in enumerate(all_list):
        count = 0
        distance = 0
        for m in marker_list:
            if find_pair(m, l, page_width, horizontal_threshold)[1] >= 0:
                count += 1
                distance += find_pair(m, l, page_width, horizontal_threshold)[2]
        if count > max_count:
            max_count = count
            index_flag = index
            min_distance = distance / count
        elif count == max_count and count > 0:
            distance /= count
            if distance < min_distance:
                min_distance = distance
                index_flag = index
    if debug == 1:
        if index_flag >= 0:
            print('page width:', abs(marker_list[0][4][0] - all_list[index_flag][0][4][0]), 'anchor width:', page_width)

    return all_list[index_flag], index_flag, min_distance


def find_column(anchors, width, column_num=2, debug=0):
    #   确定栏数，单栏宽度及第一栏和最后一栏的定位
    double_page_width_ratio = 0.42  # 默认双栏宽度比例
    three_page_width_ratio = 0.29  # 默认三栏宽度比例
    double_page_separation = 250  # 默认双栏栏间间距
    three_page_separation = 100  # 默认三栏栏间间距
    horizontal_threshold = 80  # 单栏宽度比例阈值

    top_anchors, bottom_anchors = anchors[:2]

    page_width = width
    if len(top_anchors) >= 2:
        for i in range(len(top_anchors)-1):
            page_width_0 = top_anchors[i+1][4][0] - top_anchors[i][4][0]
            page_width_1 = (top_anchors[i + 1][4][0] - top_anchors[i][4][0]) // 2
            page_width_2 = (top_anchors[i + 1][4][0] - top_anchors[i][4][0]) // 3
            if abs(page_width_0 - width * double_page_width_ratio) < horizontal_threshold:
                column_num = 2
                if page_width_0 < page_width:
                    page_width = page_width_0
            elif abs(page_width_0 - width * three_page_width_ratio) < horizontal_threshold:
                column_num = 3
                if page_width_0 < page_width:
                    page_width = page_width_0
            elif abs(page_width_1 - width * double_page_width_ratio) < horizontal_threshold:
                column_num = 2
                if page_width_1 < page_width:
                    page_width = page_width_1
            elif abs(page_width_1 - width * three_page_width_ratio) < horizontal_threshold:
                column_num = 3
                if page_width_1 < page_width:
                    page_width = page_width_1
            elif abs(page_width_2 - width * double_page_width_ratio) < horizontal_threshold:
                column_num = 2
                if page_width_2 < page_width:
                    page_width = page_width_2
            elif abs(page_width_2 - width * three_page_width_ratio) < horizontal_threshold:
                column_num = 3
                if page_width_2 < page_width:
                    page_width = page_width_2
    if page_width == width:
        if column_num == 2:
            page_width = int(width * double_page_width_ratio)    # 如果没有找到合适的大定位点，使用默认的双栏宽度
        elif column_num == 3:
            page_width = int(width * three_page_width_ratio)     # 如果没有找到合适的大定位点，使用默认的三栏宽度

    #   寻找第一栏和最后一栏的定位
    column_pos = []
    if len(top_anchors) >= 1:
        for i in range(4):
            if top_anchors[0][4][0] - (i + 1) * page_width < 0:
                column_pos.append(top_anchors[0][4][0] - i * page_width)
                break
        for i in range(4):
            if top_anchors[-1][4][0] + (i + 1) * page_width > width:
                column_pos.append(top_anchors[-1][4][0] + (i - 1) * page_width)
                break
    elif len(bottom_anchors) == 2:
        column_pos = [bottom_anchors[0][4][0], bottom_anchors[-1][4][0] - page_width]
    elif column_num == 2:
        column_pos = [(width - double_page_separation) // 2 - page_width, (width + double_page_separation) // 2]
    elif column_num == 3:
        column_pos = [width // 2 - three_page_separation - page_width * 3 // 2,
                      width // 2 + three_page_separation + page_width // 2]

    if debug == 1:
        print('top anchors')
        for t in top_anchors:
            print(t[4])
        print('bottom anchors')
        for b in bottom_anchors:
            print(b[4])
        print('page width:', page_width, 'column number:', column_num, 'column position:', column_pos)

    return page_width, column_num, column_pos