在Python中的图像中的表格上创建边框

我有一个图像，其中有一个表格和一些其他数据。我需要为表格绘制边框以分隔每个单元格。

My image looks like this

What i am trying: 1) dilating the image to create continuous spots, which looks like

2）寻找轮廓并绘图

问题：我无法正确绘制，因为我的表格单元格看起来太接近，并且在扩张时它们变成了一个连续的点 **我从互联网上获取了这段代码并尝试修改但对于这张图像来说效果不佳

code :

    import os
    import cv2
    import imutils

    # This only works if there's only one table on a page
    # Important parameters:
    #  - morph_size
    #  - min_text_height_limit
    #  - max_text_height_limit
    #  - cell_threshold
    #  - min_columns


    def pre_process_image(img, save_in_file, morph_size=(7, 7)):
        # get rid of the color
        pre = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # Otsu threshold
        pre = cv2.threshold(pre,250, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
        # dilate the text to make it solid spot
        cpy = pre.copy()
        struct = cv2.getStructuringElement(cv2.MORPH_RECT, morph_size)
        cpy = cv2.dilate(~cpy, struct, anchor=(-1, -1), iterations=1)
        # cpy = cv2.dilate(img,kernel,iterations = 1)

        pre = ~cpy
        # pre=cpy
        if save_in_file is not None:
            cv2.imwrite(save_in_file, pre)
        return pre


    def find_text_boxes(pre, min_text_height_limit=3, max_text_height_limit=30):
        # Looking for the text spots contours
        contours = cv2.findContours(pre, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        # contours = contours[0] if imutils.is_cv2() else contours[1]
        contours = contours[0]
        # Getting the texts bounding boxes based on the text size assumptions
        boxes = []
        for contour in contours:
            box = cv2.boundingRect(contour)
            h = box[3]

            if min_text_height_limit < h < max_text_height_limit:
                boxes.append(box)

        return boxes


    def find_table_in_boxes(boxes, cell_threshold=10, min_columns=2):
        rows = {}
        cols = {}

        # Clustering the bounding boxes by their positions
        for box in boxes:
            (x, y, w, h) = box
            col_key = x // cell_threshold
            row_key = y // cell_threshold
            cols[row_key] = [box] if col_key not in cols else cols[col_key] + [box]
            rows[row_key] = [box] if row_key not in rows else rows[row_key] + [box]

        # Filtering out the clusters having less than 2 cols
        table_cells = list(filter(lambda r: len(r) >= min_columns, rows.values()))
        # Sorting the row cells by x coord
        table_cells = [list(sorted(tb)) for tb in table_cells]
        # Sorting rows by the y coord
        table_cells = list(sorted(table_cells, key=lambda r: r[0][1]))

        return table_cells


    def build_lines(table_cells):
        if table_cells is None or len(table_cells) <= 0:
            return [], []

        max_last_col_width_row = max(table_cells, key=lambda b: b[-1][2])
        max_x = max_last_col_width_row[-1][0] + max_last_col_width_row[-1][2]

        max_last_row_height_box = max(table_cells[-1], key=lambda b: b[3])
        max_y = max_last_row_height_box[1] + max_last_row_height_box[3]

        hor_lines = []
        ver_lines = []

        for box in table_cells:
            x = box[0][0]
            y = box[0][1]
            hor_lines.append((x, y, max_x, y))

        for box in table_cells[0]:
            x = box[0]
            y = box[1]
            ver_lines.append((x, y, x, max_y))

        (x, y, w, h) = table_cells[0][-1]
        ver_lines.append((max_x, y, max_x, max_y))
        (x, y, w, h) = table_cells[0][0]
        hor_lines.append((x, max_y, max_x, max_y))

        return hor_lines, ver_lines

if __name__ == "__main__":
    in_file = os.path.join("data", "page1.jpg")
    pre_file = os.path.join("data", "pre.png")
    out_file = os.path.join("data", "out.png")

    img = cv2.imread(os.path.join(in_file))

    pre_processed = pre_process_image(img, pre_file)
    text_boxes = find_text_boxes(pre_processed)
    cells = find_table_in_boxes(text_boxes)
    hor_lines, ver_lines = build_lines(cells)

    # Visualize the result
    vis = img.copy()

    # for box in text_boxes:
    #     (x, y, w, h) = box
    #     cv2.rectangle(vis, (x, y), (x + w - 2, y + h - 2), (0, 255, 0), 1)

    for line in hor_lines:
        [x1, y1, x2, y2] = line
        cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255), 1)

    for line in ver_lines:
        [x1, y1, x2, y2] = line
        cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255), 1)

    cv2.imwrite(out_file, vis)

非常有趣的应用程序。

原始拨号可能不是最好的方法。

我建议使用 OCR 路由。像下面这样

输出是这样的

因此只要有两排彼此距离较近即可。例如，row1-row2

在我的示例中，如果 |292-335|

对于 OCR 包，如果您坚持使用 python，可以尝试使用 tesseract。

https://pypi.org/project/pytesseract/

请参阅此处了解 python 文本坐标Tesseract OCR 文本位置 https://stackoverflow.com/questions/51282214/tesseract-ocr-text-position

Tesseract.PageIteratorLevel myLevel = /*TODO*/;
using (var page = Engine.Process(img))
using (var iter = page.GetIterator())
{
    iter.Begin();
    do
    {
        if (iter.TryGetBoundingBox(myLevel, out var rect))
        {
            var curText = iter.GetText(myLevel);
            // Your code here, 'rect' should containt the location of the text, 'curText' contains the actual text itself
        }
    } while (iter.Next(myLevel));
}

rect包含您想要的部分 x y 高度宽度

我在这里展示的演示实际上使用类似于 Windows OCR 示例的东西

https://github.com/microsoft/Windows-universal-samples/tree/master/Samples/OCR

请随意尝试任何方法来获得您想要的表格线。