在python中的圖像中的表格上創建邊框

Question

我有一張有一張桌子和一些其他數據的圖像。 我需要為表格繪制邊框以分隔每個單元格。

我的形象看起來像這樣

我正在嘗試的是：1）放大圖像以創建連續的斑點，看起來像

2）查找輪廓和繪圖

問題：我無法正確繪制，因為它看起來像我的表格單元格太近了，並且膨脹時它們變成了連續的點**我從Internet上獲取了這段代碼並試圖進行修改，但是這樣做並不理想圖片

代碼：

    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)

Answer 1

非常有趣的應用程序。

原始撥號可能不是最好的方法。

我確實建議使用OCR路由。 像下面

輸出是這樣的

因此，只要有兩排彼此靠近。 例如，row1-row2 <npixel。 那是封閉線。 在（row1 + height1）和row2之間找到中心位置。 這條線應該很准確。

在我的樣本中，如果| 292-335 | <50。然后在（292 + 27 + 335）/ 2之間畫一條線，表示它在資產線和屬性線之間。

對於OCR軟件包，如果您堅持使用python，則可以嘗試使用tesseract。

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

請參閱此處獲取python文本坐標Tesseract OCR文本位置

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xy高度寬度

我在這里展示的演示實際上使用的是類似於Windows OCR示例的東西

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

隨意嘗試任何一種方法來獲取所需的表格行。