利用scipy包计算表格线的峰值

import cv2
import numpy as np
from scipy.signal import find_peaks, peak_widths


def get_lines_from_image(img_bin, axis, kernel_len_div = 20, kernel_len = None, iters = 3):
    """
    :param img_bin: opencv img
    :param axis: 0 对应竖直， 1对应水平线
    :param kernel_len_div: 相对于边长的几分之几
    :param kernel_len: 直接给定和长度，如果这个长度不为０，　上述例子失效
    :return:
    """
    DEBUG = True
    # Defining a kernel length
    if kernel_len is not None:
        assert kernel_len > 0
        kernel_length = kernel_len
    else:
        kernel_length = max(np.array(img_bin).shape[axis] // kernel_len_div, 1)

    if axis == 0:
        # A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
        verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))

        # Morphological operation to detect verticle lines from an image
        img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=iters)
        verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=iters)
        if DEBUG:
            cv2.imwrite("verticle_lines.jpg", verticle_lines_img)
        return verticle_lines_img

    else:
        # A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
        hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))

        # Morphological operation to detect horizontal lines from an image
        img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=iters)
        horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=iters)
        if DEBUG:
            cv2.imwrite("horizontal_lines.jpg", horizontal_lines_img)
        return horizontal_lines_img

def line_img_add(verticle_lines_img, horizontal_lines_img):
    # 把检测出来的横线和竖线相加
    alpha = 0.5
    beta = 1.0 - alpha
    img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)
    return img_final_bin


def project(np_arr, axis):
    # 水平或垂直投影, 0竖直，1水平
    return np.count_nonzero(np_arr == 0, axis=axis)

def get_grid_coordinate(img_bin, prominence_ratio = 0.3, height_ratio=None, distance=None, DEBUG=0):
    """
    计算格点水平(x)和竖直(y)坐标和线宽
    :param img_bin: 白底黑线
    :return:
    """
    #参数
    # prominence_ratio 峰值的突出程度, 相对于表格长宽
    h, w = img_bin.shape
    # print("size",h,w)
    x_prj = project(img_bin, 0)
    y_prj = project(img_bin, 1)
    # 检测峰值
    # high_ratio = 0.1 # todo 这也是一个参数
    height_x = height_y = None
    if height_ratio is not None:
        height_x = height_ratio * h
        height_y = height_ratio * w
    # x_peaks, _ = find_peaks(x_prj, height=high_ratio*h, distance = max(1,w/20), prominence=(h*prominence_ratio, None))
    # y_peaks, _ = find_peaks(y_prj, height=high_ratio*w, distance = max(1,w/50), prominence=(w*prominence_ratio, None))
    print('height_x,height_y:', height_x, height_y)
    x_peaks, _ = find_peaks(x_prj, height=height_x, distance=distance,  prominence=(h * prominence_ratio, None))
    y_peaks, _ = find_peaks(y_prj, height=height_y, distance=distance, prominence=(w * prominence_ratio, None))

    x_peaks = list(x_peaks)
    y_peaks = list(y_peaks)

    DEBUG =True
    if DEBUG:
        #plot
        import matplotlib.pyplot as plt
        img = img_bin
        plt.subplot(211)
        plt.title("x")
        print('range(x_prj.shape[0]):',range(x_prj.shape[0]))
        plt.plot(range(x_prj.shape[0]), x_prj)
        plt.plot(x_peaks, x_prj[x_peaks], "x")
        plt.subplot(212)
        plt.title("y")
        plt.plot(range(y_prj.shape[0]), y_prj)
        plt.plot(y_peaks, y_prj[y_peaks], "x")
        plt.show()

    if len(x_peaks) == 0: # 如果没检测到峰值, 把检测框边界峰值
        x_peaks = [0, w]
        print("x_peaks is None !!!!!!!")
    if len(y_peaks) == 0:
        y_peaks = [0, h]
        print("y_peaks is None !!!!!!!")

    # 计算线宽, 假设线宽一定, 横有m根线, 竖有n根线, 表格高为h, 宽为w, 线宽为x
    # n_nonzero = m*w*x + n*h*x - m*n*x^2
    #　n_nonzero 约等于 m*w*x + n*h*x
    h,w = img_bin.shape
    m,n = len(y_peaks), len(x_peaks)
    line_width = np.count_nonzero(img_bin == 0) / (m*w + n*h)
    line_width = round(line_width) + 1
    return x_peaks, y_peaks, line_width

if __name__ == '__main__':
    path= './test_page_debug_out_debug/table_crop_fix_rm_char.jpg'
    img = cv2.imread(path)
    img_bin = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    verticle_lines_img = get_lines_from_image(img_bin, 0, kernel_len_div=40)
    horizontal_lines_img = get_lines_from_image(img_bin, 1, kernel_len_div=40)
    # 表格线提取
    img_final_bin_lines = line_img_add(verticle_lines_img, horizontal_lines_img)
    cv2.imwrite('./img_final_bin_lines.jpg',img_final_bin_lines)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    # 膨胀并二值化
    img_final_bin_lines = cv2.erode(~img_final_bin_lines, kernel, iterations=2)
    (thresh, img_final_bin_lines) = cv2.threshold(img_final_bin_lines, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    cv2.imwrite('./img_final_bin_lines_fix.jpg', img_final_bin_lines)
    # 根据表格线计算格点坐标 -----------------------------------
    x_grids, y_grids, line_w = get_grid_coordinate(img_final_bin_lines)
    

输入：

提取竖直线：

提取水平线：

水平线与竖直线峰值查找：