基于OpenCV补全无网格/半网格表格图像网格线的技术求助

• 处理多种尺寸的表格图像，表格状态包括：全网格仅部分空白、仅含垂直网格线、仅含水平网格线
• 参考相关代码后，仅能在图像左右两侧各绘制一条线，无法满足需求
• 作为OpenCV新手，不确定如何调整代码

更新1 #

• 尝试用指定代码移除水平线，仅移除了大部分；移除垂直线完全无效，测试官方示例代码也无效果

更新2 #

• 已成功用以下代码移除所有线条：

def removeLines(result, axis) -> np.ndarray:
    img = result.copy()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
    if axis == "horizontal":
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 25))
    elif axis == "vertical":
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 1))
    else:
        raise ValueError("Axis must be either 'horizontal' or 'vertical'")
    detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
    cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    result = img.copy()
    for c in cnts:
        cv2.drawContours(result, [c], -1, (255, 255, 255), 2)
    return result

gridless = removeLines(removeLines(cv2.imread(image_path), 'horizontal'), 'vertical')

• 但绘制垂直线时出现异常，使用代码如下：

# read image
img = old_image.copy() # cv2.imread(image_path1)
hh, ww = img.shape[:2]
# convert to grayscale 
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# average gray image to one column
column = cv2.resize(gray, (ww,1), interpolation = cv2.INTER_AREA)
# threshold on white
thresh = cv2.threshold(column, 248, 255, cv2.THRESH_BINARY)[1]
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# Draw vertical
for cntr in contours_v:
    x,y,w,h = cv2.boundingRect(cntr)
    xcenter = x+w//2
    cv2.line(original_image, (xcenter,0), (xcenter,hh-1), (0, 0, 0), 1)

更新3 #

• 调整阈值（245-254区间）后，要么绘制过多垂直线，要么线条数量不足，无法实现每列仅一条线的需求

针对阈值不稳定导致的垂直线绘制问题，可通过以下思路优化：

1. 替换固定阈值为自适应阈值 #

固定阈值对不同光照、对比度的图像适应性差，改用自适应阈值能根据局部区域调整阈值，避免整体阈值偏差：

# 替换原阈值步骤
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 自适应阈值，blockSize和C参数可根据图像调整
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

2. 优化列平均后的轮廓处理 #

原代码直接对列平均图像找轮廓，易因局部噪点产生多余轮廓，先做形态学操作过滤噪点：

# 在列平均后添加形态学闭操作，消除小间隙
column = cv2.resize(gray, (ww,1), interpolation = cv2.INTER_AREA)
# 反转图像（表格列边界对应原图像深色区域）
column_inv = 255 - column
# 形态学闭操作，连接断裂区域
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,1))
column_clean = cv2.morphologyEx(column_inv, cv2.MORPH_CLOSE, kernel, iterations=1)
# 自动阈值化
thresh = cv2.threshold(column_clean, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

3. 轮廓去重与筛选 #

找到轮廓后，计算相邻轮廓间距，过滤过近的重复线条（表格列间距相对均匀）：

contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

# 提取所有轮廓的中心x坐标
x_centers = []
for cntr in contours:
    x,y,w,h = cv2.boundingRect(cntr)
    xcenter = x + w//2
    x_centers.append(xcenter)

# 排序并去重，保留间距大于最小列宽的线条（可根据图像估算最小列宽）
x_centers.sort()
filtered_centers = []
min_gap = ww // 10  # 假设最小列宽为图像宽度的1/10，可调整
prev_x = -min_gap * 2
for x in x_centers:
    if x - prev_x > min_gap:
        filtered_centers.append(x)
        prev_x = x

# 绘制过滤后的垂直线
for xcenter in filtered_centers:
    cv2.line(original_image, (xcenter,0), (xcenter,hh-1), (0,0,0), 1)

4. 结合表格结构特征 #

若已知表格列数，可直接根据图像宽度均分绘制线条，避免依赖阈值：

# 假设已知表格有n列
n_columns = 5  # 替换为实际列数
step = ww // n_columns
for i in range(1, n_columns):
    x = i * step
    cv2.line(original_image, (x,0), (x,hh-1), (0,0,0), 1)

内容的提问来源于stack exchange，提问作者Dolev Mitz