Question

我想从下图中删除边框

到目前为止我尝试过使用OpenCV获取边缘代码：

def autocrop(image, threshold=0):
    """Crops any edges below or equal to threshold

    Crops blank image to 1x1.

    Returns cropped image.

    """
    if len(image.shape) == 3:
        flatImage = np.max(image, 2)
    else:
        flatImage = image
    assert len(flatImage.shape) == 2

    rows = np.where(np.max(flatImage, 0) > threshold)[0]
    if rows.size:
        cols = np.where(np.max(flatImage, 1) > threshold)[0]
        image = image[cols[0]: cols[-1] + 1, rows[0]: rows[-1] + 1]
    else:
        image = image[:1, :1]

    return image

no_border = autocrop(new_image)


cv2.imwrite('no_border.png',no_border)

结果是这个图像，接下来如何删除那些框

更新：

我发现该解决方案适用于白色背景，但是当我更改背景时，颜色边框不会被删除

被修改

我已尝试过此图片的解决方案

但结果是这样的

如何完全删除边界框。

Answer 1

为此，我们使用floodFill函数。

import cv2
import numpy as np

if __name__ == '__main__':
    # read image and convert to gray
    img = cv2.imread('image.png',cv2.IMREAD_UNCHANGED)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # threshold the gray image to binarize, and negate it
    _,binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
    binary = cv2.bitwise_not(binary)

    # find external contours of all shapes
    _,contours,_ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

    # create a mask for floodfill function, see documentation
    h,w,_ = img.shape
    mask = np.zeros((h+2,w+2), np.uint8)

    # determine which contour belongs to a square or rectangle
    for cnt in contours:
        poly = cv2.approxPolyDP(cnt, 0.02*cv2.arcLength(cnt,True),True)
        if len(poly) == 4:
            # if the contour has 4 vertices then floodfill that contour with black color
            cnt = np.vstack(cnt).squeeze()
            _,binary,_,_ = cv2.floodFill(binary, mask, tuple(cnt[0]), 0)
    # convert image back to original color
    binary = cv2.bitwise_not(binary)        

    cv2.imshow('Image', binary)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Answer 2

还有另一个可以找到图像中的字符。这在轮廓中使用层次结构的概念。

实现在python中：

path = r'C:\Desktop\Stack'
filename = '2.png'

img = cv2.imread(os.path.join(path, filename), 1)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU)

_, contours2, hierarchy2 = cv2.findContours(binary, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)

请注意，cv2.findContours()函数在RETR_CCOMP参数中传递，以根据不同的层次结构级别存储轮廓。当一个轮廓位于另一个轮廓内时，层次结构非常有用，从而实现了父子关系。 RETR_CCOMP有助于确定这种关系。

img2 = img.copy()
l = []
for h in hierarchy2[0]:
    if h[0] > -1 and h[2] > -1:
        l.append(h[2])

在上面的代码段中，我将所有有孩子的轮廓传递到列表l中。使用l我在下面的代码段中绘制了这些轮廓。

for cnt in l:
    if cnt > 0:
        cv2.drawContours(img2, [contours2[cnt]], 0, (0,255,0), 2)

cv2.imshow('img2', img2)

查看DOCUMENTATION HERE以了解有关轮廓中层次结构的更多信息。

使用轮廓层次结构的python opencv裁剪

2 个答案: