我設(shè)法找到了一種對我有用的方法，盡管如果圖像中有其他水平和垂直形狀則它不起作用。
我使用的想法是簡單地從邊框是水平和垂直形狀的假設(shè)開始，并從這些僅存在于邊框中的假設(shè)出發(fā)（意味著圖像本身沒有垂直或水平線，這是一種拉伸，但我的用例有那個假設(shè)）。
這是我使用的代碼：
# extract horizontal and vertical lines
only_box = extract_all_squares(box, kernel_length=7)
# build up a mask of the same size as the image
mask = np.zeros(box.shape, dtype='uint8')
# get contours of horizontal and vetical lines
contours, hierarchy = cv2.findContours(only_box, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# draw contours on mask
mask = cv2.drawContours(mask, contours, -1, (255, 255, 255), thickness=cv2.FILLED)
# threhold mask and image
ret, mask = cv2.threshold(mask, 20, 255, cv2.THRESH_BINARY)
ret, box = cv2.threshold(box, 20, 255, cv2.THRESH_BINARY)
# remove the bits we don't want
box[mask == 0] = 255
具有以下輔助功能
def extract_all_squares(image, kernel_length):
"""
Binarizes image, keeping only vertical and horizontal lines
hopefully, it'll help us detect squares
Args:
image: image (cropped around circonstances)
kernel_length: length of kernel to use. Too long and you will catch everything,
too short and you catch nothing
Returns:
image binarized and keeping only vertical and horizozntal lines
"""
# thresholds image : anything beneath a certain value is set to zero
(thresh, img_bin) = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
# A vertical kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
vertical_ksize = (1, kernel_length)
# Morphological operation to detect vertical lines from an image
verticle_lines_img = extract_lines(img_bin, vertical_ksize)
# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
horizontal_ksize = (kernel_length, 1)
# Morphological operation to detect horizontal lines from an image
horizontal_lines_img = extract_lines(img_bin, horizontal_ksize)
img_final_bin = add_lines_together(verticle_lines_img, horizontal_lines_img)
return img_final_bin
def extract_lines(image, ksize):
"""
extract lines (horizontal or vertical, depending on ksize)
Args:
image: binarized image
ksize: size of kernel to use. Possible values :
horizontal_ksize = (kernel_length, 1)
vertical_ksize = (1, kernel_length)
Returns:
lines from image (vertical or horizontal, depending on ksize)
"""
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, ksize)
img_temp = cv2.erode(image, kernel, iterations=3)
lines_img = cv2.dilate(img_temp, kernel, iterations=3)
return lines_img
def add_lines_together(verticle_lines_img, horizontal_lines_img, alpha=0.5, beta=0.5):
"""
extract lines (horizontal or vertical, depending on ksize)
Args:
verticle_lines_img: image with vertical lines
horizontal_lines_img: image with horizontal lines
alpha : weight of first image. Keep at 0.5 for balance
beta : weight of second image. Keep at 0.5 for balance
alpha and beta are weighting parameters, this will
decide the quantity of an image to be added to make a new image
Returns:
image with an addition of both vertical and horizontal lines
"""
# This function helps to add two image with specific weight parameter to get a third image as summation of two image.
img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)
# A kernel of (3 X 3) nes.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# erodes boundaries of features, gets rid of some noise
img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)
# further kill noise by thresholding
(thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
return img_final_bin
