虚线检测 CV2 Python

Question

我正在尝试使用一些变换来去除图像中的虚线和实线，但我只能使用变形变换和霍夫线检测来去除其中的一些。

这里是一个例子：我需要删除虚线和长垂直线，同时不影响其他任何东西。

灰度图像输入：

到目前为止，这是我的代码：

thresh = cv2.threshold(num_bloc, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove vertical
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_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]
for c in cnts:
    cv2.drawContours(num_bloc, [c], -1, (255,255,255), 2)

edges = cv2.Canny(num_bloc, 75, 150)

rho = 1              #Distance resolution of the accumulator in pixels.
theta = np.pi/180    #Angle resolution of the accumulator in radians.
threshold = 300       #Only lines that are greater than threshold will be returned.
minLineLength = 50   #Line segments shorter than that are rejected.
maxLineGap = 10     #Maximum allowed gap between points on the same line to link them
lines = cv2.HoughLinesP(edges, rho = rho, theta = theta, threshold = threshold,
                       minLineLength = minLineLength, maxLineGap = maxLineGap)

if lines is not None:
    if lines.size>0 : 
        a,b,c = lines.shape
        for i in range(a):

            x1=lines[i][0][0]
            y1=lines[i][0][1]-5
            x2=lines[i][0][2]
            y2=lines[i][0][3]+5


            area = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]])

        #         cv2.line(table, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
        #         cv2.rectangle(table, (x1,y1-10 ), (x2,y2+10), (36,255,12), 2)
            cv2.fillPoly(num_bloc, [area], color=(255,255,255))

我得到的输出（在输入图像的一个子集上）：

如您所见，垂直虚线仍在此处。

关于如何删除所有线条（虚线和实线）、垂直、水平或任何角度的任何建议？

Answer 1

可以使用形态闭合运算来闭合虚线。

试试这个：

import cv2
import numpy as np

num_bloc = cv2.imread('KZpu3.png',1)
gray = cv2.cvtColor(num_bloc, cv2.COLOR_BGR2GRAY)

thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
kernel = np.ones((5,5),np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# Remove vertical
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_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]
for c in cnts:
    cv2.drawContours(num_bloc, [c], -1, (33, 227, 253), 2)

edges = cv2.Canny(num_bloc, 75, 150)

rho = 1              #Distance resolution of the accumulator in pixels.
theta = np.pi/180    #Angle resolution of the accumulator in radians.
threshold = 300       #Only lines that are greater than threshold will be returned.
minLineLength = 800   #Line segments shorter than that are rejected.
maxLineGap = 7    #Maximum allowed gap between points on the same line to link them
lines = cv2.HoughLinesP(edges, rho = rho, theta = theta, threshold = threshold,
                       minLineLength = minLineLength, maxLineGap = maxLineGap)

if lines is not None:
    if lines.size>0 : 
        a,b,c = lines.shape
        for i in range(a):

            x1=lines[i][0][0]
            y1=lines[i][0][1]-5
            x2=lines[i][0][2]
            y2=lines[i][0][3]+5


            area = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]])

        #         cv2.line(table, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
        #         cv2.rectangle(table, (x1,y1-10 ), (x2,y2+10), (36,255,12), 2)
            cv2.fillPoly(num_bloc, [area], color=(33, 227, 253))

width = int(num_bloc.shape[1] * 0.5)
height = int(num_bloc.shape[0] * 0.5)
dim = (width, height)
# resize image
resized = cv2.resize(num_bloc, dim, interpolation = cv2.INTER_AREA)
resizedthres = cv2.resize(thresh, dim, interpolation = cv2.INTER_AREA)
cv2.imshow('threshold',resizedthres)
cv2.imshow('num_bloc',resized)

输出