如何通过检测直线来检测主要结构轮廓

Question

我试图floor plan通过检测直线和边缘来检测许多图片的主要结构，参考here。

上面的例子是我需要处理的一个例子，是否可以通过检测线来获得主要结构opencv HoughLinesP？提前感谢您的帮助。

import cv2
import numpy as np

def get_lines(lines_in):
    if cv2.__version__ < '3.0':
        return lines_in[0]
    return [l[0] for l in lines]

img = cv2.imread('./test.jpg', 1)
img_gray = gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cannied = cv2.Canny(img_gray, threshold1=50, threshold2=200, apertureSize=3)
lines = cv2.HoughLinesP(cannied, rho=1, theta=np.pi / 180, threshold=80, minLineLength=30, maxLineGap=10)

for line in get_lines(lines):
    leftx, boty, rightx, topy = line
    cv2.line(img, (leftx, boty), (rightx,topy), (255, 255, 0), 2)

cv2.imwrite('./lines.png', img)
cv2.imwrite('./canniedHouse.png', cannied)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果：

线条.png

canniedHouse.png

其他参考：

如何在python中获取平面图的外部轮廓？

平面图边缘检测 - 图像处理？

Answer 1

这是一个方法

• 将图像转换为灰度
• 自适应阈值获取二值图像
• 执行形态变换以平滑图像
• 创建水平内核并检测水平线
• 创建垂直内核并检测垂直线

转换为灰度后，我们自适应阈值以获得二值图像

image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

从这里我们执行形态变换来平滑图像

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)

现在我们创建一个水平内核cv2.getStructuringElement()并检测水平线

# Find horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (35,2))
detect_horizontal = cv2.morphologyEx(close, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original, [c], -1, (36,255,12), -1)

同样，我们创建一个垂直内核并检测垂直线

# Find vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,35))
detect_vertical = cv2.morphologyEx(close, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original, [c], -1, (36,255,12), -1)

这是结果

import cv2
import numpy as np

image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)

# Find horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (35,2))
detect_horizontal = cv2.morphologyEx(close, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original, [c], -1, (36,255,12), -1)

# Find vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,35))
detect_vertical = cv2.morphologyEx(close, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original, [c], -1, (36,255,12), -1)

cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('original', original)
cv2.waitKey()

如果您只想找到外部轮廓，您可以在形态学关闭操作后找到轮廓

cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original, [c], -1, (36,255,12), 3)