如何通过检测直线来检测主体结构轮廓

Question

如何通过检测直线来检测主体结构轮廓

pythonopencvimage-processingcomputer-visioncontour

3

我正在尝试通过检测直线和边缘来检测许多“楼层平面图”图片的主要结构，参考自这里。

上面的例子是我需要处理的一个示例，是否可以通过检测带有 opencv HoughLinesP 的行来获取主要结构？非常感谢您的帮助。

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：

lines.png

canniedHouse.png

其他参考资料：

如何在Python中获取楼层平面图的外轮廓？

楼层平面图边缘检测-图像处理？

- ah bon

你有代码。结果是什么？ - fmw42

我更新了结果图像，在其中线条不可见，请检查。 - ah bon

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- nathancy · Accepted Answer

以下是一种方法

将图像转换为灰度
使用自适应阈值获取二进制图像
执行形态学变换以平滑图像
创建水平卷积核并检测水平线条
创建垂直卷积核并检测垂直线条

将图像转换为灰度后，我们使用自适应阈值法获得二值图像

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]

从这里开始，我们对图像进行形态学变换以平滑图像。

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

现在我们使用cv2.getStructuringElement()创建一个水平内核，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)

同样地，我们创建一个垂直核并检测垂直线。

# 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()

如果你只想找到外部轮廓，可以在形态学闭合操作后找到轮廓。

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)