如何在OpenCV中检测行?

本文介绍了如何在OpenCV中检测行?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正尝试检测停车处的线路，如下所示.

I am trying to detect lines in parking as shown below.

我希望得到的是清晰的线条和交叉线上的(x，y)位置.但是，结果并不十分令人满意.

What I hope to get is the clear lines and (x,y) position in the crossed line. However, the result is not very promising.

我想这是由于两个主要原因:

I guess it is due to two main reasons:

某些行非常中断或丢失.即使人眼也可以清楚地看到找出他们.即使HoughLine可以帮助连接一些丢失的物品线，因为HoughLine有时会连接不必要的线在一起，我宁愿手动进行.

Some lines are very broken or missing. Even human eyes can clearlyidentify them. Even though HoughLine can help to connect some missinglines, since HoughLine sometimes would connect unnecessary linestogether, I 'd rather to do it manually.

有些重复的行.

工作的一般管道如下所示:

The general pipeline for the work is shown as below:

import cv2
import numpy as np
import matplotlib
from matplotlib.pyplot import imshow
from matplotlib import pyplot as plt

# white color mask
img = cv2.imread(filein)
#converted = convert_hls(img)
image = cv2.cvtColor(img,cv2.COLOR_BGR2HLS)
lower = np.uint8([0, 200, 0])
upper = np.uint8([255, 255, 255])
white_mask = cv2.inRange(image, lower, upper)
# yellow color mask
lower = np.uint8([10, 0,   100])
upper = np.uint8([40, 255, 255])
yellow_mask = cv2.inRange(image, lower, upper)
# combine the mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
result = img.copy()
cv2.imshow("mask",mask)

height,width = mask.shape
skel = np.zeros([height,width],dtype=np.uint8)      #[height,width,3]
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
temp_nonzero = np.count_nonzero(mask)
while(np.count_nonzero(mask) != 0 ):
    eroded = cv2.erode(mask,kernel)
    cv2.imshow("eroded",eroded)
    temp = cv2.dilate(eroded,kernel)
    cv2.imshow("dilate",temp)
    temp = cv2.subtract(mask,temp)
    skel = cv2.bitwise_or(skel,temp)
    mask = eroded.copy()

cv2.imshow("skel",skel)
#cv2.waitKey(0)

edges = cv2.Canny(skel, 50, 150)
cv2.imshow("edges",edges)
lines = cv2.HoughLinesP(edges,1,np.pi/180,40,minLineLength=30,maxLineGap=30)
i = 0
for x1,y1,x2,y2 in lines[0]:
    i+=1
    cv2.line(result,(x1,y1),(x2,y2),(255,0,0),1)
print i

cv2.imshow("res",result)
cv2.waitKey(0)

我想知道为什么在选择某些颜色的第一步之后，线条断了并且有噪音.我认为在这一步中，我们应该做一些事情以使折线成为完整的，噪音较小的线.然后尝试应用某些方法来执行Canny和Hough线.有任何想法吗?

I wonder why after the first step of selecting certain color, the lines are broken and with noises. I would think in this step we should do something to make the broken line a complete, less noisy line. And then try to apply something to do the Canny and Hough lines. Any ideas?

推荐答案

这是我的管道，也许可以给您一些帮助.

Here is my pipeline, maybe it can give you some help.

首先，获取灰色图像并处理高斯模糊.

img = cv2.imread('src.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

第二，使用Canny进行过程边缘检测.

low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

然后，使用HoughLinesP来获取线条.您可以调整参数以获得更好的性能.

Then, use HoughLinesP to get the lines. You can adjust the parameters for better performance.

rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 15  # minimum number of votes (intersections in Hough grid cell)
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 20  # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                    min_line_length, max_line_gap)

for line in lines:
    for x1,y1,x2,y2 in line:
    cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)

最后，在srcImage上绘制线条.

# Draw the lines on the  image
lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

这是我的最后一场演出.

Here is my final performance.

最终图像:

这篇关于如何在OpenCV中检测行?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！

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