我正在尝试在管中应用霍夫概率变换,我已经有一个过滤良好的图像(边(。
我需要识别管子中间的任何一条直线(附图(,这样我就可以检测液位,但我不能这样做。有谁知道我该如何解决这个问题?
import cv2
import numpy as np
img = cv2.imread('tube.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite('gray.png',gray)
edges = cv2.Canny(gray,350,720,apertureSize = 3)
cv2.imwrite('edges.png',edges)
minLineLength = 30
maxLineGap = 0
lines = cv2.HoughLinesP(edges,1,np.pi/180,10,minLineLength,maxLineGap)
for x1,y1,x2,y2 in lines[0]:
cv2.line(img,(x1,y1),(x2,y2),(0,255,0),4)
cv2.imwrite('houghlines.png',img)
我的实际结果在"hoghlines"附图中。出现的是一条绿色的垂直线,但我需要一条水平线,这样我才能检测液位。
提前谢谢。
管
边缘
霍格林
我查看了您的代码并修改了一些内容,我在这里看到了一些 OpenCV 输入链接描述的文档。
我有这个结果,我不知道这是否是你需要的。
import cv2
import numpy as np
img = cv2.imread('tube.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite('gray.png',gray)
edges = cv2.Canny(gray,350,720, apertureSize = 3)
cv2.imwrite('edges.png',edges)
rho = 1 # distance resolution in pixels of the Hough grid
theta = np.pi / 180 # angular resolution in radians of the Hough grid
threshold = 10 # 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)
lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)
cv2.imwrite('houghlines.png',lines_edges)
霍格林.png
在此处查找类似问题 在此处输入链接说明
祝你好运。
检查它是否是你需要的,问候。
import cv2
import numpy as np
import math
img = cv2.imread('tube.png')
#img = cv2.resize(img,(360,480))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,350,720, apertureSize = 3)
#cv2.imshow("edges", edges)
rho = 1
#theta = np.pi / 180 #CHANGE FOR MATH.pi/1
threshold = 10 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 2 # minimum number of pixels making up a line
max_line_gap = 480 # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0 # creating a blank to draw lines on
lines = cv2.HoughLinesP(edges, rho, math.pi/1, threshold, np.array([]),
min_line_length, max_line_gap);
#coordinates
dot1 = (lines[0][0][0],lines[0][0][1])
dot2 = (lines[0][0][2],lines[0][0][3])
dot3 = (lines[0][0][1],lines[0][0][1])
cv2.line(img, dot1, dot2, (255,0,0), 3)
cv2.line(img, dot1, dot3, (0,255,0), 3)
cv2.imshow("output", img)
length = lines[0][0][1] - lines[0][0][3]
print ('Pixels Level', length)
if cv2.waitKey(0) & 0xFF == 27:
cv2.destroyAllWindows()
线条图像
端子输出
祝你好运。