我正在嘗試在管中應(yīng)用霍夫概率變換，并且我已經(jīng)有了一個(gè)經(jīng)過良好過濾的圖像（邊緣）。我需要識別管中間的任何這些直線（附圖），這樣我就可以檢測到液位，但我不能這樣做。有誰知道我該如何解決這個(gè)問題？import cv2import numpy as npimg = 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 = 30maxLineGap = 0lines = 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)我的實(shí)際結(jié)果在“houghlines”附圖中。出現(xiàn)的是一條綠色的垂直線，但我需要一條水平線，這樣我才能檢測到液位。
2 回答
慕的地8271018
TA貢獻(xiàn)1796條經(jīng)驗(yàn) 獲得超4個(gè)贊
檢查它是否是你需要的，問候。
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()
猛跑小豬
TA貢獻(xiàn)1858條經(jīng)驗(yàn) 獲得超8個(gè)贊
我查看了你的代碼并修改了一些東西，我看到了一些 OpenCV 的文檔在此處輸入鏈接描述。
我有這個(gè)結(jié)果，不知道是不是你需要的。
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)
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