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kandi X-RAY | hough-transform Summary
kandi X-RAY | hough-transform Summary
http://fremycompany.com/BG/2016/Detect-lines-in-an-image-using-JavaScript-353/
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- Run the final step
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- Increments the MD - value of a quadrant .
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QUESTION
I want to find the middle of a coin and find the radii. The radii can tell me if the coin is 5 cents of 50 cents. After finding the image, I must be able to detect the middle of the circles so I can give them a color
I already made a beginning with writing a code for Hough-transform
Hopefully someone can help me solve this exercise.
Code for hough transform:
...ANSWER
Answered 2022-Mar-29 at 11:59You have 2 choices to find matching circles.
first one: sliding window
smaller windows is a template that you seek for it in input image, here two images with different radii 19 and 21 respectively. First at all, find edge of template and input image, after that, multiplying cropped part of image with template. each region that is near to template should have higher value.
QUESTION
I am working on a detection of LEGO City Rails to obtain if there is a curve and and whats the curve radius or angle.
As you can see I already cropped the picture. I also used different algorithms for edge detection like canny or just sobel operators.
Unfortunately the railroad ties make it really hard to detect the curve properly. I already tried hough transform, but this doesn't work well. Also the OpenCV-function cv2.findContours
doesn't work very well.
The following picture shows my current status which uses Canny Edge Detection. The red lines in the picture show the lines generated by the probablistic Hough-Transform. I already removed all horizontal lines and all lines with an angle smaller than 10 degree generated by the hough transform.
Why doesn't the hough transform detect all lines in this picture?
Do you guys have any suggestions how I can detect the rails properly?
Thank you in advance!
...ANSWER
Answered 2021-Apr-23 at 12:49import cv2
import numpy as np
def empty(a):
pass
path = 'media/SVIi0.png'
cv2.namedWindow("TrackBars")
cv2.resizeWindow("TrackBars",640,240)
cv2.createTrackbar("Hue Min","TrackBars",11,179,empty)
cv2.createTrackbar("Hue Max","TrackBars",66,179,empty)
cv2.createTrackbar("Sat Min","TrackBars",0,255,empty)
cv2.createTrackbar("Sat Max","TrackBars",255,255,empty)
cv2.createTrackbar("Val Min","TrackBars",21,255,empty)
cv2.createTrackbar("Val Max","TrackBars",255,255,empty)
while True:
img = cv2.imread(path)
imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
h_min = cv2.getTrackbarPos("Hue Min","TrackBars")
h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
print(h_min,h_max,s_min,s_max,v_min,v_max)
lower = np.array([h_min,s_min,v_min])
upper = np.array([h_max,s_max,v_max])
mask = cv2.inRange(imgHSV,lower,upper)
imgResult = cv2.bitwise_and(img,img,mask=mask)
cv2.imshow("Mask Images", mask)
cv2.imshow("Original Images", img)
cv2.waitKey(1)
QUESTION
I have a binary image (BW_roi.mat), displayed below. And I wanted to measure the length of every line segment.
I tried the solution given in this link hough-transform. But it does not work out for me. It just measured the length of some lines as shown below.
I tried the other code
...ANSWER
Answered 2021-Feb-12 at 05:15I'd propose using convexhull in combination with the algorithm here to reduce the number of vertices in the detected polygons. Seems to work pretty well. I'll leave it to you to use the adjacency matrix produced by bwboundaries
to throw away duplicates.
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