CODEWITHSUNDEEP
pythonopencv
Alaa Ali
Alaa Ali

Reputation: 926

How can I find the angle of a T shape in OpenCV

I'm using OpenCV 2.4 to do some tracking, and I can get a contour of the shape I want, which is a T.

Input image: enter image description here

I can use cv2.minAreaRect(my_t_contour) and get the angle of that rect, but that only gives me 0-180 degrees. But this is a T shape though, so I want to be able to tell 0-360. I was thinking of:

  1. Split the contour into two rects
  2. Get a line through the rects (either using skeletonize > HoughLinesP)
  3. Determine which line is which, determine their gradient (using the coordinates I get from HoughLinesP) and then determine the direction of the T.

But I'm stuck at number 1, how can I split a contour into two shapes?

Method 1: draw center of contour and center of minAreaRect of contour

dst = cv2.cvtColor(r_target, cv2.COLOR_BGR2GRAY)
dst = cv2.GaussianBlur(dst, (11, 11), 0)
ret,dst = cv2.threshold(dst,110,255,cv2.THRESH_BINARY_INV)
cnts = cv2.findContours(dst, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
    # get minAreaRect around contour and draw its center in red
    rect = cv2.minAreaRect(c)
    cv2.circle(r_target, (int(rect[0][0]), int(rect[0][1])), 7, (0, 0, 255), -1)

    # get moments of contour to get center and draw it in white
    M = cv2.moments(c)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    cv2.circle(r_target, (cX, cY), 7, (255, 255, 255), -1)

enter image description here

Next step would probably calculate a simple gradient between the centers to determine the angle.

Method 2: skeletonize the image and get lines using HoughLinesP.

dst = cv2.cvtColor(r_target, cv2.COLOR_BGR2GRAY)
dst = cv2.GaussianBlur(dst, (11, 11), 0)
ret,dst = cv2.threshold(dst,110,255,cv2.THRESH_BINARY)
dst = 1 - dst / 255
dst = skimage.morphology.skeletonize(dst).astype(np.uint8)
rho = 1
theta = np.pi / 180
threshold = 1
minLineLength = 30
maxLineGap = 15
lines = cv2.HoughLinesP(dst, rho, theta, threshold, minLineLength=minLineLength, maxLineGap=maxLineGap)
for line in lines[0]:
    cv2.line(r_target, (line[0], line[1]), (line[2], line[3]), (0, 255, 0), 1, 8)

But the lines don't come out nicely. This is how the skeleton looks like:

enter image description here

I'm still experimenting with the variables but is there a specific thought process around using HoughLinesP?

Upvotes: 3

Views: 2478

Answers (1)

Andrey Smorodov
Andrey Smorodov

Reputation: 10850

As a variant you can use PCA, find first component direction, and use it as an searced angle. You can check here for an example: http://docs.opencv.org/trunk/d1/dee/tutorial_introduction_to_pca.html

Upvotes: 1

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