I'm hoping that I can get some help in improving my ellipse fitting method. I'm thinking of trying to use a RANSAC style method but I'm not sure if it is the right direction to go in. Any help on the direction I should begin going in would be greatly appreciated, even if it is just an improvement on my edge finding.
I've been working on this problem for a while, and I'm not making very much progress. I think the main problem is the quality of the images but I can only work which what I have now.
My current method I am testing is to use edge detection on the image, and then try and to fit ellipses around the edges I find. The images below will highlight my main problem which is that my method deals very poorly with noise.
Original Image: https://i.sstatic.net/QRYCD.jpg
After Canny Edge detection: https://i.sstatic.net/4Yl8o.png
After Ellipse Fitting: https://i.sstatic.net/waHzJ.jpg
Below is my code which I use. For the Canny edge detection I found some values and are using them statically for now. It is code taken from online which I have then modified, and is a bit hacky for now sorry.
#!/usr/bin/python import cv2 import numpy as np import sys from numpy.linalg import eig, inv # param is the result of canny edge detection def process_image(img): # for every pixel in the image: for (x,y), intensity in np.ndenumerate(img): # if the pixel is part of an edge: if intensity == 255: # determine if the edge is similar to an ellipse ellipse_test(img, x, y) def ellipse_test(img, i, j): #poor coding practice but what I'm doing for now global output, image i_array = [] j_array = [] # flood fill i,j while storing all unique i,j values in arrays flood_fill(img, i, j, i_array, j_array) i_array = np.array(i_array) j_array = np.array(j_array) if i_array.size >= 10: #put those values in a numpy array #which can have an ellipse fit around it array = [] for i, elm in enumerate(i_array): array.append([int(j_array[i]), int(i_array[i])]) array = np.array([array]) ellp = cv2.fitEllipse(array) cv2.ellipse(image, ellp, (0,0,0)) cv2.ellipse(output, ellp, (0,0,0)) def flood_fill(img, i, j, i_array, j_array): if img[i][j] != 255: return # store i,j values i_array.append(float(i)) j_array.append(float(j)) # mark i,j as 'visited' img[i][j] = 250 # flood_fill adjacent and diagonal pixels (i_max, j_max) = img.shape if i - 1 > 0 and j - 1 > 0: flood_fill(img, i - 1, j - 1, i_array, j_array) if j - 1 > 0: flood_fill(img, i, j - 1, i_array, j_array) if i - 1 > 0: flood_fill(img, i - 1, j, i_array, j_array) if i + 1 < i_max and j + 1 < j_max: flood_fill(img, i + 1, j + 1, i_array, j_array) if j + 1 < j_max: flood_fill(img, i, j + 1, i_array, j_array) if i + 1 < i_max: flood_fill(img, i + 1, j, i_array, j_array) if i + 1 < i_max and j - 1 > 0: flood_fill(img, i + 1, j - 1, i_array, j_array) if i - 1 > 0 and j + 1 < j_max: flood_fill(img, i - 1, j + 1, i_array, j_array) image = cv2.imread(sys.argv[1], 0) canny_result = cv2.GaussianBlur(image, (3,3), 0) canny_result = cv2.Canny(canny_result, 107, 208, apertureSize=3, L2gradient=False) #output is a blank images which the ellipses are drawn on output = np.zeros(image.shape, np.uint8) output[:] = [255] cv2.waitKey(0) cv2.namedWindow("Canny result:", cv2.WINDOW_NORMAL) cv2.imshow('Canny result:', canny_result) print "Press any key to find the edges" cv2.waitKey(0) print "Now finding ellipses" process_image(canny_result) print "Ellipses found!" cv2.namedWindow("Original image:", cv2.WINDOW_NORMAL) cv2.imshow('Original image:', image) cv2.namedWindow("Output image:", cv2.WINDOW_NORMAL) cv2.imshow("Output image:", output) cv2.waitKey(0) 
