How to detect ellipse and remove outliers in image using opencv Python

Question

I'm trying to extract the dots that form an ellipse then draw it . But because of some points that can be considered as outliers, I got an invalid mask of ellipse. Like this:

Here is the code that I'm executing, but it always selects the outlier

`cv2.rectangle(cleanedpartiallyimage, (0, 0), (1200, 10), (0, 0, 0), -1)

cv2.rectangle(cleanedpartiallyimage, (0, 0), (47, 1200), (0, 0, 0), -1)

image = cv2.cvtColor(cleanedpartiallyimage, cv2.COLOR_BGR2HSV) lower = np.array([85, 0, 20], dtype="uint8")
 upper = np.array([95, 255, 255], dtype="uint8") mygray = cv2.inRange(image, lower, upper)

#--- Gaussian and Canny filters to make it easy to get the contours

blurred = cv2.GaussianBlur(mygray, (5, 5), 0) imageCanny = cv2.Canny(blurred, 0, 100, 0)

ret,th = cv2.threshold(imageCanny,127,255, 0)

#--- Find all the contours in the binary image --- 
contours,hierarchy = cv2.findContours(th,3,1) 
cnt = contours big_contour = [] max = 0 for i in cnt:

area = cv2.contourArea(i) #--- find the contour having biggest area --- 
if(area > max): max = area big_contour = i

final = cv2.drawContours(imageCanny, big_contour, -1, (0,255,0), 3)

actualcontours, hierarchy = cv2.findContours(final, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

#---Removing side contour points

actualcontours = getactualcontours(actualcontours, 60)

empty = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)

#---Removes linear contour points

ConvexHullPoints = contoursConvexHull(actualcontours)

#---Converts the points to Ellipse using fitEllipse

test41 = cv2.polylines(empty, [ConvexHullPoints], True, (255, 255, 255), 3) 
imageCannyee = cv2.Canny(test41, 0, 100, 0) 
contours, hierarchy = cv2.findContours(imageCannyee, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) 
for cont in contours: 
   if len(cont) < 20: 
    break 
   elps = cv2.fitEllipse(cont) 
anotherempty = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
#---Drawing the ellipse into the empty mask
cv2.ellipse(anotherempty, elps, (255, 255, 255), 2) plt.imshow(anotherempty)

Answer 1

Here's a simple approach:

1. Obtain binary image. We load the image, convert to grayscale, Gaussian blur, then Otsu's threshold to obtain a binary image.

2. Dilate to form single contour. Next we create an elliptical shaped kernel using cv2.getStructuringElement with the cv2.MORPH_ELLIPSE parameter and dilate to combine small individual contours into a single large contour.

3. Identify ellipse. Next we find contours, filter using contour area and then detect the ellipse with cv2.fitEllipse().

---

import cv2

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Dilate with elliptical shaped kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
dilate = cv2.dilate(thresh, kernel, iterations=2)

# Find contours, filter using contour threshold area, draw ellipse
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 5000:
        ellipse = cv2.fitEllipse(c)
        cv2.ellipse(image, ellipse, (36,255,12), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('dilate', dilate)
cv2.imshow('image', image)
cv2.waitKey()