Using OpenCV in Python, how do I get the extreme point of a distinct detected contour?

We may find minimum area rectangle of each contour, and mark the points as the center point between each of the vertical edges of the rectangle.

We have to assume that the contours are in horizontal pose (small angle is allowed).

For filtering small contours (considered to be noise), we may find contour area and limit the minimum area to say 100 pixels.

Finding the extreme points (as centers if edges) is not so trivial, because the cv2.minAreaRect and cv2.boxPoints(rect) don't sort the points.

• We may have to check the angle of the box, and rotate the box by 90 degrees if box is vertical (if box angle is vertical):

   rect = cv2.minAreaRect(c)  # Find minimum area rectangle for finding the line width
   cx, cy = rect[0]
   w, h = rect[1]
   alpha = rect[2]
  
   # Rotate the box by 90 degrees if line is vertical (it's probably not the best solution...)
   if np.abs(alpha) > 45:
       rect = ((cx, cy), (h, w), 90 - alpha)
  

• Find the extreme points as the centers of the vertical edges:

   box = cv2.boxPoints(rect)
   x0 = (box[0, 0] + box[1, 0])/2
   y0 = (box[0, 1] + box[1, 1])/2
   x1 = (box[2, 0] + box[3, 0])/2
   y1 = (box[2, 1] + box[3, 1])/2
   extLeft = (int(x0), int(y0))
   extRight = (int(x1), int(y1))
  

• Swap left and right points if required:

   if x0 > x1:
       extLeft, extRight = extRight, extLeft  # Swap left and right
  

Updated code sample:

#!/usr/bin/env python
import numpy as np
import cv2

image = cv2.imread("img.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)

thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)

#ocv = cv2.ximgproc.thinning(thresh,20)

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]

for c in cnts:
    area = cv2.contourArea(c)  # Compute the area
    if area > 100:  # Assume area above 100 applies valid contour.
        rect = cv2.minAreaRect(c)  # Find minimum area rectangle for finding the line width
        cx, cy = rect[0]
        w, h = rect[1]
        alpha = rect[2]

        # Rotate the box by 90 degrees if line is vertical (it's probably not the best solution...)
        if np.abs(alpha) > 45:
            rect = ((cx, cy), (h, w), 90 - alpha)

        box = cv2.boxPoints(rect)
        x0 = (box[0, 0] + box[1, 0])/2
        y0 = (box[0, 1] + box[1, 1])/2
        x1 = (box[2, 0] + box[3, 0])/2
        y1 = (box[2, 1] + box[3, 1])/2
        #cv2.line(image, (int(x0), int(y0)), (int(x1), int(y1)), (0, 255, 0))  # Draw the line for testing
        #cv2.drawContours(image,np.int0(box),0,(0,0,255),2)

        extLeft = (int(x0), int(y0))
        extRight = (int(x1), int(y1))

        if x0 > x1:
            extLeft, extRight = extRight, extLeft  # Swap left and right

        cv2.circle(image, extLeft, 8, (0, 0, 255), -1)
        cv2.circle(image, extRight, 8, (0, 255, 0), -1)

        
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output image:

We may also use trigonometry:

#!/usr/bin/env python
import numpy as np
import cv2

image = cv2.imread("img.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)

thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]

for c in cnts:
    area = cv2.contourArea(c)  # Compute the area
    if area > 100:  # Assume area above 100 applies valid contour.
        rect = cv2.minAreaRect(c)  # Find minimum area rectangle for finding the line width
        cx, cy = rect[0]
        w, h = rect[1]
        alpha = rect[2]

        if alpha > 180:
            alpha -= 360

        # Rotate the box by 90 degrees if line is vertical (it's probably not the best solution...)
        if np.abs(alpha) > 45:
            alpha = 90 - alpha
            rect = ((cx, cy), (h, w), alpha)

        w, h = rect[1]
        alpha = np.deg2rad(alpha)
        x0 = cx - w/2*np.cos(alpha)
        y0 = cy - h/2*np.sin(alpha)
        x1 = cx + w/2*np.cos(alpha)
        y1 = cy + h/2*np.sin(alpha)
        
        extLeft = (int(x0), int(y0))
        extRight = (int(x1), int(y1))

        cv2.circle(image, extLeft, 8, (0, 0, 255), -1)
        cv2.circle(image, extRight, 8, (0, 255, 0), -1)
        
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output of the other sample image: