'How to find corner (x, y) coordinate points on image Python OpenCV?

Canny Edge Detection

This is a truck container image but from the top view. First, I need to find the rectangle and know each corner position. The goal is to know the dimension of the container.



Solution 1:[1]

Here's a simple approach:

  1. Obtain binary image. Load image, convert to grayscale, Gaussian blur, then Otsu's threshold.

  2. Find distorted bounding rectangle contour and corners. We find contours then filter using contour area to isolate the rectangular contour. Next we find the distorted bounding rectangle with cv2.minAreaRect() and the corners with cv2.boxPoints()


Detected bounding rectangle -> Mask -> Detected corners

Corner points

(188, 351)
(47, 348)
(194, 32)
(53, 29)

Code

import cv2
import numpy as np

# Load image, grayscale, blur, Otsu's threshold
image = cv2.imread('1.png')
mask = np.zeros(image.shape[:2], dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find distorted bounding rect
cnts = cv2.findContours(thresh, 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:
        # Find distorted bounding rect
        rect = cv2.minAreaRect(c)
        corners = cv2.boxPoints(rect)
        corners = np.int0(corners)
        cv2.fillPoly(mask, [corners], (255,255,255))
        
        # Draw corner points
        corners = corners.tolist()
        print(corners)
        for corner in corners:
            x, y = corner
            cv2.circle(image, (x, y), 5, (36,255,12), -1)

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

Solution 2:[2]

minAreaRect applied to the contour is your best friend. There is no need for a corner detector.

https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga3d476a3417130ae5154aea421ca7ead9

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source
Solution 1
Solution 2 Yves Daoust