how to find corners points of a shape in an image in opencv?

I have to find corners of shapes in an image. i have used Harris corner detection algorithm to find corner, but it is giving total corners present in an image and for finding corners for a particular shape in that image it is not feasible. please suggest some other approach.

You could use Harris corner detection algorithm. Corners are junction of two edges, where an edge is a sudden change in image brightness. This algorithm takes the differential of the corner score into account with reference to direction directly (wikipedia). Function cornerSubPix() refines the corner location - it iterates to find the sub-pixel accurate location of corners or radial saddle points (opencv documentation).

Example in code:

import cv2
import numpy as np


img = cv2.imread('edges.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,5,3,0.04)
ret, dst = cv2.threshold(dst,0.1*dst.max(),255,0)
dst = np.uint8(dst)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria)
for i in range(1, len(corners)):
    print(corners[i])
img[dst>0.1*dst.max()]=[0,0,255]
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows

to check if they are the real values you can add:

for i in range(1, len(corners)):
    print(corners[i,0])
    cv2.circle(img, (int(corners[i,0]), int(corners[i,1])), 7, (0,255,0), 2)

Result:

EDIT:

If you want to extract corners seperatly for every shape you could first search for contours then apply the Harris corner detection for each contour (you can draw it out on a mask with cv2.fillPolly() ). You can even define their shape based on their caracteristics (for example angle of rotation, number of corners,...). I have made an example code to help understand but note that there are other shapes that could fit the criteria I made up and you would have make other criteria (trapezoid, circle,...). This is just a simple example:

import cv2
import numpy as np


img = cv2.imread('edges.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,150,255,cv2.THRESH_BINARY)
im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

for i in contours:
    img = cv2.imread('edges.png')
    size = cv2.contourArea(i)
    rect = cv2.minAreaRect(i)
    if size <10000:
        gray = np.float32(gray)
        mask = np.zeros(gray.shape, dtype="uint8")
        cv2.fillPoly(mask, [i], (255,255,255))
        dst = cv2.cornerHarris(mask,5,3,0.04)
        ret, dst = cv2.threshold(dst,0.1*dst.max(),255,0)
        dst = np.uint8(dst)
        ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
        corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria)
        if rect[2] == 0 and len(corners) == 5:
            x,y,w,h = cv2.boundingRect(i)
            if w == h or w == h +3: #Just for the sake of example
                print('Square corners: ')
                for i in range(1, len(corners)):
                    print(corners[i])
            else:
                print('Rectangle corners: ')
                for i in range(1, len(corners)):
                    print(corners[i])
        if len(corners) == 5 and rect[2] != 0:
            print('Rombus corners: ')
            for i in range(1, len(corners)):
                print(corners[i])
        if len(corners) == 4:
            print('Triangle corners: ')
            for i in range(1, len(corners)):
                print(corners[i])
        if len(corners) == 6:
            print('Pentagon corners: ')
            for i in range(1, len(corners)):
                print(corners[i])
        img[dst>0.1*dst.max()]=[0,0,255]
        cv2.imshow('image', img)
        cv2.waitKey(0)
        cv2.destroyAllWindows