Star shape detection in opencv-python

Question

I need to identify the shapes and count the presence of each shape in the image. First, I discovered the contours and approximated them, and counted the vertices in each of the present contours. My code is as follows:

import cv2 import numpy as np import collections import sys img = cv2.imread(str(sys.argv[1]),0) ret,thresh = cv2.threshold(img,127,255,0) contours,hierarchy = cv2.findContours(thresh,1,2) no_of_vertices = [] i = 0 mask = np.zeros(img.shape,np.uint8) for contour in contours: cnt = contour area = cv2.contourArea(cnt) if area>150: epsilon = 0.02*cv2.arcLength(cnt,True) approx = cv2.approxPolyDP(cnt,epsilon,True) no_of_vertices.append(len(approx)) counter = collections.Counter(no_of_vertices) a,b = counter.keys(),counter.values() i=0 while i<len(counter): print a[i],b[i] i = i + 1 

My code does not work to detect stars in this image:

What changes should be made to the code?

Answer 1

What worked for me was comparing the square root of the area around the perimeter of the shape. This is about 0.145 for a star (+/-. 0015, because some of the edges did not come out perfectly). 0.255 for a hexagon, .21 for triangles, 0.247 for a square, and 0.250 for a pentagon.

Circularity also works (in which triangles range from 0.26 to 0.27), and it differentiates similarly (.83 for a hexagon, .55 -.56 for a triangle, .77 for a square, and .78 for a pentagon)

Below is the C ++ code (I don't have python here on my PC, but the idea is the same):

 #include "stdafx.h" #include <opencv/cxcore.h> #include <opencv2\core\mat.hpp> #include <opencv2/highgui/highgui.hpp> #include <iostream> #include <opencv/cxcore.h> #include <opencv/highgui.h> #include <opencv/cv.h> #include <opencv2/opencv.hpp> #include <opencv2/core/core.hpp> using namespace cv; using namespace std; RNG rngee(12345); int main() { Mat im = imread("C:/this/is.a/path/image.png", CV_LOAD_IMAGE_COLOR); Mat imgrey = im.clone(); cvtColor(im, imgrey, CV_RGB2GRAY); vector<vector<Point> > imContours; vector<Vec4i> hierarchy; double divMaxSize = 0.175, divMinSize = 0.125; namedWindow("Image", CV_WINDOW_NORMAL| CV_WINDOW_KEEPRATIO | CV_GUI_EXPANDED); threshold(imgrey, imgrey, 100, 255, 0); findContours(imgrey, imContours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) ); for (int i=0; i < imContours.size(); i++) { Scalar color = Scalar( rngee.uniform(0, 255), rngee.uniform(0,255), rngee.uniform(0,255) ); cout << "sqrt(Area)/arcLength = " << sqrt(contourArea(imContours[i]))/arcLength(imContours[i], true ) << endl; if(sqrt(contourArea(imContours[i]))/arcLength(imContours[i], true ) < divMaxSize && sqrt(contourArea(imContours[i]))/arcLength( imContours[i], true ) > divMinSize) { drawContours(im, imContours, i, color, 2, 8, hierarchy, 0, Point() ); cout << "I'm a star!" << endl; } imshow("Image", im); waitKey(0); } imshow("Image", im); waitKey(0); } 

In both cases - either using roundness, or using the sqrt (area) / arclength method - the result: