Detect star shape in opencv-python

Question

I need to detect the shapes and count the occurence of each shape in the image.I initially detected the contours and approximated them ,and counted the vertices in each of the contour present.My code looks like this:

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 doesnt work for detecting the stars in this image:

What changes should I make in the code?

from (dense or sparse) contour, try matchShape function: docs.opencv.org/2.4/modules/imgproc/doc/… — Micka
– Micka, Commented Dec 10, 2015 at 13:46
You can use circularity to detect shapes: (4*pi*area) / (perimeter^2). Star shapes have circularity around 0.25, for example — Miki
– Miki, Commented Dec 10, 2015 at 14:01

Rachel L · Accepted Answer · 2015-12-11 19:37:49Z

What worked for me was a comparison between the square root of the area over the perimeter of the shape. It's about 0.145 for a star (+/- .0015 because some of the edges didn't come out perfectly). 0.255 for the hexagon, .21 for the triangles, .247 for the square, and .250 for the pentagon.

Circularity also does work (which has the triangles come in at 0.26 to .27), and it differentiates similarly (.83 for the hexagon, .55-.56 for the triangle, .77 for the square, and .78 for the pentagon)

Below is the C++ code for it (I don't have python on my PC here, but the idea's 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); }

Both ways - either using circularity or my sqrt(area)/arclength method - result in: image with stars highlighted

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