Circle Detection

HoughTransform.py

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+import cv2
+import numpy as np
+
+
+#originalImage = cv2.imread('Input.jpg',1)
+#originalImage = cv2.imread('/Users/mitalibhiwande/Downloads/coins1.jpg',0)
+originalImage = cv2.imread('/Users/mitalibhiwande/Downloads/coins.jpg',1)
+cv2.imshow('Original Image',originalImage)
+
+output = originalImage.copy()
+
+#Applying Gausssian Blur on input image
+blurredImage = cv2.GaussianBlur(originalImage,(3,3),0)
+cv2.imshow('Gaussian Blurred Image',blurredImage)
+
+#Detecting edges in Image using Canny edge Detector
+edgeDetectedImage = cv2.Canny(blurredImage,60,100)
+cv2.imshow('Edge Detected Image', edgeDetectedImage)
+
+im = cv2.imread("/Users/mitalibhiwande/Downloads/coins.jpg",cv2.IMREAD_GRAYSCALE)
+#im = cv2.imread("Input.jpg",cv2.IMREAD_GRAYSCALE)
+params = cv2.SimpleBlobDetector_Params()
+
+# Change thresholds
+params.minThreshold = 50
+params.maxThreshold = 200
+
+
+# Filter by Area.
+#params.filterByArea = True
+#params.minArea = 1500
+
+## Filter by Circularity
+#params.filterByCircularity = True
+#params.minCircularity = 0.8
+#
+## Filter by Convexity
+#params.filterByConvexity = True
+#params.minConvexity = 0.87
+#    
+## Filter by Inertia
+#params.filterByInertia = True
+#params.minInertiaRatio = 0.01
+
+detector = cv2.SimpleBlobDetector()
+
+keypoints=detector.detect(im)
+#print keypoints.
+keys=cv2.drawKeypoints(edgeDetectedImage,keypoints,np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+cv2.imshow("a", keys)
+sizes=[]
+for keyPoint in keypoints:
+
+    x = keyPoint.pt[0]
+    y = keyPoint.pt[1]
+    s = keyPoint.size
+    sizes.append(s)
+
+print sizes
+
+height,width = edgeDetectedImage.shape
+print height,width
+radiivalue = int(min(height,width)/2)
+
+accumulator_array = np.zeros(((height,width,radiivalue)))
+
+def fill_accumulator_array(x0,y0,radius):
+    x = radius
+    y=0
+    decision=1-x
+
+    while(y<x):
+        if(x + x0<height and y + y0<width):
+            accumulator_array[ x + x0,y + y0,radius]+=1; # Octant 1
+        if(y + x0<height and x + y0<width):
+            accumulator_array[ y + x0,x + y0,radius]+=1; # Octant 2
+        if(-x + x0<height and y + y0<width):
+            accumulator_array[-x + x0,y + y0,radius]+=1; # Octant 4
+        if(-y + x0<height and x + y0<width):
+            accumulator_array[-y + x0,x + y0,radius]+=1; # Octant 3
+        if(-x + x0<height and -y + y0<width):
+            accumulator_array[-x + x0,-y + y0,radius]+=1; # Octant 5
+        if(-y + x0<height and -x + y0<width):
+            accumulator_array[-y + x0,-x + y0,radius]+=1; # Octant 6
+        if(x + x0<height and -y + y0<width):
+            accumulator_array[ x + x0,-y + y0,radius]+=1; # Octant 8
+        if(y + x0<height and -x + y0<width):
+            accumulator_array[ y + x0,-x + y0,radius]+=1; # Octant 7
+        y+=1
+        if(decision<=0):
+            decision += 2 * y + 1
+        else:
+            x=x-1;
+            decision += 2 * (y - x) + 1
+
+
+edges = np.where(edgeDetectedImage==255)
+for i in xrange(0,len(edges[0])):
+    x=edges[0][i]
+    y=edges[1][i]
+    for radius in xrange(int(min(sizes)/2),int(max(sizes)*2)):
+        fill_accumulator_array(x,y,radius)
+
+print accumulator_array
+
+i=0
+j=0
+filter3D = np.zeros((30,30,radiivalue))
+filter3D[:,:,:]=1
+
+while(i<height-30):
+    while(j<width-30):
+        filter3D=accumulator_array[i:i+30,j:j+30,:]*filter3D
+        max_pt = np.where(filter3D==filter3D.max())
+        a = max_pt[0]       
+        b = max_pt[1]
+        c = max_pt[2]
+        b=b+j
+        a=a+i
+        if(filter3D.max()>90):
+            cv2.circle(output,(b,a),c,(255,0,0),3)
+        j=j+30
+        filter3D[:,:,:]=1
+    j=0
+    i=i+30
+
+
+cv2.imshow('Detected circle',output)
+
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+