Added code for hough circle in coin_detection.py file.

Author: VaneeshK

size-of-objects/coin_detection.py

@@ -5,27 +5,28 @@
 # load the image, clone it for output, and then convert it to grayscale
 image_file = '/Users/vaneesh_k/PycharmProjects/Albie_ML/size-of-objects/images/coin.png'
 image = cv2.imread(image_file)
-image = cv2.resize(image,(800,800))
+image = cv2.resize(image, (800, 800))
 output = image.copy()
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+# mask =
 
+cv2.imshow('mask',gray)
 # detect circles in the image
-circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 1,minRadius=5,maxRadius=10)
-print('FOund some circles')
+circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 4.0, 100, 100.0, 30.0, 20, 200)
+print('Found some circles')
 # ensure at least some circles were found
 if circles is not None:
-	print('Found circle')
-	# convert the (x, y) coordinates and radius of the circles to integers
-	circles = np.round(circles[0, :]).astype("int")
+    print('filterd circles')
+    # convert the (x, y) coordinates and radius of the circles to integers
+    circles = np.round(circles[0, :]).astype("int")
 
+    # loop over the (x, y) coordinates and radius of the circles
+    for (x, y, r) in circles:
+        # draw the circle in the output image, then draw a rectangle
+        # corresponding to the center of the circle
+        cv2.circle(output, (x, y), r, (0, 255, 0), 4)
+        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
 
-	# loop over the (x, y) coordinates and radius of the circles
-	for (x, y, r) in circles:
-		# draw the circle in the output image, then draw a rectangle
-		# corresponding to the center of the circle
-		cv2.circle(output, (x, y), r, (0, 255, 0), 4)
-		cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
-
-	# show the output image
-	cv2.imshow("output", np.hstack([image, output]))
-	cv2.waitKey(0)
+    # show the output image
+    cv2.imshow("output", np.hstack([image, output]))
+    cv2.waitKey(0)

size-of-objects/images/coin.png

@@ -12,7 +12,7 @@ def midpoint(ptA, ptB):
 
 
 
-image = '/Users/vaneesh_k/PycharmProjects/Albie_ML/size-of-objects/images/card2.png'
+image = '/Users/vaneesh_k/PycharmProjects/Albie_ML/size-of-objects/images/coin.png'
 # load the image, convert it to grayscale, and blur it slightly
 image = cv2.imread(image)
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -40,7 +40,7 @@ def midpoint(ptA, ptB):
 # loop over the contours individually
 for c in cnts:
     # if the contour is not sufficiently large, ignore it
-    if not 150 <= cv2.contourArea(c) <= 200:
+    if not 20 <= cv2.contourArea(c) <= 150:
         continue
 
     # compute the rotated bounding box of the contour