def main(file_name):


	# import the necessary packages
	from imutils import contours
	from skimage import measure
	import numpy as np
	import argparse
	import imutils
	import cv2
	import random



	''' 
	# construct the argument parse and parse the arguments
	ap = argparse.ArgumentParser()
	ap.add_argument("-i", "--image", required=True,
		help="path to the image file")
	args = vars(ap.parse_args())'''

	# load the image, convert it to grayscale, and blur it
	image = cv2.imread(file_name)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	blurred = cv2.GaussianBlur(gray, (11, 11), 0)

	'''cv2.imshow("image", blurred)
	cv2.waitKey(0)'''


	thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]

	# perform a series of erosions and dilations to remove
	# any small blobs of noise from the thresholded image
	thresh = cv2.erode(thresh, None, iterations=2)
	thresh = cv2.dilate(thresh, None, iterations=4)

	# perform a connected component analysis on the thresholded
	# image, then initialize a mask to store only the "large"
	# components
	labels = measure.label(thresh, neighbors=8, background=0)
	mask = np.zeros(thresh.shape, dtype="uint8")
	
	# loop over the unique components
	for label in np.unique(labels):
		# if this is the background label, ignore it
		if label == 0:
			continue
	
		# otherwise, construct the label mask and count the
		# number of pixels 
		labelMask = np.zeros(thresh.shape, dtype="uint8")
		labelMask[labels == label] = 255
		numPixels = cv2.countNonZero(labelMask)
	
		# if the number of pixels in the component is sufficiently
		# large, then add it to our mask of "large blobs"
		if numPixels > 300:
			mask = cv2.add(mask, labelMask)

		# find the contours in the mask, then sort them from left to
	# right
	cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	cnts = imutils.grab_contours(cnts)
	cnts = contours.sort_contours(cnts)[0]
	
	# loop over the contours
	for (i, c) in enumerate(cnts):
		# draw the bright spot on the image
		(x, y, w, h) = cv2.boundingRect(c)
		((cX, cY), radius) = cv2.minEnclosingCircle(c)
		cv2.circle(image, (int(cX), int(cY)), int(radius),
			(0, 0, 255), 2)
		'''cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)'''
	

	t=random.randint(1,100)
	# show the output image
	cv2.imshow("Image", image)
	#cv2.waitKey(0)

	cv2.imwrite("%s.jpg"%t, image)	
	cv2.destroyAllWindows()






def main(file_name):


	# import the necessary packages
	from imutils import contours
	from skimage import measure
	import numpy as np
	import argparse
	import imutils
	import cv2
	import random



	''' 
	# construct the argument parse and parse the arguments
	ap = argparse.ArgumentParser()
	ap.add_argument("-i", "--image", required=True,
		help="path to the image file")
	args = vars(ap.parse_args())'''

	# load the image, convert it to grayscale, and blur it
	image = cv2.imread(file_name)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	blurred = cv2.GaussianBlur(gray, (11, 11), 0)

	'''cv2.imshow("image", blurred)
	cv2.waitKey(0)'''


	thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]

	# perform a series of erosions and dilations to remove
	# any small blobs of noise from the thresholded image
	thresh = cv2.erode(thresh, None, iterations=2)
	thresh = cv2.dilate(thresh, None, iterations=4)

	# perform a connected component analysis on the thresholded
	# image, then initialize a mask to store only the "large"
	# components
	labels = measure.label(thresh, neighbors=8, background=0)
	mask = np.zeros(thresh.shape, dtype="uint8")
	
	# loop over the unique components
	for label in np.unique(labels):
		# if this is the background label, ignore it
		if label == 0:
			continue
	
		# otherwise, construct the label mask and count the
		# number of pixels 
		labelMask = np.zeros(thresh.shape, dtype="uint8")
		labelMask[labels == label] = 255
		numPixels = cv2.countNonZero(labelMask)
	
		# if the number of pixels in the component is sufficiently
		# large, then add it to our mask of "large blobs"
		if numPixels > 300:
			mask = cv2.add(mask, labelMask)

		# find the contours in the mask, then sort them from left to
	# right
	cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	cnts = imutils.grab_contours(cnts)
	cnts = contours.sort_contours(cnts)[0]
	
	# loop over the contours
	for (i, c) in enumerate(cnts):
		# draw the bright spot on the image
		(x, y, w, h) = cv2.boundingRect(c)
		((cX, cY), radius) = cv2.minEnclosingCircle(c)
		cv2.circle(image, (int(cX), int(cY)), int(radius),
			(0, 0, 255), 2)
		'''cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)'''
	

	t=random.randint(1,100)
	# show the output image
	cv2.imshow("Image", image)
	#cv2.waitKey(0)

	cv2.imwrite("%s.jpg"%t, image)	
	cv2.destroyAllWindows()






# import the necessary packages # construct the argument parse and parse the arguments 	ap = argparse.ArgumentParser() 	ap.add_argument("-i", "--image", required=True, 		help="path to the image file") 	args = vars(ap.parse_args()) # load the image, convert it to grayscale, and blur it cv2.imshow("image", blurred) 	cv2.waitKey(0) # perform a series of erosions and dilations to remove # any small blobs of noise from the thresholded image # perform a connected component analysis on the thresholded # image, then initialize a mask to store only the "large" # components # loop over the unique components # if this is the background label, ignore it # otherwise, construct the label mask and count the # number of pixels # if the number of pixels in the component is sufficiently # large, then add it to our mask of "large blobs" # find the contours in the mask, then sort them from left to # right # loop over the contours # draw the bright spot on the image cv2.putText(image, "#{}".format(i + 1), (x, y - 15), 			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # show the output image #cv2.waitKey(0)