# perform a series of erosions and dilations to remove# any small blobs of noise from the thresholded imagethresh = 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"# componentslabels = measure.label(thresh, neighbors=8, background=0)mask = np.zeros(thresh.shape, dtype="uint8")# loop over the unique componentsfor 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# rightcnts = 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 contoursfor (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), 3) cv2.putText(image, "#{}".format(i + 1), (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)# show the output imagecv2.imshow("Image", image)cv2.waitKey(0)