九色国产,午夜在线视频,新黄色网址,九九色综合,天天做夜夜做久久做狠狠,天天躁夜夜躁狠狠躁2021a,久久不卡一区二区三区

# import the necessary packagesfrom skimage.feature import peak_local_maxfrom skimage.morphology import watershedfrom scipy import ndimageimport numpy as npimport argparseimport imutilsimport cv2 # load the image and perform pyramid mean shift filtering# to aid the thresholding stepimage = cv2.imread('1.jpg')shifted = cv2.pyrMeanShiftFiltering(image, 21, 51)cv2.imshow('Input', image) # convert the mean shift image to grayscale, then apply# Otsu's thresholdinggray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)thresh = cv2.threshold(gray, 0, 255,  cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]cv2.imshow('Thresh', thresh)
# compute the exact Euclidean distance from every binary# pixel to the nearest zero pixel, then find peaks in this# distance mapD = ndimage.distance_transform_edt(thresh)localMax = peak_local_max(D, indices=False, min_distance=10,  labels=thresh) # perform a connected component analysis on the local peaks,# using 8-connectivity, then appy the Watershed algorithmmarkers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]labels = watershed(-D, markers, mask=thresh)print('[INFO] {} unique segments found'.format(len(np.unique(labels)) - 1))
# loop over the unique labels returned by the Watershed# algorithmfor label in np.unique(labels):  # if the label is zero, we are examining the 'background'  # so simply ignore it  if label == 0:    continue   # otherwise, allocate memory for the label region and draw  # it on the mask  mask = np.zeros(gray.shape, dtype='uint8')  mask[labels == label] = 255   # detect contours in the mask and grab the largest one  cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,    cv2.CHAIN_APPROX_SIMPLE)  cnts = imutils.grab_contours(cnts)  c = max(cnts, key=cv2.contourArea)   # draw a circle enclosing the object  ((x, y), r) = cv2.minEnclosingCircle(c)  cv2.circle(image, (int(x), int(y)), int(r), (0, 255, 0), 2)  cv2.putText(image, '{}'.format(label), (int(x) - 10, int(y)),    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) # show the output imagecv2.imshow('Output', image)cv2.waitKey(0)cv2.destroyAllWindows()