如何从分水岭分割中节省投资回报？

Question

我正在尝试使用分水岭算法从原始图像中分割白细胞。我想在分割过程之后将ROI或boundingbox保存为新图像，但我不知道如何在python中做

这是我的主要代码

import numpy as np
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from skimage.morphology import extrema
from skimage.morphology import watershed as skwater

def ShowImage(title,img,ctype):
  if ctype=='bgr':
    b,g,r = cv2.split(img)       # get b,g,r
    rgb_img = cv2.merge([r,g,b])     # switch it to rgb
    plt.imshow(rgb_img)
  elif ctype=='hsv':
    rgb = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
    plt.imshow(rgb)
  elif ctype=='gray':
    plt.imshow(img,cmap='gray')
  elif ctype=='rgb':
    plt.imshow(img)
  else:
    raise Exception("Unknown colour type")
  plt.title(title)
  plt.show()

img=cv2.imread('29-39.jpg')
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Color Threshold',img,'bgr')

cell_hsvmin  = (120,100,100)  #Lower end of the HSV range defining the nuclei
cell_hsvmax  = (155,255,255) #Upper end of the HSV range defining the nuclei
#Transform image to HSV color space
hsv          = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) 
#Threshold based on HSV values
color_thresh = cv2.inRange(hsv, cell_hsvmin, cell_hsvmax) 
#ShowImage('Color Threshold',color_thresh,'gray')
'''
masked = cv2.bitwise_and(img,img, mask=color_thresh)
ShowImage('Color Threshold Maksed',masked,'bgr')'''
kernel  = np.ones((8,8),np.uint8)
opening = cv2.morphologyEx(color_thresh,cv2.MORPH_OPEN, kernel, iterations = 2)
#ShowImage('Opening',opening,'gray')
'''
fraction_foreground = 0.6
dist         = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist,fraction_foreground*dist.max(),255,0)
ShowImage('Distance',dist_transform,'gray')
ShowImage('Surely Foreground',sure_fg,'gray')'''
#Identify areas which are surely foreground
h_fraction = 0.1
dist     = cv2.distanceTransform(opening,cv2.DIST_L2,5)
maxima   = extrema.h_maxima(dist, h_fraction*dist.max())
print("Peaks found: {0}".format(np.sum(maxima)))
#Dilate the maxima so we can see them
maxima   = cv2.dilate(maxima, kernel, iterations=2)
ShowImage('Distance',dist,'gray')
ShowImage('Surely Foreground',maxima,'gray')

# Finding unknown region
unknown = cv2.subtract(opening,maxima)
#ShowImage('Unknown',unknown,'gray')

# Marker labelling
ret, markers = cv2.connectedComponents(maxima)
ShowImage('Connected Components',markers,'rgb')

# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

# Now, mark the region of unknown with zero
markers[unknown==np.max(unknown)] = 0

ShowImage('markers',markers,'rgb')

dist    = cv2.distanceTransform(opening,cv2.DIST_L2,5)
labels = skwater(-dist,markers,watershed_line=True)

ShowImage('Watershed',labels,'rgb')
"""imgout = img.copy()
imgout[markers == 0] = [0,0,255] #Label the watershed_line

ShowImage('img',imgout,'bgr')
"""
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))

def grab_contours(cnts):
    # if the length the contours tuple returned by cv2.findContours
    # is '2' then we are using either OpenCV v2.4, v4-beta, or
    # v4-official
    if len(cnts) == 2:
        cnts = cnts[0]

    # if the length of the contours tuple is '3' then we are using
    # either OpenCV v3, v4-pre, or v4-alpha
    elif len(cnts) == 3:
        cnts = cnts[1]

    # otherwise OpenCV has changed their cv2.findContours return
    # signature yet again and I have no idea WTH is going on
    else:
        raise Exception(("Contours tuple must have length 2 or 3, "
            "otherwise OpenCV changed their cv2.findContours return "
            "signature yet again. Refer to OpenCV's documentation "
            "in that case"))

    # return the actual contours array
    return cnts
for 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 = grab_contours(cnts)
    c = max(cnts, key=cv2.contourArea)

    # draw a circle enclosing the object
    ((x, y), r) = cv2.minEnclosingCircle(c)
    cv2.circle(img, (int(x), int(y)), int(r), (0, 255, 0), 2)
    cv2.putText(img, "#{}".format(label), (int(x) - 10, int(y)),
        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

source和结果图像like this。我需要将每个标签（＃1，＃2，＃3等）分开作为新图像

如何解决这些问题？非常感谢您的帮助。