skimage.morphology.convex_hull_image(image)

import matplotlib.pyplot as plt
from skimage import data,color,morphology

#生成二值測(cè)試圖像
img=color.rgb2gray(data.horse())
img=(img<0.5)*1

chull = morphology.convex_hull_image(img)

#繪制輪廓
fig, axes = plt.subplots(1,2,figsize=(8,8))
ax0, ax1= axes.ravel()
ax0.imshow(img,plt.cm.gray)
ax0.set_title('original image')

ax1.imshow(chull,plt.cm.gray)
ax1.set_title('convex_hull image')

import matplotlib.pyplot as plt
from skimage import data,color,morphology,feature

#生成二值測(cè)試圖像
img=color.rgb2gray(data.coins())
#檢測(cè)canny邊緣,得到二值圖片
edgs=feature.canny(img, sigma=3, low_threshold=10, high_threshold=50) 

chull = morphology.convex_hull_object(edgs)

#繪制輪廓
fig, axes = plt.subplots(1,2,figsize=(8,8))
ax0, ax1= axes.ravel()
ax0.imshow(edgs,plt.cm.gray)
ax0.set_title('many objects')
ax1.imshow(chull,plt.cm.gray)
ax1.set_title('convex_hull image')
plt.show()

skimage.measure.label（image,connectivity=None)

import numpy as np
import scipy.ndimage as ndi
from skimage import measure,color
import matplotlib.pyplot as plt

#編寫(xiě)一個(gè)函數(shù)來(lái)生成原始二值圖像
def microstructure(l=256):
  n = 5
  x, y = np.ogrid[0:l, 0:l] #生成網(wǎng)絡(luò)
  mask = np.zeros((l, l))
  generator = np.random.RandomState(1) #隨機(jī)數(shù)種子
  points = l * generator.rand(2, n**2)
  mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
  mask = ndi.gaussian_filter(mask, sigma=l/(4.*n)) #高斯濾波
  return mask > mask.mean()

data = microstructure(l=128)*1 #生成測(cè)試圖片

labels=measure.label(data,connectivity=2) #8連通區(qū)域標(biāo)記
dst=color.label2rgb(labels) #根據(jù)不同的標(biāo)記顯示不同的顏色
print('regions number:',labels.max()+1) #顯示連通區(qū)域塊數(shù)(從0開(kāi)始標(biāo)記)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(data, plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.imshow(dst,interpolation='nearest')
ax2.axis('off')

fig.tight_layout()
plt.show()

skimage.measure.regionprops(label_image)

import numpy as np
import scipy.ndimage as ndi
from skimage import morphology
import matplotlib.pyplot as plt

#編寫(xiě)一個(gè)函數(shù)來(lái)生成原始二值圖像
def microstructure(l=256):
  n = 5
  x, y = np.ogrid[0:l, 0:l] #生成網(wǎng)絡(luò)
  mask = np.zeros((l, l))
  generator = np.random.RandomState(1) #隨機(jī)數(shù)種子
  points = l * generator.rand(2, n**2)
  mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
  mask = ndi.gaussian_filter(mask, sigma=l/(4.*n)) #高斯濾波
  return mask > mask.mean()

data = microstructure(l=128) #生成測(cè)試圖片

dst=morphology.remove_small_objects(data,min_size=300,connectivity=1)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(data, plt.cm.gray, interpolation='nearest')
ax2.imshow(dst,plt.cm.gray,interpolation='nearest')

fig.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from skimage import data,filter,segmentation,measure,morphology,color

#加載并裁剪硬幣圖片
image = data.coins()[50:-50, 50:-50]

thresh =filter.threshold_otsu(image) #閾值分割
bw =morphology.closing(image > thresh, morphology.square(3)) #閉運(yùn)算

cleared = bw.copy() #復(fù)制
segmentation.clear_border(cleared) #清除與邊界相連的目標(biāo)物

label_image =measure.label(cleared) #連通區(qū)域標(biāo)記
borders = np.logical_xor(bw, cleared) #異或
label_image[borders] = -1
image_label_overlay =color.label2rgb(label_image, image=image) #不同標(biāo)記用不同顏色顯示

fig,(ax0,ax1)= plt.subplots(1,2, figsize=(8, 6))
ax0.imshow(cleared,plt.cm.gray)
ax1.imshow(image_label_overlay)

for region in measure.regionprops(label_image): #循環(huán)得到每一個(gè)連通區(qū)域?qū)傩约?
  
  #忽略小區(qū)域
  if region.area < 100:
    continue

  #繪制外包矩形
  minr, minc, maxr, maxc = region.bbox
  rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
               fill=False, edgecolor='red', linewidth=2)
  ax1.add_patch(rect)
fig.tight_layout()
plt.show()