Python基于纹理背景和聚类算法实现图像分割详解

# -*- coding: utf-8 -*-
# By: Eastmount 
import cv2  
import numpy as np  
import matplotlib.pyplot as plt
import matplotlib

#读取图像
img = cv2.imread('nv.png')

#灰度化处理图像
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#设置掩码、fgbModel、bgModel
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1,65), np.float64)
fgdModel = np.zeros((1,65), np.float64)

#矩形坐标
rect = (100, 100, 500, 800)

#图像分割
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5,
            cv2.GC_INIT_WITH_RECT)

#设置新掩码：0和2做背景
mask2 = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')

#设置字体
matplotlib.rcParams['font.sans-serif']=['SimHei']

#显示原图
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.subplot(1,2,1)
plt.imshow(img)
plt.title('(a)原始图像')

plt.xticks([]), plt.yticks([])

#使用蒙板来获取前景区域
img = img*mask2[:, :, np.newaxis]
plt.subplot(1,2,2)
plt.imshow(img)
plt.title('(b)目标图像')

plt.colorbar()
plt.xticks([]), plt.yticks([])
plt.show()

# -*- coding: utf-8 -*-
# By: Eastmount
import cv2
import numpy as np
import matplotlib.pyplot as plt

#读取原始图像灰度颜色
img = cv2.imread('scenery.png', 0) 

#获取图像高度、宽度
rows, cols = img.shape[:]

#图像二维像素转换为一维
data = img.reshape((rows * cols, 1))
data = np.float32(data)

#定义中心 (type,max_iter,epsilon)
criteria = (cv2.TERM_CRITERIA_EPS +
            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

#设置标签
flags = cv2.KMEANS_RANDOM_CENTERS

#K-Means聚类 聚集成4类
compactness, labels, centers = cv2.kmeans(data, 4, None, criteria, 10, flags)

#生成最终图像
dst = labels.reshape((img.shape[0], img.shape[1]))

#用来正常显示中文标签
plt.rcParams['font.sans-serif']=['SimHei']

#显示图像
titles = ['原始图像', '聚类图像']  
images = [img, dst]  
for i in range(2):  
   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray'), 
   plt.title(titles[i])  
   plt.xticks([]),plt.yticks([])  
plt.show()

# -*- coding: utf-8 -*-
# By: Eastmount
import cv2
import numpy as np
import matplotlib.pyplot as plt

#读取原始图像
img = cv2.imread('scenery.png') 

#图像二维像素转换为一维
data = img.reshape((-1,3))
data = np.float32(data)

#定义中心 (type,max_iter,epsilon)
criteria = (cv2.TERM_CRITERIA_EPS +
            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

#设置标签
flags = cv2.KMEANS_RANDOM_CENTERS

#K-Means聚类 聚集成2类
compactness, labels2, centers2 = cv2.kmeans(data, 2, None, criteria, 10, flags)

#K-Means聚类 聚集成4类
compactness, labels4, centers4 = cv2.kmeans(data, 4, None, criteria, 10, flags)

#K-Means聚类 聚集成8类
compactness, labels8, centers8 = cv2.kmeans(data, 8, None, criteria, 10, flags)

#K-Means聚类 聚集成16类
compactness, labels16, centers16 = cv2.kmeans(data, 16, None, criteria, 10, flags)

#K-Means聚类 聚集成64类
compactness, labels64, centers64 = cv2.kmeans(data, 64, None, criteria, 10, flags)

#图像转换回uint8二维类型
centers2 = np.uint8(centers2)
res = centers2[labels2.flatten()]
dst2 = res.reshape((img.shape))

centers4 = np.uint8(centers4)
res = centers4[labels4.flatten()]
dst4 = res.reshape((img.shape))

centers8 = np.uint8(centers8)
res = centers8[labels8.flatten()]
dst8 = res.reshape((img.shape))

centers16 = np.uint8(centers16)
res = centers16[labels16.flatten()]
dst16 = res.reshape((img.shape))

centers64 = np.uint8(centers64)
res = centers64[labels64.flatten()]
dst64 = res.reshape((img.shape))

#图像转换为RGB显示
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dst2 = cv2.cvtColor(dst2, cv2.COLOR_BGR2RGB)
dst4 = cv2.cvtColor(dst4, cv2.COLOR_BGR2RGB)
dst8 = cv2.cvtColor(dst8, cv2.COLOR_BGR2RGB)
dst16 = cv2.cvtColor(dst16, cv2.COLOR_BGR2RGB)
dst64 = cv2.cvtColor(dst64, cv2.COLOR_BGR2RGB)

#用来正常显示中文标签
plt.rcParams['font.sans-serif']=['SimHei']

#显示图像
titles = ['原始图像', '聚类图像 K=2', '聚类图像 K=4',
          '聚类图像 K=8', '聚类图像 K=16', '聚类图像 K=64']  
images = [img, dst2, dst4, dst8, dst16, dst64]  
for i in range(6):  
   plt.subplot(2,3,i+1), plt.imshow(images[i], 'gray'), 
   plt.title(titles[i])  
   plt.xticks([]),plt.yticks([])  
plt.show()