超像素概念是2003年Xiaofeng Ren提出和发展起来的图像分割技术,是指具有相似纹理、颜色、亮度等特征的相邻像素构成的有一定视觉意义的不规则像素块。它利用像素之间特征的相似性将像素分组,用少量的超像素代替大量的像素来表达图片特征,很大程度上降低了图像后处理的复杂度,所以通常作为分割算法的预处理步骤。
常见的超像素分割方法包括: Graph-based 、NCut 、Turbopixel 、 Quick-shift 、 Graph-cut a、Graph-cut b 以及 SLIC 。
其中,SLIC(simple linear iterativeclustering),即 简单线性迭代聚类 。
它是2010年提出的一种思想简单、实现方便的算法,将彩色图像转化为CIELAB颜色空间和XY坐标下的5维特征向量,然后对5维特征向量构造距离度量标准,对图像像素进行局部聚类的过程。
SLIC主要优点如下:
经过观察发现,在迭代至第10轮后,分割效果基本不再发生变化。
代码是我上网找来的,稍微改动了一丢丢。
原代码出处:SLIC算法分割超像素原理及Python实现
import math
from skimage import io, color
import numpy as np
from tqdm import trange
class Cluster(object):
cluster_index = 1
def __init__(self, h, w, l=0, a=0, b=0):
self.update(h, w, l, a, b)
self.pixels = []
self.no = self.cluster_index
self.cluster_index += 1
def update(self, h, w, l, a, b):
self.h = h
self.w = w
self.l = l
self.a = a
self.b = b
def __str__(self):
return "{},{}:{} {} {} ".format(self.h, self.w, self.l, self.a, self.b)
def __repr__(self):
return self.__str__()
class SLICProcessor(object):
@staticmethod
def open_image(path):
"""
Return:
3D array, row col [LAB]
"""
rgb = io.imread(path)
lab_arr = color.rgb2lab(rgb)
return lab_arr
@staticmethod
def save_lab_image(path, lab_arr):
"""
Convert the array to RBG, then save the image
"""
rgb_arr = color.lab2rgb(lab_arr)
io.imsave(path, rgb_arr)
def make_cluster(self, h, w):
return Cluster(h, w,
self.data[h][w][0],
self.data[h][w][1],
self.data[h][w][2])
def __init__(self, filename, K, M):
self.K = K
self.M = M
self.data = self.open_image(filename)
self.image_height = self.data.shape[0]
self.image_width = self.data.shape[1]
self.N = self.image_height * self.image_width
self.S = int(math.sqrt(self.N / self.K))
self.clusters = []
self.label = {}
self.dis = np.full((self.image_height, self.image_width), np.inf)
def init_clusters(self):
h = self.S / 2
w = self.S / 2
while h < self.image_height:
while w < self.image_width:
self.clusters.append(self.make_cluster(h, w))
w += self.S
w = self.S / 2
h += self.S
def get_gradient(self, h, w):
if w + 1 >= self.image_width:
w = self.image_width - 2
if h + 1 >= self.image_height:
h = self.image_height - 2
gradient = self.data[w + 1][h + 1][0] - self.data[w][h][0] + \
self.data[w + 1][h + 1][1] - self.data[w][h][1] + \
self.data[w + 1][h + 1][2] - self.data[w][h][2]
return gradient
def move_clusters(self):
for cluster in self.clusters:
cluster_gradient = self.get_gradient(cluster.h, cluster.w)
for dh in range(-1, 2):
for dw in range(-1, 2):
_h = cluster.h + dh
_w = cluster.w + dw
new_gradient = self.get_gradient(_h, _w)
if new_gradient < cluster_gradient:
cluster.update(_h, _w, self.data[_h][_w][0], self.data[_h][_w][1], self.data[_h][_w][2])
cluster_gradient = new_gradient
def assignment(self):
for cluster in self.clusters:
for h in range(cluster.h - 2 * self.S, cluster.h + 2 * self.S):
if h < 0 or h >= self.image_height: continue
for w in range(cluster.w - 2 * self.S, cluster.w + 2 * self.S):
if w < 0 or w >= self.image_width: continue
L, A, B = self.data[h][w]
Dc = math.sqrt(
math.pow(L - cluster.l, 2) +
math.pow(A - cluster.a, 2) +
math.pow(B - cluster.b, 2))
Ds = math.sqrt(
math.pow(h - cluster.h, 2) +
math.pow(w - cluster.w, 2))
D = math.sqrt(math.pow(Dc / self.M, 2) + math.pow(Ds / self.S, 2))
if D < self.dis[h][w]:
if (h, w) not in self.label:
self.label[(h, w)] = cluster
cluster.pixels.append((h, w))
else:
self.label[(h, w)].pixels.remove((h, w))
self.label[(h, w)] = cluster
cluster.pixels.append((h, w))
self.dis[h][w] = D
def update_cluster(self):
for cluster in self.clusters:
sum_h = sum_w = number = 0
for p in cluster.pixels:
sum_h += p[0]
sum_w += p[1]
number += 1
_h = sum_h / number
_w = sum_w / number
cluster.update(_h, _w, self.data[_h][_w][0], self.data[_h][_w][1], self.data[_h][_w][2])
def save_current_image(self, name):
image_arr = np.copy(self.data)
for cluster in self.clusters:
for p in cluster.pixels:
image_arr[p[0]][p[1]][0] = cluster.l
image_arr[p[0]][p[1]][1] = cluster.a
image_arr[p[0]][p[1]][2] = cluster.b
image_arr[cluster.h][cluster.w][0] = 0
image_arr[cluster.h][cluster.w][1] = 0
image_arr[cluster.h][cluster.w][2] = 0
self.save_lab_image(name, image_arr)
def iterate_10times(self):
self.init_clusters()
self.move_clusters()
for i in trange(20):
self.assignment()
self.update_cluster()
name = 'Elegent_Girl_M{m}_K{k}_loop{loop}.jpg'.format(loop=i, m=self.M, k=self.K)
self.save_current_image(name)
if __name__ == '__main__':
for k in [64, 128, 256, 1024]:
p = SLICProcessor('800.jpg', k, 30)
p.iterate_10times()
打印结果:
0%| | 0/20 [00:007/site-packages/skimage/util/dtype.py:111: UserWarning: Possible precision loss when converting from float64 to uint8
"%s to %s" % (dtypeobj_in, dtypeobj))
100%|##########| 20/20 [32:36<00:00, 97.83s/it]
100%|##########| 20/20 [24:37<00:00, 73.88s/it]
100%|##########| 20/20 [21:30<00:00, 64.55s/it]
100%|##########| 20/20 [18:49<00:00, 56.46s/it]
Process finished with exit code 0