我正在考虑使用 OpenCV 的 Kmeans 实现，因为它说更快......
现在我使用包 cv2 和函数 kmeans，
我无法理解参考文献中参数的描述：
Python: cv2.kmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) → retval, bestLabels, centers
samples – Floating-point matrix of input samples, one row per sample.
clusterCount – Number of clusters to split the set by.
labels – Input/output integer array that stores the cluster indices for every sample.
criteria – The algorithm termination criteria, that is, the maximum number of iterations and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
attempts – Flag to specify the number of times the algorithm is executed using different initial labelings. The algorithm returns the labels that yield the best compactness (see the last function parameter).
flags –
Flag that can take the following values:
KMEANS_RANDOM_CENTERS Select random initial centers in each attempt.
KMEANS_PP_CENTERS Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].
KMEANS_USE_INITIAL_LABELS During the first (and possibly the only) attempt, use the user-supplied labels instead of computing them from the initial centers. For the second and further attempts, use the random or semi-random centers. Use one of KMEANS_*_CENTERS flag to specify the exact method.
centers – Output matrix of the cluster centers, one row per each cluster center.
论据是什么flags[, bestLabels[, centers]])意思是？那他的呢：→ retval, bestLabels, centers ?
这是我的代码：
import cv, cv2
import scipy.io
import numpy
# read data from .mat file
mat = scipy.io.loadmat('...')
keys = mat.keys()
values = mat.viewvalues()
data_1 = mat[keys[0]]
nRows = data_1.shape[1]
nCols = data_1.shape[0]
samples = cv.CreateMat(nRows, nCols, cv.CV_32FC1)
labels = cv.CreateMat(nRows, 1, cv.CV_32SC1)
centers = cv.CreateMat(nRows, 100, cv.CV_32FC1)
#centers = numpy.
for i in range(0, nCols):
for j in range(0, nRows):
samples[j, i] = data_1[i, j]
cv2.kmeans(data_1.transpose,
100,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 0.1, 10),
attempts=cv2.KMEANS_PP_CENTERS,
flags=cv2.KMEANS_PP_CENTERS,
)
我遇到这样的错误：
flags=cv2.KMEANS_PP_CENTERS,
TypeError: <unknown> is not a numpy array
我应该如何理解cv2.kmeans的参数列表和用法？谢谢
有关此功能的文档几乎不可能找到。我有点匆忙地编写了以下 Python 代码，但它可以在我的机器上运行。它生成两个具有不同均值的多元高斯分布，然后使用 cv2.kmeans() 对它们进行分类。您可以参考这篇博文 http://www.aishack.in/tutorials/kmeans-clustering-opencv/了解参数。
处理进口：
import cv
import cv2
import numpy as np
import numpy.random as r
生成一些随机点并适当地调整它们的形状：
samples = cv.CreateMat(50, 2, cv.CV_32FC1)
random_points = r.multivariate_normal((100,100), np.array([[150,400],[150,150]]), size=(25))
random_points_2 = r.multivariate_normal((300,300), np.array([[150,400],[150,150]]), size=(25))
samples_list = np.append(random_points, random_points_2).reshape(50,2)
random_points_list = np.array(samples_list, np.float32)
samples = cv.fromarray(random_points_list)
绘制分类前后的点：
blank_image = np.zeros((400,400,3))
blank_image_classified = np.zeros((400,400,3))
for point in random_points_list:
cv2.circle(blank_image, (int(point[0]),int(point[1])), 1, (0,255,0),-1)
temp, classified_points, means = cv2.kmeans(data=np.asarray(samples), K=2, bestLabels=None,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1, 10), attempts=1,
flags=cv2.KMEANS_RANDOM_CENTERS) #Let OpenCV choose random centers for the clusters
for point, allocation in zip(random_points_list, classified_points):
if allocation == 0:
color = (255,0,0)
elif allocation == 1:
color = (0,0,255)
cv2.circle(blank_image_classified, (int(point[0]),int(point[1])), 1, color,-1)
cv2.imshow("Points", blank_image)
cv2.imshow("Points Classified", blank_image_classified)
cv2.waitKey()
在这里你可以看到原始点：
Here are the points after they have been classified:
我希望这个答案可以帮助你，它不是 k-means 的完整指南，但它至少会告诉你如何将参数传递给 OpenCV。