cv2.kmeans在Python中的用法 [英] cv2.kmeans usage in Python

问题描述

我正在考虑使用OpenCV的Kmeans实现，因为它说速度更快...

I am considering to use OpenCV's Kmeans implementation since it says to be faster...

现在我正在使用软件包cv2和功能kmeans，

Now I am using package cv2 and function kmeans,

我无法理解其参考中的参数说明:

I can not understand the parameters' description in their reference:

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.

自变量flags[, bestLabels[, centers]])是什么意思?那他的那个呢:→ retval, bestLabels, centers?

what is the argument flags[, bestLabels[, centers]]) mean? and what about his one: → 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

我应该如何理解参数列表和cv2.kmeans的用法?谢谢

How should I understand the parameter list and the usage of cv2.kmeans? Thanks

推荐答案

几乎找不到该函数的文档.我匆忙编写了以下Python代码，但它在我的机器上可以工作.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.您可以参考此博客文章以了解一些参数.

the documentation on this function is almost impossible to find. I wrote the following Python code in a bit of a hurry, but it works on my machine. It generates two multi-variate Gaussian Distributions with different means and then classifies them using cv2.kmeans(). You may refer to this blog post to get some idea of the parameters.

处理导入:

import cv
import cv2
import numpy as np
import numpy.random as r

生成一些随机点并适当调整它们的形状:

Generate some random points and shape them appropriately:

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()

在这里您可以看到原始点:

Here you can see the original points:

以下是对它们进行分类后的要点:

Here are the points after they have been classified:

我希望这个答案可以对您有所帮助，它不是k-means的完整指南，但至少将向您展示如何将参数传递给OpenCV.

I hope that this answer may help you, it is not a complete guide to k-means, but it will at least show you how to pass the parameters to OpenCV.

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