9

我正在考虑使用 OpenCV 的 Kmeans 实现，因为它说更快......

现在我正在使用包 cv2 和函数 kmeans，

我无法理解他们参考中的参数描述：

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flags[, bestLabels[, centers]])是什么意思？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

cv2.kmeans的参数列表和用法应该怎么理解？谢谢

4

2 回答 2

21

几乎找不到有关此功能的文档。我有点匆忙写了下面的 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来了解这些参数。

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塑造它们： 

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：

分类前的点

以下是分类后的要点： 分类后的积分

我希望这个答案可以帮助你，它不是一个完整的 k-means 指南，但它至少会告诉你如何将参数传递给 OpenCV。

于 2012-08-13T09:40:25.253 回答
2

这里的问题是你data_1.transpose不是一个 numpy 数组。

numpy arrayOpenCV 2.3.1 和更高版本的 python 绑定除了作为图像/数组参数之外不接受任何内容。所以，data_1.transpose必须是一个numpy数组。

通常，OpenCV 中的所有点都是类型numpy.ndarray

例如。

array([[[100., 433.]],
       [[157., 377.]],
       .
       .  
       [[147., 247.]], dtype=float32)

其中数组的每个元素是

array([[100., 433.]], dtype=float32)

并且该数组的元素是

array([100., 433.], dtype=float32)
于 2012-08-13T13:32:41.267 回答