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updated classifying code to OpenCV 3.x (bug in function to load classification models)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Mar 2016 16:37:37 -0400
parents 0a428b449b80
children 1158a6e2d28e
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1 #! /usr/bin/env python
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2 '''Libraries for machine learning algorithms'''
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3
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4 from os import path
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5 from random import shuffle
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6 from copy import copy, deepcopy
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7
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8 import numpy as np
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9 from matplotlib.pylab import text
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10 import matplotlib as mpl
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11 import matplotlib.pyplot as plt
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12 from scipy.cluster.vq import kmeans, whiten, vq
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13 from sklearn import mixture
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14 import cv2
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15
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16 import utils
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17
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18 #####################
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19 # OpenCV ML models
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20 #####################
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21
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22 class StatModel(object):
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23 '''Abstract class for loading/saving model'''
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24 def load(self, filename):
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25 if path.exists(filename):
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26 self.model.load(filename)
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27 else:
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28 print('Provided filename {} does not exist: model not loaded!'.format(filename))
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29
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30 def save(self, filename):
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31 self.model.save(filename)
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32
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33 class SVM(StatModel):
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34 '''wrapper for OpenCV SimpleVectorMachine algorithm'''
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35 def __init__(self, svmType = cv2.ml.SVM_C_SVC, kernelType = cv2.ml.SVM_RBF, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0):
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36 self.model = cv2.ml.SVM_create()
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37 self.model.setType(svmType)
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38 self.model.setKernel(kernelType)
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39 self.model.setDegree(degree)
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40 self.model.setGamma(gamma)
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41 self.model.setCoef0(coef0)
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42 self.model.setC(Cvalue)
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43 self.model.setNu(nu)
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44 self.model.setP(p)
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45
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46 def train(self, samples, layout, responses):
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47 self.model.train(samples, layout, responses)
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48
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49 def predict(self, hog):
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50 return self.model.predict(hog)
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52
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53 #####################
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54 # Clustering
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55 #####################
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56
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57 class Centroid(object):
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58 'Wrapper around instances to add a counter'
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59
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60 def __init__(self, instance, nInstances = 1):
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61 self.instance = instance
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62 self.nInstances = nInstances
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63
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64 # def similar(instance2):
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65 # return self.instance.similar(instance2)
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66
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67 def add(self, instance2):
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68 self.instance = self.instance.multiply(self.nInstances)+instance2
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69 self.nInstances += 1
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70 self.instance = self.instance.multiply(1/float(self.nInstances))
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71
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72 def average(c):
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73 inst = self.instance.multiply(self.nInstances)+c.instance.multiply(instance.nInstances)
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74 inst.multiply(1/(self.nInstances+instance.nInstances))
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75 return Centroid(inst, self.nInstances+instance.nInstances)
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76
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77 def plot(self, options = ''):
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78 self.instance.plot(options)
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79 text(self.instance.position.x+1, self.instance.position.y+1, str(self.nInstances))
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80
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81 def kMedoids(similarityMatrix, initialCentroids = None, k = None):
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82 '''Algorithm that clusters any dataset based on a similarity matrix
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83 Either the initialCentroids or k are passed'''
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84 pass
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85
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86 def assignCluster(data, similarFunc, initialCentroids = None, shuffleData = True):
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87 '''k-means algorithm with similarity function
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88 Two instances should be in the same cluster if the sameCluster function returns true for two instances. It is supposed that the average centroid of a set of instances can be computed, using the function.
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89 The number of clusters will be determined accordingly
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90
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91 data: list of instances
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92 averageCentroid: '''
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93 localdata = copy(data) # shallow copy to avoid modifying data
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94 if shuffleData:
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95 shuffle(localdata)
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96 if initialCentroids is None:
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97 centroids = [Centroid(localdata[0])]
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98 else:
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99 centroids = deepcopy(initialCentroids)
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100 for instance in localdata[1:]:
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101 i = 0
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102 while i<len(centroids) and not similarFunc(centroids[i].instance, instance):
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103 i += 1
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104 if i == len(centroids):
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105 centroids.append(Centroid(instance))
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106 else:
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107 centroids[i].add(instance)
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108
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109 return centroids
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110
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111 # TODO recompute centroids for each cluster: instance that minimizes some measure to all other elements
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112
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113 def spectralClustering(similarityMatrix, k, iter=20):
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114 '''Spectral Clustering algorithm'''
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115 n = len(similarityMatrix)
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116 # create Laplacian matrix
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117 rowsum = np.sum(similarityMatrix,axis=0)
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118 D = np.diag(1 / np.sqrt(rowsum))
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119 I = np.identity(n)
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120 L = I - np.dot(D,np.dot(similarityMatrix,D))
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121 # compute eigenvectors of L
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122 U,sigma,V = np.linalg.svd(L)
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123 # create feature vector from k first eigenvectors
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124 # by stacking eigenvectors as columns
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125 features = np.array(V[:k]).T
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126 # k-means
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127 features = whiten(features)
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128 centroids,distortion = kmeans(features,k, iter)
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129 code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster)
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130 return code,sigma
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131
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132 def prototypeCluster(instances, similarities, minSimilarity, similarityFunc = None, minClusterSize = None, randomInitialization = False):
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133 '''Finds exemplar (prototype) instance that represent each cluster
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134 Returns the prototype indices (in the instances list) and the cluster label of each instance
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135
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136 the elements in the instances list must have a length (method __len__), or one can use the random initialization
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137 the positions in the instances list corresponds to the similarities
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138 if similarityFunc is provided, the similarities are calculated as needed (this is faster) if not in similarities (negative if not computed)
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139 similarities must still be allocated with the right size
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140
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141 if an instance is different enough (<minSimilarity),
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142 it will become a new prototype.
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143 Non-prototype instances will be assigned to an existing prototype
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144 if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters
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145 and reassigning all elements in the cluster until no cluster is smaller than minClusterSize'''
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146
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147 # sort instances based on length
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148 indices = range(len(instances))
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149 if randomInitialization:
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150 indices = np.random.permutation(indices)
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151 else:
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152 def compare(i, j):
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153 if len(instances[i]) > len(instances[j]):
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154 return -1
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155 elif len(instances[i]) == len(instances[j]):
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156 return 0
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157 else:
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158 return 1
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159 indices.sort(compare)
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160 # go through all instances
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161 prototypeIndices = [indices[0]]
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162 for i in indices[1:]:
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163 if similarityFunc is not None:
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164 for j in prototypeIndices:
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165 if similarities[i][j] < 0:
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166 similarities[i][j] = similarityFunc(instances[i], instances[j])
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167 similarities[j][i] = similarities[i][j]
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168 if similarities[i][prototypeIndices].max() < minSimilarity:
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169 prototypeIndices.append(i)
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170
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171 # assignment
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172 indices = [i for i in range(similarities.shape[0]) if i not in prototypeIndices]
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173 assign = True
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174 while assign:
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175 labels = [-1]*similarities.shape[0]
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176 for i in prototypeIndices:
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177 labels[i] = i
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178 for i in indices:
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179 if similarityFunc is not None:
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180 for j in prototypeIndices:
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181 if similarities[i][j] < 0:
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182 similarities[i][j] = similarityFunc(instances[i], instances[j])
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183 similarities[j][i] = similarities[i][j]
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184 prototypeIdx = similarities[i][prototypeIndices].argmax()
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185 if similarities[i][prototypeIndices[prototypeIdx]] >= minSimilarity:
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186 labels[i] = prototypeIndices[prototypeIdx]
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187 else:
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188 labels[i] = -1 # outlier
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189 clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}
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190 smallestClusterIndex = min(clusterSizes, key = clusterSizes.get)
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191 assign = (clusterSizes[smallestClusterIndex] < minClusterSize)
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192 if assign:
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193 prototypeIndices.remove(smallestClusterIndex)
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194 indices.append(smallestClusterIndex)
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195
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196 return prototypeIndices, labels
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197
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198 def computeClusterSizes(labels, prototypeIndices, outlierIndex = -1):
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199 clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}
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200 clusterSizes['outlier'] = sum(np.array(labels) == outlierIndex)
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201 return clusterSizes
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202
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203 # Gaussian Mixture Models
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204 def plotGMMClusters(model, dataset = None, fig = None, colors = utils.colors, nPixelsPerUnit = 1., alpha = 0.3):
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205 '''plot the ellipse corresponding to the Gaussians
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206 and the predicted classes of the instances in the dataset'''
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207 if fig is None:
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208 fig = plt.figure()
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209 labels = model.predict(dataset)
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210 tmpDataset = nPixelsPerUnit*dataset
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211 for i in xrange(model.n_components):
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212 mean = nPixelsPerUnit*model.means_[i]
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213 covariance = nPixelsPerUnit*model.covars_[i]
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214 if dataset is not None:
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215 plt.scatter(tmpDataset[labels == i, 0], tmpDataset[labels == i, 1], .8, color=colors[i])
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216 plt.annotate(str(i), xy=(mean[0]+1, mean[1]+1))
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217
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218 # Plot an ellipse to show the Gaussian component
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219 v, w = np.linalg.eigh(covariance)
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220 angle = np.arctan2(w[0][1], w[0][0])
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221 angle = 180*angle/np.pi # convert to degrees
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222 v *= 4
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223 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180+angle, color=colors[i])
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224 ell.set_clip_box(fig.bbox)
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225 ell.set_alpha(alpha)
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226 fig.axes[0].add_artist(ell)