<p>最终发现了问题-我错用了NormalBayesClassifier。它并不意味着要直接提供几十张高清图像:首先应该使用OpenCV的其他算法来获取这些图像。在</p>
<p>我最后做了以下事情:
+将图像裁剪到可能包含对象的区域
+将图像转为灰度
+使用cv2.goodFeaturesToTrack()从裁剪区域收集特征以训练分类器。在</p>
<p>有一小部分功能对我有用,也许是因为我直接裁剪了图像,幸运的是它包含了高对比度的对象,这些对象在一个类中变得模糊。在</p>
<p>以下代码可使95%的人群正确:</p>
<pre><code>#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2
import sys, os.path, getopt
import numpy, random
def _usage():
print
print "cvbayes trainer"
print
print "Options:"
print
print "-m ham= path to dir of ham images"
print "-s spam= path to dir of spam images"
print "-h help this help text"
print "-v verbose lots more output"
print
def _parseOpts(argv):
"""
Turn options + args into a dict of config we'll follow. Merge in default conf.
"""
try:
opts, args = getopt.getopt(argv[1:], "hm:s:v", ["help", "ham=", 'spam=', 'verbose'])
except getopt.GetoptError as err:
print(err) # will print something like "option -a not recognized"
_usage()
sys.exit(2)
optsDict = {}
for o, a in opts:
if o == "-v":
optsDict['verbose'] = True
elif o in ("-h", " help"):
_usage()
sys.exit()
elif o in ("-m", " ham"):
optsDict['ham'] = a
elif o in ('-s', ' spam'):
optsDict['spam'] = a
else:
assert False, "unhandled option"
for mandatory_arg in ('ham', 'spam'):
if mandatory_arg not in optsDict:
print "Mandatory argument '%s' was missing; cannot continue" % mandatory_arg
sys.exit(0)
return optsDict
class ClassifierWrapper(object):
"""
Setup and encapsulate a naive bayes classifier based on OpenCV's
NormalBayesClassifier. Presently we do not use it intelligently,
instead feeding in flattened arrays of B&W pixels.
"""
def __init__(self):
super(ClassifierWrapper,self).__init__()
self.classifier = cv2.NormalBayesClassifier()
self.data = []
self.responses = []
def _load_image_features(self, f):
image_colour = cv2.imread(f)
image_crop = image_colour[327:390, 784:926] # Use the junction boxes, luke
image_grey = cv2.cvtColor(image_crop, cv2.COLOR_BGR2GRAY)
features = cv2.goodFeaturesToTrack(image_grey, 4, 0.02, 3)
return features.flatten()
def train_from_file(self, f, cl):
features = self._load_image_features(f)
self.data.append(features)
self.responses.append(cl)
def train(self, update=False):
matrix_data = numpy.matrix( self.data ).astype('float32')
matrix_resp = numpy.matrix( self.responses ).astype('float32')
self.classifier.train(matrix_data, matrix_resp, update=update)
self.data = []
self.responses = []
def predict_from_file(self, f):
features = self._load_image_features(f)
features_matrix = numpy.matrix( [ features ] ).astype('float32')
retval, results = self.classifier.predict( features_matrix )
return results
if __name__ == "__main__":
opts = _parseOpts(sys.argv)
cw = ClassifierWrapper()
ham = os.listdir(opts['ham'])
spam = os.listdir(opts['spam'])
n_training_samples = min( [len(ham),len(spam)])
print "Will train on %d samples for equal sets" % n_training_samples
for f in random.sample(ham, n_training_samples):
img_path = os.path.join(opts['ham'], f)
print "ham: %s" % img_path
cw.train_from_file(img_path, 2)
for f in random.sample(spam, n_training_samples):
img_path = os.path.join(opts['spam'], f)
print "spam: %s" % img_path
cw.train_from_file(img_path, 1)
cw.train()
print
print
# spam dir much bigger so mostly unused, let's try predict() on all of it
print "predicting on all spam..."
n_wrong = 0
n_files = len(os.listdir(opts['spam']))
for f in os.listdir(opts['spam']):
img_path = os.path.join(opts['spam'], f)
result = cw.predict_from_file(img_path)
print "%s\t%s" % (result, img_path)
if result[0][0] == 2:
n_wrong += 1
print
print "got %d of %d wrong = %.1f%%" % (n_wrong, n_files, float(n_wrong)/n_files * 100, )
</code></pre>
<p>现在我用垃圾邮件的随机子集来训练它,只是因为它的数量要多得多,而且每个类的训练数据量应该大致相等。如果有更好的数据(例如,当光线不同时,总是包括黎明和黄昏的样本),它可能会更高。在</p>
<p>也许即使是NormalBayesClassifier也不适合这项工作,我应该在连续的帧中进行运动检测实验,但至少现在互联网上有一个例子值得我们去挑选。在</p>