python训练svm分类器_如何使用Python中的OpenCV 3.0中的HOG功能训练SVM分类器？
目前我有同样的问题,我看过OpenCV的以下文件：
在哪里可以找到答案的一部分：
deskewed = [map(deskew,row) for row in train_cells]
hogdata = [map(hog,row) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1,64)
responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis])
svm = cv2.ml.SVM_create()
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.setType(cv2.ml.SVM_C_SVC)
svm.setC(2.67)
svm.setGamma(5.383)
svm.train(trainData, cv2.ml.ROW_SAMPLE, responses)
svm.save('svm_data.dat')
那是我正在合作的.一旦我解决了,我会更新答案.但此刻我希望它对你有所帮助.
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您可以在此opencv目录中找到名为digits.py的示例：
\opencv\sources\samples\python
根据您的opencv版本,SVM类的方法存在一些差异.这是opencv 3.1的一个例子.
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_RBF) # cv2.ml.SVM_LINEAR
# svm.setDegree(0.0)
svm.setGamma(5.383)
# svm.setCoef0(0.0)
svm.setC(2.67)
# svm.setNu(0.0)
# svm.setP(0.0)
# svm.setClassWeights(None)
svm.train(samples_train, cv2.ml.ROW_SAMPLE, labels_train)
resp = svm.predict(samples_test)[1].ravel()
print resp, labels_test
err = (labels_test != resp).mean()
print('error: %.2f %%' % (err*100))
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels_test, resp):
confusion[i, j] += 1
print('confusion matrix:')
print(confusion)
print()
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最后我通过这种方式得到它：
samples = []
labels = []
# Get positive samples
for filename in glob.glob(os.path.join(positive_path, '*.jpg')):
img = cv2.imread(filename, 1)
hist = hog(img)
samples.append(hist)
labels.append(1)
# Get negative samples
for filename in glob.glob(os.path.join(negative_path, '*.jpg')):
img = cv2.imread(filename, 1)
hist = hog(img)
samples.append(hist)
labels.append(0)
# Convert objects to Numpy Objects
samples = np.float32(samples)
labels = np.array(labels)
# Shuffle Samples
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(samples))
samples = samples[shuffle]
labels = labels[shuffle]
# Create SVM classifier
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_RBF) # cv2.ml.SVM_LINEAR
# svm.setDegree(0.0)
svm.setGamma(5.383)
# svm.setCoef0(0.0)
svm.setC(2.67)
# svm.setNu(0.0)
# svm.setP(0.0)
# svm.setClassWeights(None)
# Train
svm.train(samples, cv2.ml.ROW_SAMPLE, labels)
svm.save('svm_data.dat')
问候.