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Merge pull request #6190 from berak:fix_ml_python_tutorial

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Alexander Alekhin 2016-03-01 14:05:42 +00:00
commit cb012010c6
3 changed files with 18 additions and 16 deletions
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12
doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown
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@ -42,9 +42,9 @@ train_labels = np.repeat(k,250)[:,np.newaxis]
test_labels = train_labels.copy() test_labels = train_labels.copy()
# Initiate kNN, train the data, then test it with test data for k=1 # Initiate kNN, train the data, then test it with test data for k=1
knn = cv2.KNearest() knn = cv2.ml.KNearest_create()
knn.train(train,train_labels) knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
ret,result,neighbours,dist = knn.find_nearest(test,k=5) ret,result,neighbours,dist = knn.findNearest(test,k=5)
# Now we check the accuracy of classification # Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong # For that, compare the result with test_labels and check which are wrong
@ -103,9 +103,9 @@ responses, trainData = np.hsplit(train,[1])
labels, testData = np.hsplit(test,[1]) labels, testData = np.hsplit(test,[1])
# Initiate the kNN, classify, measure accuracy. # Initiate the kNN, classify, measure accuracy.
knn = cv2.KNearest() knn = cv2.ml.KNearest_create()
knn.train(trainData, responses) knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, result, neighbours, dist = knn.find_nearest(testData, k=5) ret, result, neighbours, dist = knn.findNearest(testData, k=5)
correct = np.count_nonzero(result == labels) correct = np.count_nonzero(result == labels)
accuracy = correct*100.0/10000 accuracy = correct*100.0/10000

8
doc/py_tutorials/py_ml/py_knn/py_knn_understanding/py_knn_understanding.markdown
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@ -114,9 +114,9 @@ So let's see how it works. New comer is marked in green color.
newcomer = np.random.randint(0,100,(1,2)).astype(np.float32) newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o') plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')
knn = cv2.KNearest() knn = cv2.ml.KNearest_create()
knn.train(trainData,responses) knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, results, neighbours ,dist = knn.find_nearest(newcomer, 3) ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)
print "result: ", results,"\n" print "result: ", results,"\n"
print "neighbours: ", neighbours,"\n" print "neighbours: ", neighbours,"\n"
@ -140,7 +140,7 @@ obtained as arrays.
@code{.py} @code{.py}
# 10 new comers # 10 new comers
newcomers = np.random.randint(0,100,(10,2)).astype(np.float32) newcomers = np.random.randint(0,100,(10,2)).astype(np.float32)
ret, results,neighbours,dist = knn.find_nearest(newcomer, 3) ret, results,neighbours,dist = knn.findNearest(newcomer, 3)
# The results also will contain 10 labels. # The results also will contain 10 labels.
@endcode @endcode
Additional Resources Additional Resources

14
doc/py_tutorials/py_ml/py_svm/py_svm_opencv/py_svm_opencv.markdown
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@ -64,9 +64,6 @@ import numpy as np
SZ=20 SZ=20
bin_n = 16 # Number of bins bin_n = 16 # Number of bins
svm_params = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
C=2.67, gamma=5.383 )
affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
@ -105,8 +102,13 @@ hogdata = [map(hog,row) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1,64) trainData = np.float32(hogdata).reshape(-1,64)
responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis]) responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis])
svm = cv2.SVM() svm = cv2.ml.SVM_create()
svm.train(trainData,responses, params=svm_params) 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') svm.save('svm_data.dat')
###### Now testing ######################## ###### Now testing ########################
@ -114,7 +116,7 @@ svm.save('svm_data.dat')
deskewed = [map(deskew,row) for row in test_cells] deskewed = [map(deskew,row) for row in test_cells]
hogdata = [map(hog,row) for row in deskewed] hogdata = [map(hog,row) for row in deskewed]
testData = np.float32(hogdata).reshape(-1,bin_n*4) testData = np.float32(hogdata).reshape(-1,bin_n*4)
result = svm.predict_all(testData) result = svm.predict(testData)
####### Check Accuracy ######################## ####### Check Accuracy ########################
mask = result==responses mask = result==responses