def trainSVMInMyWay(target, labels):
print(len(target))
print(len(target[0]))
# First half is trainData, remaining is testData
train_cells = target
###### Now training ########################
trainData = np.float32(train_cells).reshape(-1, 400)
#print trainData.shape
responses = np.float32(labels)[:, np.newaxis]
svm = cv2.SVM()
svm.train(trainData, responses, params=svm_params)
svm.save('svm_data.dat')
return svm
def trainSVM(target, labels):
print(len(target))
print(len(target[0]))
# First half is trainData, remaining is testData
train_cells = target
###### Now training ########################
deskewed = [map(deskew, row) for row in train_cells]
hogdata = [map(hog, row) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1, 64)
#print trainData.shape
responses = np.float32(labels)[:, np.newaxis]
svm = cv2.SVM()
svm.train(trainData, responses, params=svm_params)
svm.save('svm_data.dat')
return svm
if 'sad' in j:
L.append(1)
if 'angry' in j:
L.append(2)
if 'surprise' in j:
L.append(3)
if 'natural' in j:
L.append(4)
if 'fear' in j:
L.append(5)
if 'disgust' in j:
L.append(6)
h = hog.compute(resized_image)
Big.append(h)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
BN = np.array(Big, np.float32)
LN = np.array(L, np.int)
print(BN.shape)
print(LN.shape)
svm.train(BN, cv2.ml.ROW_SAMPLE, LN)
svm.save('emotions.dat')
#filename = 'SVModel.sav'
#pickle.dump(svm, open(filename, 'wb'))
def store_svm(svm, path):
svm.save(path)
# Set up SVM for OpenCV 3
svm = cv2.ml.SVM_create()
# Set SVM type
svm.setType(cv2.ml.SVM_C_SVC)
# Set SVM Kernel to Radial Basis Function (RBF)
#svm.setKernel(cv2.ml.SVM_RBF)
# Set parameter C
#svm.setC(C)
# Set parameter Gamma
#svm.setGamma(gamma)
# Train SVM on training data
svm.trainAuto(X_tr, cv2.ml.ROW_SAMPLE, y_tr)
# Save trained model
svm.save("apples_svm_model.yml")
'''
svm = cv2.ml.SVM_load('apples_svm_model.yml')
pred = svm.predict(X_te)[1]
'''
a = []
for i in range(len(pred)):
a.append(int(pred[i][0]))
mask = a == y_te
correct = np.count_nonzero(mask)
print("svm model accuracy with HOG descriptor:", end=" ")
print(correct * 100.0 / pred.size)
validation_fruit_images = []
Cropped=image[y:y + h, x:x + w]
resized_image = cv2.resize(Cropped, (64, 128))
# c=0
# while(c!=''):
# if c""
if 'man' in j:
L.append(0)
if 'bnat' in j:
L.append(1)
h = hog.compute(resized_image)
Big.append(h)
print (h.mean(), L[-1])
BN = np.array(Big, np.float32)
LN = np.array(L, np.int)
print(BN.shape)
print(LN.shape)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.train(BN, cv2.ml.ROW_SAMPLE, LN)
svm.save('gender.dat')
#filename = 'SVModel.sav'
#pickle.dump(svm, open(filename, 'wb'))