def create_svm(svm_options):
svm = cv2.ml.SVM_create()
svm.setType(svm_options.svm_type)
svm.setKernel(svm_options.kernel_type)
svm.setDegree(svm_options.degree)
svm.setGamma(svm_options.gamma)
svm.setC(svm_options.C)
svm.setP(svm_options.e)
svm.setTermCriteria((cv2.TERM_CRITERIA_COUNT + cv2.TERM_CRITERIA_EPS, svm_options.max_iter, 1e-09))
return svm
def __init__(self):
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR) # (cv2.ml.SVM_RBF)
# 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)
super().__init__(svm)
def create_svm(svm_options):
"""
Create an instance of SVM with svm_options.
:param svm_options: SVM options.
:return: An instance of OpenCV SVM model.
"""
svm = cv2.ml.SVM_create()
svm.setType(svm_options.svm_type)
svm.setKernel(svm_options.kernel_type)
svm.setDegree(svm_options.degree)
svm.setGamma(svm_options.gamma)
svm.setC(svm_options.C)
svm.setP(svm_options.e)
svm.setTermCriteria((cv2.TERM_CRITERIA_COUNT + cv2.TERM_CRITERIA_EPS, svm_options.max_iter, 1e-09))
return svm
def main():
hog = cv2.HOGDescriptor()
#GET IMAGES AND THEIR LABEL
print("Loading pictures...")
train_img, train_labels = get_images("train", "train_labels.csv")
test_img, test_labels = get_images("test", "test_labels.csv")
print("Loaded...")
#RESIZE
print("Resizing images...")
train_img = resize_images(train_img)
test_img = resize_images(test_img)
print("Resized...")
#MAP LABEL TO INT
train_labels = list(map(strToNumberLabels, train_labels))
test_labels = list(map(strToNumberLabels, test_labels))
#EXTRACT FEATURES
print("Before hog extraction")
features_train = hog_compute(hog, train_img)
features_test = hog_compute(hog, test_img)
print("passed hog extraction")
#
trainingDataMat = np.array(features_train)
labelsMat = np.array(train_labels)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.setTermCriteria((cv2.TERM_CRITERIA_COUNT, 100, 1.e-10))
svm.train(trainingDataMat, cv2.ml.ROW_SAMPLE, labelsMat)
sample_data = np.array(features_test, np.float32)
svm.setC(100)
#svm.setGamma(0.1)
print("Training model...")
svm.train(trainingDataMat, cv2.ml.ROW_SAMPLE, labelsMat)
response = svm.predict(sample_data)
final = []
for y in response[1]:
final.append(int(y[0]))
countAccuracy(final, test_labels)
def OpenCvSVM(X,y):
X_train,X_test,y_train,y_test=train_test_split(X, y, test_size=0.2,random_state=1)
X_train=np.array(X_train,dtype=np.float32)
X_test=np.array(X_test,dtype=np.float32)
y_test=np.array(y_test,dtype=np.int32)
sc=StandardScaler()
sc.fit(X_train)
X_train=sc.transform(X_train)
X_test=sc.transform(X_test)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.train(X_train, cv2.ml.ROW_SAMPLE, y_train)
y_predict = svm.predict(X_test)
y_pred=np.zeros(len(y_predict[1]))
for i in range(len(y_predict[1])):
y_pred[i]=y_predict[1][i][0]
return y_pred,y_test
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 create_svm():
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER, 100, 1e-6))
return svm
