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 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)
if __name__ == '__main__':
train_img, train_labels = load_images("train", "train_labels.csv")
test_img, test_labels = load_images("test", "test_labels.csv")
train_labels_int = encode_labels(train_labels)
test_labels_int = encode_labels(test_labels)
train_img = resize_images(train_img, (128, 128))
test_img = resize_images(test_img, (128, 128))
features_train = extract_features(train_img)
features_test = extract_features(test_img)
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.setC(100)
svm.setGamma(0.1)
svm.train(np.array(features_train), cv2.ml.ROW_SAMPLE,
np.array(train_labels_int))
predicted = svm.predict(np.array(features_test, np.float32))
result = []
for p in predicted[1]:
result.append(int(p[0]))
print("Acurracy: " + str(count_accuracy(result, test_labels_int)))
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