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
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)))
labels.append(0)
test = []
play = [1, 2, 3]
test.append(play)
print(type(hist))
# print(hist.type())
# samples = np.array([np.float32(j) for j in samples])
samples = np.float32(test)
labels = np.array(labels)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_RBF)
svm.setGamma(5.383)
svm.setC(2.67)
svm.train(samples, cv2.ml.ROW_SAMPLE, labels)
# print(samples)
# [l.tolist() for l in samples]
# print(samples)
# samples = np.float32(samples)
#
# print("samples", samples)
# [print("labels", labels)]
# positive_path = "archive/train/ben_afflek"
# # img = cv2.imread(positive_path)
# img = cv2.imread(glob.glob(os.path.join(positive_path, '*jpg'))[0])