def train(self, svm_kernel, k, des_name, test_image, des_option=constants.ORB_FEAT_OPTION, is_interactive=True):
"""
Gets the descriptors for the training set and then calculates the SVM for them.
Args:
svm_kernel (constant): The kernel of the SVM that will be created.
codebook (NumPy float matrix): Each row is a center of a codebook of Bag of Words approach.
des_option (integer): The option of the feature that is going to be used as local descriptor.
is_interactive (boolean): If it is the user can choose to load files or generate.
Returns:
cv2.SVM: The Support Vector Machine obtained in the training phase.
"""
isTrain= True
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
x_filename = filenames.vlads_train(k, des_name)
print("Getting global descriptors for the training set.")
start = time.time()
x, y, x_test, y1, testimg_h, cluster_model = self.get_data_and_labels(self.dataset.get_train_set(), test_image,
KMeans(n_clusters=k), k, des_name ,isTrain,des_option,isTrain)
utils.save(x_filename, x)
end = time.time()
svm_filename = filenames.svm(k, des_name, svm_kernel)
utils.save(svm_filename, x)
print("Calculating the Support Vector Machine for the training set...")
return x, y, x_test, y1, testimg_h, cluster_model
def train(self, svm_kernel, codebook, des_option=constants.ORB_FEAT_OPTION, is_interactive=True):
"""
Gets the descriptors for the training set and then calculates the SVM for them.
Args:
svm_kernel (constant): The kernel of the SVM that will be created.
codebook (NumPy float matrix): Each row is a center of a codebook of Bag of Words approach.
des_option (integer): The option of the feature that is going to be used as local descriptor.
is_interactive (boolean): If it is the user can choose to load files or generate.
Returns:
cv2.SVM: The Support Vector Machine obtained in the training phase.
"""
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
k = len(codebook)
x_filename = filenames.vlads_train(k, des_name)
if is_interactive:
data_option = input("Enter [1] to calculate VLAD vectors for the training set or [2] to load them.\n")
else:
data_option = constants.GENERATE_OPTION
if data_option == constants.GENERATE_OPTION:
# Getting the global vectors for all of the training set
print("Getting global descriptors for the training set.")
start = time.time()
x, y = self.get_data_and_labels(self.dataset.get_train_set(), codebook, des_option)
utils.save(x_filename, x)
end = time.time()
print("VLADs training vectors saved on file {0}".format(x_filename))
self.log.train_vlad_time(end - start)
else:
# Loading the global vectors for all of the training set
print("Loading global descriptors for the training set.")
x = utils.load(x_filename)
y = self.dataset.get_train_y()
x = np.matrix(x, dtype=np.float32)
svm = cv2.SVM()
svm_filename = filenames.svm(k, des_name, svm_kernel)
if is_interactive:
svm_option = input("Enter [1] for generating a SVM or [2] to load one\n")
else:
svm_option = constants.GENERATE_OPTION
if svm_option == constants.GENERATE_OPTION:
# Calculating the Support Vector Machine for the training set
print("Calculating the Support Vector Machine for the training set...")
svm_params = dict(kernel_type=svm_kernel, svm_type=cv2.SVM_C_SVC, C=1)
start = time.time()
svm.train_auto(x, y, None, None, svm_params)
end = time.time()
self.log.svm_time(end - start)
# Storing the SVM in a file
svm.save(svm_filename)
else:
svm.load(svm_filename)
return svm
def test_one_img_classification():
img = cv2.imread("test.jpg")
resize_to = 640
h, w, channels = img.shape
img = utils.resize(img, resize_to, h, w)
des = descriptors.sift(img)
k = 128
des_name = "SIFT"
codebook_filename = filenames.codebook(k, des_name)
codebook = utils.load(codebook_filename)
img_vlad = descriptors.vlad(des, codebook)
svm_filename = filenames.svm(k, des_name)
svm = cv2.SVM()
svm.load(svm_filename)
result = svm.predict(img_vlad)
print("result is {0}".format(result))
def train(self,
svm_kernel,
k,
des_name,
des_option=constants.ORB_FEAT_OPTION,
is_interactive=True):
"""
Gets the descriptors for the training set and then calculates the SVM for them.
Args:
svm_kernel (constant): The kernel of the SVM that will be created.
codebook (NumPy float matrix): Each row is a center of a codebook of Bag of Words approach.
des_option (integer): The option of the feature that is going to be used as local descriptor.
is_interactive (boolean): If it is the user can choose to load files or generate.
Returns:
cv2.SVM: The Support Vector Machine obtained in the training phase.
"""
isTrain = True
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
x_filename = filenames.vlads_train(k, des_name)
print("Getting global descriptors for the training set.")
start = time.time()
x, y, cluster_model = self.get_data_and_labels(
self.dataset.get_train_set(), None, k, des_name, des_option,
isTrain)
utils.save(x_filename, x)
end = time.time()
svm_filename = filenames.svm(k, des_name, svm_kernel)
print("Calculating the Support Vector Machine for the training set...")
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(svm_kernel)
svm.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER, 100, 1e-6))
svm.train(x, cv2.ml.ROW_SAMPLE, y)
return svm, cluster_model
