def test_vlad():
img = cv2.imread(constants.TESTING_IMG_PATH)
option = input("Enter [1] for using ORB features or [2] to use SIFT features.\n")
if option == 1:
des = descriptors.orb(img)
else:
des = descriptors.sift(img)
des_name = constants.ORB_FEAT_NAME if option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
k = 128
codebook_filename = "codebook_{0}_{1}.csv".format(k, des_name)
centers = np.loadtxt(codebook_filename, delimiter=constants.NUMPY_DELIMITER)
vlad_vector = descriptors.vlad(des, centers)
print(vlad_vector)
return vlad_vector
def get_data_and_labels(self, img_set, codebook, des_option = constants.ORB_FEAT_OPTION):
"""
Calculates all the local descriptors for an image set and then uses a codebook to calculate the VLAD global
descriptor for each image and stores the label with the class of the image.
Args:
img_set (string array): The list of image paths for the set.
codebook (numpy float matrix): Each row is a center and each column is a dimension of the centers.
des_option (integer): The option of the feature that is going to be used as local descriptor.
Returns:
NumPy float matrix: Each row is the global descriptor of an image and each column is a dimension.
NumPy float array: Each element is the number of the class for the corresponding image.
"""
target = open("result.txt", 'a')
y = []
x = None
#print len(img_set)
#print img_set[0]
for class_number in range(len(img_set)):
img_paths = img_set[class_number]
#print img_paths
step = round(constants.STEP_PERCENTAGE * len(img_paths) / 100)
for i in range(len(img_paths)):
end = img_paths[i].find('.png', 5)
target.write(img_paths[i][5:end]+",\n")
if (step > 0) and (i % step == 0):
percentage = (100 * i) / len(img_paths)
print("Calculating global descriptors for image number {0} of {1}({2}%)".format(
i, len(img_paths), percentage)
)
#print img_paths[i]
img = cv2.imread(img_paths[i])
if des_option == constants.ORB_FEAT_OPTION:
des = descriptors.orb(img)
else:
des = descriptors.sift(img)
if des is not None:
des = nmpy.array(des, dtype=nmpy.float32)
vlad_vector = descriptors.vlad(des, codebook)
if x is None:
x = vlad_vector
y.append(class_number)
else:
x = nmpy.vstack((x, vlad_vector))
y.append(class_number)
else:
print("Img with None descriptor: {0}".format(img_paths[i]))
y = nmpy.float32(y)[:, nmpy.newaxis]
x = nmpy.matrix(x)
return x, y
def get_data_and_labels(self, img_set, codebook, des_option = constants.ORB_FEAT_OPTION):
"""
Calculates all the local descriptors for an image set and then uses a codebook to calculate the VLAD global
descriptor for each image and stores the label with the class of the image.
Args:
img_set (string array): The list of image paths for the set.
codebook (numpy float matrix): Each row is a center and each column is a dimension of the centers.
des_option (integer): The option of the feature that is going to be used as local descriptor.
Returns:
NumPy float matrix: Each row is the global descriptor of an image and each column is a dimension.
NumPy float array: Each element is the number of the class for the corresponding image.
"""
y = []
x = None
for class_number in range(len(img_set)):
img_paths = img_set[class_number]
step = round(constants.STEP_PERCENTAGE * len(img_paths) / 100)
for i in range(len(img_paths)):
if (step > 0) and (i % step == 0):
percentage = (100 * i) / len(img_paths)
print("Calculating global descriptors for image number {0} of {1}({2}%)".format(
i, len(img_paths), percentage)
)
img = cv2.imread(img_paths[i])
if des_option == constants.ORB_FEAT_OPTION:
des = descriptors.orb(img)
else:
des = descriptors.sift(img)
if des is not None:
des = np.array(des, dtype=np.float32)
vlad_vector = descriptors.vlad(des, codebook)
if x is None:
x = vlad_vector
y.append(class_number)
else:
x = np.vstack((x, vlad_vector))
y.append(class_number)
else:
print("Img with None descriptor: {0}".format(img_paths[i]))
y = np.float32(y)[:, np.newaxis]
x = np.matrix(x)
return x, y
def test_des_type():
img = cv2.imread(constants.TESTING_IMG_PATH)
kp, des = descriptors.orb(img)
return des
