def test_pca_validity(self, pca_postfix):
cnn_model = CNNModel()
pca_utility = PCAUtility()
tf_record_utility = TFRecordUtility()
image_utility = ImageUtility()
eigenvalues, eigenvectors, meanvector = pca_utility.load_pca_obj(
dataset_name=DatasetName.ibug, pca_postfix=pca_postfix)
lbl_arr, img_arr, pose_arr = tf_record_utility.retrieve_tf_record(
tfrecord_filename=IbugConf.tf_train_path,
number_of_records=30,
only_label=False)
for i in range(20):
b_vector_p = self.calculate_b_vector(lbl_arr[i], True, eigenvalues,
eigenvectors, meanvector)
lbl_new = meanvector + np.dot(eigenvectors, b_vector_p)
labels_true_transformed, landmark_arr_x_t, landmark_arr_y_t = image_utility. \
create_landmarks_from_normalized(lbl_arr[i], 224, 224, 112, 112)
labels_true_transformed_pca, landmark_arr_x_pca, landmark_arr_y_pca = image_utility. \
create_landmarks_from_normalized(lbl_new, 224, 224, 112, 112)
image_utility.print_image_arr(i, img_arr[i], landmark_arr_x_t,
landmark_arr_y_t)
image_utility.print_image_arr(i * 1000, img_arr[i],
landmark_arr_x_pca,
landmark_arr_y_pca)
def test_pca_validity(self, dataset_name, pca_postfix):
image_utility = ImageUtility()
eigenvalues = load('pca_obj/' + dataset_name + self.eigenvalues_prefix + str(pca_postfix)+".npy")
eigenvectors = load('pca_obj/' + dataset_name + self.eigenvectors_prefix + str(pca_postfix)+".npy")
meanvector = load('pca_obj/' + dataset_name + self.meanvector_prefix + str(pca_postfix)+".npy")
'''load data: '''
lbl_arr = []
img_arr = []
path = None
if dataset_name == DatasetName.ibug:
path = IbugConf.rotated_img_path_prefix # rotated is ok, since advs_aug is the same as rotated
num_of_landmarks = IbugConf.num_of_landmarks
elif dataset_name == DatasetName.cofw:
path = CofwConf.rotated_img_path_prefix
num_of_landmarks = CofwConf.num_of_landmarks
elif dataset_name == DatasetName.wflw:
path = WflwConf.rotated_img_path_prefix
num_of_landmarks = WflwConf.num_of_landmarks
for file in tqdm(os.listdir(path)):
if file.endswith(".pts"):
pts_file = os.path.join(path, file)
img_file = pts_file[:-3] + "jpg"
if not os.path.exists(img_file):
continue
points_arr = []
with open(pts_file) as fp:
line = fp.readline()
cnt = 1
while line:
if 3 < cnt <= num_of_landmarks+3:
x_y_pnt = line.strip()
x = float(x_y_pnt.split(" ")[0])
y = float(x_y_pnt.split(" ")[1])
points_arr.append(x)
points_arr.append(y)
line = fp.readline()
cnt += 1
lbl_arr.append(points_arr)
img_arr.append(Image.open(img_file))
for i in range(30):
b_vector_p = self.calculate_b_vector(lbl_arr[i], True, eigenvalues, eigenvectors, meanvector)
lbl_new = meanvector + np.dot(eigenvectors, b_vector_p)
lbl_new = lbl_new.tolist()
labels_true_transformed, landmark_arr_x_t, landmark_arr_y_t = image_utility. \
create_landmarks(lbl_arr[i], 1, 1)
labels_true_transformed_pca, landmark_arr_x_pca, landmark_arr_y_pca = image_utility. \
create_landmarks(lbl_new, 1, 1)
image_utility.print_image_arr(i, img_arr[i], landmark_arr_x_t, landmark_arr_y_t)
image_utility.print_image_arr(i * 1000, img_arr[i], landmark_arr_x_pca, landmark_arr_y_pca)
