def create_pca(self, dataset_name, pca_postfix):
tf_record_util = TFRecordUtility()
lbl_arr = []
pose_arr = []
if dataset_name == DatasetName.ibug:
lbl_arr, img_arr, pose_arr = tf_record_util.retrieve_tf_record(
IbugConf.tf_train_path,
IbugConf.sum_of_train_samples,
only_label=True,
only_pose=True)
lbl_arr = np.array(lbl_arr)
print('PCA-retrieved')
'''need to be normalized based on the hyper face paper?'''
# reduced_lbl_arr, eigenvalues, eigenvectors = self.__svd_func(lbl_arr, pca_postfix)
reduced_lbl_arr, eigenvalues, eigenvectors = self.__func_PCA(
lbl_arr, pca_postfix)
mean_lbl_arr = np.mean(lbl_arr, axis=0)
eigenvectors = eigenvectors.T
self.__save_obj(
eigenvalues,
dataset_name + self.__eigenvalues_prefix + str(pca_postfix))
self.__save_obj(
eigenvectors,
dataset_name + self.__eigenvectors_prefix + str(pca_postfix))
self.__save_obj(
mean_lbl_arr,
dataset_name + self.__meanvector_prefix + str(pca_postfix))
'''calculate pose min max'''
p_1_arr = []
p_2_arr = []
p_3_arr = []
for p_item in pose_arr:
p_1_arr.append(p_item[0])
p_2_arr.append(p_item[1])
p_3_arr.append(p_item[2])
p_1_min = min(p_1_arr)
p_1_max = max(p_1_arr)
p_2_min = min(p_2_arr)
p_2_max = max(p_2_arr)
p_3_min = min(p_3_arr)
p_3_max = max(p_3_arr)
self.__save_obj(p_1_min, 'p_1_min')
self.__save_obj(p_1_max, 'p_1_max')
self.__save_obj(p_2_min, 'p_2_min')
self.__save_obj(p_2_max, 'p_2_max')
self.__save_obj(p_3_min, 'p_3_min')
self.__save_obj(p_3_max, 'p_3_max')
print('PCA-->done')
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)
