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AffectnetConf, D300wConf, W300Conf, InputDataSize, LearningConfig, CofwConf, WflwConf
from image_utility import ImageUtility
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.decomposition import TruncatedSVD
import numpy as np
import pickle
import os
from tqdm import tqdm
from numpy import save, load
import math
from PIL import Image
class PCAUtility:
eigenvalues_prefix = "_eigenvalues_"
eigenvectors_prefix = "_eigenvectors_"
meanvector_prefix = "_meanvector_"
def create_pca_from_npy(self, dataset_name, pca_postfix):
lbl_arr = []
path = None
if dataset_name == DatasetName.ibug:
path = D300wConf.normalized_point # normalized
# elif dataset_name == DatasetName.cofw:
# path = CofwConf.normalized_points_npy_dir # normalized
# elif dataset_name == DatasetName.wflw:
# path = WflwConf.normalized_points_npy_dir # normalized
lbl_arr = []
for file in tqdm(os.listdir(path)):
if file.endswith(".npy"):
npy_file = os.path.join(path, file)
lbl_arr.append(load(npy_file))
lbl_arr = np.array(lbl_arr)
print('PCA calculation started')
''' no normalization is needed, since we want to generate hm'''
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))
#
save('pca_obj/' + dataset_name + self.eigenvalues_prefix + str(pca_postfix), eigenvalues)
save('pca_obj/' + dataset_name + self.eigenvectors_prefix + str(pca_postfix), eigenvectors)
save('pca_obj/' + dataset_name + self.meanvector_prefix + str(pca_postfix), mean_lbl_arr)
def create_pca_from_points(self, dataset_name, pca_postfix):
lbl_arr = []
path = None
if dataset_name == DatasetName.ibug:
path = D300wConf.rotated_img_path_prefix # rotated is ok, since advs_aug is the same as rotated
num_of_landmarks = D300wConf.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)
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)
lbl_arr = np.array(lbl_arr)
print('PCA calculation started')
''' no normalization is needed, since we want to generate hm'''
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))
#
save('pca_obj/' + dataset_name + self.eigenvalues_prefix + str(pca_postfix), eigenvalues)
save('pca_obj/' + dataset_name + self.eigenvectors_prefix + str(pca_postfix), eigenvectors)
save('pca_obj/' + dataset_name + self.meanvector_prefix + str(pca_postfix), mean_lbl_arr)
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 = D300wConf.rotated_img_path_prefix # rotated is ok, since advs_aug is the same as rotated
num_of_landmarks = D300wConf.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)
def calculate_b_vector(self, predicted_vector, correction, eigenvalues, eigenvectors, meanvector):
tmp1 = predicted_vector - meanvector
b_vector = np.dot(eigenvectors.T, tmp1)
# put b in -3lambda =>
if correction:
i = 0
for b_item in b_vector:
lambda_i_sqr = 3 * math.sqrt(eigenvalues[i])
if b_item > 0:
b_item = min(b_item, lambda_i_sqr)
else:
b_item = max(b_item, -1 * lambda_i_sqr)
b_vector[i] = b_item
i += 1
return b_vector
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(D300wConf.tf_train_path,
D300wConf.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 __save_obj(self, obj, name):
with open('obj/' + name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_pose_obj(self):
with open('obj/p_1_min.pkl', 'rb') as f:
p_1_min = pickle.load(f)
with open('obj/p_1_max.pkl', 'rb') as f:
p_1_max = pickle.load(f)
with open('obj/p_2_min.pkl', 'rb') as f:
p_2_min = pickle.load(f)
with open('obj/p_2_max.pkl', 'rb') as f:
p_2_max = pickle.load(f)
with open('obj/p_3_min.pkl', 'rb') as f:
p_3_min = pickle.load(f)
with open('obj/p_3_max.pkl', 'rb') as f:
p_3_max = pickle.load(f)
return p_1_min, p_1_max, p_2_min, p_2_max, p_3_min, p_3_max
def load_pca_obj(self, dataset_name, pca_postfix=97):
with open('obj/' + dataset_name + self.eigenvalues_prefix + str(pca_postfix) + '.pkl', 'rb') as f:
eigenvalues = pickle.load(f)
with open('obj/' + dataset_name + self.eigenvectors_prefix + str(pca_postfix) + '.pkl', 'rb') as f:
eigenvectors = pickle.load(f)
with open('obj/' + dataset_name + self.meanvector_prefix + str(pca_postfix) + '.pkl', 'rb') as f:
meanvector = pickle.load(f)
return eigenvalues, eigenvectors, meanvector
def _func_PCA(self, input_data, pca_postfix):
input_data = np.array(input_data)
pca = PCA(n_components=pca_postfix/100)
# pca = PCA(n_components=0.98)
# pca = IncrementalPCA(n_components=50, batch_size=50)
pca.fit(input_data)
pca_input_data = pca.transform(input_data)
eigenvalues = pca.explained_variance_
eigenvectors = pca.components_
return pca_input_data, eigenvalues, eigenvectors
def __svd_func(self, input_data, pca_postfix):
svd = TruncatedSVD(n_components=50)
svd.fit(input_data)
pca_input_data = svd.transform(input_data)
eigenvalues = svd.explained_variance_
eigenvectors = svd.components_
return pca_input_data, eigenvalues, eigenvectors
# U, S, VT = svd(input_data)
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