class LinearRegressor:
def __init__(self, n, cost=RMSE()):
self.n = n
self.w = np.random.randn(n + 1)
self.normalize = True
self.cost = cost
self.scaler = Scaler()
def preprocess(self, X):
if self.normalize:
X = self.scaler.transform(X)
ones = np.ones(shape=len(X))
return np.c_[ones, X]
def predict_preproccessed(self, X):
return np.dot(X, self.w)
def predict(self, X):
if np.sum(X[:, 0] == 1) != X.shape[0]:
X = self.preprocess(X)
return self.predict_preproccessed(X)
def ccost(self, X, y):
X = self.preprocess(X)
return self.cost.compute(self.predict_preproccessed(X), y)
def fit(self, X, y, optimizer, epoch, plot):
if str(optimizer) == 'equation' or str(optimizer) == 'genetic':
self.normalize = False
if str(optimizer) == 'genetic':
optimizer.pr = lambda X, w: np.dot(X, w)
self.scaler.fit(X)
X = self.preprocess(X)
loss = [self.cost.compute(self.predict_preproccessed(X), y)]
for _ in range(epoch):
self.w = optimizer.optimize(X, y, self.w, self.cost)
loss.append(self.cost.compute(self.predict_preproccessed(X), y))
if plot:
plt.xlabel('iterations')
plt.ylabel('Emperical risk')
plt.title('Emperical rist optimization')
plt.plot(np.arange(epoch + 1), loss)
plt.show()
def embed_imgs(x):
npload = np.load('data/mean_std2.npz')
mean, std = npload['mean'], npload['std']
scaler = Scaler(mean=mean, std=std)
# model_path = 'data/cnn_model/epoch_232_val_loss1.351451.hdf5'
# model_emb_path = 'data/emb_model/model_1_epoch_0_test_eer0.114874.hdf5'
# model_path = 'data/cnn_model/epoch_57_val_loss1.699622.hdf5'
# model_emb_path = 'data/emb_model/model_2_epoch_25_test_eer0.106689.hdf5'
# model_path = 'data/cnn_model/epoch_29_val_loss1.441430.hdf5'
# model_emb_path = 'data/emb_model/model_5_epoch_2_test_eer0.143211.hdf5'
# model_emb_path = 'data/emb_model/model_6_epoch_6_test_eer_0.135497_test2_err0.254601.hdf5'
# model_emb_path = '../data/Modeltpe2/epoch_0_test_eer0.139840.hdf5'
# model_emb_path = '../data/Modeltpe3/epoch_12_test_eer0.107399.hdf5'
# model_emb_path = 'data/emb_model/model_4_epoch_1_test_eer0.108006.hdf5'
# model_path = 'data/cnn_model/epoch_16_val_loss1.231896.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_15_test_eer0.127431_test2_err0.218662.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_1_test_eer0.133520_test2_err0.216839.hdf5'
# model_emb_path = 'data/emb_model/model_9_epoch_5_test_eer0.127574_test2_err0.229637.hdf5'
model_path = 'data/cnn_model/epoch_66_val_loss1.206078.hdf5'
model_emb_path = 'data/emb_model/model_10_epoch_10_test_eer0.169731_test2_err0.204908.hdf5'
model = keras.models.load_model(model_path)
model_emb = keras.models.load_model(model_emb_path)
bottleneck = Bottleneck(model)
x = scaler.transform(x)
x = bottleneck.predict(transpose_matrix(x))
x = model_emb.predict(x)
# model_emb_path = 'data/epoch_17_test_eer0.191621.hdf5'
# model_emb = keras.models.load_model(model_emb_path)
# x = scaler.transform(x)
# x = model_emb.predict(transpose_matrix(x))
return x
class Recognizer(object):
def __init__(self):
self.resize_shape = (100, 100)
shape_predictor_path = 'data/shape_predictor_68_face_landmarks.dat'
self.shape_predictor = dlib.shape_predictor(shape_predictor_path)
self.eye_and_mouth_indices = [39, 42, 57]
self.template_landmarks = get_template_landmarks(
self.eye_and_mouth_indices, self.resize_shape[0])
npload = np.load('data/mean_std2.npz')
mean, std = npload['mean'], npload['std']
self.scaler = Scaler(mean=mean, std=std)
# model_emb_path = 'data/epoch_17_test_eer0.191621.hdf5'
model_path = 'data/cnn_model/epoch_66_val_loss1.206078.hdf5'
model_emb_path = 'data/emb_model/model_10_epoch_10_test_eer0.169731_test2_err0.204908.hdf5'
# model_path = 'data/cnn_model/epoch_16_val_loss1.231896.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_15_test_eer0.127431_test2_err0.218662.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_1_test_eer0.133520_test2_err0.216839.hdf5'
# model_emb_path = 'data/emb_model/model_9_epoch_5_test_eer0.127574_test2_err0.229637.hdf5'
# model_path = 'data/cnn_model/epoch_232_val_loss1.351451.hdf5'
# model_emb_path = 'data/emb_model/model_1_epoch_0_test_eer0.114874.hdf5'
#
# model_path = 'data/cnn_model/epoch_57_val_loss1.699622.hdf5'
# model_emb_path = 'data/emb_model/model_2_epoch_25_test_eer0.106689.hdf5'
# model_path = 'data/cnn_model/epoch_29_val_loss1.441430.hdf5'
# model_emb_path = 'data/emb_model/model_5_epoch_2_test_eer0.143211.hdf5'
# model_emb_path = 'data/emb_model/model_6_epoch_6_test_eer_0.135497_test2_err0.254601.hdf5'
# model_emb_path = '../data/Modeltpe2/epoch_0_test_eer0.139840.hdf5'
# model_emb_path = '../data/Modeltpe3/epoch_12_test_eer0.107399.hdf5'
# model_emb_path = 'data/emb_model/model_4_epoch_1_test_eer0.108006.hdf5'
model = keras.models.load_model(model_path)
self.model_emb = keras.models.load_model(model_emb_path)
self.bottleneck = Bottleneck(model)
npload = np.load('data/face_base.npz')
self.x, self.y = npload['x'], npload['y']
print(self.x.shape, self.y.shape)
with open('data/labels_dict.pkl', 'rb') as file:
self.labels_dict = pickle.load(file)
self.knn = KNeighborsClassifier(n_neighbors=1, metric=metric, n_jobs=1)
self.knn.fit(self.x, self.y)
def iterate_similarities(self, emb):
for i, person_emb in enumerate(self.x):
sim = person_emb @ emb.T
yield sim, i
def predict(self, img, img_gray, rect):
img = align_img(img, img_gray, rect, self.shape_predictor,
self.template_landmarks, self.eye_and_mouth_indices,
self.resize_shape)
batch_x = [img]
import matplotlib.pyplot as plt
# plt.imshow(img)
# plt.show()
batch_x = self.scaler.transform(batch_x)
batch_x = self.bottleneck.predict(transpose_matrix(batch_x))
batch_x = self.model_emb.predict(batch_x)
# batch_x = self.model_emb.predict(transpose_matrix(batch_x))
pred_labels = self.knn.predict(batch_x)
neighbors = self.knn.kneighbors(batch_x)
label_neighbors = [
self.labels_dict[self.y[ind]] for ind in neighbors[1][0]
]
print(label_neighbors, neighbors[0])
# label_ind = max(self.iterate_similarities(batch_x[0]), key=lambda x: x[0])[1]
# label = self.y[label_ind]
label = pred_labels[0]
return self.labels_dict[label], label_neighbors