recognition_t = 0.3
required_size = (160, 160)
encoding_dict = load_pickle(encodings_path)
face_detector = mtcnn.MTCNN()
face_encoder = load_model(encoder_model)
img = cv2.imread(test_img_path)
# plt_show(img)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = face_detector.detect_faces(img_rgb)
for res in results:
face, pt_1, pt_2 = get_face(img_rgb, res['box'])
encode = get_encode(face_encoder, face, required_size)
encode = l2_normalizer.transform(np.expand_dims(encode, axis=0))[0]
name = 'unknown'
distance = float("inf")
for db_name, db_encode in encoding_dict.items():
dist = cosine(db_encode, encode)
if dist < recognition_t and dist < distance:
name = db_name
distance = dist
if name == 'unknown':
cv2.rectangle(img, pt_1, pt_2, (0, 0, 255), 2)
cv2.putText(img, name, pt_1, cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
else:
cv2.rectangle(img, pt_1, pt_2, (0, 255, 0), 2)
# Train the network
net.cae_train()
# save result
net.cae_save('mnist/net.pkl')
# Plot the loss curve
# net.cae_eval()
# Test the network
imgs = X_test.reshape(-1,28,28)
img_small = imgs[30,:,:]
# encode
img_en = utils.get_encode(net.cae, img_small)
# decode
img_de = utils.get_decode(net.cae, img_en)
# Compare
img_pre = np.rint(img_de.reshape(28,28) * 256).astype(int)
img_pre = np.clip(img_pre, a_min = 0, a_max = 255)
img_pre = img_pre.astype('uint8')
plt.imshow(img_pre)
def get_picture_array(X, rescale=4):
array = X.reshape(28,28)
array = np.clip(array, a_min = 0, a_max = 255)
return array.repeat(rescale, axis = 0).repeat(rescale, axis = 1).astype(np.uint8())
def compare_images(img, img_pre):