def get_image_tensor(self):
image_path = self.data_dict['path']
face_finder = FaceRecognizer()
face_finder.new_image(image_path)
np_tensor = np.zeros(DataInterpreter.num_pixels)
try:
np_tensor = face_finder.get_face_1D_numpy()
except (AssertionError, FileNotFoundError) as e:
pass
return torch.from_numpy(np_tensor).double()
def get_image_and_name_tensor(self):
np_tensor = np.zeros(DataInterpreter.num_pixels + 6)
image_path = self.data_dict['path']
face_finder = FaceRecognizer()
face_finder.new_image(image_path)
np_face_tensor = np.zeros(DataInterpreter.num_pixels)
try:
np_face_tensor = face_finder.get_face_1D_numpy()
except AssertionError:
pass
assert np_face_tensor.size < np_tensor.size
ctr = 0
for i in range(np_face_tensor.size):
np_tensor[i] = np_face_tensor[i]
ctr += 1
name = ""
try:
name = self.data_dict['name']
if name == None or name == "":
raise NameError
if " " in name:
name = name[:name.index(" ")]
first_letter = DataInterpreter.hash_str(name[0])
first_two = DataInterpreter.hash_str(name[1])
first_three = DataInterpreter.hash_str(name[2])
last_three = DataInterpreter.hash_str(name[-3])
last_two = DataInterpreter.hash_str(name[-2])
last_letter = DataInterpreter.hash_str(name[-1])
np_tensor[ctr] = first_letter
np_tensor[ctr + 1] = first_two
np_tensor[ctr + 2] = first_three
np_tensor[ctr + 3] = last_three
np_tensor[ctr + 4] = last_two
np_tensor[ctr + 5] = last_letter
except NameError:
np_tensor[ctr] = 0
np_tensor[ctr + 1] = 0
np_tensor[ctr + 2] = 0
np_tensor[ctr + 3] = 0
np_tensor[ctr + 4] = 0
np_tensor[ctr + 5] = 0
return torch.from_numpy(np_tensor).double()
