def get_data_file(dataset, thread_num):
detector = FaceRecognizer()
temp_dataset = []
ctr = 0
file_ctr = 0
gc.collect()
while len(dataset) > 0:
datum = dataset.pop(0)
print("File ", thread_num, file_ctr, "datapoint", ctr)
detector.new_image(datum['path'])
try:
a = detector.get_face_1D_numpy().tolist()
datum["as_tensor"] = a
datum["landmarks"] = detector.get_facial_landmarks(thread_num)
temp_dataset.append(datum)
gc.collect()
except (LookupError, AssertionError, NameError,
FileNotFoundError) as e:
gc.collect()
pass
ctr += 1
file_name = 'FinalDataset/FinalDataset' + str(thread_num) + "t_" + str(
file_ctr) + "f_" + '.json'
file_ctr += 1
with open(file_name, 'w') as outfile:
json.dump(temp_dataset, outfile)
del temp_dataset
gc.collect()
print("Thread Num: ", thread_num, " Done")
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()
def forward_img(self, img_filepath):
fr = FaceRecognizer()
fr.new_image(img_filepath)
np_face_arr = fr.get_face_as_numpy()
input_tensor = torch.from_numpy(np_face_arr)
return self.forward(input_tensor)
