def __init__(self, input_datadir, output_datadir):

self.input_datadir = input_datadir

self.output_datadir = output_datadir

def collect_data(self):

output_dir = os.path.expanduser(self.output_datadir)

if not os.path.exists(output_dir):

os.makedirs(output_dir)

dataset = facenet.get_dataset(self.input_datadir)

with tf.Graph().as_default():

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)

sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))

with sess.as_default():

pnet, rnet, onet = detect_face.create_mtcnn(sess, './npy')

minsize = 20 # minimum size of face

threshold = [0.6, 0.7, 0.7] # three steps's threshold

factor = 0.709 # scale factor

margin = 44

image_size = 182

# Add a random key to the filename to allow alignment using multiple processes

random_key = np.random.randint(0, high=99999)

bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)

with open(bounding_boxes_filename, "w") as text_file:

nrof_images_total = 0

nrof_successfully_aligned = 0

for cls in dataset:

output_class_dir = os.path.join(output_dir, cls.name)

if not os.path.exists(output_class_dir):

os.makedirs(output_class_dir)

for image_path in cls.image_paths:

nrof_images_total += 1

filename = os.path.splitext(os.path.split(image_path)[1])[0]

output_filename = os.path.join(output_class_dir, filename + '.png')

print("Image: %s" % image_path)

if not os.path.exists(output_filename):

try:

img = misc.imread(image_path)

except (IOError, ValueError, IndexError) as e:

errorMessage = '{}: {}'.format(image_path, e)

print(errorMessage)

else:

if img.ndim < 2:

print('Unable to align "%s"' % image_path)

text_file.write('%s\n' % (output_filename))

continue

if img.ndim == 2:

img = facenet.to_rgb(img)

print('to_rgb data dimension: ', img.ndim)

img = img[:, :, 0:3]

bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold,

factor)

nrof_faces = bounding_boxes.shape[0]

print('No of Detected Face: %d' % nrof_faces)

if nrof_faces > 0:

det = bounding_boxes[:, 0:4]

img_size = np.asarray(img.shape)[0:2]

if nrof_faces > 1:

bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])

img_center = img_size / 2

offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],

(det[:, 1] + det[:, 3]) / 2 - img_center[0]])

offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)

index = np.argmax(

bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering

det = det[index, :]

det = np.squeeze(det)

bb_temp = np.zeros(4, dtype=np.int32)

bb_temp[0] = det[0]

bb_temp[1] = det[1]

bb_temp[2] = det[2]

bb_temp[3] = det[3]

try:

cropped_temp = img[bb_temp[1]:bb_temp[3], bb_temp[0]:bb_temp[2], :]

scaled_temp = misc.imresize(cropped_temp, (image_size, image_size), interp='bilinear')

nrof_successfully_aligned += 1

misc.imsave(output_filename, scaled_temp)

text_file.write('%s %d %d %d %d\n' % (

output_filename, bb_temp[0], bb_temp[1], bb_temp[2], bb_temp[3]))

except:

continue

else:

print('Unable to align "%s"' % image_path)

text_file.write('%s\n' % (output_filename))

return (nrof_images_total,nrof_successfully_aligned)