def main(args):
    sleep(random.random())
    output_dir = os.path.expanduser(args.output_dir)
    if (not os.path.exists(output_dir)):
        os.makedirs(output_dir)
    (src_path, _) = os.path.split(os.path.realpath(__file__))
    facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
    dataset = facenet.get_dataset(args.input_dir, False)
    print('Creating networks and loading parameters')
    with tf.Graph().as_default():
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
        with sess.as_default():
            (pnet, rnet, onet) = create_mtcnn(sess, None)
    minsize = 20
    threshold = [0.6, 0.7, 0.7]
    factor = 0.709
    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
        if args.random_order:
            random.shuffle(dataset)
        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)
                if args.random_order:
                    random.shuffle(cls.image_paths)
            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_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)
                        img = img[:, :, 0:3]
                        (bounding_boxes, _) = detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
                        nrof_faces = bounding_boxes.shape[0]
                        if (nrof_faces > 0):
                            det = bounding_boxes[:, 0:4]
                            det_arr = []
                            img_size = np.asarray(img.shape)[0:2]
                            if (nrof_faces > 1):
                                if args.detect_multiple_faces:
                                    for i in range(nrof_faces):
                                        det_arr.append(np.squeeze(det[i]))
                                else:
                                    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)))
                                    det_arr.append(det[index, :])
                            else:
                                det_arr.append(np.squeeze(det))
                            for (i, det) in enumerate(det_arr):
                                det = np.squeeze(det)
                                bb = np.zeros(4, dtype=np.int32)
                                bb[0] = np.maximum((det[0] - (args.margin / 2)), 0)
                                bb[1] = np.maximum((det[1] - (args.margin / 2)), 0)
                                bb[2] = np.minimum((det[2] + (args.margin / 2)), img_size[1])
                                bb[3] = np.minimum((det[3] + (args.margin / 2)), img_size[0])
                                cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
                                scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
                                nrof_successfully_aligned += 1
                                (filename_base, file_extension) = os.path.splitext(output_filename)
                                if args.detect_multiple_faces:
                                    output_filename_n = '{}_{}{}'.format(filename_base, i, file_extension)
                                else:
                                    output_filename_n = '{}{}'.format(filename_base, file_extension)
                                misc.imsave(output_filename_n, scaled)
                                text_file.write(('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3])))
                        else:
                            print(('Unable to align "%s"' % image_path))
                            text_file.write(('%s\n' % output_filename))
    print(('Total number of images: %d' % nrof_images_total))
    print(('Number of successfully aligned images: %d' % nrof_successfully_aligned))
def main(args):
    sleep(random.random())
    output_dir = os.path.expanduser(args.output_dir)
    if (not os.path.exists(output_dir)):
        os.makedirs(output_dir)
    (src_path, _) = os.path.split(os.path.realpath(__file__))
    facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
    dataset = facenet.get_dataset(args.input_dir, False)
    print('Creating networks and loading parameters')
    with tf.Graph().as_default():
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
        with sess.as_default():
            (pnet, rnet, onet) = create_mtcnn(sess, None)
    minsize = 20
    threshold = [0.6, 0.7, 0.7]
    factor = 0.709
    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
        if args.random_order:
            random.shuffle(dataset)
        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)
                if args.random_order:
                    random.shuffle(cls.image_paths)
            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_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)
                        img = img[:, :, 0:3]
                        (bounding_boxes, _) = detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
                        nrof_faces = bounding_boxes.shape[0]
                        if (nrof_faces > 0):
                            det = bounding_boxes[:, 0:4]
                            det_arr = []
                            img_size = np.asarray(img.shape)[0:2]
                            if (nrof_faces > 1):
                                if args.detect_multiple_faces:
                                    for i in range(nrof_faces):
                                        det_arr.append(np.squeeze(det[i]))
                                else:
                                    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)))
                                    det_arr.append(det[index, :])
                            else:
                                det_arr.append(np.squeeze(det))
                            for (i, det) in enumerate(det_arr):
                                det = np.squeeze(det)
                                bb = np.zeros(4, dtype=np.int32)
                                bb[0] = np.maximum((det[0] - (args.margin / 2)), 0)
                                bb[1] = np.maximum((det[1] - (args.margin / 2)), 0)
                                bb[2] = np.minimum((det[2] + (args.margin / 2)), img_size[1])
                                bb[3] = np.minimum((det[3] + (args.margin / 2)), img_size[0])
                                cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
                                scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
                                nrof_successfully_aligned += 1
                                (filename_base, file_extension) = os.path.splitext(output_filename)
                                if args.detect_multiple_faces:
                                    output_filename_n = '{}_{}{}'.format(filename_base, i, file_extension)
                                else:
                                    output_filename_n = '{}{}'.format(filename_base, file_extension)
                                misc.imsave(output_filename_n, scaled)
                                text_file.write(('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3])))
                        else:
                            print(('Unable to align "%s"' % image_path))
                            text_file.write(('%s\n' % output_filename))
    print(('Total number of images: %d' % nrof_images_total))
    print(('Number of successfully aligned images: %d' % nrof_successfully_aligned))