text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from easydict import EasyDict as edict
  import time
  import sys
  import numpy as np
  import argparse
  import struct
  import cv2
  import sklearn
  from sklearn.preprocessing import normalize
  import mxnet as mx
  from mxnet import ndarray as nd
  
  
  def read_img(image_path):
      img = cv2.imread(image_path, cv2.CV_LOAD_IMAGE_COLOR)
      return img
  
  
  def get_feature(imgs, nets):
      count = len(imgs)
      data = mx.nd.zeros(shape=(count * 2, 3, imgs[0].shape[0],
                                imgs[0].shape[1]))
      for idx, img in enumerate(imgs):
          img = img[:, :, ::-1]  #to rgb
          img = np.transpose(img, (2, 0, 1))
          for flipid in [0, 1]:
              _img = np.copy(img)
              if flipid == 1:
                  _img = _img[:, :, ::-1]
              _img = nd.array(_img)
              data[count * flipid + idx] = _img
  
      F = []
      for net in nets:
          db = mx.io.DataBatch(data=(data, ))
          net.model.forward(db, is_train=False)
          x = net.model.get_outputs()[0].asnumpy()
          embedding = x[0:count, :] + x[count:, :]
          embedding = sklearn.preprocessing.normalize(embedding)
          #print('emb', embedding.shape)
          F.append(embedding)
      F = np.concatenate(F, axis=1)
      F = sklearn.preprocessing.normalize(F)
      #print('F', F.shape)
      return F
  
  
  def write_bin(path, feature):
      feature = list(feature)
      with open(path, 'wb') as f:
          f.write(struct.pack('4i', len(feature), 1, 4, 5))
          f.write(struct.pack("%df" % len(feature), *feature))
  
  
  def get_and_write(buffer, nets):
      imgs = []
      for k in buffer:
          imgs.append(k[0])
      features = get_feature(imgs, nets)
      #print(np.linalg.norm(feature))
      assert features.shape[0] == len(buffer)
      for ik, k in enumerate(buffer):
          out_path = k[1]
          feature = features[ik].flatten()
          write_bin(out_path, feature)
  
  
  def main(args):
  
      print(args)
      gpuid = args.gpu
      ctx = mx.gpu(gpuid)
      nets = []
      image_shape = [int(x) for x in args.image_size.split(',')]
      for model in args.model.split('|'):
          vec = model.split(',')
          assert len(vec) > 1
          prefix = vec[0]
          epoch = int(vec[1])
          print('loading', prefix, epoch)
          net = edict()
          net.ctx = ctx
          net.sym, net.arg_params, net.aux_params = mx.model.load_checkpoint(
              prefix, epoch)
          all_layers = net.sym.get_internals()
          net.sym = all_layers['fc1_output']
          net.model = mx.mod.Module(symbol=net.sym,
                                    context=net.ctx,
                                    label_names=None)
          net.model.bind(data_shapes=[('data', (1, 3, image_shape[1],
                                                image_shape[2]))])
          net.model.set_params(net.arg_params, net.aux_params)
          nets.append(net)
  
      facescrub_out = os.path.join(args.output, 'facescrub')
      megaface_out = os.path.join(args.output, 'megaface')
  
      i = 0
      succ = 0
      buffer = []
      for line in open(args.facescrub_lst, 'r'):
          if i % 1000 == 0:
              print("writing fs", i, succ)
          i += 1
          image_path = line.strip()
          _path = image_path.split('/')
          a, b = _path[-2], _path[-1]
          out_dir = os.path.join(facescrub_out, a)
          if not os.path.exists(out_dir):
              os.makedirs(out_dir)
          image_path = os.path.join(args.facescrub_root, image_path)
          img = read_img(image_path)
          if img is None:
              print('read error:', image_path)
              continue
          out_path = os.path.join(out_dir, b + "_%s.bin" % (args.algo))
          item = (img, out_path)
          buffer.append(item)
          if len(buffer) == args.batch_size:
              get_and_write(buffer, nets)
              buffer = []
          succ += 1
      if len(buffer) > 0:
          get_and_write(buffer, nets)
          buffer = []
      print('fs stat', i, succ)
  
      i = 0
      succ = 0
      buffer = []
      for line in open(args.megaface_lst, 'r'):
          if i % 1000 == 0:
              print("writing mf", i, succ)
          i += 1
          image_path = line.strip()
          _path = image_path.split('/')
          a1, a2, b = _path[-3], _path[-2], _path[-1]
          out_dir = os.path.join(megaface_out, a1, a2)
          if not os.path.exists(out_dir):
              os.makedirs(out_dir)
              #continue
          #print(landmark)
          image_path = os.path.join(args.megaface_root, image_path)
          img = read_img(image_path)
          if img is None:
              print('read error:', image_path)
              continue
          out_path = os.path.join(out_dir, b + "_%s.bin" % (args.algo))
          item = (img, out_path)
          buffer.append(item)
          if len(buffer) == args.batch_size:
              get_and_write(buffer, nets)
              buffer = []
          succ += 1
      if len(buffer) > 0:
          get_and_write(buffer, nets)
          buffer = []
      print('mf stat', i, succ)
  
  
  def parse_arguments(argv):
      parser = argparse.ArgumentParser()
  
      parser.add_argument('--batch_size', type=int, help='', default=8)
      parser.add_argument('--image_size', type=str, help='', default='3,112,112')
      parser.add_argument('--gpu', type=int, help='', default=0)
      parser.add_argument('--algo', type=str, help='', default='insightface')
      parser.add_argument('--facescrub-lst',
                          type=str,
                          help='',
                          default='./data/facescrub_lst')
      parser.add_argument('--megaface-lst',
                          type=str,
                          help='',
                          default='./data/megaface_lst')
      parser.add_argument('--facescrub-root',
                          type=str,
                          help='',
                          default='./data/facescrub_images')
      parser.add_argument('--megaface-root',
                          type=str,
                          help='',
                          default='./data/megaface_images')
      parser.add_argument('--output', type=str, help='', default='./feature_out')
      parser.add_argument('--model', type=str, help='', default='')
      return parser.parse_args(argv)
  
  
  if __name__ == '__main__':
      main(parse_arguments(sys.argv[1:]))
  