text: import argparse
  import cv2
  import sys
  import numpy as np
  import os
  import mxnet as mx
  import datetime
  from skimage import transform as trans
  import insightface
  
  
  def square_crop(im, S):
      if im.shape[0] > im.shape[1]:
          height = S
          width = int(float(im.shape[1]) / im.shape[0] * S)
          scale = float(S) / im.shape[0]
      else:
          width = S
          height = int(float(im.shape[0]) / im.shape[1] * S)
          scale = float(S) / im.shape[1]
      resized_im = cv2.resize(im, (width, height))
      det_im = np.zeros((S, S, 3), dtype=np.uint8)
      det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
      return det_im, scale
  
  
  def transform(data, center, output_size, scale, rotation):
      scale_ratio = scale
      rot = float(rotation) * np.pi / 180.0
      #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
      t1 = trans.SimilarityTransform(scale=scale_ratio)
      cx = center[0] * scale_ratio
      cy = center[1] * scale_ratio
      t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
      t3 = trans.SimilarityTransform(rotation=rot)
      t4 = trans.SimilarityTransform(translation=(output_size / 2,
                                                  output_size / 2))
      t = t1 + t2 + t3 + t4
      M = t.params[0:2]
      cropped = cv2.warpAffine(data,
                               M, (output_size, output_size),
                               borderValue=0.0)
      return cropped, M
  
  
  def trans_points2d(pts, M):
      new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
      for i in range(pts.shape[0]):
          pt = pts[i]
          new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
          new_pt = np.dot(M, new_pt)
          #print('new_pt', new_pt.shape, new_pt)
          new_pts[i] = new_pt[0:2]
  
      return new_pts
  
  
  def trans_points3d(pts, M):
      scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
      #print(scale)
      new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
      for i in range(pts.shape[0]):
          pt = pts[i]
          new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
          new_pt = np.dot(M, new_pt)
          #print('new_pt', new_pt.shape, new_pt)
          new_pts[i][0:2] = new_pt[0:2]
          new_pts[i][2] = pts[i][2] * scale
  
      return new_pts
  
  
  def trans_points(pts, M):
      if pts.shape[1] == 2:
          return trans_points2d(pts, M)
      else:
          return trans_points3d(pts, M)
  
  
  class Handler:
      def __init__(self, prefix, epoch, im_size=192, det_size=224, ctx_id=0):
          print('loading', prefix, epoch)
          if ctx_id >= 0:
              ctx = mx.gpu(ctx_id)
          else:
              ctx = mx.cpu()
          image_size = (im_size, im_size)
          self.detector = insightface.model_zoo.get_model(
              'retinaface_mnet025_v2')  #can replace with your own face detector
          #self.detector = insightface.model_zoo.get_model('retinaface_r50_v1')
          self.detector.prepare(ctx_id=ctx_id)
          self.det_size = det_size
          sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
          all_layers = sym.get_internals()
          sym = all_layers['fc1_output']
          self.image_size = image_size
          model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
          model.bind(for_training=False,
                     data_shapes=[('data', (1, 3, image_size[0], image_size[1]))
                                  ])
          model.set_params(arg_params, aux_params)
          self.model = model
          self.image_size = image_size
  
      def get(self, img, get_all=False):
          out = []
          det_im, det_scale = square_crop(img, self.det_size)
          bboxes, _ = self.detector.detect(det_im)
          if bboxes.shape[0] == 0:
              return out
          bboxes /= det_scale
          if not get_all:
              areas = []
              for i in range(bboxes.shape[0]):
                  x = bboxes[i]
                  area = (x[2] - x[0]) * (x[3] - x[1])
                  areas.append(area)
              m = np.argsort(areas)[-1]
              bboxes = bboxes[m:m + 1]
          for i in range(bboxes.shape[0]):
              bbox = bboxes[i]
              input_blob = np.zeros((1, 3) + self.image_size, dtype=np.float32)
              w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
              center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2
              rotate = 0
              _scale = self.image_size[0] * 2 / 3.0 / max(w, h)
              rimg, M = transform(img, center, self.image_size[0], _scale,
                                  rotate)
              rimg = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
              rimg = np.transpose(rimg, (2, 0, 1))  #3*112*112, RGB
              input_blob[0] = rimg
              data = mx.nd.array(input_blob)
              db = mx.io.DataBatch(data=(data, ))
              self.model.forward(db, is_train=False)
              pred = self.model.get_outputs()[-1].asnumpy()[0]
              if pred.shape[0] >= 3000:
                  pred = pred.reshape((-1, 3))
              else:
                  pred = pred.reshape((-1, 2))
              pred[:, 0:2] += 1
              pred[:, 0:2] *= (self.image_size[0] // 2)
              if pred.shape[1] == 3:
                  pred[:, 2] *= (self.image_size[0] // 2)
  
              IM = cv2.invertAffineTransform(M)
              pred = trans_points(pred, IM)
              out.append(pred)
          return out
  
  
  if __name__ == '__main__':
      handler = Handler('./model/2d106_det', 0, ctx_id=7, det_size=640)
      im = cv2.imread('../../sample-images/t1.jpg')
      tim = im.copy()
      preds = handler.get(im, get_all=True)
      color = (200, 160, 75)
      for pred in preds:
          pred = np.round(pred).astype(np.int)
          for i in range(pred.shape[0]):
              p = tuple(pred[i])
              cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)
      cv2.imwrite('./test_out.jpg', tim)
  