repo_description: DeepFaceLab is the leading software for creating deepfakes.
repo_topics: arxiv creating-deepfakes deep-face-swap deep-learning deep-neural-networks deepface deepfacelab deepfakes deeplearning face-swap faceswap fakeapp machine-learning neural-nets neural-networks
code: import numpy as np
  import cv2
  from scipy.spatial import Delaunay
  
  
  def applyAffineTransform(src, srcTri, dstTri, size) :
      warpMat = cv2.getAffineTransform( np.float32(srcTri), np.float32(dstTri) )
      return cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 )
  
  def morphTriangle(dst_img, src_img, st, dt) :
      (h,w,c) = dst_img.shape
      sr = np.array( cv2.boundingRect(np.float32(st)) )
      dr = np.array( cv2.boundingRect(np.float32(dt)) )
      sRect = st - sr[0:2]
      dRect = dt - dr[0:2]
      d_mask = np.zeros((dr[3], dr[2], c), dtype = np.float32)
      cv2.fillConvexPoly(d_mask, np.int32(dRect), (1.0,)*c, 8, 0);
      imgRect = src_img[sr[1]:sr[1] + sr[3], sr[0]:sr[0] + sr[2]]
      size = (dr[2], dr[3])
      warpImage1 = applyAffineTransform(imgRect, sRect, dRect, size)
  
      if c == 1:
          warpImage1 = np.expand_dims( warpImage1, -1 )
  
      dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]] = dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]]*(1-d_mask) + warpImage1 * d_mask
  
  def morph_by_points (image, sp, dp):
      if sp.shape != dp.shape:
          raise ValueError ('morph_by_points() sp.shape != dp.shape')
      (h,w,c) = image.shape
  
      result_image = np.zeros(image.shape, dtype = image.dtype)
  
      for tri in Delaunay(dp).simplices:
          morphTriangle(result_image, image, sp[tri], dp[tri])
  
      return result_image