# based on deepfakes sample project
# https://github.com/deepfakes/faceswap
import cv2
import numpy
from umeyama import umeyama
# apply a random transformation on the training data to create a more robust model
def random_transform( image, rotation_range, zoom_range, shift_range, random_flip ):
h,w = image.shape[0:2]
rotation = numpy.random.uniform( -rotation_range, rotation_range )
scale = numpy.random.uniform( 1 - zoom_range, 1 + zoom_range )
tx = numpy.random.uniform( -shift_range, shift_range ) * w
ty = numpy.random.uniform( -shift_range, shift_range ) * h
mat = cv2.getRotationMatrix2D( (w//2,h//2), rotation, scale )
mat[:,2] += (tx,ty)
result = cv2.warpAffine( image, mat, (w,h), borderMode=cv2.BORDER_REPLICATE )
if numpy.random.random() < random_flip:
result = result[:,::-1]
return result
# get pair of random warped images from aligened face image
def random_warp( image ):
assert image.shape == (256,256,3)
range_ = numpy.linspace( 128-80, 128+80, 5 )
mapx = numpy.broadcast_to( range_, (5,5) )
mapy = mapx.T
mapx = mapx + numpy.random.normal( size=(5,5), scale=5 )
mapy = mapy + numpy.random.normal( size=(5,5), scale=5 )
interp_mapx = cv2.resize( mapx, (80,80) )[8:72,8:72].astype('float32')
interp_mapy = cv2.resize( mapy, (80,80) )[8:72,8:72].astype('float32')
warped_image = cv2.remap( image, interp_mapx, interp_mapy, cv2.INTER_LINEAR )
src_points = numpy.stack( [ mapx.ravel(), mapy.ravel() ], axis=-1 )
dst_points = numpy.mgrid[0:65:16,0:65:16].T.reshape(-1,2)
mat = umeyama( src_points, dst_points, True )[0:2]
target_image = cv2.warpAffine( image, mat, (64,64) )
return warped_image, target_image
