December 21, 2017 16:05 · Dec 21, 2017
diff --git a/align_images.py b/align_images.py
@@ -0,0 +1,110 @@
+import argparse
+import cv2
+import dlib
+import json
+import numpy
+import skimage
+from pathlib import Path
+from tqdm import tqdm
+from umeyama import umeyama
+
+from face_alignment import FaceAlignment, LandmarksType
+
+def monkey_patch_face_detector(_):
+    detector = dlib.get_frontal_face_detector()
+    class Rect(object):
+        def __init__(self,rect):
+            self.rect=rect
+    def detect( *args ):
+        return [ Rect(x) for x in detector(*args) ]
+    return detect
+
+dlib.cnn_face_detection_model_v1 = monkey_patch_face_detector
+FACE_ALIGNMENT = FaceAlignment( LandmarksType._2D, enable_cuda=True, flip_input=False )
+
+mean_face_x = numpy.array([
+0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
+0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
+0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
+0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
+0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
+0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
+0.553364, 0.490127, 0.42689 ])
+
+mean_face_y = numpy.array([
+0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
+0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
+0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
+0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
+0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
+0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
+0.784792, 0.824182, 0.831803, 0.824182 ])
+
+landmarks_2D = numpy.stack( [ mean_face_x, mean_face_y ], axis=1 )
+
+def transform( image, mat, size, padding=0 ):
+    mat = mat * size
+    mat[:,2] += padding
+    new_size = int( size + padding * 2 )
+    return cv2.warpAffine( image, mat, ( new_size, new_size ) )
+
+def main( args ):
+    input_dir = Path( args.input_dir )
+    assert input_dir.is_dir()
+
+    output_dir = input_dir / args.output_dir
+    output_dir.mkdir( parents=True, exist_ok=True )
+
+    output_file = input_dir / args.output_file
+
+    input_files = list( input_dir.glob( "*." + args.file_type ) )
+    assert len( input_files ) > 0, "Can't find input files"
+
+    def iter_face_alignments():
+        for fn in tqdm( input_files ):
+            image = cv2.imread( str(fn) )
+            if image is None:
+                tqdm.write( "Can't read image file: ", fn )
+                continue
+
+            faces = FACE_ALIGNMENT.get_landmarks( skimage.io.imread( str(fn) ) )
+
+            if faces is None: continue
+            if len(faces) == 0: continue
+            if args.only_one_face and len(faces) != 1: continue
+
+            for i,points in enumerate(faces):
+                alignment = umeyama( points[17:], landmarks_2D, True )[0:2]
+                aligned_image = transform( image, alignment, 160, 48 )
+
+                if len(faces) == 1:
+                    out_fn = "{}.jpg".format( Path(fn).stem )
+                else:
+                    out_fn = "{}_{}.jpg".format( Path(fn).stem, i )
+
+                out_fn = output_dir / out_fn
+                cv2.imwrite( str(out_fn), aligned_image )
+
+                yield str(fn.relative_to(input_dir)), str(out_fn.relative_to(input_dir)), list( alignment.ravel() )
+
+    face_alignments = list( iter_face_alignments() )
+
+    with output_file.open('w') as f:
+        results = json.dumps( face_alignments, ensure_ascii=False )
+        f.write( results )
+
+    print( "Save face alignments to output file:", output_file )
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument( "input_dir"  , type=str )
+    parser.add_argument( "output_dir" , type=str, nargs='?', default='aligned' )
+    parser.add_argument( "output_file", type=str, nargs='?', default='alignments.json' )
+
+    parser.set_defaults( only_one_face=False )
+    parser.add_argument('--one-face' , dest='only_one_face', action='store_true'  )
+    parser.add_argument('--all-faces', dest='only_one_face', action='store_false' )
+
+    parser.add_argument( "--file-type", type=str, default='jpg' )
+
+    main( parser.parse_args() )
diff --git a/merge_faces.py b/merge_faces.py
@@ -0,0 +1,62 @@
+import argparse
+import cv2
+import json
+import numpy
+from pathlib import Path
+from tqdm import tqdm
+
+from model import autoencoder_A
+from model import autoencoder_B
+from model import encoder, decoder_A, decoder_B
+
+encoder  .load_weights( "models/encoder.h5"   )
+decoder_A.load_weights( "models/decoder_A.h5" )
+decoder_B.load_weights( "models/decoder_B.h5" )
+
+def convert_one_image( autoencoder, image, mat ):
+    size = 64
+    face = cv2.warpAffine( image, mat * size, (size,size) )
+    face = numpy.expand_dims( face, 0 )
+    new_face = autoencoder.predict( face / 255.0 )[0]
+    new_face = numpy.clip( new_face * 255, 0, 255 ).astype( image.dtype )
+    new_image = numpy.copy( image )
+    image_size = image.shape[1], image.shape[0]
+    cv2.warpAffine( new_face, mat * size, image_size, new_image, cv2.WARP_INVERSE_MAP, cv2.BORDER_TRANSPARENT )
+    return new_image
+
+def main( args ):
+    input_dir = Path( args.input_dir )
+    assert input_dir.is_dir()
+
+    alignments = input_dir / args.alignments
+    with alignments.open() as f:
+        alignments = json.load(f)
+
+    output_dir = input_dir / args.output_dir
+    output_dir.mkdir( parents=True, exist_ok=True )
+
+    if args.direction == 'AtoB': autoencoder = autoencoder_B
+    if args.direction == 'BtoA': autoencoder = autoencoder_A
+
+    for image_file, face_file, mat in tqdm( alignments ):
+        image = cv2.imread( str( input_dir / image_file ) )
+        face  = cv2.imread( str( input_dir / face_file  ) )
+
+        mat = numpy.array(mat).reshape(2,3)
+
+        if image is None: continue
+        if face  is None: continue
+
+        new_image = convert_one_image( autoencoder, image, mat )
+
+        output_file = output_dir / Path(image_file).name
+        cv2.imwrite( str(output_file), new_image )
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument( "input_dir", type=str )
+    parser.add_argument( "alignments", type=str, nargs='?', default='alignments.json' )
+    parser.add_argument( "output_dir", type=str, nargs='?', default='merged' )
+    parser.add_argument( "--direction", type=str, default="AtoB", choices=["AtoB", "BtoA"])
+    main( parser.parse_args() )
+
diff --git a/umeyama.py b/umeyama.py
@@ -0,0 +1,84 @@
+## License (Modified BSD)
+## Copyright (C) 2011, the scikit-image team All rights reserved.
+##
+## Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+##
+## Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+## Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+## Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+## THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# umeyama function from scikit-image/skimage/transform/_geometric.py
+
+import numpy as np
+
+def umeyama( src, dst, estimate_scale ):
+    """Estimate N-D similarity transformation with or without scaling.
+    Parameters
+    ----------
+    src : (M, N) array
+        Source coordinates.
+    dst : (M, N) array
+        Destination coordinates.
+    estimate_scale : bool
+        Whether to estimate scaling factor.
+    Returns
+    -------
+    T : (N + 1, N + 1)
+        The homogeneous similarity transformation matrix. The matrix contains
+        NaN values only if the problem is not well-conditioned.
+    References
+    ----------
+    .. [1] "Least-squares estimation of transformation parameters between two
+            point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
+    """
+
+    num = src.shape[0]
+    dim = src.shape[1]
+
+    # Compute mean of src and dst.
+    src_mean = src.mean(axis=0)
+    dst_mean = dst.mean(axis=0)
+
+    # Subtract mean from src and dst.
+    src_demean = src - src_mean
+    dst_demean = dst - dst_mean
+
+    # Eq. (38).
+    A = np.dot(dst_demean.T, src_demean) / num
+
+    # Eq. (39).
+    d = np.ones((dim,), dtype=np.double)
+    if np.linalg.det(A) < 0:
+        d[dim - 1] = -1
+
+    T = np.eye(dim + 1, dtype=np.double)
+
+    U, S, V = np.linalg.svd(A)
+
+    # Eq. (40) and (43).
+    rank = np.linalg.matrix_rank(A)
+    if rank == 0:
+        return np.nan * T
+    elif rank == dim - 1:
+        if np.linalg.det(U) * np.linalg.det(V) > 0:
+            T[:dim, :dim] = np.dot(U, V)
+        else:
+            s = d[dim - 1]
+            d[dim - 1] = -1
+            T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V))
+            d[dim - 1] = s
+    else:
+        T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V.T))
+
+    if estimate_scale:
+        # Eq. (41) and (42).
+        scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d)
+    else:
+        scale = 1.0
+
+    T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T)
+    T[:dim, :dim] *= scale
+
+    return T
+