Preprocessing steps

During training, face photos and drawings are aligned and have nose,eyes,lips mask detected.

During test, the alignment step is optional and the masks are not needed.

All training and testing images in our model are aligned using facial landmarks. And landmarks after alignment are needed in our code.

First, 5 facial landmark for a face photo need to be detected (we detect using MTCNN(MTCNNv1)).
Then, we provide a matlab function in face_align_512.m to align, resize and crop face photos (and corresponding drawings) to 512x512. Call this function in MATLAB to align the image to 512x512. For example, for ia_selfie_10515.jpg in example dir, 5 detected facial landmark is saved in example/ia_selfie_10515_facial5point.mat. Call following in MATLAB:

load('example/ia_selfie_10515_facial5point.mat');
[trans_img]=face_align_512('example/ia_selfie_10515.jpg',facial5point,'example');

This will align the image and output aligned image in example folder. See face_align_512.m for more instructions.

In our work, we use the face parsing network in https://github.com/cientgu/Mask_Guided_Portrait_Editing to get nose,eyes,lips regions and then dilate the regions to make them cover these facial features (some examples are shown in example folder).

The background masks need to be copied to datasets/portrait_drawing/train/A(B)(_eyes)(_lips), and has the same filename with aligned face photos.

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