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Note: You will need to add the shape_predictor_68_face_landmarks.dat file in the root for this to work. Url is provided in aligner.py
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import cv2
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import numpy
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from aligner import Aligner
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from model import autoencoder_A
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from model import autoencoder_B
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from model import encoder, decoder_A, decoder_B
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encoder .load_weights( "models/encoder.h5" )
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decoder_A.load_weights( "models/decoder_A.h5" )
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decoder_B.load_weights( "models/decoder_B.h5" )
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autoencoder = autoencoder_B
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# landmark file can be found in http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
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# unzip it in the same folder as the main scripts
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aligner = Aligner("shape_predictor_68_face_landmarks.dat")
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def convert_one_image( image ):
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assert image.shape == (256,256,3)
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crop = slice(48,208)
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face = image[crop,crop]
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face = cv2.resize( face, (64,64) )
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face = numpy.expand_dims( face, 0 )
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new_face = autoencoder.predict( face / 255.0 )[0]
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new_face = numpy.clip( new_face * 255, 0, 255 ).astype( image.dtype )
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new_face = cv2.resize( new_face, (160,160) )
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result = aligner.align(image.copy(), new_face)
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if result is None:
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return superpose(image, new_face, crop)
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else:
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return result
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def superpose(image, new_face, crop):
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new_image = image.copy()
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new_image[crop,crop] = new_face
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return new_image
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