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https://github.com/deepfakes/faceswap
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e76ab16bfe
* Add preview functionality to effmpeg. (#435) * Add preview functionality to effmpeg. effmpeg tool: Preview for actions that have a video output now available. Preview does not work when muxing audio. * Model json unicode fix1 (#443) * fixed Windows 10 path error while loading weights * - fixed TypeError: the JSON object must be str, not 'bytes' with OriginalHighRes Model * MTCNN Extractor and Extraction refactor (#453) * implement mtcnn extractor * mtcnn refactor and vram management changes * cli arguments update for mtcnn/dlib split * Add mtcnn models to gitignore * Change multiprocessing on extract * GUI changes to handle nargs defaults * Early exit bugfix (#455) * Fix extract early termination bug * Fix extract early exit bug * Multi face detection bugfix (#456) * Multi face extraction fix * Original high res cleanup 1 (#457) * slight model re-factoring - removed excess threading code - added random kernel initialization to dense layer * Slight OriginalHighRes re-factoring an code cleanup
62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
import numpy
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from lib.umeyama import umeyama
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from lib.align_eyes import align_eyes
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from numpy.linalg import inv
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import cv2
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mean_face_x = numpy.array([
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0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
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0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
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0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
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0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
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0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
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0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
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0.553364, 0.490127, 0.42689 ])
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mean_face_y = numpy.array([
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0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
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0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
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0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
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0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
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0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
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0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
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0.784792, 0.824182, 0.831803, 0.824182 ])
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landmarks_2D = numpy.stack( [ mean_face_x, mean_face_y ], axis=1 )
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def get_align_mat(face, size, should_align_eyes):
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mat_umeyama = umeyama(numpy.array(face.landmarks_as_xy()[17:]), landmarks_2D, True)[0:2]
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if should_align_eyes is False:
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return mat_umeyama
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mat_umeyama = mat_umeyama * size
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# Convert to matrix
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landmarks = numpy.matrix(face.landmarks_as_xy())
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# cv2 expects points to be in the form np.array([ [[x1, y1]], [[x2, y2]], ... ]), we'll expand the dim
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landmarks = numpy.expand_dims(landmarks, axis=1)
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# Align the landmarks using umeyama
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umeyama_landmarks = cv2.transform(landmarks, mat_umeyama, landmarks.shape)
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# Determine a rotation matrix to align eyes horizontally
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mat_align_eyes = align_eyes(umeyama_landmarks, size)
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# Extend the 2x3 transform matrices to 3x3 so we can multiply them
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# and combine them as one
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mat_umeyama = numpy.matrix(mat_umeyama)
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mat_umeyama.resize((3, 3))
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mat_align_eyes = numpy.matrix(mat_align_eyes)
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mat_align_eyes.resize((3, 3))
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mat_umeyama[2] = mat_align_eyes[2] = [0, 0, 1]
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# Combine the umeyama transform with the extra rotation matrix
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transform_mat = mat_align_eyes * mat_umeyama
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# Remove the extra row added, shape needs to be 2x3
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transform_mat = numpy.delete(transform_mat, 2, 0)
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transform_mat = transform_mat / size
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return transform_mat
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