mirror of
https://github.com/deepfakes/faceswap.git
synced 2026-10-25 03:10:11 +08:00
Othniel Cundangan
810bd0bce7
* Clearer requirements for each platform
* Refactoring of old plugins (Model_Original + Extract_Align) + Cleanups
* Adding GAN128
* Update GAN to v2
* Create instance_normalization.py
* Fix decoder output
* Revert "Fix decoder output"
This reverts commit 3a8ecb8957.
* Fix convert
* Enable all options except perceptual_loss by default
* Disable instance norm
* Update Model.py
* Update Trainer.py
* Match GAN128 to shaoanlu's latest v2
* Add first_order to GAN128
* Disable `use_perceptual_loss`
* Fix call to `self.first_order`
* Switch to average loss in output
* Constrain average to last 100 iterations
* Fix math, constrain average to intervals of 100
* Fix math averaging again
* Remove math and simplify this damn averagin
* Add gan128 conversion
* Update convert.py
* Use non-warped images in masked preview
* Add K.set_learning_phase(1) to gan64
* Add K.set_learning_phase(1) to gan128
* Add missing keras import
* Use non-warped images in masked preview for gan128
* Exclude deleted faces from conversion
* --input-aligned-dir defaults to "{input_dir}/aligned"
* Simplify map operation
* port 'face_alignment' from PyTorch to Keras. It works x2 faster, but initialization takes 20secs.
2DFAN-4.h5 and mmod_human_face_detector.dat included in lib\FaceLandmarksExtractor
fixed dlib vs tensorflow conflict: dlib must do op first, then load keras model, otherwise CUDA OOM error
if face location not found by CNN, its try to find by HOG.
removed this:
- if face.landmarks == None:
- print("Warning! landmarks not found. Switching to crop!")
- return cv2.resize(face.image, (size, size))
because DetectedFace always has landmarks
* Enabled masked converter for GAN models
* Histogram matching, cli option for perceptual loss
* Fix init() positional args error
* Add backwards compatibility for aligned filenames
* Fix masked converter
* Remove GAN converters
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
# Based on the original https://www.reddit.com/r/deepfakes/ code sample
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# Adjust code made by https://github.com/yangchen8710
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import cv2
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import numpy
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import os
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class Convert(object):
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def __init__(self, encoder, smooth_mask=True, avg_color_adjust=True, **kwargs):
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self.encoder = encoder
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self.use_smooth_mask = smooth_mask
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self.use_avg_color_adjust = avg_color_adjust
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def patch_image( self, original, face_detected, size ):
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#assert image.shape == (256, 256, 3)
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image = cv2.resize(face_detected.image, (256, 256))
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crop = slice(48, 208)
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face = image[crop, crop]
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old_face = face.copy()
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face = cv2.resize(face, (size, size))
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face = numpy.expand_dims(face, 0)
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new_face = self.encoder(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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if self.use_avg_color_adjust:
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self.adjust_avg_color(old_face,new_face)
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if self.use_smooth_mask:
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self.smooth_mask(old_face,new_face)
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new_face = self.superpose(image, new_face, crop)
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original[slice(face_detected.y, face_detected.y + face_detected.h), slice(face_detected.x, face_detected.x + face_detected.w)] = cv2.resize(new_face, (face_detected.w, face_detected.h))
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return original
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def adjust_avg_color(self,img_old,img_new):
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w,h,c = img_new.shape
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for i in range(img_new.shape[-1]):
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old_avg = img_old[:, :, i].mean()
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new_avg = img_new[:, :, i].mean()
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diff_int = (int)(old_avg - new_avg)
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for m in range(img_new.shape[0]):
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for n in range(img_new.shape[1]):
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temp = (img_new[m,n,i] + diff_int)
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if temp < 0:
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img_new[m,n,i] = 0
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elif temp > 255:
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img_new[m,n,i] = 255
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else:
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img_new[m,n,i] = temp
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def smooth_mask(self,img_old,img_new):
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w,h,c = img_new.shape
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crop = slice(0,w)
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mask = numpy.zeros_like(img_new)
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mask[h//15:-h//15,w//15:-w//15,:] = 255
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mask = cv2.GaussianBlur(mask,(15,15),10)
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img_new[crop,crop] = mask/255*img_new + (1-mask/255)*img_old
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def superpose(self,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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