DeepFaceLab/core/imagelib/common.py
Colombo 76ca79216e Upgraded to TF version 1.13.2
Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
2020-01-25 21:58:19 +04:00

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1.3 KiB
Python

import numpy as np
def normalize_channels(img, target_channels):
img_shape_len = len(img.shape)
if img_shape_len == 2:
h, w = img.shape
c = 0
elif img_shape_len == 3:
h, w, c = img.shape
else:
raise ValueError("normalize: incorrect image dimensions.")
if c == 0 and target_channels > 0:
img = img[...,np.newaxis]
c = 1
if c == 1 and target_channels > 1:
img = np.repeat (img, target_channels, -1)
c = target_channels
if c > target_channels:
img = img[...,0:target_channels]
c = target_channels
return img
def cut_odd_image(img):
h, w, c = img.shape
wm, hm = w % 2, h % 2
if wm + hm != 0:
img = img[0:h-hm,0:w-wm,:]
return img
def overlay_alpha_image(img_target, img_source, xy_offset=(0,0) ):
(h,w,c) = img_source.shape
if c != 4:
raise ValueError("overlay_alpha_image, img_source must have 4 channels")
x1, x2 = xy_offset[0], xy_offset[0] + w
y1, y2 = xy_offset[1], xy_offset[1] + h
alpha_s = img_source[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
img_target[y1:y2, x1:x2, c] = (alpha_s * img_source[:, :, c] +
alpha_l * img_target[y1:y2, x1:x2, c])