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New script:

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5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat
Copies faces containing XSeg polygons to aligned_xseg\ dir.
Useful only if you want to collect labeled faces and reuse them in other fakes.

Now you can use trained XSeg mask in the SAEHD training process.
It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model.
use
5.XSeg.optional) trained mask for data_dst/data_src - apply.bat
5.XSeg.optional) trained mask for data_dst/data_src - remove.bat

Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions.

XSeg trainer : now you can choose type of face
XSeg trainer : now you can restart training in “override settings”
Merger: XSeg-* modes now can be used with all types of faces.

Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed,
because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
This commit is contained in:
Colombo 2020-03-30 14:00:40 +04:00
parent e5bad483ca
commit 6d3607a13d
30 changed files with 279 additions and 1520 deletions
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27
mainscripts/Sorter.py
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@ -6,7 +6,6 @@ import sys
import tempfile
from functools import cmp_to_key
from pathlib import Path
from shutil import copyfile
import cv2
import numpy as np
@ -35,7 +34,7 @@ class BlurEstimatorSubprocessor(Subprocessor):
else:
image = cv2_imread( str(filepath) )
return [ str(filepath), estimate_sharpness(image) ]
#override
def get_data_name (self, data):
@ -146,7 +145,7 @@ def sort_by_face_pitch(input_path):
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def sort_by_face_source_rect_size(input_path):
io.log_info ("Sorting by face rect size...")
img_list = []
@ -163,15 +162,15 @@ def sort_by_face_source_rect_size(input_path):
source_rect = dflimg.get_source_rect()
rect_area = mathlib.polygon_area(np.array(source_rect[[0,2,2,0]]).astype(np.float32), np.array(source_rect[[1,1,3,3]]).astype(np.float32))
img_list.append( [str(filepath), rect_area ] )
io.log_info ("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
return img_list, trash_img_list
class HistSsimSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
@ -444,13 +443,13 @@ class FinalLoaderSubprocessor(Subprocessor):
raise Exception ("Unable to load %s" % (filepath.name) )
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
if self.faster:
source_rect = dflimg.get_source_rect()
sharpness = mathlib.polygon_area(np.array(source_rect[[0,2,2,0]]).astype(np.float32), np.array(source_rect[[1,1,3,3]]).astype(np.float32))
else:
sharpness = estimate_sharpness(gray)
pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll ( dflimg.get_landmarks(), size=dflimg.get_shape()[1] )
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
@ -586,12 +585,12 @@ class FinalHistDissimSubprocessor(Subprocessor):
def get_result(self):
return self.result
def sort_best_faster(input_path):
def sort_best_faster(input_path):
return sort_best(input_path, faster=True)
def sort_best(input_path, faster=False):
target_count = io.input_int ("Target number of faces?", 2000)
io.log_info ("Performing sort by best faces.")
if faster:
io.log_info("Using faster algorithm. Faces will be sorted by source-rect-area instead of blur.")
@ -630,7 +629,7 @@ def sort_best(input_path, faster=False):
imgs_per_grad += total_lack // grads
sharpned_imgs_per_grad = imgs_per_grad*10
for g in io.progress_bar_generator ( range (grads), "Sort by blur"):
img_list = yaws_sample_list[g]
@ -770,7 +769,7 @@ def sort_by_absdiff(input_path):
outputs_full = []
outputs_remain = []
for i in range(batch_size):
diff_t = tf.reduce_sum( tf.abs(i_t-j_t[i]), axis=[1,2,3] )
outputs_full.append(diff_t)