DeepFaceLab/merger/InteractiveMergerSubprocessor.py

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import multiprocessing
import os
import pickle
import sys
import traceback
from pathlib import Path
import numpy as np
from core import imagelib, pathex
from core.cv2ex import *
from core.interact import interact as io
from core.joblib import Subprocessor
from merger import MergeFaceAvatar, MergeMasked, MergerConfig
from .MergerScreen import Screen, ScreenManager
MERGER_DEBUG = False
class InteractiveMergerSubprocessor(Subprocessor):
class Frame(object):
def __init__(self, prev_temporal_frame_infos=None,
frame_info=None,
next_temporal_frame_infos=None):
self.prev_temporal_frame_infos = prev_temporal_frame_infos
self.frame_info = frame_info
self.next_temporal_frame_infos = next_temporal_frame_infos
self.output_filepath = None
self.output_mask_filepath = None
self.idx = None
self.cfg = None
self.is_done = False
self.is_processing = False
self.is_shown = False
self.image = None
class ProcessingFrame(object):
def __init__(self, idx=None,
cfg=None,
prev_temporal_frame_infos=None,
frame_info=None,
next_temporal_frame_infos=None,
output_filepath=None,
output_mask_filepath=None,
need_return_image = False):
self.idx = idx
self.cfg = cfg
self.prev_temporal_frame_infos = prev_temporal_frame_infos
self.frame_info = frame_info
self.next_temporal_frame_infos = next_temporal_frame_infos
self.output_filepath = output_filepath
self.output_mask_filepath = output_mask_filepath
self.need_return_image = need_return_image
if self.need_return_image:
self.image = None
class Cli(Subprocessor.Cli):
#override
def on_initialize(self, client_dict):
self.log_info ('Running on %s.' % (client_dict['device_name']) )
self.device_idx = client_dict['device_idx']
self.device_name = client_dict['device_name']
self.predictor_func = client_dict['predictor_func']
self.predictor_input_shape = client_dict['predictor_input_shape']
self.face_enhancer_func = client_dict['face_enhancer_func']
added XSeg model. with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
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self.xseg_256_extract_func = client_dict['xseg_256_extract_func']
#transfer and set stdin in order to work code.interact in debug subprocess
stdin_fd = client_dict['stdin_fd']
if stdin_fd is not None:
sys.stdin = os.fdopen(stdin_fd)
return None
#override
def process_data(self, pf): #pf=ProcessingFrame
cfg = pf.cfg.copy()
frame_info = pf.frame_info
filepath = frame_info.filepath
if len(frame_info.landmarks_list) == 0:
if cfg.mode == 'raw-predict':
h,w,c = self.predictor_input_shape
img_bgr = np.zeros( (h,w,3), dtype=np.uint8)
img_mask = np.zeros( (h,w,1), dtype=np.uint8)
else:
self.log_info (f'no faces found for {filepath.name}, copying without faces')
img_bgr = cv2_imread(filepath)
imagelib.normalize_channels(img_bgr, 3)
h,w,c = img_bgr.shape
img_mask = np.zeros( (h,w,1), dtype=img_bgr.dtype)
cv2_imwrite (pf.output_filepath, img_bgr)
cv2_imwrite (pf.output_mask_filepath, img_mask)
if pf.need_return_image:
pf.image = np.concatenate ([img_bgr, img_mask], axis=-1)
else:
if cfg.type == MergerConfig.TYPE_MASKED:
try:
final_img = MergeMasked (self.predictor_func, self.predictor_input_shape,
face_enhancer_func=self.face_enhancer_func,
added XSeg model. with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
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xseg_256_extract_func=self.xseg_256_extract_func,
cfg=cfg,
frame_info=frame_info)
except Exception as e:
e_str = traceback.format_exc()
if 'MemoryError' in e_str:
raise Subprocessor.SilenceException
else:
raise Exception( f'Error while merging file [{filepath}]: {e_str}' )
elif cfg.type == MergerConfig.TYPE_FACE_AVATAR:
final_img = MergeFaceAvatar (self.predictor_func, self.predictor_input_shape,
cfg, pf.prev_temporal_frame_infos,
pf.frame_info,
pf.next_temporal_frame_infos )
cv2_imwrite (pf.output_filepath, final_img[...,0:3] )
cv2_imwrite (pf.output_mask_filepath, final_img[...,3:4] )
if pf.need_return_image:
pf.image = final_img
return pf
#overridable
def get_data_name (self, pf):
#return string identificator of your data
return pf.frame_info.filepath
#override
def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter, subprocess_count=4):
if len (frames) == 0:
raise ValueError ("len (frames) == 0")
super().__init__('Merger', InteractiveMergerSubprocessor.Cli, io_loop_sleep_time=0.001)
self.is_interactive = is_interactive
self.merger_session_filepath = Path(merger_session_filepath)
self.merger_config = merger_config
self.predictor_func = predictor_func
self.predictor_input_shape = predictor_input_shape
self.face_enhancer_func = face_enhancer_func
added XSeg model. with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
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self.xseg_256_extract_func = xseg_256_extract_func
self.frames_root_path = frames_root_path
self.output_path = output_path
self.output_mask_path = output_mask_path
self.model_iter = model_iter
self.prefetch_frame_count = self.process_count = subprocess_count
session_data = None
if self.is_interactive and self.merger_session_filepath.exists():
io.input_skip_pending()
if io.input_bool ("Use saved session?", True):
try:
with open( str(self.merger_session_filepath), "rb") as f:
session_data = pickle.loads(f.read())
except Exception as e:
pass
rewind_to_frame_idx = None
self.frames = frames
self.frames_idxs = [ *range(len(self.frames)) ]
self.frames_done_idxs = []
if self.is_interactive and session_data is not None:
# Loaded session data, check it
s_frames = session_data.get('frames', None)
s_frames_idxs = session_data.get('frames_idxs', None)
s_frames_done_idxs = session_data.get('frames_done_idxs', None)
s_model_iter = session_data.get('model_iter', None)
frames_equal = (s_frames is not None) and \
(s_frames_idxs is not None) and \
(s_frames_done_idxs is not None) and \
(s_model_iter is not None) and \
(len(frames) == len(s_frames)) # frames count must match
if frames_equal:
for i in range(len(frames)):
frame = frames[i]
s_frame = s_frames[i]
# frames filenames must match
if frame.frame_info.filepath.name != s_frame.frame_info.filepath.name:
frames_equal = False
if not frames_equal:
break
if frames_equal:
io.log_info ('Using saved session from ' + '/'.join (self.merger_session_filepath.parts[-2:]) )
for frame in s_frames:
if frame.cfg is not None:
# recreate MergerConfig class using constructor with get_config() as dict params
# so if any new param will be added, old merger session will work properly
frame.cfg = frame.cfg.__class__( **frame.cfg.get_config() )
self.frames = s_frames
self.frames_idxs = s_frames_idxs
self.frames_done_idxs = s_frames_done_idxs
if self.model_iter != s_model_iter:
# model was more trained, recompute all frames
rewind_to_frame_idx = -1
for frame in self.frames:
frame.is_done = False
elif len(self.frames_idxs) == 0:
# all frames are done?
rewind_to_frame_idx = -1
if len(self.frames_idxs) != 0:
cur_frame = self.frames[self.frames_idxs[0]]
cur_frame.is_shown = False
if not frames_equal:
session_data = None
if session_data is None:
for filename in pathex.get_image_paths(self.output_path): #remove all images in output_path
Path(filename).unlink()
for filename in pathex.get_image_paths(self.output_mask_path): #remove all images in output_mask_path
Path(filename).unlink()
frames[0].cfg = self.merger_config.copy()
for i in range( len(self.frames) ):
frame = self.frames[i]
frame.idx = i
frame.output_filepath = self.output_path / ( frame.frame_info.filepath.stem + '.png' )
frame.output_mask_filepath = self.output_mask_path / ( frame.frame_info.filepath.stem + '.png' )
if not frame.output_filepath.exists() or \
not frame.output_mask_filepath.exists():
# if some frame does not exist, recompute and rewind
frame.is_done = False
frame.is_shown = False
if rewind_to_frame_idx is None:
rewind_to_frame_idx = i-1
else:
rewind_to_frame_idx = min(rewind_to_frame_idx, i-1)
if rewind_to_frame_idx is not None:
while len(self.frames_done_idxs) > 0:
if self.frames_done_idxs[-1] > rewind_to_frame_idx:
prev_frame = self.frames[self.frames_done_idxs.pop()]
self.frames_idxs.insert(0, prev_frame.idx)
else:
break
#override
def process_info_generator(self):
r = [0] if MERGER_DEBUG else range(self.process_count)
for i in r:
yield 'CPU%d' % (i), {}, {'device_idx': i,
'device_name': 'CPU%d' % (i),
'predictor_func': self.predictor_func,
'predictor_input_shape' : self.predictor_input_shape,
'face_enhancer_func': self.face_enhancer_func,
added XSeg model. with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
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'xseg_256_extract_func' : self.xseg_256_extract_func,
'stdin_fd': sys.stdin.fileno() if MERGER_DEBUG else None
}
#overridable optional
def on_clients_initialized(self):
io.progress_bar ("Merging", len(self.frames_idxs)+len(self.frames_done_idxs), initial=len(self.frames_done_idxs) )
self.process_remain_frames = not self.is_interactive
self.is_interactive_quitting = not self.is_interactive
if self.is_interactive:
help_images = {
MergerConfig.TYPE_MASKED : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_masked.jpg') ),
MergerConfig.TYPE_FACE_AVATAR : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_face_avatar.jpg') ),
}
self.main_screen = Screen(initial_scale_to_width=1368, image=None, waiting_icon=True)
self.help_screen = Screen(initial_scale_to_height=768, image=help_images[self.merger_config.type], waiting_icon=False)
self.screen_manager = ScreenManager( "Merger", [self.main_screen, self.help_screen], capture_keys=True )
self.screen_manager.set_current (self.help_screen)
self.screen_manager.show_current()
self.masked_keys_funcs = {
'`' : lambda cfg,shift_pressed: cfg.set_mode(0),
'1' : lambda cfg,shift_pressed: cfg.set_mode(1),
'2' : lambda cfg,shift_pressed: cfg.set_mode(2),
'3' : lambda cfg,shift_pressed: cfg.set_mode(3),
'4' : lambda cfg,shift_pressed: cfg.set_mode(4),
'5' : lambda cfg,shift_pressed: cfg.set_mode(5),
'6' : lambda cfg,shift_pressed: cfg.set_mode(6),
'q' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(1 if not shift_pressed else 5),
'a' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(-1 if not shift_pressed else -5),
'w' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(1 if not shift_pressed else 5),
's' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(-1 if not shift_pressed else -5),
'e' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(1 if not shift_pressed else 5),
'd' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(-1 if not shift_pressed else -5),
'r' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(1 if not shift_pressed else 5),
'f' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(-1 if not shift_pressed else -5),
't' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(1 if not shift_pressed else 5),
'g' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(-1 if not shift_pressed else -5),
'y' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(1 if not shift_pressed else 5),
'h' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(-1 if not shift_pressed else -5),
'u' : lambda cfg,shift_pressed: cfg.add_output_face_scale(1 if not shift_pressed else 5),
'j' : lambda cfg,shift_pressed: cfg.add_output_face_scale(-1 if not shift_pressed else -5),
'i' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(1 if not shift_pressed else 5),
'k' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(-1 if not shift_pressed else -5),
'o' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(1 if not shift_pressed else 5),
'l' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(-1 if not shift_pressed else -5),
'p' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(1 if not shift_pressed else 5),
';' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-1),
':' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-5),
'z' : lambda cfg,shift_pressed: cfg.toggle_masked_hist_match(),
'x' : lambda cfg,shift_pressed: cfg.toggle_mask_mode(),
'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
}
self.masked_keys = list(self.masked_keys_funcs.keys())
#overridable optional
def on_clients_finalized(self):
io.progress_bar_close()
if self.is_interactive:
self.screen_manager.finalize()
for frame in self.frames:
frame.output_filepath = None
frame.output_mask_filepath = None
frame.image = None
session_data = {
'frames': self.frames,
'frames_idxs': self.frames_idxs,
'frames_done_idxs': self.frames_done_idxs,
'model_iter' : self.model_iter,
}
self.merger_session_filepath.write_bytes( pickle.dumps(session_data) )
io.log_info ("Session is saved to " + '/'.join (self.merger_session_filepath.parts[-2:]) )
#override
def on_tick(self):
io.process_messages()
go_prev_frame = False
go_first_frame = False
go_prev_frame_overriding_cfg = False
go_first_frame_overriding_cfg = False
go_next_frame = self.process_remain_frames
go_next_frame_overriding_cfg = False
go_last_frame_overriding_cfg = False
cur_frame = None
if len(self.frames_idxs) != 0:
cur_frame = self.frames[self.frames_idxs[0]]
if self.is_interactive:
screen_image = None if self.process_remain_frames else \
self.main_screen.get_image()
self.main_screen.set_waiting_icon( self.process_remain_frames or \
self.is_interactive_quitting )
if cur_frame is not None and not self.is_interactive_quitting:
if not self.process_remain_frames:
if cur_frame.is_done:
if not cur_frame.is_shown:
if cur_frame.image is None:
image = cv2_imread (cur_frame.output_filepath, verbose=False)
image_mask = cv2_imread (cur_frame.output_mask_filepath, verbose=False)
if image is None or image_mask is None:
# unable to read? recompute then
cur_frame.is_done = False
else:
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image = imagelib.normalize_channels(image, 3)
image_mask = imagelib.normalize_channels(image_mask, 1)
cur_frame.image = np.concatenate([image, image_mask], -1)
if cur_frame.is_done:
io.log_info (cur_frame.cfg.to_string( cur_frame.frame_info.filepath.name) )
cur_frame.is_shown = True
screen_image = cur_frame.image
else:
self.main_screen.set_waiting_icon(True)
self.main_screen.set_image(screen_image)
self.screen_manager.show_current()
key_events = self.screen_manager.get_key_events()
key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
if key == 9: #tab
self.screen_manager.switch_screens()
else:
if key == 27: #esc
self.is_interactive_quitting = True
elif self.screen_manager.get_current() is self.main_screen:
if self.merger_config.type == MergerConfig.TYPE_MASKED and chr_key in self.masked_keys:
self.process_remain_frames = False
if cur_frame is not None:
cfg = cur_frame.cfg
prev_cfg = cfg.copy()
if cfg.type == MergerConfig.TYPE_MASKED:
self.masked_keys_funcs[chr_key](cfg, shift_pressed)
if prev_cfg != cfg:
io.log_info ( cfg.to_string(cur_frame.frame_info.filepath.name) )
cur_frame.is_done = False
cur_frame.is_shown = False
else:
if chr_key == ',' or chr_key == 'm':
self.process_remain_frames = False
go_prev_frame = True
if chr_key == ',':
if shift_pressed:
go_first_frame = True
elif chr_key == 'm':
if not shift_pressed:
go_prev_frame_overriding_cfg = True
else:
go_first_frame_overriding_cfg = True
elif chr_key == '.' or chr_key == '/':
self.process_remain_frames = False
go_next_frame = True
if chr_key == '.':
if shift_pressed:
self.process_remain_frames = not self.process_remain_frames
elif chr_key == '/':
if not shift_pressed:
go_next_frame_overriding_cfg = True
else:
go_last_frame_overriding_cfg = True
elif chr_key == '-':
self.screen_manager.get_current().diff_scale(-0.1)
elif chr_key == '=':
self.screen_manager.get_current().diff_scale(0.1)
elif chr_key == 'v':
self.screen_manager.get_current().toggle_show_checker_board()
if go_prev_frame:
if cur_frame is None or cur_frame.is_done:
if cur_frame is not None:
cur_frame.image = None
while True:
if len(self.frames_done_idxs) > 0:
prev_frame = self.frames[self.frames_done_idxs.pop()]
self.frames_idxs.insert(0, prev_frame.idx)
prev_frame.is_shown = False
io.progress_bar_inc(-1)
if cur_frame is not None and (go_prev_frame_overriding_cfg or go_first_frame_overriding_cfg):
if prev_frame.cfg != cur_frame.cfg:
prev_frame.cfg = cur_frame.cfg.copy()
prev_frame.is_done = False
cur_frame = prev_frame
if go_first_frame_overriding_cfg or go_first_frame:
if len(self.frames_done_idxs) > 0:
continue
break
elif go_next_frame:
if cur_frame is not None and cur_frame.is_done:
cur_frame.image = None
cur_frame.is_shown = True
self.frames_done_idxs.append(cur_frame.idx)
self.frames_idxs.pop(0)
io.progress_bar_inc(1)
f = self.frames
if len(self.frames_idxs) != 0:
next_frame = f[ self.frames_idxs[0] ]
next_frame.is_shown = False
if go_next_frame_overriding_cfg or go_last_frame_overriding_cfg:
if go_next_frame_overriding_cfg:
to_frames = next_frame.idx+1
else:
to_frames = len(f)
for i in range( next_frame.idx, to_frames ):
f[i].cfg = None
for i in range( min(len(self.frames_idxs), self.prefetch_frame_count) ):
frame = f[ self.frames_idxs[i] ]
if frame.cfg is None:
if i == 0:
frame.cfg = cur_frame.cfg.copy()
else:
frame.cfg = f[ self.frames_idxs[i-1] ].cfg.copy()
frame.is_done = False #initiate solve again
frame.is_shown = False
if len(self.frames_idxs) == 0:
self.process_remain_frames = False
return (self.is_interactive and self.is_interactive_quitting) or \
(not self.is_interactive and self.process_remain_frames == False)
#override
def on_data_return (self, host_dict, pf):
frame = self.frames[pf.idx]
frame.is_done = False
frame.is_processing = False
#override
def on_result (self, host_dict, pf_sent, pf_result):
frame = self.frames[pf_result.idx]
frame.is_processing = False
if frame.cfg == pf_result.cfg:
frame.is_done = True
frame.image = pf_result.image
#override
def get_data(self, host_dict):
if self.is_interactive and self.is_interactive_quitting:
return None
for i in range ( min(len(self.frames_idxs), self.prefetch_frame_count) ):
frame = self.frames[ self.frames_idxs[i] ]
if not frame.is_done and not frame.is_processing and frame.cfg is not None:
frame.is_processing = True
return InteractiveMergerSubprocessor.ProcessingFrame(idx=frame.idx,
cfg=frame.cfg.copy(),
prev_temporal_frame_infos=frame.prev_temporal_frame_infos,
frame_info=frame.frame_info,
next_temporal_frame_infos=frame.next_temporal_frame_infos,
output_filepath=frame.output_filepath,
output_mask_filepath=frame.output_mask_filepath,
need_return_image=True )
return None
#override
def get_result(self):
return 0