mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 21:12:07 -07:00
407ce3b1ca
With interactive converter you can change any parameter of any frame and see the result in real time. Converter: added motion_blur_power param. Motion blur is applied by precomputed motion vectors. So the moving face will look more realistic. RecycleGAN model is removed. Added experimental AVATAR model. Minimum required VRAM is 6GB (NVIDIA), 12GB (AMD) Usage: 1) place data_src.mp4 10-20min square resolution video of news reporter sitting at the table with static background, other faces should not appear in frames. 2) process "extract images from video data_src.bat" with FULL fps 3) place data_dst.mp4 video of face who will control the src face 4) process "extract images from video data_dst FULL FPS.bat" 5) process "data_src mark faces S3FD best GPU.bat" 6) process "data_dst extract unaligned faces S3FD best GPU.bat" 7) train AVATAR.bat stage 1, tune batch size to maximum for your card (32 for 6GB), train to 50k+ iters. 8) train AVATAR.bat stage 2, tune batch size to maximum for your card (4 for 6GB), train to decent sharpness. 9) convert AVATAR.bat 10) converted to mp4.bat updated versions of modules
642 lines
27 KiB
Python
642 lines
27 KiB
Python
import math
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import multiprocessing
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import operator
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import os
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import shutil
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import sys
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import time
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import traceback
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from pathlib import Path
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import cv2
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import numpy as np
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import numpy.linalg as npla
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import imagelib
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from converters import FrameInfo, ConverterConfig, ConvertMasked, ConvertFaceAvatar
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from facelib import FaceType, FANSegmentator, LandmarksProcessor
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from interact import interact as io
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from joblib import SubprocessFunctionCaller, Subprocessor
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from utils import Path_utils
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from utils.cv2_utils import *
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from utils.DFLJPG import DFLJPG
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from utils.DFLPNG import DFLPNG
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from .ConverterScreen import Screen, ScreenManager
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CONVERTER_DEBUG = False
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class ConvertSubprocessor(Subprocessor):
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class Frame(object):
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def __init__(self, prev_temporal_frame_infos=None,
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frame_info=None,
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next_temporal_frame_infos=None):
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self.prev_temporal_frame_infos = prev_temporal_frame_infos
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self.frame_info = frame_info
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self.next_temporal_frame_infos = next_temporal_frame_infos
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self.output_filename = None
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self.idx = None
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self.cfg = None
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self.is_done = False
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self.is_processing = False
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self.is_shown = False
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self.image = None
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class ProcessingFrame(object):
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def __init__(self, idx=None,
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cfg=None,
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prev_temporal_frame_infos=None,
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frame_info=None,
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next_temporal_frame_infos=None,
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output_filename=None,
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need_return_image = False):
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self.idx = idx
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self.cfg = cfg
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self.prev_temporal_frame_infos = prev_temporal_frame_infos
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self.frame_info = frame_info
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self.next_temporal_frame_infos = next_temporal_frame_infos
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self.output_filename = output_filename
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self.need_return_image = need_return_image
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if self.need_return_image:
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self.image = None
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class Cli(Subprocessor.Cli):
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#override
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def on_initialize(self, client_dict):
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self.log_info ('Running on %s.' % (client_dict['device_name']) )
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self.device_idx = client_dict['device_idx']
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self.device_name = client_dict['device_name']
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self.predictor_func = client_dict['predictor_func']
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self.dcscn_upscale_func = client_dict['dcscn_upscale_func']
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#transfer and set stdin in order to work code.interact in debug subprocess
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stdin_fd = client_dict['stdin_fd']
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if stdin_fd is not None:
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sys.stdin = os.fdopen(stdin_fd)
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from nnlib import nnlib
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#model process ate all GPU mem,
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#so we cannot use GPU for any TF operations in converter processes
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#therefore forcing active_DeviceConfig to CPU only
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nnlib.active_DeviceConfig = nnlib.DeviceConfig (cpu_only=True)
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print(nnlib.backend)
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self.fanseg_by_face_type = {}
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self.fanseg_input_size = 256
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def fanseg_extract(face_type, *args, **kwargs):
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fanseg = self.fanseg_by_face_type.get(face_type, None)
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if self.fanseg_by_face_type.get(face_type, None) is None:
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fanseg = FANSegmentator( self.fanseg_input_size , FaceType.toString( face_type ) )
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self.fanseg_by_face_type[face_type] = fanseg
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return fanseg.extract(*args, **kwargs)
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self.fanseg_extract_func = fanseg_extract
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return None
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#override
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def process_data(self, pf): #pf=ProcessingFrame
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cfg = pf.cfg.copy()
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cfg.predictor_func = self.predictor_func
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frame_info = pf.frame_info
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filename = frame_info.filename
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landmarks_list = frame_info.landmarks_list
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filename_path = Path(filename)
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output_filename = pf.output_filename
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need_return_image = pf.need_return_image
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if len(landmarks_list) == 0:
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self.log_info ( 'no faces found for %s, copying without faces' % (filename_path.name) )
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if filename_path.suffix == '.png':
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shutil.copy (filename, output_filename )
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else:
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img_bgr = cv2_imread(filename)
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cv2_imwrite (output_filename, img_bgr)
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if need_return_image:
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img_bgr = cv2_imread(filename)
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pf.image = img_bgr
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else:
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if cfg.type == ConverterConfig.TYPE_MASKED:
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cfg.dcscn_upscale_func = self.dcscn_upscale_func
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cfg.fanseg_input_size = self.fanseg_input_size
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cfg.fanseg_extract_func = self.fanseg_extract_func
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try:
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final_img = ConvertMasked (cfg, frame_info)
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except Exception as e:
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e_str = traceback.format_exc()
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if 'MemoryError' in e_str:
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raise Subprocessor.SilenceException
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else:
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raise Exception( 'Error while converting file [%s]: %s' % (filename, e_str) )
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elif cfg.type == ConverterConfig.TYPE_FACE_AVATAR:
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final_img = ConvertFaceAvatar (cfg, pf.prev_temporal_frame_infos,
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pf.frame_info,
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pf.next_temporal_frame_infos )
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if output_filename is not None and final_img is not None:
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cv2_imwrite (output_filename, final_img )
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if need_return_image:
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pf.image = final_img
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return pf
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#overridable
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def get_data_name (self, pf):
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#return string identificator of your data
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return pf.frame_info.filename
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#override
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def __init__(self, is_interactive, converter_config, frames, output_path):
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if len (frames) == 0:
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raise ValueError ("len (frames) == 0")
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super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if CONVERTER_DEBUG else 60, io_loop_sleep_time=0.001, initialize_subprocesses_in_serial=False)# if debug == True else 60)
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self.is_interactive = is_interactive
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self.converter_config = converter_config
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#dummy predict and sleep, tensorflow caching kernels. If remove it, sometime conversion speed can be x2 slower
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self.converter_config.predictor_func (dummy_predict=True)
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time.sleep(2)
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self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(self.converter_config.predictor_func)
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self.converter_config.predictor_func = None
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self.dcscn = None
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def DCSCN_upscale(*args, **kwargs):
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if self.dcscn is None:
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self.dcscn = imagelib.DCSCN()
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return self.dcscn.upscale(*args, **kwargs)
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self.dcscn_host, self.dcscn_upscale_func = SubprocessFunctionCaller.make_pair(DCSCN_upscale)
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self.frames = frames
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self.output_path = output_path
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self.prefetch_frame_count = self.process_count = min(6,multiprocessing.cpu_count())
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self.frames_idxs = [ *range(len(self.frames)) ]
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self.frames_done_idxs = []
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for i in range( len(self.frames) ):
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frame = self.frames[i]
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frame.idx = i
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frame.output_filename = self.output_path / ('%.5d.png' % i)
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frames[0].cfg = self.converter_config.copy()
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#override
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def process_info_generator(self):
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r = [0] if CONVERTER_DEBUG else range(self.process_count)
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for i in r:
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yield 'CPU%d' % (i), {}, {'device_idx': i,
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'device_name': 'CPU%d' % (i),
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'predictor_func': self.predictor_func,
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'dcscn_upscale_func': self.dcscn_upscale_func,
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'stdin_fd': sys.stdin.fileno() if CONVERTER_DEBUG else None
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}
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#overridable optional
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def on_clients_initialized(self):
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io.progress_bar ("Converting", len (self.frames_idxs) )
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self.process_remain_frames = not self.is_interactive
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self.is_interactive_quitting = not self.is_interactive
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if self.is_interactive:
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help_images = {
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ConverterConfig.TYPE_MASKED : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_converter_masked.jpg') ),
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ConverterConfig.TYPE_FACE_AVATAR : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_converter_face_avatar.jpg') ),
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}
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self.main_screen = Screen(initial_scale_to_width=1368, image=None, waiting_icon=True)
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self.help_screen = Screen(initial_scale_to_height=768, image=help_images[self.converter_config.type], waiting_icon=False)
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self.screen_manager = ScreenManager( "Converter", [self.main_screen, self.help_screen], capture_keys=True )
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self.screen_manager.set_current (self.help_screen)
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self.screen_manager.show_current()
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#overridable optional
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def on_clients_finalized(self):
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if self.is_interactive:
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self.screen_manager.finalize()
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io.progress_bar_close()
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cfg_change_keys = ['`','1', '2', '3', '4', '5', '6', '7', '8', '9',
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'q', 'a', 'w', 's', 'e', 'd', 'r', 'f', 't', 'g','y','h',
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'z', 'x', 'c', 'v', 'b' ]
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#override
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def on_tick(self):
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self.predictor_func_host.process_messages()
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self.dcscn_host.process_messages()
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go_prev_frame = False
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go_next_frame = self.process_remain_frames
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go_next_frame_overriding_cfg = False
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cur_frame = None
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if len(self.frames_idxs) != 0:
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cur_frame = self.frames[self.frames_idxs[0]]
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if self.is_interactive:
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self.main_screen.set_waiting_icon(False)
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if not self.is_interactive_quitting and not self.process_remain_frames:
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if cur_frame is not None:
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if not cur_frame.is_shown:
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if cur_frame.is_done:
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cur_frame.is_shown = True
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io.log_info (cur_frame.cfg)
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if cur_frame.image is None:
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cur_frame.image = cv2_imread ( cur_frame.output_filename)
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if cur_frame.image is None:
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cur_frame.is_done = False #unable to read? recompute then
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cur_frame.is_shown = False
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self.main_screen.set_image(cur_frame.image)
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else:
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self.main_screen.set_waiting_icon(True)
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else:
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self.main_screen.set_image(None)
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else:
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self.main_screen.set_image(None)
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self.main_screen.set_waiting_icon(True)
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self.screen_manager.show_current()
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key_events = self.screen_manager.get_key_events()
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key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
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if key == 9: #tab
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self.screen_manager.switch_screens()
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else:
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if key == 27: #esc
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self.is_interactive_quitting = True
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elif self.screen_manager.get_current() is self.main_screen:
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if chr_key in self.cfg_change_keys:
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self.process_remain_frames = False
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if cur_frame is not None:
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cfg = cur_frame.cfg
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prev_cfg = cfg.copy()
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if cfg.type == ConverterConfig.TYPE_MASKED:
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if chr_key == '`':
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cfg.set_mode(0)
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elif key >= ord('1') and key <= ord('9'):
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cfg.set_mode( key - ord('0') )
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elif chr_key == 'q':
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cfg.add_hist_match_threshold(1 if not shift_pressed else 5)
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elif chr_key == 'a':
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cfg.add_hist_match_threshold(-1 if not shift_pressed else -5)
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elif chr_key == 'w':
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cfg.add_erode_mask_modifier(1 if not shift_pressed else 5)
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elif chr_key == 's':
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cfg.add_erode_mask_modifier(-1 if not shift_pressed else -5)
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elif chr_key == 'e':
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cfg.add_blur_mask_modifier(1 if not shift_pressed else 5)
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elif chr_key == 'd':
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cfg.add_blur_mask_modifier(-1 if not shift_pressed else -5)
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elif chr_key == 'r':
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cfg.add_motion_blur_power(1 if not shift_pressed else 5)
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elif chr_key == 'f':
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cfg.add_motion_blur_power(-1 if not shift_pressed else -5)
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elif chr_key == 't':
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cfg.add_color_degrade_power(1 if not shift_pressed else 5)
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elif chr_key == 'g':
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cfg.add_color_degrade_power(-1 if not shift_pressed else -5)
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elif chr_key == 'y':
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cfg.add_output_face_scale(1 if not shift_pressed else 5)
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elif chr_key == 'h':
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cfg.add_output_face_scale(-1 if not shift_pressed else -5)
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elif chr_key == 'z':
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cfg.toggle_masked_hist_match()
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elif chr_key == 'x':
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cfg.toggle_mask_mode()
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elif chr_key == 'c':
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cfg.toggle_color_transfer_mode()
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elif chr_key == 'v':
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cfg.toggle_super_resolution()
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elif chr_key == 'b':
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cfg.toggle_export_mask_alpha()
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else:
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if chr_key == 's':
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cfg.toggle_add_source_image()
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if prev_cfg != cfg:
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io.log_info (cfg)
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cur_frame.is_done = False
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cur_frame.is_shown = False
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else:
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if chr_key == ',':
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self.process_remain_frames = False
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go_prev_frame = True
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elif chr_key == '.' or chr_key == '/':
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self.process_remain_frames = False
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go_next_frame = True
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go_next_frame_overriding_cfg = chr_key == '/'
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elif chr_key == '\r' or chr_key == '\n':
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self.process_remain_frames = not self.process_remain_frames
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elif chr_key == '-':
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self.screen_manager.get_current().diff_scale(-0.1)
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elif chr_key == '=':
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self.screen_manager.get_current().diff_scale(0.1)
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if go_prev_frame:
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if cur_frame is not None and cur_frame.is_done:
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cur_frame.image = None
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if len(self.frames_done_idxs) > 0:
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prev_frame = self.frames[self.frames_done_idxs.pop()]
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self.frames_idxs.insert(0, prev_frame.idx)
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prev_frame.is_shown = False
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io.progress_bar_inc(-1)
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elif go_next_frame:
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if cur_frame is not None and cur_frame.is_done:
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cur_frame.image = None
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cur_frame.is_shown = True
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self.frames_done_idxs.append(cur_frame.idx)
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self.frames_idxs.pop(0)
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io.progress_bar_inc(1)
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if len(self.frames_idxs) != 0:
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next_frame = self.frames[ self.frames_idxs[0] ]
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if go_next_frame_overriding_cfg:
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f = self.frames
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for i in range( next_frame.idx, len(self.frames) ):
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f[i].cfg = None
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f[i].is_shown = False
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if next_frame.cfg is None or next_frame.is_shown == False: #next frame is never shown or override current cfg to next frames and the prefetches
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for i in range( min(len(self.frames_idxs), self.prefetch_frame_count) ):
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frame = self.frames[ self.frames_idxs[i] ]
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if frame.cfg is None or frame.cfg != cur_frame.cfg:
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frame.cfg = cur_frame.cfg.copy()
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frame.is_done = False #initiate solve again
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next_frame.is_shown = False
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if len(self.frames_idxs) == 0:
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self.process_remain_frames = False
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return (self.is_interactive and self.is_interactive_quitting) or \
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(not self.is_interactive and self.process_remain_frames == False)
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#override
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def on_data_return (self, host_dict, pf):
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frame = self.frames[pf.idx]
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frame.is_done = False
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frame.is_processing = False
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#override
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def on_result (self, host_dict, pf_sent, pf_result):
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frame = self.frames[pf_result.idx]
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frame.is_processing = False
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if frame.cfg == pf_result.cfg:
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frame.is_done = True
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frame.image = pf_result.image
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#override
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def get_data(self, host_dict):
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if self.is_interactive and self.is_interactive_quitting:
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return None
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for i in range ( min(len(self.frames_idxs), self.prefetch_frame_count) ):
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frame = self.frames[ self.frames_idxs[i] ]
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if not frame.is_done and not frame.is_processing and frame.cfg is not None:
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frame.is_processing = True
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return ConvertSubprocessor.ProcessingFrame(idx=frame.idx,
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cfg=frame.cfg.copy(),
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prev_temporal_frame_infos=frame.prev_temporal_frame_infos,
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frame_info=frame.frame_info,
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next_temporal_frame_infos=frame.next_temporal_frame_infos,
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output_filename=frame.output_filename,
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need_return_image=True )
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return None
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#override
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def get_result(self):
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return 0
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def main (args, device_args):
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io.log_info ("Running converter.\r\n")
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aligned_dir = args.get('aligned_dir', None)
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avaperator_aligned_dir = args.get('avaperator_aligned_dir', None)
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try:
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input_path = Path(args['input_dir'])
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output_path = Path(args['output_dir'])
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model_path = Path(args['model_dir'])
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|
|
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if not input_path.exists():
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|
io.log_err('Input directory not found. Please ensure it exists.')
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|
return
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|
|
|
if output_path.exists():
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for filename in Path_utils.get_image_paths(output_path):
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|
Path(filename).unlink()
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|
else:
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if not model_path.exists():
|
|
io.log_err('Model directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
is_interactive = io.input_bool ("Use interactive converter? (y/n skip:y) : ", True) if not io.is_colab() else False
|
|
|
|
import models
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|
model = models.import_model( args['model_name'] )(model_path, device_args=device_args)
|
|
|
|
cfg = model.get_ConverterConfig()
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|
|
|
if not is_interactive:
|
|
cfg.ask_settings()
|
|
|
|
input_path_image_paths = Path_utils.get_image_paths(input_path)
|
|
|
|
if cfg.type == ConverterConfig.TYPE_MASKED:
|
|
if aligned_dir is None:
|
|
io.log_err('Aligned directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
aligned_path = Path(aligned_dir)
|
|
if not aligned_path.exists():
|
|
io.log_err('Aligned directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
alignments = {}
|
|
multiple_faces_detected = False
|
|
aligned_path_image_paths = Path_utils.get_image_paths(aligned_path)
|
|
for filepath in io.progress_bar_generator(aligned_path_image_paths, "Collecting alignments"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix == '.png':
|
|
dflimg = DFLPNG.load( str(filepath) )
|
|
elif filepath.suffix == '.jpg':
|
|
dflimg = DFLJPG.load ( str(filepath) )
|
|
else:
|
|
dflimg = None
|
|
|
|
if dflimg is None:
|
|
io.log_err ("%s is not a dfl image file" % (filepath.name) )
|
|
continue
|
|
|
|
source_filename_stem = Path( dflimg.get_source_filename() ).stem
|
|
if source_filename_stem not in alignments.keys():
|
|
alignments[ source_filename_stem ] = []
|
|
|
|
alignments_ar = alignments[ source_filename_stem ]
|
|
alignments_ar.append (dflimg.get_source_landmarks())
|
|
if len(alignments_ar) > 1:
|
|
multiple_faces_detected = True
|
|
|
|
if multiple_faces_detected:
|
|
io.log_info ("Warning: multiple faces detected. Strongly recommended to process them separately.")
|
|
|
|
frames = [ ConvertSubprocessor.Frame( frame_info=FrameInfo(filename=p, landmarks_list=alignments.get(Path(p).stem, None))) for p in input_path_image_paths ]
|
|
|
|
if multiple_faces_detected:
|
|
io.log_info ("Warning: multiple faces detected. Motion blur will not be used.")
|
|
else:
|
|
s = 256
|
|
local_pts = [ (s//2-1, s//2-1), (s//2-1,0) ] #center+up
|
|
frames_len = len(frames)
|
|
for i in io.progress_bar_generator( range(len(frames)) , "Computing motion vectors"):
|
|
fi_prev = frames[max(0, i-1)].frame_info
|
|
fi = frames[i].frame_info
|
|
fi_next = frames[min(i+1, frames_len-1)].frame_info
|
|
if len(fi_prev.landmarks_list) == 0 or \
|
|
len(fi.landmarks_list) == 0 or \
|
|
len(fi_next.landmarks_list) == 0:
|
|
continue
|
|
|
|
mat_prev = LandmarksProcessor.get_transform_mat ( fi_prev.landmarks_list[0], s, face_type=FaceType.FULL)
|
|
mat = LandmarksProcessor.get_transform_mat ( fi.landmarks_list[0] , s, face_type=FaceType.FULL)
|
|
mat_next = LandmarksProcessor.get_transform_mat ( fi_next.landmarks_list[0], s, face_type=FaceType.FULL)
|
|
|
|
pts_prev = LandmarksProcessor.transform_points (local_pts, mat_prev, True)
|
|
pts = LandmarksProcessor.transform_points (local_pts, mat, True)
|
|
pts_next = LandmarksProcessor.transform_points (local_pts, mat_next, True)
|
|
|
|
prev_vector = pts[0]-pts_prev[0]
|
|
next_vector = pts_next[0]-pts[0]
|
|
|
|
motion_vector = pts_next[0] - pts_prev[0]
|
|
fi.motion_power = npla.norm(motion_vector)
|
|
|
|
motion_vector = motion_vector / fi.motion_power if fi.motion_power != 0 else np.array([0,0],dtype=np.float32)
|
|
|
|
fi.motion_deg = -math.atan2(motion_vector[1],motion_vector[0])*180 / math.pi
|
|
|
|
|
|
elif cfg.type == ConverterConfig.TYPE_FACE_AVATAR:
|
|
filesdata = []
|
|
for filepath in io.progress_bar_generator(input_path_image_paths, "Collecting info"):
|
|
filepath = Path(filepath)
|
|
|
|
if filepath.suffix == '.png':
|
|
dflimg = DFLPNG.load( str(filepath) )
|
|
elif filepath.suffix == '.jpg':
|
|
dflimg = DFLJPG.load ( str(filepath) )
|
|
else:
|
|
dflimg = None
|
|
|
|
if dflimg is None:
|
|
io.log_err ("%s is not a dfl image file" % (filepath.name) )
|
|
continue
|
|
filesdata += [ ( FrameInfo(filename=str(filepath), landmarks_list=[dflimg.get_landmarks()] ), dflimg.get_source_filename() ) ]
|
|
|
|
filesdata = sorted(filesdata, key=operator.itemgetter(1)) #sort by filename
|
|
frames = []
|
|
filesdata_len = len(filesdata)
|
|
for i in range(len(filesdata)):
|
|
frame_info = filesdata[i][0]
|
|
|
|
prev_temporal_frame_infos = []
|
|
next_temporal_frame_infos = []
|
|
|
|
for t in range (cfg.temporal_face_count):
|
|
prev_frame_info = filesdata[ max(i -t, 0) ][0]
|
|
next_frame_info = filesdata[ min(i +t, filesdata_len-1 )][0]
|
|
|
|
prev_temporal_frame_infos.insert (0, prev_frame_info )
|
|
next_temporal_frame_infos.append ( next_frame_info )
|
|
|
|
frames.append ( ConvertSubprocessor.Frame(prev_temporal_frame_infos=prev_temporal_frame_infos,
|
|
frame_info=frame_info,
|
|
next_temporal_frame_infos=next_temporal_frame_infos) )
|
|
|
|
if len(frames) == 0:
|
|
io.log_info ("No frames to convert in input_dir.")
|
|
else:
|
|
ConvertSubprocessor (
|
|
is_interactive = is_interactive,
|
|
converter_config = cfg,
|
|
frames = frames,
|
|
output_path = output_path,
|
|
).run()
|
|
|
|
model.finalize()
|
|
|
|
except Exception as e:
|
|
print ( 'Error: %s' % (str(e)))
|
|
traceback.print_exc()
|
|
|
|
#interpolate landmarks
|
|
#from facelib import LandmarksProcessor
|
|
#from facelib import FaceType
|
|
#a = sorted(alignments.keys())
|
|
#a_len = len(a)
|
|
#
|
|
#box_pts = 3
|
|
#box = np.ones(box_pts)/box_pts
|
|
#for i in range( a_len ):
|
|
# if i >= box_pts and i <= a_len-box_pts-1:
|
|
# af0 = alignments[ a[i] ][0] ##first face
|
|
# m0 = LandmarksProcessor.get_transform_mat (af0, 256, face_type=FaceType.FULL)
|
|
#
|
|
# points = []
|
|
#
|
|
# for j in range(-box_pts, box_pts+1):
|
|
# af = alignments[ a[i+j] ][0] ##first face
|
|
# m = LandmarksProcessor.get_transform_mat (af, 256, face_type=FaceType.FULL)
|
|
# p = LandmarksProcessor.transform_points (af, m)
|
|
# points.append (p)
|
|
#
|
|
# points = np.array(points)
|
|
# points_len = len(points)
|
|
# t_points = np.transpose(points, [1,0,2])
|
|
#
|
|
# p1 = np.array ( [ int(np.convolve(x[:,0], box, mode='same')[points_len//2]) for x in t_points ] )
|
|
# p2 = np.array ( [ int(np.convolve(x[:,1], box, mode='same')[points_len//2]) for x in t_points ] )
|
|
#
|
|
# new_points = np.concatenate( [np.expand_dims(p1,-1),np.expand_dims(p2,-1)], -1 )
|
|
#
|
|
# alignments[ a[i] ][0] = LandmarksProcessor.transform_points (new_points, m0, True).astype(np.int32)
|