mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 13:32:09 -07:00
046649e6be
* superb improved fanseg * _ * _ * added FANseg extractor for src and dst faces to use it in training * - * _ * _ * update to 'partial' func * _ * trained FANSeg_256_full_face.h5, new experimental models: AVATAR, RecycleGAN * _ * _ * _ * fix for TCC mode cards(tesla), was conflict with plaidML initialization. * _ * update manuals * _
391 lines
15 KiB
Python
391 lines
15 KiB
Python
import sys
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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 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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from converters import Converter
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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 imagelib import normalize_channels
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class ConvertSubprocessor(Subprocessor):
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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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io.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.converter = client_dict['converter']
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self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None
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self.alignments = client_dict['alignments']
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self.avatar_image_paths = client_dict['avatar_image_paths']
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self.debug = client_dict['debug']
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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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self.converter.on_cli_initialize()
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return None
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#override
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def process_data(self, data):
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idx, filename = data
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filename_path = Path(filename)
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files_processed = 1
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faces_processed = 0
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output_filename_path = self.output_path / (filename_path.stem + '.png')
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if (self.converter.type == Converter.TYPE_FACE or self.converter.type == Converter.TYPE_FACE_AVATAR ) \
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and filename_path.stem not in self.alignments.keys():
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if not self.debug:
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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 ( str(filename_path), str(output_filename_path) )
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else:
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image = cv2_imread(str(filename_path))
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cv2_imwrite ( str(output_filename_path), image )
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else:
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image = (cv2_imread(str(filename_path)) / 255.0).astype(np.float32)
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image = normalize_channels (image, 3)
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if self.converter.type == Converter.TYPE_IMAGE:
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image = self.converter.cli_convert_image(image, None, self.debug)
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if self.debug:
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return (1, image)
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faces_processed = 1
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elif self.converter.type == Converter.TYPE_IMAGE_WITH_LANDMARKS:
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#currently unused
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if filename_path.suffix == '.png':
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dflimg = DFLPNG.load( str(filename_path) )
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elif filename_path.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filename_path) )
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else:
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dflimg = None
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if dflimg is not None:
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image_landmarks = dflimg.get_landmarks()
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image = self.converter.convert_image(image, image_landmarks, self.debug)
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if self.debug:
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raise NotImplementedError
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#for img in image:
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# io.show_image ('Debug convert', img )
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# cv2.waitKey(0)
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faces_processed = 1
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else:
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self.log_err ("%s is not a dfl image file" % (filename_path.name) )
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elif self.converter.type == Converter.TYPE_FACE or self.converter.type == Converter.TYPE_FACE_AVATAR:
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ava_face = None
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if self.converter.type == Converter.TYPE_FACE_AVATAR:
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ava_filename_path = self.avatar_image_paths[idx]
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ava_face = (cv2_imread(str(ava_filename_path)) / 255.0).astype(np.float32)
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ava_face = normalize_channels (ava_face, 3)
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faces = self.alignments[filename_path.stem]
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if self.debug:
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debug_images = []
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for face_num, image_landmarks in enumerate(faces):
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try:
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if self.debug:
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self.log_info ( '\nConverting face_num [%d] in file [%s]' % (face_num, filename_path) )
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if self.debug:
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debug_images += self.converter.cli_convert_face(image, image_landmarks, self.debug, avaperator_face_bgr=ava_face)
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else:
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image = self.converter.cli_convert_face(image, image_landmarks, self.debug, avaperator_face_bgr=ava_face)
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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 face_num [%d] in file [%s]: %s' % (face_num, filename_path, e_str) )
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if self.debug:
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return (1, debug_images)
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faces_processed = len(faces)
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if not self.debug:
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cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
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return (0, files_processed, faces_processed)
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#overridable
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def get_data_name (self, data):
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#return string identificator of your data
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idx, filename = data
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return filename
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#override
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def __init__(self, converter, input_path_image_paths, output_path, alignments, avatar_image_paths=None, debug = False):
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super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if debug == True else 60)
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self.converter = converter
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self.input_data = self.input_path_image_paths = input_path_image_paths
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self.input_data_idxs = [ *range(len(self.input_data)) ]
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self.output_path = output_path
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self.alignments = alignments
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self.avatar_image_paths = avatar_image_paths
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self.debug = debug
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self.files_processed = 0
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self.faces_processed = 0
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#override
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def process_info_generator(self):
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r = [0] if self.debug else range( min(6,multiprocessing.cpu_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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'converter' : self.converter,
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'output_dir' : str(self.output_path),
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'alignments' : self.alignments,
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'avatar_image_paths' : self.avatar_image_paths,
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'debug': self.debug,
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'stdin_fd': sys.stdin.fileno() if self.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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if self.debug:
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io.named_window ("Debug convert")
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io.progress_bar ("Converting", len (self.input_data_idxs) )
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#overridable optional
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def on_clients_finalized(self):
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io.progress_bar_close()
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if self.debug:
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io.destroy_all_windows()
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#override
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def get_data(self, host_dict):
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if len (self.input_data_idxs) > 0:
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idx = self.input_data_idxs.pop(0)
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return (idx, self.input_data[idx])
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return None
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#override
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def on_data_return (self, host_dict, data):
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idx, filename = data
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self.input_data_idxs.insert(0, idx)
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#override
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def on_result (self, host_dict, data, result):
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if result[0] == 0:
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self.files_processed += result[0]
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self.faces_processed += result[1]
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elif result[0] == 1:
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for img in result[1]:
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io.show_image ('Debug convert', (img*255).astype(np.uint8) )
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io.wait_any_key()
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io.progress_bar_inc(1)
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#override
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def on_tick(self):
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self.converter.on_host_tick()
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#override
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def get_result(self):
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return self.files_processed, self.faces_processed
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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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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:
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output_path.mkdir(parents=True, exist_ok=True)
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if not model_path.exists():
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io.log_err('Model directory not found. Please ensure it exists.')
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return
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import models
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model = models.import_model( args['model_name'] )(model_path, device_args=device_args)
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converter = model.get_converter()
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input_path_image_paths = Path_utils.get_image_paths(input_path)
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alignments = None
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avatar_image_paths = None
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if converter.type == Converter.TYPE_FACE or converter.type == Converter.TYPE_FACE_AVATAR:
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if aligned_dir is None:
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io.log_err('Aligned directory not found. Please ensure it exists.')
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return
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aligned_path = Path(aligned_dir)
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if not aligned_path.exists():
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io.log_err('Aligned directory not found. Please ensure it exists.')
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return
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alignments = {}
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aligned_path_image_paths = Path_utils.get_image_paths(aligned_path)
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for filepath in io.progress_bar_generator(aligned_path_image_paths, "Collecting alignments"):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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continue
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source_filename_stem = Path( dflimg.get_source_filename() ).stem
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if source_filename_stem not in alignments.keys():
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alignments[ source_filename_stem ] = []
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alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
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if converter.type == Converter.TYPE_FACE_AVATAR:
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if avaperator_aligned_dir is None:
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io.log_err('Avatar operator aligned directory not found. Please ensure it exists.')
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return
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avaperator_aligned_path = Path(avaperator_aligned_dir)
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if not avaperator_aligned_path.exists():
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io.log_err('Avatar operator aligned directory not found. Please ensure it exists.')
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return
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avatar_image_paths = []
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for filename in io.progress_bar_generator( Path_utils.get_image_paths(avaperator_aligned_path) , "Sorting avaperator faces"):
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filepath = Path(filename)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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if dflimg is None:
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io.log_err ("Fatal error: %s is not a dfl image file" % (filepath.name) )
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return
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avatar_image_paths += [ (filename, dflimg.get_source_filename() ) ]
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avatar_image_paths = [ p[0] for p in sorted(avatar_image_paths, key=operator.itemgetter(1)) ]
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if len(input_path_image_paths) < len(avatar_image_paths):
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io.log_err("Input faces count must be >= avatar operator faces count.")
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return
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files_processed, faces_processed = ConvertSubprocessor (
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converter = converter,
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input_path_image_paths = input_path_image_paths,
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output_path = output_path,
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alignments = alignments,
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avatar_image_paths = avatar_image_paths,
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debug = args.get('debug',False)
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).run()
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model.finalize()
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except Exception as e:
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print ( 'Error: %s' % (str(e)))
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traceback.print_exc()
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'''
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if model_name == 'AVATAR':
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output_path_image_paths = Path_utils.get_image_paths(output_path)
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last_ok_frame = -1
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for filename in output_path_image_paths:
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filename_path = Path(filename)
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stem = Path(filename).stem
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try:
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frame = int(stem)
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except:
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raise Exception ('Aligned avatars must be created from indexed sequence files.')
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if frame-last_ok_frame > 1:
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start = last_ok_frame + 1
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end = frame - 1
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print ("Filling gaps: [%d...%d]" % (start, end) )
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for i in range (start, end+1):
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shutil.copy ( str(filename), str( output_path / ('%.5d%s' % (i, filename_path.suffix )) ) )
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last_ok_frame = frame
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'''
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#interpolate landmarks
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#from facelib import LandmarksProcessor
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#from facelib import FaceType
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#a = sorted(alignments.keys())
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#a_len = len(a)
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#
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#box_pts = 3
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#box = np.ones(box_pts)/box_pts
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#for i in range( a_len ):
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# if i >= box_pts and i <= a_len-box_pts-1:
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# af0 = alignments[ a[i] ][0] ##first face
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# m0 = LandmarksProcessor.get_transform_mat (af0, 256, face_type=FaceType.FULL)
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#
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# points = []
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#
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# for j in range(-box_pts, box_pts+1):
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# af = alignments[ a[i+j] ][0] ##first face
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# m = LandmarksProcessor.get_transform_mat (af, 256, face_type=FaceType.FULL)
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# p = LandmarksProcessor.transform_points (af, m)
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# points.append (p)
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#
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# points = np.array(points)
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# points_len = len(points)
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# t_points = np.transpose(points, [1,0,2])
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#
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# p1 = np.array ( [ int(np.convolve(x[:,0], box, mode='same')[points_len//2]) for x in t_points ] )
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# p2 = np.array ( [ int(np.convolve(x[:,1], box, mode='same')[points_len//2]) for x in t_points ] )
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#
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# new_points = np.concatenate( [np.expand_dims(p1,-1),np.expand_dims(p2,-1)], -1 )
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#
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# alignments[ a[i] ][0] = LandmarksProcessor.transform_points (new_points, m0, True).astype(np.int32)
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