mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
85792d232a
'show alpha mask' now on 'V' button 'super resolution mode' is replaced by 'super resolution power' (0..100) which can be modified via 'T' 'G' buttons
866 lines
39 KiB
Python
866 lines
39 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 pickle
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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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from core import imagelib
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import samplelib
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from merger import (MergerConfig, MergeFaceAvatar, MergeMasked,
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FrameInfo)
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from DFLIMG import DFLIMG
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from facelib import FaceEnhancer, FaceType, LandmarksProcessor, TernausNet
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from core.interact import interact as io
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from core.joblib import SubprocessFunctionCaller, Subprocessor
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from core.leras import nn
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from core import pathex
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from core.cv2ex import *
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from .MergerScreen import Screen, ScreenManager
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MERGER_DEBUG = False
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class MergeSubprocessor(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_filepath = None
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self.output_mask_filepath = 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_filepath=None,
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output_mask_filepath=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_filepath = output_filepath
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self.output_mask_filepath = output_mask_filepath
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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.predictor_input_shape = client_dict['predictor_input_shape']
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self.superres_func = client_dict['superres_func']
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self.fanseg_input_size = client_dict['fanseg_input_size']
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self.fanseg_extract_func = client_dict['fanseg_extract_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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def blursharpen_func (img, sharpen_mode=0, kernel_size=3, amount=100):
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if kernel_size % 2 == 0:
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kernel_size += 1
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if amount > 0:
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if sharpen_mode == 1: #box
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kernel = np.zeros( (kernel_size, kernel_size), dtype=np.float32)
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kernel[ kernel_size//2, kernel_size//2] = 1.0
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box_filter = np.ones( (kernel_size, kernel_size), dtype=np.float32) / (kernel_size**2)
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kernel = kernel + (kernel - box_filter) * amount
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return cv2.filter2D(img, -1, kernel)
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elif sharpen_mode == 2: #gaussian
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blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
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img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
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return img
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elif amount < 0:
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n = -amount
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while n > 0:
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img_blur = cv2.medianBlur(img, 5)
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if int(n / 10) != 0:
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img = img_blur
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else:
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pass_power = (n % 10) / 10.0
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img = img*(1.0-pass_power)+img_blur*pass_power
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n = max(n-10,0)
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return img
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return img
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self.blursharpen_func = blursharpen_func
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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.blursharpen_func = self.blursharpen_func
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cfg.superres_func = self.superres_func
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frame_info = pf.frame_info
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filepath = frame_info.filepath
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if len(frame_info.landmarks_list) == 0:
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self.log_info (f'no faces found for {filepath.name}, copying without faces')
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img_bgr = cv2_imread(filepath)
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imagelib.normalize_channels(img_bgr, 3)
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cv2_imwrite (pf.output_filepath, img_bgr)
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h,w,c = img_bgr.shape
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img_mask = np.zeros( (h,w,1), dtype=img_bgr.dtype)
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cv2_imwrite (pf.output_mask_filepath, img_mask)
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if pf.need_return_image:
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pf.image = np.concatenate ([img_bgr, img_mask], axis=-1)
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else:
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if cfg.type == MergerConfig.TYPE_MASKED:
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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 = MergeMasked (self.predictor_func, self.predictor_input_shape, 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( f'Error while merging file [{filepath}]: {e_str}' )
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elif cfg.type == MergerConfig.TYPE_FACE_AVATAR:
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final_img = MergeFaceAvatar (self.predictor_func, self.predictor_input_shape,
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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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cv2_imwrite (pf.output_filepath, final_img[...,0:3] )
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cv2_imwrite (pf.output_mask_filepath, final_img[...,3:4] )
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if pf.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.filepath
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#override
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def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter):
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if len (frames) == 0:
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raise ValueError ("len (frames) == 0")
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super().__init__('Merger', MergeSubprocessor.Cli, 86400 if MERGER_DEBUG else 60, io_loop_sleep_time=0.001)
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self.is_interactive = is_interactive
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self.merger_session_filepath = Path(merger_session_filepath)
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self.merger_config = merger_config
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self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
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self.predictor_input_shape = predictor_input_shape
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self.face_enhancer = None
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def superres_func(face_bgr):
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if self.face_enhancer is None:
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self.face_enhancer = FaceEnhancer(place_model_on_cpu=True)
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return self.face_enhancer.enhance (face_bgr, is_tanh=True, preserve_size=False)
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self.superres_host, self.superres_func = SubprocessFunctionCaller.make_pair(superres_func)
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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_func(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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cpu_only = len(nn.getCurrentDeviceConfig().devices) == 0
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with nn.tf.device('/CPU:0' if cpu_only else '/GPU:0'):
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fanseg = TernausNet("FANSeg", self.fanseg_input_size , FaceType.toString( face_type ), place_model_on_cpu=True )
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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_host, self.fanseg_extract_func = SubprocessFunctionCaller.make_pair(fanseg_extract_func)
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self.frames_root_path = frames_root_path
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self.output_path = output_path
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self.output_mask_path = output_mask_path
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self.model_iter = model_iter
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self.prefetch_frame_count = self.process_count = multiprocessing.cpu_count()
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session_data = None
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if self.is_interactive and self.merger_session_filepath.exists():
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io.input_skip_pending()
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if io.input_bool ("Use saved session?", True):
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try:
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with open( str(self.merger_session_filepath), "rb") as f:
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session_data = pickle.loads(f.read())
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except Exception as e:
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pass
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rewind_to_frame_idx = None
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self.frames = frames
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self.frames_idxs = [ *range(len(self.frames)) ]
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self.frames_done_idxs = []
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if self.is_interactive and session_data is not None:
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# Loaded session data, check it
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s_frames = session_data.get('frames', None)
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s_frames_idxs = session_data.get('frames_idxs', None)
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s_frames_done_idxs = session_data.get('frames_done_idxs', None)
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s_model_iter = session_data.get('model_iter', None)
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frames_equal = (s_frames is not None) and \
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(s_frames_idxs is not None) and \
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(s_frames_done_idxs is not None) and \
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(s_model_iter is not None) and \
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(len(frames) == len(s_frames)) # frames count must match
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if frames_equal:
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for i in range(len(frames)):
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frame = frames[i]
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s_frame = s_frames[i]
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# frames filenames must match
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if frame.frame_info.filepath.name != s_frame.frame_info.filepath.name:
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frames_equal = False
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if not frames_equal:
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break
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if frames_equal:
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io.log_info ('Using saved session from ' + '/'.join (self.merger_session_filepath.parts[-2:]) )
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for frame in s_frames:
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if frame.cfg is not None:
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# recreate MergerConfig class using constructor with get_config() as dict params
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# so if any new param will be added, old merger session will work properly
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frame.cfg = frame.cfg.__class__( **frame.cfg.get_config() )
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self.frames = s_frames
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self.frames_idxs = s_frames_idxs
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self.frames_done_idxs = s_frames_done_idxs
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if self.model_iter != s_model_iter:
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# model was more trained, recompute all frames
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rewind_to_frame_idx = -1
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for frame in self.frames:
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frame.is_done = False
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elif len(self.frames_idxs) == 0:
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# all frames are done?
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rewind_to_frame_idx = -1
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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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cur_frame.is_shown = False
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if not frames_equal:
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session_data = None
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if session_data is None:
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for filename in pathex.get_image_paths(self.output_path): #remove all images in output_path
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Path(filename).unlink()
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for filename in pathex.get_image_paths(self.output_mask_path): #remove all images in output_mask_path
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Path(filename).unlink()
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frames[0].cfg = self.merger_config.copy()
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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_filepath = self.output_path / ( frame.frame_info.filepath.stem + '.png' )
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frame.output_mask_filepath = self.output_mask_path / ( frame.frame_info.filepath.stem + '.png' )
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if not frame.output_filepath.exists() or \
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not frame.output_mask_filepath.exists():
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# if some frame does not exist, recompute and rewind
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frame.is_done = False
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frame.is_shown = False
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if rewind_to_frame_idx is None:
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rewind_to_frame_idx = i-1
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else:
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rewind_to_frame_idx = min(rewind_to_frame_idx, i-1)
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if rewind_to_frame_idx is not None:
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while len(self.frames_done_idxs) > 0:
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if self.frames_done_idxs[-1] > rewind_to_frame_idx:
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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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else:
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break
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#override
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def process_info_generator(self):
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r = [0] if MERGER_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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'predictor_input_shape' : self.predictor_input_shape,
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'superres_func': self.superres_func,
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'fanseg_input_size' : self.fanseg_input_size,
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'fanseg_extract_func' : self.fanseg_extract_func,
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'stdin_fd': sys.stdin.fileno() if MERGER_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 ("Merging", len(self.frames_idxs)+len(self.frames_done_idxs), initial=len(self.frames_done_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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MergerConfig.TYPE_MASKED : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_masked.jpg') ),
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MergerConfig.TYPE_FACE_AVATAR : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_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.merger_config.type], waiting_icon=False)
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self.screen_manager = ScreenManager( "Merger", [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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self.masked_keys_funcs = {
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'`' : lambda cfg,shift_pressed: cfg.set_mode(0),
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'1' : lambda cfg,shift_pressed: cfg.set_mode(1),
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'2' : lambda cfg,shift_pressed: cfg.set_mode(2),
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'3' : lambda cfg,shift_pressed: cfg.set_mode(3),
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'4' : lambda cfg,shift_pressed: cfg.set_mode(4),
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'5' : lambda cfg,shift_pressed: cfg.set_mode(5),
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'q' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(1 if not shift_pressed else 5),
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'a' : lambda cfg,shift_pressed: cfg.add_hist_match_threshold(-1 if not shift_pressed else -5),
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'w' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(1 if not shift_pressed else 5),
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's' : lambda cfg,shift_pressed: cfg.add_erode_mask_modifier(-1 if not shift_pressed else -5),
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'e' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(1 if not shift_pressed else 5),
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'd' : lambda cfg,shift_pressed: cfg.add_blur_mask_modifier(-1 if not shift_pressed else -5),
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'r' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(1 if not shift_pressed else 5),
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'f' : lambda cfg,shift_pressed: cfg.add_motion_blur_power(-1 if not shift_pressed else -5),
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't' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(1 if not shift_pressed else 5),
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'g' : lambda cfg,shift_pressed: cfg.add_super_resolution_power(-1 if not shift_pressed else -5),
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'y' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(1 if not shift_pressed else 5),
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'h' : lambda cfg,shift_pressed: cfg.add_blursharpen_amount(-1 if not shift_pressed else -5),
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'u' : lambda cfg,shift_pressed: cfg.add_output_face_scale(1 if not shift_pressed else 5),
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'j' : lambda cfg,shift_pressed: cfg.add_output_face_scale(-1 if not shift_pressed else -5),
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'i' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(1 if not shift_pressed else 5),
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'k' : lambda cfg,shift_pressed: cfg.add_image_denoise_power(-1 if not shift_pressed else -5),
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'o' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(1 if not shift_pressed else 5),
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'l' : lambda cfg,shift_pressed: cfg.add_bicubic_degrade_power(-1 if not shift_pressed else -5),
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'p' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(1 if not shift_pressed else 5),
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';' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-1),
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':' : lambda cfg,shift_pressed: cfg.add_color_degrade_power(-5),
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'z' : lambda cfg,shift_pressed: cfg.toggle_masked_hist_match(),
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'x' : lambda cfg,shift_pressed: cfg.toggle_mask_mode(),
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'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
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'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
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}
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self.masked_keys = list(self.masked_keys_funcs.keys())
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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.is_interactive:
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self.screen_manager.finalize()
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for frame in self.frames:
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frame.output_filepath = None
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frame.output_mask_filepath = None
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frame.image = None
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session_data = {
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'frames': self.frames,
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'frames_idxs': self.frames_idxs,
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'frames_done_idxs': self.frames_done_idxs,
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'model_iter' : self.model_iter,
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}
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self.merger_session_filepath.write_bytes( pickle.dumps(session_data) )
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io.log_info ("Session is saved to " + '/'.join (self.merger_session_filepath.parts[-2:]) )
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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.superres_host.process_messages()
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self.fanseg_host.process_messages()
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go_prev_frame = False
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go_first_frame = False
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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:
|
|
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 MergeSubprocessor.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
|
|
|
|
def main (model_class_name=None,
|
|
saved_models_path=None,
|
|
training_data_src_path=None,
|
|
force_model_name=None,
|
|
input_path=None,
|
|
output_path=None,
|
|
output_mask_path=None,
|
|
aligned_path=None,
|
|
force_gpu_idxs=None,
|
|
cpu_only=None):
|
|
io.log_info ("Running merger.\r\n")
|
|
|
|
try:
|
|
if not input_path.exists():
|
|
io.log_err('Input directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
if not output_path.exists():
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if not output_mask_path.exists():
|
|
output_mask_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if not saved_models_path.exists():
|
|
io.log_err('Model directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
is_interactive = io.input_bool ("Use interactive merger?", True) if not io.is_colab() else False
|
|
|
|
import models
|
|
model = models.import_model(model_class_name)(is_training=False,
|
|
saved_models_path=saved_models_path,
|
|
training_data_src_path=training_data_src_path,
|
|
force_gpu_idxs=force_gpu_idxs,
|
|
cpu_only=cpu_only)
|
|
merger_session_filepath = model.get_strpath_storage_for_file('merger_session.dat')
|
|
predictor_func, predictor_input_shape, cfg = model.get_MergerConfig()
|
|
|
|
if not is_interactive:
|
|
cfg.ask_settings()
|
|
|
|
input_path_image_paths = pathex.get_image_paths(input_path)
|
|
|
|
if cfg.type == MergerConfig.TYPE_MASKED:
|
|
if not aligned_path.exists():
|
|
io.log_err('Aligned directory not found. Please ensure it exists.')
|
|
return
|
|
|
|
packed_samples = None
|
|
try:
|
|
packed_samples = samplelib.PackedFaceset.load(aligned_path)
|
|
except:
|
|
io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(aligned_path)}, {traceback.format_exc()}")
|
|
|
|
|
|
if packed_samples is not None:
|
|
io.log_info ("Using packed faceset.")
|
|
def generator():
|
|
for sample in io.progress_bar_generator( packed_samples, "Collecting alignments"):
|
|
filepath = Path(sample.filename)
|
|
yield filepath, DFLIMG.load(filepath, loader_func=lambda x: sample.read_raw_file() )
|
|
else:
|
|
def generator():
|
|
for filepath in io.progress_bar_generator( pathex.get_image_paths(aligned_path), "Collecting alignments"):
|
|
filepath = Path(filepath)
|
|
yield filepath, DFLIMG.load(filepath)
|
|
|
|
alignments = {}
|
|
multiple_faces_detected = False
|
|
|
|
for filepath, dflimg in generator():
|
|
if dflimg is None:
|
|
io.log_err ("%s is not a dfl image file" % (filepath.name) )
|
|
continue
|
|
|
|
source_filename = dflimg.get_source_filename()
|
|
if source_filename is None:
|
|
continue
|
|
|
|
source_filepath = Path(source_filename)
|
|
source_filename_stem = source_filepath.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(), filepath, source_filepath ) )
|
|
|
|
if len(alignments_ar) > 1:
|
|
multiple_faces_detected = True
|
|
|
|
if multiple_faces_detected:
|
|
io.log_info ("")
|
|
io.log_info ("Warning: multiple faces detected. Only one alignment file should refer one source file.")
|
|
io.log_info ("")
|
|
|
|
for a_key in list(alignments.keys()):
|
|
a_ar = alignments[a_key]
|
|
if len(a_ar) > 1:
|
|
for _, filepath, source_filepath in a_ar:
|
|
io.log_info (f"alignment {filepath.name} refers to {source_filepath.name} ")
|
|
io.log_info ("")
|
|
|
|
if multiple_faces_detected:
|
|
io.log_info ("It is strongly recommended to process the faces separatelly.")
|
|
io.log_info ("Use 'recover original filename' to determine the exact duplicates.")
|
|
io.log_info ("")
|
|
|
|
frames = [ MergeSubprocessor.Frame( frame_info=FrameInfo(filepath=Path(p),
|
|
landmarks_list=alignments.get(Path(p).stem, (None,) )[0]
|
|
)
|
|
)
|
|
for p in input_path_image_paths ]
|
|
|
|
if multiple_faces_detected:
|
|
io.log_info ("Warning: multiple faces detected. Motion blur will not be used.")
|
|
io.log_info ("")
|
|
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 == MergerConfig.TYPE_FACE_AVATAR:
|
|
filesdata = []
|
|
for filepath in io.progress_bar_generator(input_path_image_paths, "Collecting info"):
|
|
filepath = Path(filepath)
|
|
|
|
dflimg = DFLIMG.load(filepath)
|
|
if dflimg is None:
|
|
io.log_err ("%s is not a dfl image file" % (filepath.name) )
|
|
continue
|
|
filesdata += [ ( FrameInfo(filepath=filepath, landmarks_list=[dflimg.get_landmarks()] ), dflimg.get_source_filename() ) ]
|
|
|
|
filesdata = sorted(filesdata, key=operator.itemgetter(1)) #sort by source_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 ( MergeSubprocessor.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 merge in input_dir.")
|
|
else:
|
|
MergeSubprocessor (
|
|
is_interactive = is_interactive,
|
|
merger_session_filepath = merger_session_filepath,
|
|
predictor_func = predictor_func,
|
|
predictor_input_shape = predictor_input_shape,
|
|
merger_config = cfg,
|
|
frames = frames,
|
|
frames_root_path = input_path,
|
|
output_path = output_path,
|
|
output_mask_path = output_mask_path,
|
|
model_iter = model.get_iter()
|
|
).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)
|