mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
171 lines
No EOL
6.8 KiB
Python
171 lines
No EOL
6.8 KiB
Python
import json
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import shutil
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import traceback
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from pathlib import Path
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import numpy as np
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from core import pathex
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from core.cv2ex import *
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from core.interact import interact as io
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from core.leras import nn
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from DFLIMG import *
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from facelib import XSegNet, LandmarksProcessor, FaceType
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def apply_xseg(input_path, model_path):
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if not input_path.exists():
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raise ValueError(f'{input_path} not found. Please ensure it exists.')
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if not model_path.exists():
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raise ValueError(f'{model_path} not found. Please ensure it exists.')
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face_type = io.input_str ("XSeg model face type", 'same', ['h','mf','f','wf','head','same'], help_message="Specify face type of trained XSeg model. For example if XSeg model trained as WF, but faceset is HEAD, specify WF to apply xseg only on WF part of HEAD. Default is 'same'").lower()
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if face_type == 'same':
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face_type = None
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else:
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face_type = {'h' : FaceType.HALF,
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'mf' : FaceType.MID_FULL,
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'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[face_type]
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io.log_info(f'Applying trained XSeg model to {input_path.name}/ folder.')
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device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True)
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nn.initialize(device_config)
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xseg = XSegNet(name='XSeg',
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load_weights=True,
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weights_file_root=model_path,
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data_format=nn.data_format,
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raise_on_no_model_files=True)
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xseg_res = xseg.get_resolution()
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images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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dflimg = DFLIMG.load(filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_info(f'{filepath} is not a DFLIMG')
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continue
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img = cv2_imread(filepath).astype(np.float32) / 255.0
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h,w,c = img.shape
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img_face_type = FaceType.fromString( dflimg.get_face_type() )
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if face_type is not None and img_face_type != face_type:
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lmrks = dflimg.get_source_landmarks()
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fmat = LandmarksProcessor.get_transform_mat(lmrks, w, face_type)
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imat = LandmarksProcessor.get_transform_mat(lmrks, w, img_face_type)
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g_p = LandmarksProcessor.transform_points (np.float32([(0,0),(w,0),(0,w) ]), fmat, True)
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g_p2 = LandmarksProcessor.transform_points (g_p, imat)
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mat = cv2.getAffineTransform( g_p2, np.float32([(0,0),(w,0),(0,w) ]) )
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img = cv2.warpAffine(img, mat, (w, w), cv2.INTER_LANCZOS4)
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img = cv2.resize(img, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4)
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else:
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if w != xseg_res:
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img = cv2.resize( img, (xseg_res,xseg_res), interpolation=cv2.INTER_LANCZOS4 )
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if len(img.shape) == 2:
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img = img[...,None]
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mask = xseg.extract(img)
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if face_type is not None and img_face_type != face_type:
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mask = cv2.resize(mask, (w, w), interpolation=cv2.INTER_LANCZOS4)
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mask = cv2.warpAffine( mask, mat, (w,w), np.zeros( (h,w,c), dtype=np.float), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4)
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mask = cv2.resize(mask, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4)
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mask[mask < 0.5]=0
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mask[mask >= 0.5]=1
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dflimg.set_xseg_mask(mask)
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dflimg.save()
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def fetch_xseg(input_path):
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if not input_path.exists():
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raise ValueError(f'{input_path} not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name + '_xseg')
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output_path.mkdir(exist_ok=True, parents=True)
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io.log_info(f'Copying faces containing XSeg polygons to {output_path.name}/ folder.')
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images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
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files_copied = []
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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dflimg = DFLIMG.load(filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_info(f'{filepath} is not a DFLIMG')
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continue
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ie_polys = dflimg.get_seg_ie_polys()
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if ie_polys.has_polys():
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files_copied.append(filepath)
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shutil.copy ( str(filepath), str(output_path / filepath.name) )
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io.log_info(f'Files copied: {len(files_copied)}')
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is_delete = io.input_bool (f"\r\nDelete original files?", True)
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if is_delete:
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for filepath in files_copied:
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Path(filepath).unlink()
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def remove_xseg(input_path):
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if not input_path.exists():
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raise ValueError(f'{input_path} not found. Please ensure it exists.')
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io.log_info(f'Processing folder {input_path}')
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io.log_info('!!! WARNING : APPLIED XSEG MASKS WILL BE REMOVED FROM THE FRAMES !!!')
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io.log_info('!!! WARNING : APPLIED XSEG MASKS WILL BE REMOVED FROM THE FRAMES !!!')
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io.log_info('!!! WARNING : APPLIED XSEG MASKS WILL BE REMOVED FROM THE FRAMES !!!')
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io.input_str('Press enter to continue.')
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images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
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files_processed = 0
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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dflimg = DFLIMG.load(filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_info(f'{filepath} is not a DFLIMG')
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continue
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if dflimg.has_xseg_mask():
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dflimg.set_xseg_mask(None)
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dflimg.save()
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files_processed += 1
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io.log_info(f'Files processed: {files_processed}')
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def remove_xseg_labels(input_path):
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if not input_path.exists():
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raise ValueError(f'{input_path} not found. Please ensure it exists.')
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io.log_info(f'Processing folder {input_path}')
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io.log_info('!!! WARNING : LABELED XSEG POLYGONS WILL BE REMOVED FROM THE FRAMES !!!')
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io.log_info('!!! WARNING : LABELED XSEG POLYGONS WILL BE REMOVED FROM THE FRAMES !!!')
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io.log_info('!!! WARNING : LABELED XSEG POLYGONS WILL BE REMOVED FROM THE FRAMES !!!')
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io.input_str('Press enter to continue.')
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images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
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files_processed = 0
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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dflimg = DFLIMG.load(filepath)
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if dflimg is None or not dflimg.has_data():
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io.log_info(f'{filepath} is not a DFLIMG')
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continue
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if dflimg.has_seg_ie_polys():
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dflimg.set_seg_ie_polys(None)
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dflimg.save()
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files_processed += 1
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io.log_info(f'Files processed: {files_processed}') |