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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
added XSeg model.
with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
This commit is contained in:
Colombo
parent
2be940092b
commit
45582d129d
27 changed files with 577 additions and 711 deletions
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@ -552,8 +552,8 @@ def dev_test1(input_dir):
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#import code
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#code.interact(local=dict(globals(), **locals()))
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def dev_resave_pngs(input_dir):
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def dev_resave_pngs(input_dir):
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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@ -562,26 +562,26 @@ def dev_resave_pngs(input_dir):
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for filepath in io.progress_bar_generator(images_paths,"Processing"):
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cv2_imwrite(filepath, cv2_imread(filepath))
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def dev_segmented_trash(input_dir):
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def dev_segmented_trash(input_dir):
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name+'_trash')
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output_path.mkdir(parents=True, exist_ok=True)
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images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
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trash_paths = []
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for filepath in images_paths:
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json_file = filepath.parent / (filepath.stem +'.json')
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if not json_file.exists():
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trash_paths.append(filepath)
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for filepath in trash_paths:
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try:
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filepath.rename ( output_path / filepath.name )
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except:
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@ -590,9 +590,9 @@ def dev_segmented_trash(input_dir):
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def dev_segmented_extract(input_dir, output_dir ):
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# extract and merge .json labelme files within the faces
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device_config = nn.DeviceConfig.GPUIndexes( nn.ask_choose_device_idxs(suggest_all_gpu=True) )
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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@ -600,7 +600,7 @@ def dev_segmented_extract(input_dir, output_dir ):
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output_path = Path(output_dir)
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io.log_info("Performing extract segmented faces.")
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io.log_info(f'Output dir is {output_path}')
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if output_path.exists():
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output_images_paths = pathex.get_image_paths(output_path, subdirs=True)
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if len(output_images_paths) > 0:
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@ -613,71 +613,71 @@ def dev_segmented_extract(input_dir, output_dir ):
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images_paths = pathex.get_image_paths(input_path, subdirs=True, return_Path_class=True)
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extract_data = []
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extract_data = []
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images_jsons = {}
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images_processed = 0
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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json_filepath = filepath.parent / (filepath.stem+'.json')
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json_filepath = filepath.parent / (filepath.stem+'.json')
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if json_filepath.exists():
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try:
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json_dict = json.loads(json_filepath.read_text())
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images_jsons[filepath] = json_dict
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total_points = [ [x,y] for shape in json_dict['shapes'] for x,y in shape['points'] ]
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total_points = np.array(total_points)
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if len(total_points) == 0:
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io.log_info(f"No points found in {json_filepath}, skipping.")
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continue
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l,r = int(total_points[:,0].min()), int(total_points[:,0].max())
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t,b = int(total_points[:,1].min()), int(total_points[:,1].max())
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force_output_path=output_path / filepath.relative_to(input_path).parent
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force_output_path.mkdir(exist_ok=True, parents=True)
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extract_data.append ( ExtractSubprocessor.Data(filepath,
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extract_data.append ( ExtractSubprocessor.Data(filepath,
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rects=[ [l,t,r,b] ],
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force_output_path=force_output_path ) )
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images_processed += 1
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except:
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except:
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io.log_err(f"err {filepath}, {traceback.format_exc()}")
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return
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else:
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io.log_info(f"No .json file for {filepath.relative_to(input_path)}, skipping.")
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continue
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image_size = 1024
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face_type = FaceType.HEAD
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face_type = FaceType.HEAD
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extract_data = ExtractSubprocessor (extract_data, 'landmarks', image_size, face_type, device_config=device_config).run()
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extract_data = ExtractSubprocessor (extract_data, 'final', image_size, face_type, device_config=device_config).run()
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for data in extract_data:
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filepath = data.force_output_path / (data.filepath.stem+'_0.jpg')
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dflimg = DFLIMG.load(filepath)
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image_to_face_mat = dflimg.get_image_to_face_mat()
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json_dict = images_jsons[data.filepath]
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ie_polys = IEPolys()
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for shape in json_dict['shapes']:
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for shape in json_dict['shapes']:
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ie_poly = ie_polys.add(1)
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points = np.array( [ [x,y] for x,y in shape['points'] ] )
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points = LandmarksProcessor.transform_points(points, image_to_face_mat)
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for x,y in points:
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ie_poly.add( int(x), int(y) )
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dflimg.embed_and_set (filepath, ie_polys=ie_polys)
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io.log_info(f"Images found: {len(images_paths)}")
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io.log_info(f"Images processed: {images_processed}")
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"""
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@ -685,20 +685,20 @@ def dev_segmented_extract(input_dir, output_dir ):
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for data in extract_data:
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filepath = data.filepath
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output_filepath = output_path / (filepath.stem+'.jpg')
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img = cv2_imread(filepath)
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img = imagelib.normalize_channels(img, 3)
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cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
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json_dict = images_jsons[filepath]
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ie_polys = IEPolys()
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for shape in json_dict['shapes']:
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ie_poly = ie_polys.add(1)
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for shape in json_dict['shapes']:
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ie_poly = ie_polys.add(1)
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for x,y in shape['points']:
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ie_poly.add( int(x), int(y) )
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ie_poly.add( int(x), int(y) )
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DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
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landmarks=data.landmarks[0],
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ie_polys=ie_polys,
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