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https://github.com/iperov/DeepFaceLab.git
synced 2025-08-14 02:37:00 -07:00
refactorings, improved sort by hist-dissim
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iperov
parent
4ff67ad26b
commit
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6 changed files with 128 additions and 127 deletions
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@ -3,7 +3,7 @@ from pathlib import Path
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from utils import Path_utils
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import cv2
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from tqdm import tqdm
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from utils.AlignedPNG import AlignedPNG
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from utils.DFLPNG import DFLPNG
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from utils import image_utils
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import shutil
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import numpy as np
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@ -156,12 +156,7 @@ class ConvertSubprocessor(SubprocessorBase):
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image = (cv2.imread(str(filename_path)) / 255.0).astype(np.float32)
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if self.converter.get_mode() == ConverterBase.MODE_IMAGE:
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image_landmarks = None
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a_png = AlignedPNG.load( str(filename_path) )
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if a_png is not None:
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d = a_png.getFaceswapDictData()
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if d is not None and 'landmarks' in d.keys():
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image_landmarks = np.array(d['landmarks'])
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image_landmarks = DFLPNG.load( str(filename_path), throw_on_no_embedded_data=True ).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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@ -258,20 +253,15 @@ def main (input_dir, output_dir, model_dir, model_name, aligned_dir=None, **in_o
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aligned_path_image_paths = Path_utils.get_image_paths(aligned_path)
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for filename in tqdm(aligned_path_image_paths, desc= "Collecting alignments" ):
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a_png = AlignedPNG.load( str(filename) )
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if a_png is None:
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print ( "%s - no embedded data found." % (filename) )
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continue
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d = a_png.getFaceswapDictData()
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if d is None or d['source_filename'] is None or d['source_rect'] is None or d['source_landmarks'] is None:
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print ( "%s - no embedded data found." % (filename) )
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dflpng = DFLPNG.load( str(filename), print_on_no_embedded_data=True )
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if dflpng is None:
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continue
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source_filename_stem = Path(d['source_filename']).stem
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source_filename_stem = Path( dflpng.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 ( np.array(d['source_landmarks']) )
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alignments[ source_filename_stem ].append (dflpng.get_source_landmarks())
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files_processed, faces_processed = ConvertSubprocessor (
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@ -8,7 +8,7 @@ from pathlib import Path
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import numpy as np
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import cv2
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from utils import Path_utils
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from utils.AlignedPNG import AlignedPNG
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from utils.DFLPNG import DFLPNG
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from utils import image_utils
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from facelib import FaceType
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import facelib
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@ -313,20 +313,15 @@ class ExtractSubprocessor(SubprocessorBase):
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face_image_landmarks = facelib.LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
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cv2.imwrite(output_file, face_image)
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a_png = AlignedPNG.load (output_file)
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d = {
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'face_type': FaceType.toString(self.face_type),
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'landmarks': face_image_landmarks.tolist(),
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'yaw_value': facelib.LandmarksProcessor.calc_face_yaw (face_image_landmarks),
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'pitch_value': facelib.LandmarksProcessor.calc_face_pitch (face_image_landmarks),
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'source_filename': filename_path.name,
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'source_rect': rect,
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'source_landmarks': image_landmarks.tolist()
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}
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a_png.setFaceswapDictData (d)
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a_png.save(output_file)
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DFLPNG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
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landmarks = face_image_landmarks.tolist(),
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yaw_value = facelib.LandmarksProcessor.calc_face_yaw (face_image_landmarks),
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pitch_value = facelib.LandmarksProcessor.calc_face_pitch (face_image_landmarks),
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source_filename = filename_path.name,
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source_rect= rect,
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source_landmarks = image_landmarks.tolist()
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)
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result.append (output_file)
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@ -8,7 +8,8 @@ from shutil import copyfile
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from pathlib import Path
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from utils import Path_utils
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from utils.AlignedPNG import AlignedPNG
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from utils import image_utils
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from utils.DFLPNG import DFLPNG
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from facelib import LandmarksProcessor
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from utils.SubprocessorBase import SubprocessorBase
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import multiprocessing
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@ -86,22 +87,16 @@ class BlurEstimatorSubprocessor(SubprocessorBase):
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#override
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def onClientProcessData(self, data):
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filename_path = Path( data[0] )
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image = cv2.imread( str(filename_path) )
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face_mask = None
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a_png = AlignedPNG.load( str(filename_path) )
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if a_png is not None:
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d = a_png.getFaceswapDictData()
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if (d is not None) and (d['landmarks'] is not None):
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face_mask = LandmarksProcessor.get_image_hull_mask (image, np.array(d['landmarks']))
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if face_mask is not None:
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image = (image*face_mask).astype(np.uint8)
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dflpng = DFLPNG.load( str(filename_path), print_on_no_embedded_data=True )
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if dflpng is not None:
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image = cv2.imread( str(filename_path) )
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image = ( image * \
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LandmarksProcessor.get_image_hull_mask (image, dflpng.get_landmarks()) \
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).astype(np.uint8)
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return [ str(filename_path), estimate_sharpness( image ) ]
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else:
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print ( "%s - no embedded data found." % (str(filename_path)) )
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return [ str(filename_path), 0 ]
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return [ str(filename_path), estimate_sharpness( image ) ]
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#override
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def onClientGetDataName (self, data):
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@ -164,18 +159,11 @@ def sort_by_face(input_path):
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print ("%s is not a png file required for sort_by_face" % (filepath.name) )
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continue
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a_png = AlignedPNG.load (str(filepath))
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if a_png is None:
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print ("%s failed to load" % (filepath.name) )
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continue
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d = a_png.getFaceswapDictData()
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if d is None or d['landmarks'] is None:
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print ("%s - no embedded data found required for sort_by_face" % (filepath.name) )
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dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
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if dflpng is None:
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continue
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img_list.append( [str(filepath), np.array(d['landmarks']) ] )
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img_list.append( [str(filepath), dflpng.get_landmarks()] )
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img_list_len = len(img_list)
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@ -207,18 +195,11 @@ def sort_by_face_dissim(input_path):
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print ("%s is not a png file required for sort_by_face_dissim" % (filepath.name) )
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continue
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a_png = AlignedPNG.load (str(filepath))
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if a_png is None:
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print ("%s failed to load" % (filepath.name) )
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continue
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d = a_png.getFaceswapDictData()
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if d is None or d['landmarks'] is None:
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print ("%s - no embedded data found required for sort_by_face_dissim" % (filepath.name) )
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dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
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if dflpng is None:
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continue
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img_list.append( [str(filepath), np.array(d['landmarks']), 0 ] )
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img_list.append( [str(filepath), dflpng.get_landmarks(), 0 ] )
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img_list_len = len(img_list)
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for i in tqdm( range(0, img_list_len-1), desc="Sorting"):
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@ -247,18 +228,11 @@ def sort_by_face_yaw(input_path):
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print ("%s is not a png file required for sort_by_face_dissim" % (filepath.name) )
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continue
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a_png = AlignedPNG.load (str(filepath))
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if a_png is None:
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print ("%s failed to load" % (filepath.name) )
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continue
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d = a_png.getFaceswapDictData()
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if d is None or d['yaw_value'] is None:
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print ("%s - no embedded data found required for sort_by_face_dissim" % (filepath.name) )
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dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
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if dflpng is None:
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continue
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img_list.append( [str(filepath), np.array(d['yaw_value']) ] )
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img_list.append( [str(filepath), np.array( dflpng.get_yaw_value() ) ] )
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print ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
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@ -423,9 +397,7 @@ class HistDissimSubprocessor(SubprocessorBase):
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for j in range( 0, self.img_list_len):
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if i == j:
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continue
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score_total += cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
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cv2.compareHist(self.img_list[i][2], self.img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
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cv2.compareHist(self.img_list[i][3], self.img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
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score_total += cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
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return score_total
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@ -436,7 +408,7 @@ class HistDissimSubprocessor(SubprocessorBase):
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#override
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def onHostResult (self, data, result):
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self.img_list[data[0]][4] = result
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self.img_list[data[0]][2] = result
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return 1
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#override
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@ -451,17 +423,20 @@ def sort_by_hist_dissim(input_path):
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print ("Sorting by histogram dissimilarity...")
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img_list = []
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for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
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img = cv2.imread(x)
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img_list.append ([x, cv2.calcHist([img], [0], None, [256], [0, 256]),
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cv2.calcHist([img], [1], None, [256], [0, 256]),
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cv2.calcHist([img], [2], None, [256], [0, 256]), 0
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])
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for filename_path in tqdm( Path_utils.get_image_paths(input_path), desc="Loading"):
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image = cv2.imread(filename_path)
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dflpng = DFLPNG.load( str(filename_path), print_on_no_embedded_data=True )
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if dflpng is not None:
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face_mask = LandmarksProcessor.get_image_hull_mask (image, dflpng.get_landmarks())
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image = (image*face_mask).astype(np.uint8)
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img_list.append ([filename_path, cv2.calcHist([cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)], [0], None, [256], [0, 256]), 0 ])
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img_list = HistDissimSubprocessor(img_list).process()
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print ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(4), reverse=True)
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img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
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return img_list
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@ -508,18 +483,11 @@ def sort_by_origname(input_path):
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print ("%s is not a png file required for sort_by_origname" % (filepath.name) )
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continue
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a_png = AlignedPNG.load (str(filepath))
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if a_png is None:
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print ("%s failed to load" % (filepath.name) )
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continue
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d = a_png.getFaceswapDictData()
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if d is None or d['source_filename'] is None:
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print ("%s - no embedded data found required for sort_by_origname" % (filepath.name) )
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dflpng = DFLPNG.load (str(filepath), print_on_no_embedded_data=True)
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if dflpng is None:
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continue
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img_list.append( [str(filepath), d['source_filename']] )
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img_list.append( [str(filepath), dflpng.get_source_filename()] )
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print ("Sorting...")
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img_list = sorted(img_list, key=operator.itemgetter(1))
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@ -545,4 +513,4 @@ def main (input_path, sort_by_method):
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elif sort_by_method == 'black': img_list = sort_by_black (input_path)
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elif sort_by_method == 'origname': img_list = sort_by_origname (input_path)
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final_rename (input_path, img_list)
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final_rename (input_path, img_list)
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