mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 13:32:09 -07:00
added support of non-english characters in paths
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iperov
parent
0313fd9ae6
commit
5a1fb199f4
7 changed files with 47 additions and 23 deletions
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@ -7,6 +7,7 @@ import cv2
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from tqdm import tqdm
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from utils.DFLPNG import DFLPNG
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from utils.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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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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@ -154,7 +155,7 @@ class ConvertSubprocessor(SubprocessorBase):
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print ( 'no faces found for %s, copying without faces' % (filename_path.name) )
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shutil.copy ( str(filename_path), str(output_filename_path) )
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else:
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image = (cv2.imread(str(filename_path)) / 255.0).astype(np.float32)
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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 = self.converter.convert_image(image, None, self.debug)
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@ -197,7 +198,7 @@ class ConvertSubprocessor(SubprocessorBase):
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faces_processed = len(faces)
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if not self.debug:
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cv2.imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
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cv2_imwrite (str(output_filename_path), (image*255).astype(np.uint8) )
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return (files_processed, faces_processed)
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@ -8,6 +8,7 @@ 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.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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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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@ -154,7 +155,7 @@ class ExtractSubprocessor(SubprocessorBase):
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self.param['y'] = ( ( prev_rect[1] + prev_rect[3] ) / 2 ) * self.view_scale
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if len(faces) == 0:
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self.original_image = cv2.imread(filename)
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self.original_image = cv2_imread(filename)
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(h,w,c) = self.original_image.shape
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@ -289,9 +290,9 @@ class ExtractSubprocessor(SubprocessorBase):
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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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image = cv2_imread( str(filename_path) )
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if image is None:
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print ( 'Failed to extract %s, reason: cv2.imread() fail.' % ( str(filename_path) ) )
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print ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
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else:
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if self.type == 'rects':
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rects = self.e.extract_from_bgr (image)
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@ -327,7 +328,7 @@ class ExtractSubprocessor(SubprocessorBase):
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face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
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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, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
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cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
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DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
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landmarks = face_image_landmarks.tolist(),
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@ -341,7 +342,7 @@ class ExtractSubprocessor(SubprocessorBase):
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result.append (output_file)
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if self.debug:
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cv2.imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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return result
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return None
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@ -11,6 +11,7 @@ from utils import Path_utils
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from utils import image_utils
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from utils.DFLPNG import DFLPNG
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from utils.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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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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@ -98,7 +99,7 @@ class BlurEstimatorSubprocessor(SubprocessorBase):
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dflimg = None
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if dflimg is not None:
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image = cv2.imread( str(filepath) )
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image = cv2_imread( str(filepath) )
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image = ( image * \
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LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks()) \
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).astype(np.uint8)
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@ -139,14 +140,14 @@ def sort_by_blur(input_path):
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def sort_by_brightness(input_path):
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print ("Sorting by brightness...")
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2.imread(x), cv2.COLOR_BGR2HSV)[...,2].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True) ]
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2_imread(x), cv2.COLOR_BGR2HSV)[...,2].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True) ]
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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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return img_list
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def sort_by_hue(input_path):
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print ("Sorting by hue...")
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2.imread(x), cv2.COLOR_BGR2HSV)[...,0].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True) ]
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img_list = [ [x, np.mean ( cv2.cvtColor(cv2_imread(x), cv2.COLOR_BGR2HSV)[...,0].flatten() )] for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True) ]
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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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return img_list
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@ -316,7 +317,7 @@ class HistSsimSubprocessor(SubprocessorBase):
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img_list = []
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for x in data:
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img = cv2.imread(x)
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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])
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@ -455,7 +456,7 @@ def sort_by_hist_dissim(input_path):
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print ("%s is not a dfl image file" % (filepath.name) )
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continue
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image = cv2.imread(str(filepath))
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image = cv2_imread(str(filepath))
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face_mask = LandmarksProcessor.get_image_hull_mask (image.shape, dflimg.get_landmarks())
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image = (image*face_mask).astype(np.uint8)
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@ -535,7 +536,7 @@ class FinalLoaderSubprocessor(SubprocessorBase):
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print ("%s is not a dfl image file" % (filepath.name) )
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raise Exception("")
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bgr = cv2.imread(str(filepath))
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bgr = cv2_imread(str(filepath))
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if bgr is None:
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raise Exception ("Unable to load %s" % (filepath.name) )
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@ -651,7 +652,7 @@ def sort_by_black(input_path):
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img_list = []
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for x in tqdm( Path_utils.get_image_paths(input_path), desc="Loading", ascii=True):
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img = cv2.imread(x)
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img = cv2_imread(x)
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img_list.append ([x, img[(img == 0)].size ])
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print ("Sorting...")
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@ -11,6 +11,7 @@ from utils import Path_utils
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from utils import image_utils
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from utils.DFLPNG import DFLPNG
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from utils.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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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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@ -26,9 +27,9 @@ def convert_png_to_jpg_file (filepath):
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dfl_dict = dflpng.getDFLDictData()
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img = cv2.imread (str(filepath))
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img = cv2_imread (str(filepath))
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new_filepath = str(filepath.parent / (filepath.stem + '.jpg'))
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cv2.imwrite ( new_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 85])
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cv2_imwrite ( new_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 85])
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DFLJPG.embed_data( new_filepath,
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face_type=dfl_dict.get('face_type', None),
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@ -41,11 +42,7 @@ def convert_png_to_jpg_file (filepath):
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filepath.unlink()
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def convert_png_to_jpg_folder (input_path):
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if not all(ord(c) < 128 for c in input_path):
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print ("Path to directory must contain only non unicode characters.")
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return
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input_path = Path(input_path)
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print ("Converting PNG to JPG...\r\n")
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@ -8,6 +8,7 @@ from utils import Path_utils
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from utils import std_utils
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from utils import image_utils
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from utils.console_utils import *
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from utils.cv2_utils import *
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import numpy as np
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import cv2
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from samples import SampleGeneratorBase
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@ -321,7 +322,7 @@ class ModelBase(object):
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if self.write_preview_history:
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if self.epoch % 10 == 0:
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img = (self.get_static_preview() * 255).astype(np.uint8)
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cv2.imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.epoch) )), img )
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cv2_imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.epoch) )), img )
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self.epoch += 1
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@ -1,6 +1,7 @@
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from enum import IntEnum
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import cv2
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import numpy as np
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from utils.cv2_utils import *
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class SampleType(IntEnum):
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IMAGE = 0 #raw image
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@ -37,7 +38,7 @@ class Sample(object):
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close_target_list=close_target_list if close_target_list is not None else self.close_target_list)
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def load_bgr(self):
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img = cv2.imread (self.filename).astype(np.float32) / 255.0
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img = cv2_imread (self.filename).astype(np.float32) / 255.0
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if self.mirror:
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img = img[:,::-1].copy()
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return img
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22
utils/cv2_utils.py
Normal file
22
utils/cv2_utils.py
Normal file
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@ -0,0 +1,22 @@
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import cv2
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import numpy as np
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from pathlib import Path
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#allows to open non-english characters path
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def cv2_imread(filename, flags=cv2.IMREAD_UNCHANGED):
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try:
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with open(filename, "rb") as stream:
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bytes = bytearray(stream.read())
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numpyarray = np.asarray(bytes, dtype=np.uint8)
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return cv2.imdecode(numpyarray, flags)
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except:
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return None
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def cv2_imwrite(filename, img, *args):
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ret, buf = cv2.imencode( Path(filename).suffix, img, *args)
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if ret == True:
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try:
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with open(filename, "wb") as stream:
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stream.write( buf )
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except:
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pass
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