returned back

3.optional) denoise data_dst images.bat
	Apply it if dst video is very sharp.

	Denoise dst images before face extraction.
	This technique helps neural network not to learn the noise.
	The result is less pixel shake of the predicted face.

main.py

@@ -258,10 +258,9 @@ if __name__ == "__main__":
     def process_videoed_denoise_image_sequence(arguments):
         osex.set_process_lowest_prio()
         from mainscripts import VideoEd
-        VideoEd.denoise_image_sequence (arguments.input_dir, arguments.ext, arguments.factor)
-    p = videoed_parser.add_parser( "denoise-image-sequence", help="Denoise sequence of images, keeping sharp edges. This allows you to make the final fake more believable, since the neural network is not able to make a detailed skin texture, but it makes the edges quite clear. Therefore, if the whole frame is more `blurred`, then a fake will seem more believable. Especially true for scenes of the film, which are usually very clear.")
-    p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input file to be processed. Specify .*-extension to find first file.")
-    p.add_argument('--ext', dest="ext", default=None, help="Image format (extension) of input files.")
+        VideoEd.denoise_image_sequence (arguments.input_dir, arguments.factor)
+    p = videoed_parser.add_parser( "denoise-image-sequence", help="Denoise sequence of images, keeping sharp edges. Helps to remove pixel shake from the predicted face.")
+    p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory to be processed.")
     p.add_argument('--factor', type=int, dest="factor", default=None, help="Denoise factor (1-20).")
     p.set_defaults(func=process_videoed_denoise_image_sequence)