mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 03:26:47 -04:00
e76ab16bfe
* Add preview functionality to effmpeg. (#435) * Add preview functionality to effmpeg. effmpeg tool: Preview for actions that have a video output now available. Preview does not work when muxing audio. * Model json unicode fix1 (#443) * fixed Windows 10 path error while loading weights * - fixed TypeError: the JSON object must be str, not 'bytes' with OriginalHighRes Model * MTCNN Extractor and Extraction refactor (#453) * implement mtcnn extractor * mtcnn refactor and vram management changes * cli arguments update for mtcnn/dlib split * Add mtcnn models to gitignore * Change multiprocessing on extract * GUI changes to handle nargs defaults * Early exit bugfix (#455) * Fix extract early termination bug * Fix extract early exit bug * Multi face detection bugfix (#456) * Multi face extraction fix * Original high res cleanup 1 (#457) * slight model re-factoring - removed excess threading code - added random kernel initialization to dense layer * Slight OriginalHighRes re-factoring an code cleanup
133 lines
5 KiB
Python
133 lines
5 KiB
Python
#!/usr/bin python3
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""" The script to run the extract process of faceswap """
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import os
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import sys
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from pathlib import Path
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from tqdm import tqdm
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from lib.gpu_stats import GPUStats
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from lib.multithreading import pool_process
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from scripts.fsmedia import Alignments, Faces, Images, Utils
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tqdm.monitor_interval = 0 # workaround for TqdmSynchronisationWarning
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class Extract(object):
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""" The extract process. """
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def __init__(self, arguments):
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self.args = arguments
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self.images = Images(self.args)
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self.faces = Faces(self.args)
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self.alignments = Alignments(self.args)
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self.output_dir = self.faces.output_dir
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self.export_face = True
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def process(self):
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""" Perform the extraction process """
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print('Starting, this may take a while...')
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Utils.set_verbosity(self.args.verbose)
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if self.args.multiprocess and GPUStats().device_count == 0:
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# TODO Checking that there is no available GPU is not
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# necessarily an indicator of whether the user is actually
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# using the CPU. Maybe look to implement further checks on
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# dlib/tensorflow compilations
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self.extract_multi_process()
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else:
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self.extract_single_process()
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self.alignments.write_alignments(self.faces.faces_detected)
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images, faces = Utils.finalize(self.images.images_found,
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self.faces.num_faces_detected,
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self.faces.verify_output)
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self.images.images_found = images
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self.faces.num_faces_detected = faces
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def extract_single_process(self):
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""" Run extraction in a single process """
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for filename in tqdm(self.images.input_images, file=sys.stdout):
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filename, faces = self.process_single_image(filename)
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self.faces.faces_detected[os.path.basename(filename)] = faces
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def extract_multi_process(self):
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""" Run the extraction on the correct number of processes """
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for filename, faces in tqdm(
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pool_process(
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self.process_single_image,
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self.images.input_images),
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total=self.images.images_found,
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file=sys.stdout):
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self.faces.num_faces_detected += 1
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self.faces.faces_detected[os.path.basename(filename)] = faces
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def process_single_image(self, filename):
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""" Detect faces in an image. Rotate the image the specified amount
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until at least one face is found, or until image rotations are
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depleted.
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Once at least one face has been detected, pass to
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process_single_face to process the individual faces """
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retval = filename, list()
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try:
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image = Utils.cv2_read_write('read', filename)
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for angle in self.images.rotation_angles:
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currentimage = Utils.rotate_image_by_angle(image, angle)
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faces = self.faces.get_faces(currentimage, angle)
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process_faces = [(idx, face) for idx, face in faces]
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if process_faces and angle != 0 and self.args.verbose:
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print("found face(s) by rotating image "
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"{} degrees".format(angle))
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if process_faces:
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break
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final_faces = [self.process_single_face(idx,
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face,
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filename,
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currentimage)
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for idx, face in process_faces]
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retval = filename, final_faces
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except Exception as err:
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if self.args.verbose:
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print("Failed to extract from image: "
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"{}. Reason: {}".format(filename, err))
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return retval
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def process_single_face(self, idx, face, filename, image):
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""" Perform processing on found faces """
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output_file = self.output_dir / Path(
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filename).stem if self.export_face else None
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self.faces.draw_landmarks_on_face(face, image)
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resized_face, t_mat = self.faces.extractor.extract(
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image,
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face,
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256,
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self.faces.align_eyes)
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blurry_file = self.faces.detect_blurry_faces(face,
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t_mat,
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resized_face,
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filename)
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output_file = blurry_file if blurry_file else output_file
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if self.export_face:
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filename = "{}_{}{}".format(str(output_file),
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str(idx),
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Path(filename).suffix)
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Utils.cv2_read_write('write', filename, resized_face)
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return {"r": face.r,
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"x": face.x,
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"w": face.w,
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"y": face.y,
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"h": face.h,
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"landmarksXY": face.landmarks_as_xy()}
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