mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 19:05:02 -04:00
ca63242996
* Extraction - Speed improvements (#522) * Initial Plugin restructure * Detectors to plugins. Detector speed improvements * Re-implement dlib aligner, remove models, FAN to TF. Parallel processing * Update manual, update convert, implement parallel/serial switching * linting + fix cuda check (setup.py). requirements update keras 2.2.4 * Add extract size option. Fix dlib hog init * GUI: Increase tooltip width * Update alignment tool to support new DetectedFace * Add skip existing faces option * Fix sort tool to new plugin structure * remove old align plugin * fix convert -skip faces bug * Fix convert skipping no faces frames * Convert - draw onto transparent layer * Fix blur threshold bug * fix skip_faces convert bug * Fix training
255 lines
9 KiB
Python
255 lines
9 KiB
Python
#!/usr/bin python3
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""" Utilities available across all scripts """
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import os
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import warnings
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from pathlib import Path
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from re import finditer
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from time import time
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import cv2
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import numpy as np
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import dlib
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from lib.faces_detect import DetectedFace
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from lib.training_data import TrainingDataGenerator
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# Global variables
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_image_extensions = ['.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff']
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_video_extensions = ['.avi', '.flv', '.mkv', '.mov', '.mp4', '.mpeg', '.webm']
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def get_folder(path):
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""" Return a path to a folder, creating it if it doesn't exist """
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output_dir = Path(path)
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output_dir.mkdir(parents=True, exist_ok=True)
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return output_dir
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def get_image_paths(directory, exclude=list(), debug=False):
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""" Return a list of images that reside in a folder """
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image_extensions = _image_extensions
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exclude_names = [os.path.basename(Path(x).stem[:Path(x).stem.rfind('_')] +
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Path(x).suffix) for x in exclude]
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dir_contents = list()
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if not os.path.exists(directory):
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directory = get_folder(directory)
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dir_scanned = sorted(os.scandir(directory), key=lambda x: x.name)
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for chkfile in dir_scanned:
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if any([chkfile.name.lower().endswith(ext)
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for ext in image_extensions]):
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if chkfile.name in exclude_names:
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if debug:
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print("Already processed %s" % chkfile.name)
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continue
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else:
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dir_contents.append(chkfile.path)
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return dir_contents
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def backup_file(directory, filename):
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""" Backup a given file by appending .bk to the end """
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origfile = os.path.join(directory, filename)
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backupfile = origfile + '.bk'
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if os.path.exists(backupfile):
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os.remove(backupfile)
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if os.path.exists(origfile):
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os.rename(origfile, backupfile)
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def set_system_verbosity(loglevel):
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""" Set the verbosity level of tensorflow and suppresses
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future and deprecation warnings from any modules
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From:
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https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information
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Can be set to:
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0 - all logs shown
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1 - filter out INFO logs
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2 - filter out WARNING logs
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3 - filter out ERROR logs """
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = loglevel
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if loglevel != '0':
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for warncat in (FutureWarning, DeprecationWarning):
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warnings.simplefilter(action='ignore', category=warncat)
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def rotate_image_by_angle(image, angle,
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rotated_width=None, rotated_height=None):
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""" Rotate an image by a given angle.
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From: https://stackoverflow.com/questions/22041699 """
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height, width = image.shape[:2]
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image_center = (width/2, height/2)
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rotation_matrix = cv2.getRotationMatrix2D(image_center, -1.*angle, 1.)
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if rotated_width is None or rotated_height is None:
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abs_cos = abs(rotation_matrix[0, 0])
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abs_sin = abs(rotation_matrix[0, 1])
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if rotated_width is None:
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rotated_width = int(height*abs_sin + width*abs_cos)
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if rotated_height is None:
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rotated_height = int(height*abs_cos + width*abs_sin)
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rotation_matrix[0, 2] += rotated_width/2 - image_center[0]
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rotation_matrix[1, 2] += rotated_height/2 - image_center[1]
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return (cv2.warpAffine(image,
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rotation_matrix,
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(rotated_width, rotated_height)),
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rotation_matrix)
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def rotate_landmarks(face, rotation_matrix):
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""" Rotate the landmarks and bounding box for faces
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found in rotated images.
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Pass in a DetectedFace object, Alignments dict or DLib rectangle"""
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if isinstance(face, DetectedFace):
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bounding_box = [[face.x, face.y],
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[face.x + face.w, face.y],
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[face.x + face.w, face.y + face.h],
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[face.x, face.y + face.h]]
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landmarks = face.landmarksXY
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elif isinstance(face, dict):
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bounding_box = [[face.get("x", 0), face.get("y", 0)],
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[face.get("x", 0) + face.get("w", 0),
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face.get("y", 0)],
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[face.get("x", 0) + face.get("w", 0),
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face.get("y", 0) + face.get("h", 0)],
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[face.get("x", 0),
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face.get("y", 0) + face.get("h", 0)]]
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landmarks = face.get("landmarksXY", list())
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elif isinstance(face, dlib.rectangle):
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bounding_box = [[face.left(), face.top()],
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[face.right(), face.top()],
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[face.right(), face.bottom()],
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[face.left(), face.bottom()]]
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landmarks = list()
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else:
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raise ValueError("Unsupported face type")
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rotation_matrix = cv2.invertAffineTransform(rotation_matrix)
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rotated = list()
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for item in (bounding_box, landmarks):
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if not item:
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continue
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points = np.array(item, np.int32)
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points = np.expand_dims(points, axis=0)
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transformed = cv2.transform(points,
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rotation_matrix).astype(np.int32)
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rotated.append(transformed.squeeze())
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# Bounding box should follow x, y planes, so get min/max
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# for non-90 degree rotations
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pt_x = min([pnt[0] for pnt in rotated[0]])
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pt_y = min([pnt[1] for pnt in rotated[0]])
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pt_x1 = max([pnt[0] for pnt in rotated[0]])
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pt_y1 = max([pnt[1] for pnt in rotated[0]])
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if isinstance(face, DetectedFace):
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face.x = int(pt_x)
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face.y = int(pt_y)
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face.w = int(pt_x1 - pt_x)
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face.h = int(pt_y1 - pt_y)
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face.r = 0
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if len(rotated) > 1:
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face.landmarksXY = [tuple(point) for point in rotated[1].tolist()]
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elif isinstance(face, dict):
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face["x"] = int(pt_x)
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face["y"] = int(pt_y)
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face["w"] = int(pt_x1 - pt_x)
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face["h"] = int(pt_y1 - pt_y)
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face["r"] = 0
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if len(rotated) > 1:
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face["landmarksXY"] = [tuple(point)
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for point in rotated[1].tolist()]
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else:
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face = dlib.rectangle(int(pt_x), int(pt_y), int(pt_x1), int(pt_y1))
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return face
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def camel_case_split(identifier):
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""" Split a camel case name
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from: https://stackoverflow.com/questions/29916065 """
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matches = finditer(
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".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)",
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identifier)
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return [m.group(0) for m in matches]
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class Timelapse:
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""" Time lapse function for training """
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@classmethod
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def create_timelapse(cls, input_dir_a, input_dir_b, output_dir, trainer):
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""" Create the time lapse """
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if input_dir_a is None and input_dir_b is None and output_dir is None:
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return None
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if input_dir_a is None or input_dir_b is None:
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raise ValueError("To enable the timelapse, you have to supply "
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"all the parameters (--timelapse-input-A and "
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"--timelapse-input-B).")
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if output_dir is None:
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output_dir = get_folder(os.path.join(trainer.model.model_dir,
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"timelapse"))
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return Timelapse(input_dir_a, input_dir_b, output_dir, trainer)
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def __init__(self, input_dir_a, input_dir_b, output, trainer):
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self.output_dir = output
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self.trainer = trainer
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if not os.path.isdir(self.output_dir):
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print('Error: {} does not exist'.format(self.output_dir))
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exit(1)
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self.files_a = self.read_input_images(input_dir_a)
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self.files_b = self.read_input_images(input_dir_b)
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btchsz = min(len(self.files_a), len(self.files_b))
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self.images_a = self.get_image_data(self.files_a, btchsz)
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self.images_b = self.get_image_data(self.files_b, btchsz)
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@staticmethod
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def read_input_images(input_dir):
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""" Get the image paths """
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if not os.path.isdir(input_dir):
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print('Error: {} does not exist'.format(input_dir))
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exit(1)
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if not os.listdir(input_dir):
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print('Error: {} contains no images'.format(input_dir))
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exit(1)
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return get_image_paths(input_dir)
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def get_image_data(self, input_images, batch_size):
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""" Get training images """
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random_transform_args = {
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'rotation_range': 0,
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'zoom_range': 0,
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'shift_range': 0,
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'random_flip': 0
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}
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zoom = 1
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if hasattr(self.trainer.model, 'IMAGE_SHAPE'):
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zoom = self.trainer.model.IMAGE_SHAPE[0] // 64
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generator = TrainingDataGenerator(random_transform_args, 160, zoom)
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batch = generator.minibatchAB(input_images, batch_size,
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doShuffle=False)
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return next(batch)[2]
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def work(self):
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""" Write out timelapse image """
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image = self.trainer.show_sample(self.images_a, self.images_b)
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cv2.imwrite(os.path.join(self.output_dir,
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str(int(time())) + ".png"), image)
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