def load_aligned(self, image, size=256, align_eyes=False, dtype=None): """ No need to load aligned information for all uses of this class, so only call this to load the information for easy reference to aligned properties for this face """ # Don't reload an already aligned face: if self.aligned: logger.trace( "Skipping alignment calculation for already aligned face") else: logger.trace( "Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)", size, align_eyes, dtype) padding = int(size * self.extract_ratio) // 2 self.aligned["size"] = size self.aligned["padding"] = padding self.aligned["align_eyes"] = align_eyes self.aligned["matrix"] = get_align_mat(self, size, align_eyes) self.aligned["face"] = None if image is not None and self.aligned["face"] is None: logger.trace("Getting aligned face") face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding) self.aligned["face"] = face if dtype is None else face.astype( dtype) logger.trace( "Loaded aligned face: %s", {key: val for key, val in self.aligned.items() if key != "face"})
def load_reference_face(self, image, size=64, coverage_ratio=0.625, dtype=None): """ Return a face in the correct dimensions for reference to the output from a NN Coverage ratio should be the ratio of the extracted image that was used for training """ logger.trace( "Loading reference face: (size: %s, coverage_ratio: %s, dtype: %s)", size, coverage_ratio, dtype) self.reference["size"] = size self.reference["padding"] = self.padding_from_coverage( size, coverage_ratio) self.reference["matrix"] = get_align_mat(self, size, should_align_eyes=False) face = np.clip( AlignerExtract().transform(image, self.reference["matrix"], size, self.reference["padding"])[:, :, :3] / 255.0, 0.0, 1.0) self.reference["face"] = face if dtype is None else face.astype(dtype) logger.trace("Loaded reference face. (face_shape: %s, matrix: %s)", self.reference_face.shape, self.reference_matrix)
def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None): """ Align a face in the correct dimensions for feeding into a model. Parameters ---------- image: numpy.ndarray The image that contains the face to be aligned size: int The size of the face in pixels to be fed into the model coverage_ratio: float, optional the ratio of the extracted image that was used for training. Default: `0.625` dtype: str, optional Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None`` Notes ----- This method must be executed to get access to the following `properties`: - :func:`feed_face` - :func:`feed_interpolators` """ logger.trace("Loading feed face: (size: %s, coverage_ratio: %s, dtype: %s)", size, coverage_ratio, dtype) self.feed["size"] = size self.feed["padding"] = self._padding_from_coverage(size, coverage_ratio) self.feed["matrix"] = get_align_mat(self) face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"]) face = np.clip(face[:, :, :3] / 255., 0., 1.) self.feed["face"] = face if dtype is None else face.astype(dtype) logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)", self.feed_face.shape, self._feed_matrix)
def extract(self, image, face, size): if face.landmarks == None: print("Warning! landmarks not found. Switching to crop!") return cv2.resize(face.image, (size, size)) alignment = get_align_mat( face ) return self.transform( image, alignment, size, 48 )
def extract(self, image, face, size): if face.landmarks == None: print("Warning! landmarks not found. Switching to crop!") return cv2.resize(face.image, (size, size)) alignment = get_align_mat(face) return self.transform(image, alignment, size, 48)
def load_aligned(self, image, size=256, padding=48, align_eyes=False): """ No need to load aligned information for all uses of this class, so only call this to load the information for easy reference to aligned properties for this face """ self.aligned["size"] = size self.aligned["padding"] = padding self.aligned["align_eyes"] = align_eyes self.aligned["matrix"] = get_align_mat(self, size, align_eyes) self.aligned["face"] = AlignerExtract().transform( image, self.aligned["matrix"], size, padding)
def load_aligned(self, image, size=256, align_eyes=False, dtype=None): """ Align a face from a given image. Aligning a face is a relatively expensive task and is not required for all uses of the :class:`~lib.faces_detect.DetectedFace` object, so call this function explicitly to load an aligned face. This method plugs into :mod:`lib.aligner` to perform face alignment based on this face's ``landmarks_xy``. If the face has already been aligned, then this function will return having performed no action. Parameters ---------- image: numpy.ndarray The image that contains the face to be aligned size: int The size of the output face in pixels align_eyes: bool, optional Optionally perform additional alignment to align eyes. Default: `False` dtype: str, optional Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None`` Notes ----- This method must be executed to get access to the following `properties`: - :func:`original_roi` - :func:`aligned_landmarks` - :func:`aligned_face` - :func:`adjusted_interpolators` """ if self.aligned: # Don't reload an already aligned face logger.trace( "Skipping alignment calculation for already aligned face") else: logger.trace( "Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)", size, align_eyes, dtype) padding = int(size * self._extract_ratio) // 2 self.aligned["size"] = size self.aligned["padding"] = padding self.aligned["align_eyes"] = align_eyes self.aligned["matrix"] = get_align_mat(self, size, align_eyes) self.aligned["face"] = None if image is not None and self.aligned["face"] is None: logger.trace("Getting aligned face") face = AlignerExtract().transform(image, self.aligned["matrix"], size, padding) self.aligned["face"] = face if dtype is None else face.astype( dtype) logger.trace( "Loaded aligned face: %s", {key: val for key, val in self.aligned.items() if key != "face"})
def patch_image( self, image, face_detected): size = 64 image_size = image.shape[1], image.shape[0] mat = numpy.array(get_align_mat(face_detected)).reshape(2,3) * size new_face = self.get_new_face(image,mat,size) image_mask = self.get_image_mask( image, new_face, face_detected, mat, image_size ) return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def patch_image( self, image, face_detected ): size = 64 image_size = image.shape[1], image.shape[0] mat = numpy.array(get_align_mat(face_detected)).reshape(2,3) * size new_face = self.get_new_face(image,mat,size) image_mask = self.get_image_mask( image, new_face, face_detected, mat, image_size ) return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None, is_aligned_face=False): """ Align a face in the correct dimensions for feeding into a model. Parameters ---------- image: numpy.ndarray The image that contains the face to be aligned size: int The size of the face in pixels to be fed into the model coverage_ratio: float, optional the ratio of the extracted image that was used for training. Default: `0.625` dtype: str, optional Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None`` is_aligned_face: bool, optional Indicates that the :attr:`image` is an aligned face rather than a frame. Default: ``False`` Notes ----- This method must be executed to get access to the following `properties`: - :func:`feed_face` - :func:`feed_interpolators` """ logger.trace( "Loading feed face: (size: %s, coverage_ratio: %s, dtype: %s, " "is_aligned_face: %s)", size, coverage_ratio, dtype, is_aligned_face) self.feed["size"] = size self.feed["padding"] = self._padding_from_coverage( size, coverage_ratio) self.feed["matrix"] = get_align_mat(self) if is_aligned_face: original_size = image.shape[0] interp = cv2.INTER_CUBIC if original_size < size else cv2.INTER_AREA face = cv2.resize(image, (size, size), interpolation=interp) else: face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"]) self.feed["face"] = face if dtype is None else face.astype(dtype) logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)", self.feed_face.shape, self.feed_matrix)
def load_aligned(self, image, size=256, padding=48, align_eyes=False): """ No need to load aligned information for all uses of this class, so only call this to load the information for easy reference to aligned properties for this face """ logger.trace( "Loading aligned face: (size: %s, padding: %s, align_eyes: %s)", size, padding, align_eyes) self.aligned["size"] = size self.aligned["padding"] = padding self.aligned["align_eyes"] = align_eyes self.aligned["matrix"] = get_align_mat(self, size, align_eyes) self.aligned["face"] = AlignerExtract().transform( image, self.aligned["matrix"], size, padding) logger.trace( "Loaded aligned face: %s", {key: val for key, val in self.aligned.items() if key != "face"})
def patch_image(self, image, face_detected, size): image_size = image.shape[1], image.shape[0] mat = numpy.array( get_align_mat(face_detected, size, should_align_eyes=False)).reshape(2, 3) mat = mat * size new_face = self.get_new_face(image, mat, size) image_mask = self.get_image_mask(image, new_face, face_detected.landmarksAsXY(), mat, image_size) return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def patch_image( self, image, face_detected, size ): image_size = image.shape[1], image.shape[0] mat = numpy.array(get_align_mat(face_detected, size, should_align_eyes=False)).reshape(2,3) if "GAN" not in self.trainer: mat = mat * size else: padding = int(48/256*size) mat = mat * (size - 2 * padding) mat[:,2] += padding new_face = self.get_new_face(image,mat,size) image_mask = self.get_image_mask( image, new_face, face_detected.landmarks_as_xy(), mat, image_size ) return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def load_reference_face(self, image, size=64, coverage_ratio=0.625, dtype=None): """ Return a face in the correct dimensions for reference to the output from a NN Coverage ratio should be the ratio of the extracted image that was used for training """ logger.trace("Loading reference face: (size: %s, coverage_ratio: %s, dtype: %s)", size, coverage_ratio, dtype) self.reference["size"] = size self.reference["padding"] = self.padding_from_coverage(size, coverage_ratio) self.reference["matrix"] = get_align_mat(self, size, should_align_eyes=False) face = np.clip(AlignerExtract().transform(image, self.reference["matrix"], size, self.reference["padding"])[:, :, :3] / 255.0, 0.0, 1.0) self.reference["face"] = face if dtype is None else face.astype(dtype) logger.trace("Loaded reference face. (face_shape: %s, matrix: %s)", self.reference_face.shape, self.reference_matrix)
def patch_image(self, image, face_detected, size): image_size = image.shape[1], image.shape[0] mat = numpy.array( get_align_mat(face_detected, size, should_align_eyes=False)).reshape(2, 3) if "GAN" not in self.trainer: mat = mat * size else: padding = int(48 / 256 * size) mat = mat * (size - 2 * padding) mat[:, 2] += padding new_face = self.get_new_face(image, mat, size) image_mask = self.get_image_mask(image, new_face, face_detected.landmarks_as_xy(), mat, image_size) return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def load_aligned(self, image, size=256, align_eyes=False, dtype=None): """ No need to load aligned information for all uses of this class, so only call this to load the information for easy reference to aligned properties for this face """ logger.trace("Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)", size, align_eyes, dtype) padding = int(size * self.extract_ratio) // 2 self.aligned["size"] = size self.aligned["padding"] = padding self.aligned["align_eyes"] = align_eyes self.aligned["matrix"] = get_align_mat(self, size, align_eyes) if image is None: self.aligned["face"] = None else: face = AlignerExtract().transform( image, self.aligned["matrix"], size, padding) self.aligned["face"] = face if dtype is None else face.astype(dtype) logger.trace("Loaded aligned face: %s", {key: val for key, val in self.aligned.items() if key != "face"})
def extract(self, image, face, size, align_eyes): """ Extract a face from an image """ alignment = get_align_mat(face, size, align_eyes) extracted = self.transform(image, alignment, size, 48) return extracted, alignment
def extract(self, image, face, size): if face.landmarks == None: return cv2.resize(face.image, (size, size)) alignment = get_align_mat(face) return self.transform(image, alignment, size, 48)
def extract(self, image, face, size): alignment = get_align_mat(face) return self.transform(image, alignment, size, 48)
def extract(self, image, face, size, align_eyes): alignment = get_align_mat(face, size, align_eyes) extracted = self.transform(image, alignment, size, 48) return extracted, alignment