mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
541498970e
Only show model folder deprecation warning for existing models Gracefully exit FixedProducerDispatcher on error
517 lines
23 KiB
Python
517 lines
23 KiB
Python
#!/usr/bin/env python3
|
|
""" Process training data for model training """
|
|
|
|
import logging
|
|
|
|
from hashlib import sha1
|
|
from random import random, shuffle, choice
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from scipy.interpolate import griddata
|
|
|
|
from lib.model import masks
|
|
from lib.multithreading import FixedProducerDispatcher
|
|
from lib.queue_manager import queue_manager
|
|
from lib.umeyama import umeyama
|
|
from lib.utils import cv2_read_img, FaceswapError
|
|
|
|
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
class TrainingDataGenerator():
|
|
""" Generate training data for models """
|
|
def __init__(self, model_input_size, model_output_size, training_opts, config):
|
|
logger.debug("Initializing %s: (model_input_size: %s, model_output_shape: %s, "
|
|
"training_opts: %s, landmarks: %s, config: %s)",
|
|
self.__class__.__name__, model_input_size, model_output_size,
|
|
{key: val for key, val in training_opts.items() if key != "landmarks"},
|
|
bool(training_opts.get("landmarks", None)), config)
|
|
self.batchsize = 0
|
|
self.model_input_size = model_input_size
|
|
self.model_output_size = model_output_size
|
|
self.training_opts = training_opts
|
|
self.mask_class = self.set_mask_class()
|
|
self.landmarks = self.training_opts.get("landmarks", None)
|
|
self.fixed_producer_dispatcher = None # Set by FPD when loading
|
|
self._nearest_landmarks = None
|
|
self.processing = ImageManipulation(model_input_size,
|
|
model_output_size,
|
|
training_opts.get("coverage_ratio", 0.625),
|
|
config)
|
|
logger.debug("Initialized %s", self.__class__.__name__)
|
|
|
|
def set_mask_class(self):
|
|
""" Set the mask function to use if using mask """
|
|
mask_type = self.training_opts.get("mask_type", None)
|
|
if mask_type:
|
|
logger.debug("Mask type: '%s'", mask_type)
|
|
mask_class = getattr(masks, mask_type)
|
|
else:
|
|
mask_class = None
|
|
logger.debug("Mask class: %s", mask_class)
|
|
return mask_class
|
|
|
|
def minibatch_ab(self, images, batchsize, side,
|
|
do_shuffle=True, is_preview=False, is_timelapse=False):
|
|
""" Keep a queue filled to 8x Batch Size """
|
|
logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
|
|
"is_preview, %s, is_timelapse: %s)", len(images), batchsize, side, do_shuffle,
|
|
is_preview, is_timelapse)
|
|
self.batchsize = batchsize
|
|
is_display = is_preview or is_timelapse
|
|
queue_in, queue_out = self.make_queues(side, is_preview, is_timelapse)
|
|
training_size = self.training_opts.get("training_size", 256)
|
|
batch_shape = list((
|
|
(batchsize, training_size, training_size, 3), # sample images
|
|
(batchsize, self.model_input_size, self.model_input_size, 3),
|
|
(batchsize, self.model_output_size, self.model_output_size, 3)))
|
|
if self.mask_class:
|
|
batch_shape.append((self.batchsize, self.model_output_size, self.model_output_size, 1))
|
|
|
|
self.fixed_producer_dispatcher = FixedProducerDispatcher(
|
|
method=self.load_batches,
|
|
shapes=batch_shape,
|
|
in_queue=queue_in,
|
|
out_queue=queue_out,
|
|
args=(images, side, is_display, do_shuffle, batchsize))
|
|
self.fixed_producer_dispatcher.start()
|
|
logger.debug("Batching to queue: (side: '%s', is_display: %s)", side, is_display)
|
|
return self.minibatch(side, is_display, self.fixed_producer_dispatcher)
|
|
|
|
def join_subprocess(self):
|
|
""" Join the FixedProduceerDispatcher subprocess from outside this module """
|
|
logger.debug("Joining FixedProducerDispatcher")
|
|
if self.fixed_producer_dispatcher is None:
|
|
logger.debug("FixedProducerDispatcher not yet initialized. Exiting")
|
|
return
|
|
self.fixed_producer_dispatcher.join()
|
|
logger.debug("Joined FixedProducerDispatcher")
|
|
|
|
@staticmethod
|
|
def make_queues(side, is_preview, is_timelapse):
|
|
""" Create the buffer token queues for Fixed Producer Dispatcher """
|
|
q_name = "_{}".format(side)
|
|
if is_preview:
|
|
q_name = "{}{}".format("preview", q_name)
|
|
elif is_timelapse:
|
|
q_name = "{}{}".format("timelapse", q_name)
|
|
else:
|
|
q_name = "{}{}".format("train", q_name)
|
|
q_names = ["{}_{}".format(q_name, direction) for direction in ("in", "out")]
|
|
logger.debug(q_names)
|
|
queues = [queue_manager.get_queue(queue) for queue in q_names]
|
|
return queues
|
|
|
|
def load_batches(self, mem_gen, images, side, is_display,
|
|
do_shuffle=True, batchsize=0):
|
|
""" Load the warped images and target images to queue """
|
|
logger.debug("Loading batch: (image_count: %s, side: '%s', is_display: %s, "
|
|
"do_shuffle: %s)", len(images), side, is_display, do_shuffle)
|
|
self.validate_samples(images)
|
|
# Intialize this for each subprocess
|
|
self._nearest_landmarks = dict()
|
|
|
|
def _img_iter(imgs):
|
|
while True:
|
|
if do_shuffle:
|
|
shuffle(imgs)
|
|
for img in imgs:
|
|
yield img
|
|
|
|
img_iter = _img_iter(images)
|
|
epoch = 0
|
|
for memory_wrapper in mem_gen:
|
|
memory = memory_wrapper.get()
|
|
logger.trace("Putting to batch queue: (side: '%s', is_display: %s)",
|
|
side, is_display)
|
|
for i, img_path in enumerate(img_iter):
|
|
imgs = self.process_face(img_path, side, is_display)
|
|
for j, img in enumerate(imgs):
|
|
memory[j][i][:] = img
|
|
epoch += 1
|
|
if i == batchsize - 1:
|
|
break
|
|
memory_wrapper.ready()
|
|
logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
|
|
epoch, side, is_display)
|
|
|
|
def validate_samples(self, data):
|
|
""" Check the total number of images against batchsize and return
|
|
the total number of images """
|
|
length = len(data)
|
|
msg = ("Number of images is lower than batch-size (Note that too few "
|
|
"images may lead to bad training). # images: {}, "
|
|
"batch-size: {}".format(length, self.batchsize))
|
|
try:
|
|
assert length >= self.batchsize, msg
|
|
except AssertionError as err:
|
|
msg += ("\nYou should increase the number of images in your training set or lower "
|
|
"your batch-size.")
|
|
raise FaceswapError(msg) from err
|
|
|
|
@staticmethod
|
|
def minibatch(side, is_display, load_process):
|
|
""" A generator function that yields epoch, batchsize of warped_img
|
|
and batchsize of target_img from the load queue """
|
|
logger.debug("Launching minibatch generator for queue (side: '%s', is_display: %s)",
|
|
side, is_display)
|
|
for batch_wrapper in load_process:
|
|
with batch_wrapper as batch:
|
|
logger.trace("Yielding batch: (size: %s, item shapes: %s, side: '%s', "
|
|
"is_display: %s)",
|
|
len(batch), [item.shape for item in batch], side, is_display)
|
|
yield batch
|
|
load_process.stop()
|
|
logger.debug("Finished minibatch generator for queue: (side: '%s', is_display: %s)",
|
|
side, is_display)
|
|
load_process.join()
|
|
|
|
def process_face(self, filename, side, is_display):
|
|
""" Load an image and perform transformation and warping """
|
|
logger.trace("Process face: (filename: '%s', side: '%s', is_display: %s)",
|
|
filename, side, is_display)
|
|
image = cv2_read_img(filename, raise_error=True)
|
|
if self.mask_class or self.training_opts["warp_to_landmarks"]:
|
|
src_pts = self.get_landmarks(filename, image, side)
|
|
if self.mask_class:
|
|
image = self.mask_class(src_pts, image, channels=4).mask
|
|
|
|
image = self.processing.color_adjust(image,
|
|
self.training_opts["augment_color"],
|
|
is_display)
|
|
|
|
if not is_display:
|
|
image = self.processing.random_transform(image)
|
|
if not self.training_opts["no_flip"]:
|
|
image = self.processing.do_random_flip(image)
|
|
sample = image.copy()[:, :, :3]
|
|
|
|
if self.training_opts["warp_to_landmarks"]:
|
|
dst_pts = self.get_closest_match(filename, side, src_pts)
|
|
processed = self.processing.random_warp_landmarks(image, src_pts, dst_pts)
|
|
else:
|
|
processed = self.processing.random_warp(image)
|
|
|
|
processed.insert(0, sample)
|
|
logger.trace("Processed face: (filename: '%s', side: '%s', shapes: %s)",
|
|
filename, side, [img.shape for img in processed])
|
|
return processed
|
|
|
|
def get_landmarks(self, filename, image, side):
|
|
""" Return the landmarks for this face """
|
|
logger.trace("Retrieving landmarks: (filename: '%s', side: '%s'", filename, side)
|
|
lm_key = sha1(image).hexdigest()
|
|
try:
|
|
src_points = self.landmarks[side][lm_key]
|
|
except KeyError as err:
|
|
msg = ("At least one of your images does not have a matching entry in your alignments "
|
|
"file."
|
|
"\nIf you are training with a mask or using 'warp to landmarks' then every "
|
|
"face you intend to train on must exist within the alignments file."
|
|
"\nThe specific file that caused the failure was '{}' which has a hash of {}."
|
|
"\nMost likely there will be more than just this file missing from the "
|
|
"alignments file. You can use the Alignments Tool to help identify missing "
|
|
"alignments".format(lm_key, filename))
|
|
raise FaceswapError(msg) from err
|
|
logger.trace("Returning: (src_points: %s)", src_points)
|
|
return src_points
|
|
|
|
def get_closest_match(self, filename, side, src_points):
|
|
""" Return closest matched landmarks from opposite set """
|
|
logger.trace("Retrieving closest matched landmarks: (filename: '%s', src_points: '%s'",
|
|
filename, src_points)
|
|
landmarks = self.landmarks["a"] if side == "b" else self.landmarks["b"]
|
|
closest_hashes = self._nearest_landmarks.get(filename)
|
|
if not closest_hashes:
|
|
dst_points_items = list(landmarks.items())
|
|
dst_points = list(x[1] for x in dst_points_items)
|
|
closest = (np.mean(np.square(src_points - dst_points),
|
|
axis=(1, 2))).argsort()[:10]
|
|
closest_hashes = tuple(dst_points_items[i][0] for i in closest)
|
|
self._nearest_landmarks[filename] = closest_hashes
|
|
dst_points = landmarks[choice(closest_hashes)]
|
|
logger.trace("Returning: (dst_points: %s)", dst_points)
|
|
return dst_points
|
|
|
|
|
|
class ImageManipulation():
|
|
""" Manipulations to be performed on training images """
|
|
def __init__(self, input_size, output_size, coverage_ratio, config):
|
|
""" input_size: Size of the face input into the model
|
|
output_size: Size of the face that comes out of the modell
|
|
coverage_ratio: Coverage ratio of full image. Eg: 256 * 0.625 = 160
|
|
"""
|
|
logger.debug("Initializing %s: (input_size: %s, output_size: %s, coverage_ratio: %s, "
|
|
"config: %s)", self.__class__.__name__, input_size, output_size,
|
|
coverage_ratio, config)
|
|
self.config = config
|
|
# Transform and Warp args
|
|
self.input_size = input_size
|
|
self.output_size = output_size
|
|
# Warp args
|
|
self.coverage_ratio = coverage_ratio # Coverage ratio of full image. Eg: 256 * 0.625 = 160
|
|
self.scale = 5 # Normal random variable scale
|
|
logger.debug("Initialized %s", self.__class__.__name__)
|
|
|
|
def color_adjust(self, img, augment_color, is_display):
|
|
""" Color adjust RGB image """
|
|
logger.trace("Color adjusting image")
|
|
if not is_display and augment_color:
|
|
logger.trace("Augmenting color")
|
|
face, _ = self.separate_mask(img)
|
|
face = face.astype("uint8")
|
|
face = self.random_clahe(face)
|
|
face = self.random_lab(face)
|
|
img[:, :, :3] = face
|
|
return img.astype('float32') / 255.0
|
|
|
|
def random_clahe(self, image):
|
|
""" Randomly perform Contrast Limited Adaptive Histogram Equilization """
|
|
contrast_random = random()
|
|
if contrast_random > self.config.get("color_clahe_chance", 50) / 100:
|
|
return image
|
|
|
|
base_contrast = image.shape[0] // 128
|
|
grid_base = random() * self.config.get("color_clahe_max_size", 4)
|
|
contrast_adjustment = int(grid_base * (base_contrast / 2))
|
|
grid_size = base_contrast + contrast_adjustment
|
|
logger.trace("Adjusting Contrast. Grid Size: %s", grid_size)
|
|
|
|
clahe = cv2.createCLAHE(clipLimit=2.0, # pylint: disable=no-member
|
|
tileGridSize=(grid_size, grid_size))
|
|
for chan in range(3):
|
|
image[:, :, chan] = clahe.apply(image[:, :, chan])
|
|
return image
|
|
|
|
def random_lab(self, image):
|
|
""" Perform random color/lightness adjustment in L*a*b* colorspace """
|
|
amount_l = self.config.get("color_lightness", 30) / 100
|
|
amount_ab = self.config.get("color_ab", 8) / 100
|
|
|
|
randoms = [(random() * amount_l * 2) - amount_l, # L adjust
|
|
(random() * amount_ab * 2) - amount_ab, # A adjust
|
|
(random() * amount_ab * 2) - amount_ab] # B adjust
|
|
|
|
logger.trace("Random LAB adjustments: %s", randoms)
|
|
image = cv2.cvtColor( # pylint:disable=no-member
|
|
image, cv2.COLOR_BGR2LAB).astype("float32") / 255.0 # pylint:disable=no-member
|
|
|
|
for idx, adjustment in enumerate(randoms):
|
|
if adjustment >= 0:
|
|
image[:, :, idx] = ((1 - image[:, :, idx]) * adjustment) + image[:, :, idx]
|
|
else:
|
|
image[:, :, idx] = image[:, :, idx] * (1 + adjustment)
|
|
image = cv2.cvtColor((image * 255.0).astype("uint8"), # pylint:disable=no-member
|
|
cv2.COLOR_LAB2BGR) # pylint:disable=no-member
|
|
return image
|
|
|
|
@staticmethod
|
|
def separate_mask(image):
|
|
""" Return the image and the mask from a 4 channel image """
|
|
mask = None
|
|
if image.shape[2] == 4:
|
|
logger.trace("Image contains mask")
|
|
mask = np.expand_dims(image[:, :, -1], axis=2)
|
|
image = image[:, :, :3]
|
|
else:
|
|
logger.trace("Image has no mask")
|
|
return image, mask
|
|
|
|
def get_coverage(self, image):
|
|
""" Return coverage value for given image """
|
|
coverage = int(image.shape[0] * self.coverage_ratio)
|
|
logger.trace("Coverage: %s", coverage)
|
|
return coverage
|
|
|
|
def random_transform(self, image):
|
|
""" Randomly transform an image """
|
|
logger.trace("Randomly transforming image")
|
|
height, width = image.shape[0:2]
|
|
|
|
rotation_range = self.config.get("rotation_range", 10)
|
|
rotation = np.random.uniform(-rotation_range, rotation_range)
|
|
|
|
zoom_range = self.config.get("zoom_range", 5) / 100
|
|
scale = np.random.uniform(1 - zoom_range, 1 + zoom_range)
|
|
|
|
shift_range = self.config.get("shift_range", 5) / 100
|
|
tnx = np.random.uniform(-shift_range, shift_range) * width
|
|
tny = np.random.uniform(-shift_range, shift_range) * height
|
|
|
|
mat = cv2.getRotationMatrix2D( # pylint:disable=no-member
|
|
(width // 2, height // 2), rotation, scale)
|
|
mat[:, 2] += (tnx, tny)
|
|
result = cv2.warpAffine( # pylint:disable=no-member
|
|
image, mat, (width, height),
|
|
borderMode=cv2.BORDER_REPLICATE) # pylint:disable=no-member
|
|
|
|
logger.trace("Randomly transformed image")
|
|
return result
|
|
|
|
def do_random_flip(self, image):
|
|
""" Perform flip on image if random number is within threshold """
|
|
logger.trace("Randomly flipping image")
|
|
random_flip = self.config.get("random_flip", 50) / 100
|
|
if np.random.random() < random_flip:
|
|
logger.trace("Flip within threshold. Flipping")
|
|
retval = image[:, ::-1]
|
|
else:
|
|
logger.trace("Flip outside threshold. Not Flipping")
|
|
retval = image
|
|
logger.trace("Randomly flipped image")
|
|
return retval
|
|
|
|
def random_warp(self, image):
|
|
""" get pair of random warped images from aligned face image """
|
|
logger.trace("Randomly warping image")
|
|
height, width = image.shape[0:2]
|
|
coverage = self.get_coverage(image)
|
|
try:
|
|
assert height == width and height % 2 == 0
|
|
except AssertionError as err:
|
|
msg = ("Training images should be square with an even number of pixels across each "
|
|
"side. An image was found with width: {}, height: {}."
|
|
"\nMost likely this is a frame rather than a face within your training set. "
|
|
"\nMake sure that the only images within your training set are faces generated "
|
|
"from the Extract process.".format(width, height))
|
|
raise FaceswapError(msg) from err
|
|
|
|
range_ = np.linspace(height // 2 - coverage // 2,
|
|
height // 2 + coverage // 2,
|
|
5, dtype='float32')
|
|
mapx = np.broadcast_to(range_, (5, 5)).copy()
|
|
mapy = mapx.T
|
|
# mapx, mapy = np.float32(np.meshgrid(range_,range_)) # instead of broadcast
|
|
|
|
pad = int(1.25 * self.input_size)
|
|
slices = slice(pad // 10, -pad // 10)
|
|
dst_slice = slice(0, (self.output_size + 1), (self.output_size // 4))
|
|
interp = np.empty((2, self.input_size, self.input_size), dtype='float32')
|
|
####
|
|
|
|
for i, map_ in enumerate([mapx, mapy]):
|
|
map_ = map_ + np.random.normal(size=(5, 5), scale=self.scale)
|
|
interp[i] = cv2.resize(map_, (pad, pad))[slices, slices] # pylint:disable=no-member
|
|
|
|
warped_image = cv2.remap( # pylint:disable=no-member
|
|
image, interp[0], interp[1], cv2.INTER_LINEAR) # pylint:disable=no-member
|
|
logger.trace("Warped image shape: %s", warped_image.shape)
|
|
|
|
src_points = np.stack([mapx.ravel(), mapy.ravel()], axis=-1)
|
|
dst_points = np.mgrid[dst_slice, dst_slice]
|
|
mat = umeyama(src_points, True, dst_points.T.reshape(-1, 2))[0:2]
|
|
target_image = cv2.warpAffine( # pylint:disable=no-member
|
|
image, mat, (self.output_size, self.output_size))
|
|
logger.trace("Target image shape: %s", target_image.shape)
|
|
|
|
warped_image, warped_mask = self.separate_mask(warped_image)
|
|
target_image, target_mask = self.separate_mask(target_image)
|
|
|
|
if target_mask is None:
|
|
logger.trace("Randomly warped image")
|
|
return [warped_image, target_image]
|
|
|
|
logger.trace("Target mask shape: %s", target_mask.shape)
|
|
logger.trace("Randomly warped image and mask")
|
|
return [warped_image, target_image, target_mask]
|
|
|
|
def random_warp_landmarks(self, image, src_points=None, dst_points=None):
|
|
""" get warped image, target image and target mask
|
|
From DFAKER plugin """
|
|
logger.trace("Randomly warping landmarks")
|
|
size = image.shape[0]
|
|
coverage = self.get_coverage(image)
|
|
|
|
p_mx = size - 1
|
|
p_hf = (size // 2) - 1
|
|
|
|
edge_anchors = [(0, 0), (0, p_mx), (p_mx, p_mx), (p_mx, 0),
|
|
(p_hf, 0), (p_hf, p_mx), (p_mx, p_hf), (0, p_hf)]
|
|
grid_x, grid_y = np.mgrid[0:p_mx:complex(size), 0:p_mx:complex(size)]
|
|
|
|
source = src_points
|
|
destination = (dst_points.copy().astype('float32') +
|
|
np.random.normal(size=dst_points.shape, scale=2.0))
|
|
destination = destination.astype('uint8')
|
|
|
|
face_core = cv2.convexHull(np.concatenate( # pylint:disable=no-member
|
|
[source[17:], destination[17:]], axis=0).astype(int))
|
|
|
|
source = [(pty, ptx) for ptx, pty in source] + edge_anchors
|
|
destination = [(pty, ptx) for ptx, pty in destination] + edge_anchors
|
|
|
|
indicies_to_remove = set()
|
|
for fpl in source, destination:
|
|
for idx, (pty, ptx) in enumerate(fpl):
|
|
if idx > 17:
|
|
break
|
|
elif cv2.pointPolygonTest(face_core, # pylint:disable=no-member
|
|
(pty, ptx),
|
|
False) >= 0:
|
|
indicies_to_remove.add(idx)
|
|
|
|
for idx in sorted(indicies_to_remove, reverse=True):
|
|
source.pop(idx)
|
|
destination.pop(idx)
|
|
|
|
grid_z = griddata(destination, source, (grid_x, grid_y), method="linear")
|
|
map_x = np.append([], [ar[:, 1] for ar in grid_z]).reshape(size, size)
|
|
map_y = np.append([], [ar[:, 0] for ar in grid_z]).reshape(size, size)
|
|
map_x_32 = map_x.astype('float32')
|
|
map_y_32 = map_y.astype('float32')
|
|
|
|
warped_image = cv2.remap(image, # pylint:disable=no-member
|
|
map_x_32,
|
|
map_y_32,
|
|
cv2.INTER_LINEAR, # pylint:disable=no-member
|
|
cv2.BORDER_TRANSPARENT) # pylint:disable=no-member
|
|
target_image = image
|
|
|
|
# TODO Make sure this replacement is correct
|
|
slices = slice(size // 2 - coverage // 2, size // 2 + coverage // 2)
|
|
# slices = slice(size // 32, size - size // 32) # 8px on a 256px image
|
|
warped_image = cv2.resize( # pylint:disable=no-member
|
|
warped_image[slices, slices, :], (self.input_size, self.input_size),
|
|
cv2.INTER_AREA) # pylint:disable=no-member
|
|
logger.trace("Warped image shape: %s", warped_image.shape)
|
|
target_image = cv2.resize( # pylint:disable=no-member
|
|
target_image[slices, slices, :], (self.output_size, self.output_size),
|
|
cv2.INTER_AREA) # pylint:disable=no-member
|
|
logger.trace("Target image shape: %s", target_image.shape)
|
|
|
|
warped_image, warped_mask = self.separate_mask(warped_image)
|
|
target_image, target_mask = self.separate_mask(target_image)
|
|
|
|
if target_mask is None:
|
|
logger.trace("Randomly warped image")
|
|
return [warped_image, target_image]
|
|
|
|
logger.trace("Target mask shape: %s", target_mask.shape)
|
|
logger.trace("Randomly warped image and mask")
|
|
return [warped_image, target_image, target_mask]
|
|
|
|
|
|
def stack_images(images):
|
|
""" Stack images """
|
|
logger.debug("Stack images")
|
|
|
|
def get_transpose_axes(num):
|
|
if num % 2 == 0:
|
|
logger.debug("Even number of images to stack")
|
|
y_axes = list(range(1, num - 1, 2))
|
|
x_axes = list(range(0, num - 1, 2))
|
|
else:
|
|
logger.debug("Odd number of images to stack")
|
|
y_axes = list(range(0, num - 1, 2))
|
|
x_axes = list(range(1, num - 1, 2))
|
|
return y_axes, x_axes, [num - 1]
|
|
|
|
images_shape = np.array(images.shape)
|
|
new_axes = get_transpose_axes(len(images_shape))
|
|
new_shape = [np.prod(images_shape[x]) for x in new_axes]
|
|
logger.debug("Stacked images")
|
|
return np.transpose(
|
|
images,
|
|
axes=np.concatenate(new_axes)
|
|
).reshape(new_shape)
|