Optimize Data Augmentation (#881)

* Move image utils to lib.image
* Add .pylintrc file
* Remove some cv2 pylint ignores
* TrainingData: Load images from disk in batches
* TrainingData: get_landmarks to batch
* TrainingData: transform and flip to batches
* TrainingData: Optimize color augmentation
* TrainingData: Optimize target and random_warp
* TrainingData - Convert _get_closest_match for batching
* TrainingData: Warp To Landmarks optimized
* Save models to threadpoolexecutor
* Move stack_images, Rename ImageManipulation. ImageAugmentation Docstrings
* Masks: Set dtype and threshold for lib.masks based on input face
* Docstrings and Documentation

.gitignore

@@ -28,6 +28,7 @@
 !plugins/extract/*
 !plugins/train/*
 !plugins/convert/*
+!.pylintrc
 !tools
 !tools/lib*
 !_travis

.pylintrc

@@ -0,0 +1,570 @@
+[MASTER]
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-whitelist=cv2
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS
+
+# Add files or directories matching the regex patterns to the blacklist. The
+# regex matches against base names, not paths.
+ignore-patterns=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Specify a configuration file.
+#rcfile=
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
+confidence=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=print-statement,
+        parameter-unpacking,
+        unpacking-in-except,
+        old-raise-syntax,
+        backtick,
+        long-suffix,
+        old-ne-operator,
+        old-octal-literal,
+        import-star-module-level,
+        non-ascii-bytes-literal,
+        raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-symbolic-message-instead,
+        apply-builtin,
+        basestring-builtin,
+        buffer-builtin,
+        cmp-builtin,
+        coerce-builtin,
+        execfile-builtin,
+        file-builtin,
+        long-builtin,
+        raw_input-builtin,
+        reduce-builtin,
+        standarderror-builtin,
+        unicode-builtin,
+        xrange-builtin,
+        coerce-method,
+        delslice-method,
+        getslice-method,
+        setslice-method,
+        no-absolute-import,
+        old-division,
+        dict-iter-method,
+        dict-view-method,
+        next-method-called,
+        metaclass-assignment,
+        indexing-exception,
+        raising-string,
+        reload-builtin,
+        oct-method,
+        hex-method,
+        nonzero-method,
+        cmp-method,
+        input-builtin,
+        round-builtin,
+        intern-builtin,
+        unichr-builtin,
+        map-builtin-not-iterating,
+        zip-builtin-not-iterating,
+        range-builtin-not-iterating,
+        filter-builtin-not-iterating,
+        using-cmp-argument,
+        eq-without-hash,
+        div-method,
+        idiv-method,
+        rdiv-method,
+        exception-message-attribute,
+        invalid-str-codec,
+        sys-max-int,
+        bad-python3-import,
+        deprecated-string-function,
+        deprecated-str-translate-call,
+        deprecated-itertools-function,
+        deprecated-types-field,
+        next-method-defined,
+        dict-items-not-iterating,
+        dict-keys-not-iterating,
+        dict-values-not-iterating,
+        deprecated-operator-function,
+        deprecated-urllib-function,
+        xreadlines-attribute,
+        deprecated-sys-function,
+        exception-escape,
+        comprehension-escape
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+
+
+[REPORTS]
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+#msg-template=
+
+# Set the output format. Available formats are text, parseable, colorized, json
+# and msvs (visual studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style.
+#attr-rgx=
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style.
+#class-attribute-rgx=
+
+# Naming style matching correct class names.
+class-naming-style=PascalCase
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style.
+#function-rgx=
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names=i,
+           j,
+           k,
+           ex,
+           Run,
+           _
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style.
+#method-rgx=
+
+# Naming style matching correct module names.
+module-naming-style=snake_case
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style.
+#variable-rgx=
+
+
+[LOGGING]
+
+# Format style used to check logging format string. `old` means using %
+# formatting, while `new` is for `{}` formatting.
+logging-format-style=old
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[SIMILARITIES]
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package..
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# List of optional constructs for which whitespace checking is disabled. `dict-
+# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
+# `trailing-comma` allows a space between comma and closing bracket: (a, ).
+# `empty-line` allows space-only lines.
+no-space-check=trailing-comma,
+               dict-separator
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[STRING]
+
+# This flag controls whether the implicit-str-concat-in-sequence should
+# generate a warning on implicit string concatenation in sequences defined over
+# several lines.
+check-str-concat-over-line-jumps=no
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid defining new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices=1
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement.
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=cls
+
+
+[IMPORTS]
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=optparse,tkinter.tix
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled).
+ext-import-graph=
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled).
+import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "BaseException, Exception".
+overgeneral-exceptions=BaseException,
+                       Exception

docs/full/lib.image.rst

@@ -0,0 +1,7 @@
+lib.image module
+========================
+
+.. automodule:: lib.image
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/full/lib.rst

@@ -8,6 +8,8 @@ Subpackages
 
    lib.model
    lib.faces_detect
+   lib.image
+   lib.training_data
 
 Module contents
 ---------------

docs/full/lib.training_data.rst

@@ -0,0 +1,7 @@
+lib.training\_data module
+=========================
+
+.. automodule:: lib.training_data
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/index.rst

@@ -7,7 +7,7 @@ faceswap.dev Developer Documentation
 ====================================
 
 .. toctree::
-   :maxdepth: 4
+   :maxdepth: 2
    :caption: Contents:
 
    full/modules

lib/alignments.py

@@ -8,8 +8,8 @@ from datetime import datetime
 
 import cv2
 
+from lib.faces_detect import rotate_landmarks
 from lib import Serializer
-from lib.utils import rotate_landmarks
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 

lib/face_filter.py

@@ -4,7 +4,7 @@
 import logging
 
 from lib.vgg_face import VGGFace
-from lib.utils import cv2_read_img
+from lib.image import read_image
 from plugins.extract.pipeline import Extractor
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
@@ -47,10 +47,10 @@ class FaceFilter():
         """ Load the images """
         retval = dict()
         for fpath in reference_file_paths:
-            retval[fpath] = {"image": cv2_read_img(fpath, raise_error=True),
+            retval[fpath] = {"image": read_image(fpath, raise_error=True),
                              "type": "filter"}
         for fpath in nreference_file_paths:
-            retval[fpath] = {"image": cv2_read_img(fpath, raise_error=True),
+            retval[fpath] = {"image": read_image(fpath, raise_error=True),
                              "type": "nfilter"}
         logger.debug("Loaded filter images: %s", {k: v["type"] for k, v in retval.items()})
         return retval

lib/faces_detect.py

@@ -2,6 +2,7 @@
 """ Face and landmarks detection for faceswap.py """
 import logging
 
+import cv2
 import numpy as np
 
 from lib.aligner import Extract as AlignerExtract, get_align_mat, get_matrix_scaling
@@ -399,3 +400,89 @@ class DetectedFace():
         if not self.reference:
             return None
         return get_matrix_scaling(self.reference_matrix)
+
+
+def rotate_landmarks(face, rotation_matrix):
+    """ Rotates the 68 point landmarks and detection bounding box around the given rotation matrix.
+
+    Paramaters
+    ----------
+    face: DetectedFace or dict
+        A :class:`DetectedFace` or an `alignments file` ``dict`` containing the 68 point landmarks
+        and the `x`, `w`, `y`, `h` detection bounding box points.
+    rotation_matrix: numpy.ndarray
+        The rotation matrix to rotate the given object by.
+
+    Returns
+    -------
+    DetectedFace or dict
+        The rotated :class:`DetectedFace` or `alignments file` ``dict`` with the landmarks and
+        detection bounding box points rotated by the given matrix. The return type is the same as
+        the input type for ``face``
+    """
+    logger.trace("Rotating landmarks: (rotation_matrix: %s, type(face): %s",
+                 rotation_matrix, type(face))
+    rotated_landmarks = None
+    # Detected Face Object
+    if isinstance(face, DetectedFace):
+        bounding_box = [[face.x, face.y],
+                        [face.x + face.w, face.y],
+                        [face.x + face.w, face.y + face.h],
+                        [face.x, face.y + face.h]]
+        landmarks = face.landmarks_xy
+
+    # Alignments Dict
+    elif isinstance(face, dict) and "x" in face:
+        bounding_box = [[face.get("x", 0), face.get("y", 0)],
+                        [face.get("x", 0) + face.get("w", 0),
+                         face.get("y", 0)],
+                        [face.get("x", 0) + face.get("w", 0),
+                         face.get("y", 0) + face.get("h", 0)],
+                        [face.get("x", 0),
+                         face.get("y", 0) + face.get("h", 0)]]
+        landmarks = face.get("landmarks_xy", list())
+
+    else:
+        raise ValueError("Unsupported face type")
+
+    logger.trace("Original landmarks: %s", landmarks)
+
+    rotation_matrix = cv2.invertAffineTransform(
+        rotation_matrix)
+    rotated = list()
+    for item in (bounding_box, landmarks):
+        if not item:
+            continue
+        points = np.array(item, np.int32)
+        points = np.expand_dims(points, axis=0)
+        transformed = cv2.transform(points,
+                                    rotation_matrix).astype(np.int32)
+        rotated.append(transformed.squeeze())
+
+    # Bounding box should follow x, y planes, so get min/max
+    # for non-90 degree rotations
+    pt_x = min([pnt[0] for pnt in rotated[0]])
+    pt_y = min([pnt[1] for pnt in rotated[0]])
+    pt_x1 = max([pnt[0] for pnt in rotated[0]])
+    pt_y1 = max([pnt[1] for pnt in rotated[0]])
+    width = pt_x1 - pt_x
+    height = pt_y1 - pt_y
+
+    if isinstance(face, DetectedFace):
+        face.x = int(pt_x)
+        face.y = int(pt_y)
+        face.w = int(width)
+        face.h = int(height)
+        face.r = 0
+        if len(rotated) > 1:
+            rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
+            face.landmarks_xy = rotated_landmarks
+    else:
+        face["left"] = int(pt_x)
+        face["top"] = int(pt_y)
+        face["right"] = int(pt_x1)
+        face["bottom"] = int(pt_y1)
+        rotated_landmarks = face
+
+    logger.trace("Rotated landmarks: %s", rotated_landmarks)
+    return face

lib/image.py

@@ -0,0 +1,302 @@
+#!/usr/bin python3
+""" Utilities for working with images and videos """
+
+import logging
+import subprocess
+import sys
+
+from concurrent import futures
+from hashlib import sha1
+
+import cv2
+import imageio_ffmpeg as im_ffm
+import numpy as np
+
+from lib.utils import convert_to_secs, FaceswapError
+
+logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
+
+# ################### #
+# <<< IMAGE UTILS >>> #
+# ################### #
+
+
+# <<< IMAGE IO >>> #
+
+def read_image(filename, raise_error=False):
+    """ Read an image file from a file location.
+
+    Extends the functionality of :func:`cv2.imread()` by ensuring that an image was actually
+    loaded. Errors can be logged and ignored so that the process can continue on an image load
+    failure.
+
+    Parameters
+    ----------
+    filename: str
+        Full path to the image to be loaded.
+    raise_error: bool, optional
+        If ``True``, then any failures (including the returned image being ``None``) will be
+        raised. If ``False`` then an error message will be logged, but the error will not be
+        raised. Default: ``False``
+
+    Returns
+    -------
+    numpy.ndarray
+        The image in `BGR` channel order.
+
+    Example
+    -------
+    >>> image_file = "/path/to/image.png"
+    >>> try:
+    >>>    image = read_image(image_file, raise_error=True)
+    >>> except:
+    >>>     raise ValueError("There was an error")
+    """
+    logger.trace("Requested image: '%s'", filename)
+    success = True
+    image = None
+    try:
+        image = cv2.imread(filename)
+        if image is None:
+            raise ValueError
+    except TypeError:
+        success = False
+        msg = "Error while reading image (TypeError): '{}'".format(filename)
+        logger.error(msg)
+        if raise_error:
+            raise Exception(msg)
+    except ValueError:
+        success = False
+        msg = ("Error while reading image. This is most likely caused by special characters in "
+               "the filename: '{}'".format(filename))
+        logger.error(msg)
+        if raise_error:
+            raise Exception(msg)
+    except Exception as err:  # pylint:disable=broad-except
+        success = False
+        msg = "Failed to load image '{}'. Original Error: {}".format(filename, str(err))
+        logger.error(msg)
+        if raise_error:
+            raise Exception(msg)
+    logger.trace("Loaded image: '%s'. Success: %s", filename, success)
+    return image
+
+
+def read_image_batch(filenames):
+    """ Load a batch of images from the given file locations.
+
+    Leverages multi-threading to load multiple images from disk at the same time
+    leading to vastly reduced image read times.
+
+    Parameters
+    ----------
+    filenames: list
+        A list of ``str`` full paths to the images to be loaded.
+
+    Returns
+    -------
+    numpy.ndarray
+        The batch of images in `BGR` channel order.
+
+    Notes
+    -----
+    As the images are compiled into a batch, they must be all of the same dimensions.
+
+    Example
+    -------
+    >>> image_filenames = ["/path/to/image_1.png", "/path/to/image_2.png", "/path/to/image_3.png"]
+    >>> images = read_image_batch(image_filenames)
+    """
+    logger.trace("Requested batch: '%s'", filenames)
+    executor = futures.ThreadPoolExecutor()
+    with executor:
+        images = [executor.submit(read_image, filename, raise_error=True)
+                  for filename in filenames]
+        batch = np.array([future.result() for future in futures.as_completed(images)])
+    logger.trace("Returning images: %s", batch.shape)
+    return batch
+
+
+def read_image_hash(filename):
+    """ Return the `sha1` hash of an image saved on disk.
+
+    Parameters
+    ----------
+    filename: str
+        Full path to the image to be loaded.
+
+    Returns
+    -------
+    str
+        The :func:`hashlib.hexdigest()` representation of the `sha1` hash of the given image.
+    Example
+    -------
+    >>> image_file = "/path/to/image.png"
+    >>> image_hash = read_image_hash(image_file)
+    """
+    img = read_image(filename, raise_error=True)
+    image_hash = sha1(img).hexdigest()
+    logger.trace("filename: '%s', hash: %s", filename, image_hash)
+    return image_hash
+
+
+def encode_image_with_hash(image, extension):
+    """ Encode an image, and get the encoded image back with its `sha1` hash.
+
+    Parameters
+    ----------
+    image: numpy.ndarray
+        The image to be encoded in `BGR` channel order.
+    extension: str
+        A compatible `cv2` image file extension that the final image is to be saved to.
+
+    Returns
+    -------
+    image_hash: str
+        The :func:`hashlib.hexdigest()` representation of the `sha1` hash of the encoded image
+    encoded_image: bytes
+        The image encoded into the correct file format
+
+    Example
+    -------
+    >>> image_file = "/path/to/image.png"
+    >>> image = read_image(image_file)
+    >>> image_hash, encoded_image = encode_image_with_hash(image, ".jpg")
+    """
+    encoded_image = cv2.imencode(extension, image)[1]
+    image_hash = sha1(cv2.imdecode(encoded_image, cv2.IMREAD_UNCHANGED)).hexdigest()
+    return image_hash, encoded_image
+
+
+def batch_convert_color(batch, colorspace):
+    """ Convert a batch of images from one color space to another.
+
+    Converts a batch of images by reshaping the batch prior to conversion rather than iterating
+    over the images. This leads to a significant speed up in the convert process.
+
+    Parameters
+    ----------
+    batch: numpy.ndarray
+        A batch of images.
+    colorspace: str
+        The OpenCV Color Conversion Code suffix. For example for BGR to LAB this would be
+        ``'BGR2LAB'``.
+        See https://docs.opencv.org/4.1.1/d8/d01/group__imgproc__color__conversions.html for a full
+        list of color codes.
+
+    Returns
+    -------
+    numpy.ndarray
+        The batch converted to the requested color space.
+
+    Example
+    -------
+    >>> images_bgr = numpy.array([image1, image2, image3])
+    >>> images_lab = batch_convert_color(images_bgr, "BGR2LAB")
+
+    Notes
+    -----
+    This function is only compatible for color space conversions that have the same image shape
+    for source and destination color spaces.
+
+    If you use :func:`batch_convert_color` with 8-bit images, the conversion will have some
+    information lost. For many cases, this will not be noticeable but it is recommended
+    to use 32-bit images in cases that need the full range of colors or that convert an image
+    before an operation and then convert back.
+    """
+    logger.trace("Batch converting: (batch shape: %s, colorspace: %s)", batch.shape, colorspace)
+    original_shape = batch.shape
+    batch = batch.reshape((original_shape[0] * original_shape[1], *original_shape[2:]))
+    batch = cv2.cvtColor(batch, getattr(cv2, "COLOR_{}".format(colorspace)))
+    return batch.reshape(original_shape)
+
+
+# ################### #
+# <<< VIDEO UTILS >>> #
+# ################### #
+
+def count_frames_and_secs(filename, timeout=60):
+    """ Count the number of frames and seconds in a video file.
+
+    Adapted From :mod:`ffmpeg_imageio` to handle the issue of ffmpeg occasionally hanging
+    inside a subprocess.
+
+    If the operation times out then the process will try to read the data again, up to a total
+    of 3 times. If the data still cannot be read then an exception will be raised.
+
+    Note that this operation can be quite slow for large files.
+
+    Parameters
+    ----------
+    filename: str
+        Full path to the video to be analyzed.
+    timeout: str, optional
+        The amount of time in seconds to wait for the video data before aborting.
+        Default: ``60``
+
+    Returns
+    -------
+    nframes: int
+        The number of frames in the given video file.
+    nsecs: float
+        The duration, in seconds, of the given video file.
+
+    Example
+    -------
+    >>> video = "/path/to/video.mp4"
+    >>> frames, secs = count_frames_and_secs(video)
+    """
+    # https://stackoverflow.com/questions/2017843/fetch-frame-count-with-ffmpeg
+
+    assert isinstance(filename, str), "Video path must be a string"
+    exe = im_ffm.get_ffmpeg_exe()
+    iswin = sys.platform.startswith("win")
+    logger.debug("iswin: '%s'", iswin)
+    cmd = [exe, "-i", filename, "-map", "0:v:0", "-c", "copy", "-f", "null", "-"]
+    logger.debug("FFMPEG Command: '%s'", " ".join(cmd))
+    attempts = 3
+    for attempt in range(attempts):
+        try:
+            logger.debug("attempt: %s of %s", attempt + 1, attempts)
+            out = subprocess.check_output(cmd,
+                                          stderr=subprocess.STDOUT,
+                                          shell=iswin,
+                                          timeout=timeout)
+            logger.debug("Succesfully communicated with FFMPEG")
+            break
+        except subprocess.CalledProcessError as err:
+            out = err.output.decode(errors="ignore")
+            raise RuntimeError("FFMEG call failed with {}:\n{}".format(err.returncode, out))
+        except subprocess.TimeoutExpired as err:
+            this_attempt = attempt + 1
+            if this_attempt == attempts:
+                msg = ("FFMPEG hung while attempting to obtain the frame count. "
+                       "Sometimes this issue resolves itself, so you can try running again. "
+                       "Otherwise use the Effmpeg Tool to extract the frames from your video into "
+                       "a folder, and then run the requested Faceswap process on that folder.")
+                raise FaceswapError(msg) from err
+            logger.warning("FFMPEG hung while attempting to obtain the frame count. "
+                           "Retrying %s of %s", this_attempt + 1, attempts)
+            continue
+
+    # Note that other than with the subprocess calls below, ffmpeg wont hang here.
+    # Worst case Python will stop/crash and ffmpeg will continue running until done.
+
+    nframes = nsecs = None
+    for line in reversed(out.splitlines()):
+        if not line.startswith(b"frame="):
+            continue
+        line = line.decode(errors="ignore")
+        logger.debug("frame line: '%s'", line)
+        idx = line.find("frame=")
+        if idx >= 0:
+            splitframes = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
+            nframes = int(splitframes)
+        idx = line.find("time=")
+        if idx >= 0:
+            splittime = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
+            nsecs = convert_to_secs(*splittime.split(":"))
+        logger.debug("nframes: %s, nsecs: %s", nframes, nsecs)
+        return nframes, nsecs
+
+    raise RuntimeError("Could not get number of frames")  # pragma: no cover

lib/model/masks.py

@@ -43,6 +43,8 @@ class Mask():
                      self.__class__.__name__, face.shape, channels, landmarks)
         self.landmarks = landmarks
         self.face = face
+        self.dtype = face.dtype
+        self.threshold = 255 if self.dtype == "uint8" else 255.0
         self.channels = channels
 
         mask = self.build_mask()
@@ -73,7 +75,7 @@ class Mask():
 class dfl_full(Mask):  # pylint: disable=invalid-name
     """ DFL facial mask """
     def build_mask(self):
-        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)
+        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=self.dtype)
 
         nose_ridge = (self.landmarks[27:31], self.landmarks[33:34])
         jaw = (self.landmarks[0:17],
@@ -90,14 +92,14 @@ class dfl_full(Mask):  # pylint: disable=invalid-name
 
         for item in parts:
             merged = np.concatenate(item)
-            cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member
+            cv2.fillConvexPoly(mask, cv2.convexHull(merged), self.threshold)
         return mask
 
 
 class components(Mask):  # pylint: disable=invalid-name
     """ Component model mask """
     def build_mask(self):
-        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)
+        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=self.dtype)
 
         r_jaw = (self.landmarks[0:9], self.landmarks[17:18])
         l_jaw = (self.landmarks[8:17], self.landmarks[26:27])
@@ -117,7 +119,7 @@ class components(Mask):  # pylint: disable=invalid-name
 
         for item in parts:
             merged = np.concatenate(item)
-            cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member
+            cv2.fillConvexPoly(mask, cv2.convexHull(merged), self.threshold)
         return mask
 
 
@@ -126,7 +128,7 @@ class extended(Mask):  # pylint: disable=invalid-name
         Based on components mask. Attempts to extend the eyebrow points up the forehead
     """
     def build_mask(self):
-        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)
+        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=self.dtype)
 
         landmarks = self.landmarks.copy()
         # mid points between the side of face and eye point
@@ -161,15 +163,15 @@ class extended(Mask):  # pylint: disable=invalid-name
 
         for item in parts:
             merged = np.concatenate(item)
-            cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member
+            cv2.fillConvexPoly(mask, cv2.convexHull(merged), self.threshold)
         return mask
 
 
 class facehull(Mask):  # pylint: disable=invalid-name
     """ Basic face hull mask """
     def build_mask(self):
-        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)
+        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=self.dtype)
-        hull = cv2.convexHull(  # pylint: disable=no-member
+        hull = cv2.convexHull(
             np.array(self.landmarks).reshape((-1, 2)))
-        cv2.fillConvexPoly(mask, hull, 255.0, lineType=cv2.LINE_AA)  # pylint: disable=no-member
+        cv2.fillConvexPoly(mask, hull, self.threshold, lineType=cv2.LINE_AA)
         return mask

lib/utils.py

@@ -4,27 +4,18 @@
 import json
 import logging
 import os
-import subprocess
 import sys
 import urllib
 import warnings
 import zipfile
-from hashlib import sha1
+
 from pathlib import Path
 from re import finditer
 from multiprocessing import current_process
 from socket import timeout as socket_timeout, error as socket_error
 
-import imageio_ffmpeg as im_ffm
 from tqdm import tqdm
 
-import numpy as np
-import cv2
-
-
-from lib.faces_detect import DetectedFace
-
-
 # Global variables
 _image_extensions = [  # pylint:disable=invalid-name
     ".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff"]
@@ -132,6 +123,22 @@ def get_image_paths(directory):
     return dir_contents
 
 
+def convert_to_secs(*args):
+    """ converts a time to second. Either convert_to_secs(min, secs) or
+        convert_to_secs(hours, mins, secs). """
+    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
+    logger.debug("from time: %s", args)
+    retval = 0.0
+    if len(args) == 1:
+        retval = float(args[0])
+    elif len(args) == 2:
+        retval = 60 * float(args[0]) + float(args[1])
+    elif len(args) == 3:
+        retval = 3600 * float(args[0]) + 60 * float(args[1]) + float(args[2])
+    logger.debug("to secs: %s", retval)
+    return retval
+
+
 def full_path_split(path):
     """ Split a given path into all of it's separate components """
     logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
@@ -151,147 +158,6 @@ def full_path_split(path):
     return allparts
 
 
-def cv2_read_img(filename, raise_error=False):
-    """ Read an image with cv2 and check that an image was actually loaded.
-        Logs an error if the image returned is None. or an error has occured.
-
-        Pass raise_error=True if error should be raised """
-    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
-    logger.trace("Requested image: '%s'", filename)
-    success = True
-    image = None
-    try:
-        image = cv2.imread(filename)  # pylint:disable=no-member,c-extension-no-member
-        if image is None:
-            raise ValueError
-    except TypeError:
-        success = False
-        msg = "Error while reading image (TypeError): '{}'".format(filename)
-        logger.error(msg)
-        if raise_error:
-            raise Exception(msg)
-    except ValueError:
-        success = False
-        msg = ("Error while reading image. This is most likely caused by special characters in "
-               "the filename: '{}'".format(filename))
-        logger.error(msg)
-        if raise_error:
-            raise Exception(msg)
-    except Exception as err:  # pylint:disable=broad-except
-        success = False
-        msg = "Failed to load image '{}'. Original Error: {}".format(filename, str(err))
-        logger.error(msg)
-        if raise_error:
-            raise Exception(msg)
-    logger.trace("Loaded image: '%s'. Success: %s", filename, success)
-    return image
-
-
-def hash_image_file(filename):
-    """ Return an image file's sha1 hash """
-    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
-    img = cv2_read_img(filename, raise_error=True)
-    img_hash = sha1(img).hexdigest()
-    logger.trace("filename: '%s', hash: %s", filename, img_hash)
-    return img_hash
-
-
-def hash_encode_image(image, extension):
-    """ Encode the image, get the hash and return the hash with
-        encoded image """
-    img = cv2.imencode(extension, image)[1]  # pylint:disable=no-member,c-extension-no-member
-    f_hash = sha1(
-        cv2.imdecode(  # pylint:disable=no-member,c-extension-no-member
-            img,
-            cv2.IMREAD_UNCHANGED)).hexdigest()  # pylint:disable=no-member,c-extension-no-member
-    return f_hash, img
-
-
-def convert_to_secs(*args):
-    """ converts a time to second. Either convert_to_secs(min, secs) or
-        convert_to_secs(hours, mins, secs). """
-    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
-    logger.debug("from time: %s", args)
-    retval = 0.0
-    if len(args) == 1:
-        retval = float(args[0])
-    elif len(args) == 2:
-        retval = 60 * float(args[0]) + float(args[1])
-    elif len(args) == 3:
-        retval = 3600 * float(args[0]) + 60 * float(args[1]) + float(args[2])
-    logger.debug("to secs: %s", retval)
-    return retval
-
-
-def count_frames_and_secs(path, timeout=60):
-    """
-    Adapted From ffmpeg_imageio, to handle occasional hanging issue:
-    https://github.com/imageio/imageio-ffmpeg
-
-    Get the number of frames and number of seconds for the given video
-    file. Note that this operation can be quite slow for large files.
-
-    Disclaimer: I've seen this produce different results from actually reading
-    the frames with older versions of ffmpeg (2.x). Therefore I cannot say
-    with 100% certainty that the returned values are always exact.
-    """
-    # https://stackoverflow.com/questions/2017843/fetch-frame-count-with-ffmpeg
-
-    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
-    assert isinstance(path, str), "Video path must be a string"
-    exe = im_ffm.get_ffmpeg_exe()
-    iswin = sys.platform.startswith("win")
-    logger.debug("iswin: '%s'", iswin)
-    cmd = [exe, "-i", path, "-map", "0:v:0", "-c", "copy", "-f", "null", "-"]
-    logger.debug("FFMPEG Command: '%s'", " ".join(cmd))
-    attempts = 3
-    for attempt in range(attempts):
-        try:
-            logger.debug("attempt: %s of %s", attempt + 1, attempts)
-            out = subprocess.check_output(cmd,
-                                          stderr=subprocess.STDOUT,
-                                          shell=iswin,
-                                          timeout=timeout)
-            logger.debug("Succesfully communicated with FFMPEG")
-            break
-        except subprocess.CalledProcessError as err:
-            out = err.output.decode(errors="ignore")
-            raise RuntimeError("FFMEG call failed with {}:\n{}".format(err.returncode, out))
-        except subprocess.TimeoutExpired as err:
-            this_attempt = attempt + 1
-            if this_attempt == attempts:
-                msg = ("FFMPEG hung while attempting to obtain the frame count. "
-                       "Sometimes this issue resolves itself, so you can try running again. "
-                       "Otherwise use the Effmpeg Tool to extract the frames from your video into "
-                       "a folder, and then run the requested Faceswap process on that folder.")
-                raise FaceswapError(msg) from err
-            logger.warning("FFMPEG hung while attempting to obtain the frame count. "
-                           "Retrying %s of %s", this_attempt + 1, attempts)
-            continue
-
-    # Note that other than with the subprocess calls below, ffmpeg wont hang here.
-    # Worst case Python will stop/crash and ffmpeg will continue running until done.
-
-    nframes = nsecs = None
-    for line in reversed(out.splitlines()):
-        if not line.startswith(b"frame="):
-            continue
-        line = line.decode(errors="ignore")
-        logger.debug("frame line: '%s'", line)
-        idx = line.find("frame=")
-        if idx >= 0:
-            splitframes = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
-            nframes = int(splitframes)
-        idx = line.find("time=")
-        if idx >= 0:
-            splittime = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
-            nsecs = convert_to_secs(*splittime.split(":"))
-        logger.debug("nframes: %s, nsecs: %s", nframes, nsecs)
-        return nframes, nsecs
-
-    raise RuntimeError("Could not get number of frames")  # pragma: no cover
-
-
 def backup_file(directory, filename):
     """ Backup a given file by appending .bk to the end """
     logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
@@ -348,80 +214,6 @@ def deprecation_warning(func_name, additional_info=None):
     logger.warning(msg)
 
 
-def rotate_landmarks(face, rotation_matrix):
-    # pylint:disable=c-extension-no-member
-    """ Rotate the landmarks and bounding box for faces
-        found in rotated images.
-        Pass in a DetectedFace object or Alignments dict """
-    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
-    logger.trace("Rotating landmarks: (rotation_matrix: %s, type(face): %s",
-                 rotation_matrix, type(face))
-    rotated_landmarks = None
-    # Detected Face Object
-    if isinstance(face, DetectedFace):
-        bounding_box = [[face.x, face.y],
-                        [face.x + face.w, face.y],
-                        [face.x + face.w, face.y + face.h],
-                        [face.x, face.y + face.h]]
-        landmarks = face.landmarks_xy
-
-    # Alignments Dict
-    elif isinstance(face, dict) and "x" in face:
-        bounding_box = [[face.get("x", 0), face.get("y", 0)],
-                        [face.get("x", 0) + face.get("w", 0),
-                         face.get("y", 0)],
-                        [face.get("x", 0) + face.get("w", 0),
-                         face.get("y", 0) + face.get("h", 0)],
-                        [face.get("x", 0),
-                         face.get("y", 0) + face.get("h", 0)]]
-        landmarks = face.get("landmarks_xy", list())
-
-    else:
-        raise ValueError("Unsupported face type")
-
-    logger.trace("Original landmarks: %s", landmarks)
-
-    rotation_matrix = cv2.invertAffineTransform(  # pylint:disable=no-member
-        rotation_matrix)
-    rotated = list()
-    for item in (bounding_box, landmarks):
-        if not item:
-            continue
-        points = np.array(item, np.int32)
-        points = np.expand_dims(points, axis=0)
-        transformed = cv2.transform(points,  # pylint:disable=no-member
-                                    rotation_matrix).astype(np.int32)
-        rotated.append(transformed.squeeze())
-
-    # Bounding box should follow x, y planes, so get min/max
-    # for non-90 degree rotations
-    pt_x = min([pnt[0] for pnt in rotated[0]])
-    pt_y = min([pnt[1] for pnt in rotated[0]])
-    pt_x1 = max([pnt[0] for pnt in rotated[0]])
-    pt_y1 = max([pnt[1] for pnt in rotated[0]])
-    width = pt_x1 - pt_x
-    height = pt_y1 - pt_y
-
-    if isinstance(face, DetectedFace):
-        face.x = int(pt_x)
-        face.y = int(pt_y)
-        face.w = int(width)
-        face.h = int(height)
-        face.r = 0
-        if len(rotated) > 1:
-            rotated_landmarks = [tuple(point) for point in rotated[1].tolist()]
-            face.landmarks_xy = rotated_landmarks
-    else:
-        face["left"] = int(pt_x)
-        face["top"] = int(pt_y)
-        face["right"] = int(pt_x1)
-        face["bottom"] = int(pt_y1)
-        rotated_landmarks = face
-
-    logger.trace("Rotated landmarks: %s", rotated_landmarks)
-    return face
-
-
 def camel_case_split(identifier):
     """ Split a camel case name
         from: https://stackoverflow.com/questions/29916065 """

plugins/extract/detect/_base.py

@@ -18,8 +18,7 @@ To get a :class:`~lib.faces_detect.DetectedFace` object use the function:
 import cv2
 import numpy as np
 
-from lib.faces_detect import DetectedFace
+from lib.faces_detect import DetectedFace, rotate_landmarks
-from lib.utils import rotate_landmarks
 from plugins.extract._base import Extractor, logger
 
 

plugins/train/model/_base.py

@@ -9,6 +9,7 @@ import os
 import sys
 import time
 
+from concurrent import futures
 from json import JSONDecodeError
 
 import keras
@@ -24,7 +25,6 @@ from lib.model.losses import (DSSIMObjective, PenalizedLoss, gradient_loss, mask
                               generalized_loss, l_inf_norm, gmsd_loss, gaussian_blur)
 from lib.model.nn_blocks import NNBlocks
 from lib.model.optimizers import Adam
-from lib.multithreading import MultiThread
 from lib.utils import deprecation_warning, FaceswapError
 from plugins.train._config import Config
 
@@ -466,21 +466,13 @@ class ModelBase():
         backup_func = self.backup.backup_model if self.should_backup(save_averages) else None
         if backup_func:
             logger.info("Backing up models...")
-        save_threads = list()
+        executor = futures.ThreadPoolExecutor()
-        for network in self.networks.values():
+        save_threads = [executor.submit(network.save, backup_func=backup_func)
-            name = "save_{}".format(network.name)
+                        for network in self.networks.values()]
-            save_threads.append(MultiThread(network.save,
+        save_threads.append(executor.submit(self.state.save, backup_func=backup_func))
-                                            name=name,
+        futures.wait(save_threads)
-                                            backup_func=backup_func))
+        # call result() to capture errors
-        save_threads.append(MultiThread(self.state.save,
+        _ = [thread.result() for thread in save_threads]
-                                        name="save_state",
-                                        backup_func=backup_func))
-        for thread in save_threads:
-            thread.start()
-        for thread in save_threads:
-            if thread.has_error:
-                logger.error(thread.errors[0])
-            thread.join()
         msg = "[Saved models]"
         if save_averages:
             lossmsg = ["{}_{}: {:.5f}".format(self.state.loss_names[side][0],

plugins/train/trainer/_base.py

@@ -33,7 +33,7 @@ from tensorflow.python import errors_impl as tf_errors  # pylint:disable=no-name
 
 from lib.alignments import Alignments
 from lib.faces_detect import DetectedFace
-from lib.training_data import TrainingDataGenerator, stack_images
+from lib.training_data import TrainingDataGenerator
 from lib.utils import FaceswapError, get_folder, get_image_paths
 from plugins.train._config import Config
 
@@ -292,10 +292,10 @@ class Batcher():
         """ Return the next batch from the generator
             Items should come out as: (warped, target [, mask]) """
         batch = next(self.feed)
-        feed = batch[1]
+        if self.use_mask:
-        batch = batch[2:]   # Remove full size samples and feed from batch
+            batch = [[batch["feed"], batch["masks"]], batch["targets"] + [batch["masks"]]]
-        mask = batch[-1]
+        else:
-        batch = [[feed, mask], batch] if self.use_mask else [feed, batch]
+            batch = [batch["feed"], batch["targets"]]
         self.generate_preview(do_preview)
         return batch
 
@@ -309,13 +309,10 @@ class Batcher():
         if self.preview_feed is None:
             self.set_preview_feed()
         batch = next(self.preview_feed)
-        self.samples, feed = batch[:2]
+        self.samples = batch["samples"]
-        batch = batch[2:]   # Remove full size samples and feed from batch
+        self.target = [batch["targets"][self.model.largest_face_index]]
-        self.target = batch[self.model.largest_face_index]
         if self.use_mask:
-            mask = batch[-1]
+            self.target += [batch["masks"]]
-            batch = [[feed, mask], batch]
-            self.target = [self.target, mask]
 
     def set_preview_feed(self):
         """ Set the preview dictionary """
@@ -347,15 +344,11 @@ class Batcher():
     def compile_timelapse_sample(self):
         """ Timelapse samples """
         batch = next(self.timelapse_feed)
-        samples, feed = batch[:2]
+        batchsize = len(batch["samples"])
-        batchsize = len(samples)
+        images = [batch["targets"][self.model.largest_face_index]]
-        batch = batch[2:]   # Remove full size samples and feed from batch
-        images = batch[self.model.largest_face_index]
         if self.use_mask:
-            mask = batch[-1]
+            images = images + [batch["masks"]]
-            batch = [[feed, mask], batch]
+        sample = self.compile_sample(batchsize, samples=batch["samples"], images=images)
-            images = [images, mask]
-        sample = self.compile_sample(batchsize, samples=samples, images=images)
         return sample
 
     def set_timelapse_feed(self, images, batchsize):
@@ -405,10 +398,10 @@ class Samples():
 
         for side, samples in self.images.items():
             other_side = "a" if side == "b" else "b"
-            predictions = [preds["{}_{}".format(side, side)],
+            predictions = [preds["{0}_{0}".format(side)],
                            preds["{}_{}".format(other_side, side)]]
             display = self.to_full_frame(side, samples, predictions)
-            headers[side] = self.get_headers(side, other_side, display[0].shape[1])
+            headers[side] = self.get_headers(side, display[0].shape[1])
             figures[side] = np.stack([display[0], display[1], display[2], ], axis=1)
             if self.images[side][0].shape[0] % 2 == 1:
                 figures[side] = np.concatenate([figures[side],
@@ -547,22 +540,22 @@ class Samples():
         logger.debug("Overlayed foreground. Shape: %s", retval.shape)
         return retval
 
-    def get_headers(self, side, other_side, width):
+    def get_headers(self, side, width):
         """ Set headers for images """
-        logger.debug("side: '%s', other_side: '%s', width: %s",
+        logger.debug("side: '%s', width: %s",
-                     side, other_side, width)
+                     side, width)
+        titles = ("Original", "Swap") if side == "a" else ("Swap", "Original")
         side = side.upper()
-        other_side = other_side.upper()
         height = int(64 * self.scaling)
         total_width = width * 3
         logger.debug("height: %s, total_width: %s", height, total_width)
         font = cv2.FONT_HERSHEY_SIMPLEX  # pylint: disable=no-member
-        texts = ["Target {}".format(side),
+        texts = ["{} ({})".format(titles[0], side),
-                 "{} > {}".format(side, side),
+                 "{0} > {0}".format(titles[0]),
-                 "{} > {}".format(side, other_side)]
+                 "{} > {}".format(titles[0], titles[1])]
         text_sizes = [cv2.getTextSize(texts[idx],  # pylint: disable=no-member
                                       font,
-                                      self.scaling,
+                                      self.scaling * 0.8,
                                       1)[0]
                       for idx in range(len(texts))]
         text_y = int((height + text_sizes[0][1]) / 2)
@@ -576,7 +569,7 @@ class Samples():
                         text,
                         (text_x[idx], text_y),
                         font,
-                        self.scaling,
+                        self.scaling * 0.8,
                         (0, 0, 0),
                         1,
                         lineType=cv2.LINE_AA)  # pylint: disable=no-member
@@ -703,3 +696,25 @@ class Landmarks():
                 detected_face.load_aligned(None, size=self.size)
                 landmarks[detected_face.hash] = detected_face.aligned_landmarks
         return landmarks
+
+
+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)

scripts/convert.py

@@ -17,9 +17,10 @@ from lib import Serializer
 from lib.convert import Converter
 from lib.faces_detect import DetectedFace
 from lib.gpu_stats import GPUStats
+from lib.image import read_image_hash
 from lib.multithreading import MultiThread, total_cpus
 from lib.queue_manager import queue_manager
-from lib.utils import FaceswapError, get_folder, get_image_paths, hash_image_file
+from lib.utils import FaceswapError, get_folder, get_image_paths
 from plugins.extract.pipeline import Extractor
 from plugins.plugin_loader import PluginLoader
 
@@ -682,7 +683,7 @@ class OptionalActions():
             file_list = [path for path in get_image_paths(input_aligned_dir)]
             logger.info("Getting Face Hashes for selected Aligned Images")
             for face in tqdm(file_list, desc="Hashing Faces"):
-                face_hashes.append(hash_image_file(face))
+                face_hashes.append(read_image_hash(face))
             logger.debug("Face Hashes: %s", (len(face_hashes)))
             if not face_hashes:
                 raise FaceswapError("Aligned directory is empty, no faces will be converted!")
@@ -746,5 +747,5 @@ class Legacy():
                 continue
             hash_faces = all_faces[frame]
             for index, face_path in hash_faces.items():
-                hash_faces[index] = hash_image_file(face_path)
+                hash_faces[index] = read_image_hash(face_path)
             self.alignments.add_face_hashes(frame, hash_faces)

scripts/extract.py

@@ -8,9 +8,10 @@ from pathlib import Path
 
 from tqdm import tqdm
 
+from lib.image import encode_image_with_hash
 from lib.multithreading import MultiThread
 from lib.queue_manager import queue_manager
-from lib.utils import get_folder, hash_encode_image, deprecation_warning
+from lib.utils import get_folder, deprecation_warning
 from plugins.extract.pipeline import Extractor
 from scripts.fsmedia import Alignments, Images, PostProcess, Utils
 
@@ -255,7 +256,7 @@ class Extract():
             face = detected_face["face"]
             resized_face = face.aligned_face
 
-            face.hash, img = hash_encode_image(resized_face, extension)
+            face.hash, img = encode_image_with_hash(resized_face, extension)
             self.save_queue.put((out_filename, img))
             final_faces.append(face.to_alignment())
         self.alignments.data[os.path.basename(filename)] = final_faces

scripts/fsmedia.py

@@ -16,8 +16,9 @@ import numpy as np
 from lib.aligner import Extract as AlignerExtract
 from lib.alignments import Alignments as AlignmentsBase
 from lib.face_filter import FaceFilter as FilterFunc
-from lib.utils import (camel_case_split, count_frames_and_secs, cv2_read_img, get_folder,
+from lib.image import count_frames_and_secs, read_image
-                       get_image_paths, set_system_verbosity, _video_extensions)
+from lib.utils import (camel_case_split, get_folder, get_image_paths, set_system_verbosity,
+                       _video_extensions)
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
@@ -183,7 +184,7 @@ class Images():
         """ Load frames from disk """
         logger.debug("Input is separate Frames. Loading images")
         for filename in self.input_images:
-            image = cv2_read_img(filename, raise_error=False)
+            image = read_image(filename, raise_error=False)
             if image is None:
                 continue
             yield filename, image
@@ -212,7 +213,7 @@ class Images():
                 logger.trace("Extracted frame_no %s from filename '%s'", frame_no, filename)
             retval = self.load_one_video_frame(int(frame_no))
         else:
-            retval = cv2_read_img(filename, raise_error=True)
+            retval = read_image(filename, raise_error=True)
         return retval
 
     def load_one_video_frame(self, frame_no):

scripts/train.py

@@ -12,10 +12,11 @@ import cv2
 import tensorflow as tf
 from keras.backend.tensorflow_backend import set_session
 
+from lib.image import read_image
 from lib.keypress import KBHit
 from lib.multithreading import MultiThread
-from lib.queue_manager import queue_manager
+from lib.queue_manager import queue_manager  # noqa pylint:disable=unused-import
-from lib.utils import cv2_read_img, get_folder, get_image_paths, set_system_verbosity
+from lib.utils import get_folder, get_image_paths, set_system_verbosity
 from plugins.plugin_loader import PluginLoader
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
@@ -176,7 +177,7 @@ class Train():
     @property
     def image_size(self):
         """ Get the training set image size for storing in model data """
-        image = cv2_read_img(self.images["a"][0], raise_error=True)
+        image = read_image(self.images["a"][0], raise_error=True)
         size = image.shape[0]
         logger.debug("Training image size: %s", size)
         return size

tools/lib_alignments/media.py

@@ -14,8 +14,8 @@ from tqdm import tqdm
 from lib.aligner import Extract as AlignerExtract
 from lib.alignments import Alignments
 from lib.faces_detect import DetectedFace
-from lib.utils import (_image_extensions, _video_extensions, count_frames_and_secs, cv2_read_img,
+from lib.image import count_frames_and_secs, encode_image_with_hash, read_image, read_image_hash
-                       hash_image_file, hash_encode_image)
+from lib.utils import _image_extensions, _video_extensions
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
@@ -175,7 +175,7 @@ class MediaLoader():
         else:
             src = os.path.join(self.folder, filename)
             logger.trace("Loading image: '%s'", src)
-            image = cv2_read_img(src, raise_error=True)
+            image = read_image(src, raise_error=True)
         return image
 
     def load_video_frame(self, filename):
@@ -210,7 +210,7 @@ class Faces(MediaLoader):
                 continue
             filename = os.path.splitext(face)[0]
             file_extension = os.path.splitext(face)[1]
-            face_hash = hash_image_file(os.path.join(self.folder, face))
+            face_hash = read_image_hash(os.path.join(self.folder, face))
             retval = {"face_fullname": face,
                       "face_name": filename,
                       "face_extension": file_extension,
@@ -358,7 +358,7 @@ class ExtractedFaces():
     @staticmethod
     def save_face_with_hash(filename, extension, face):
         """ Save a face and return it's hash """
-        f_hash, img = hash_encode_image(face, extension)
+        f_hash, img = encode_image_with_hash(face, extension)
         logger.trace("Saving face: '%s'", filename)
         with open(filename, "wb") as out_file:
             out_file.write(img)

tools/sort.py

@@ -16,8 +16,8 @@ from tqdm import tqdm
 from lib.cli import FullHelpArgumentParser
 from lib import Serializer
 from lib.faces_detect import DetectedFace
+from lib.image import read_image
 from lib.queue_manager import queue_manager
-from lib.utils import cv2_read_img
 from lib.vgg_face2_keras import VGGFace2 as VGGFace
 from plugins.plugin_loader import PluginLoader
 
@@ -106,7 +106,7 @@ class Sort():
     @staticmethod
     def get_landmarks(filename):
         """ Extract the face from a frame (If not alignments file found) """
-        image = cv2_read_img(filename, raise_error=True)
+        image = read_image(filename, raise_error=True)
         feed = Sort.alignment_dict(image)
         feed["filename"] = filename
         queue_manager.get_queue("in").put(feed)
@@ -161,7 +161,7 @@ class Sort():
         logger.info("Sorting by face similarity...")
 
         images = np.array(self.find_images(input_dir))
-        preds = np.array([self.vgg_face.predict(cv2_read_img(img, raise_error=True))
+        preds = np.array([self.vgg_face.predict(read_image(img, raise_error=True))
                           for img in tqdm(images, desc="loading", file=sys.stdout)])
         logger.info("Sorting. Depending on ths size of your dataset, this may take a few "
                     "minutes...")
@@ -264,7 +264,7 @@ class Sort():
         logger.info("Sorting by histogram similarity...")
 
         img_list = [
-            [img, cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256])]
+            [img, cv2.calcHist([read_image(img, raise_error=True)], [0], None, [256], [0, 256])]
             for img in
             tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout)
         ]
@@ -294,7 +294,7 @@ class Sort():
 
         img_list = [
             [img,
-             cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256]), 0]
+             cv2.calcHist([read_image(img, raise_error=True)], [0], None, [256], [0, 256]), 0]
             for img in
             tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout)
         ]
@@ -548,7 +548,7 @@ class Sort():
         input_dir = self.args.input_dir
         logger.info("Preparing to group...")
         if group_method == 'group_blur':
-            temp_list = [[img, self.estimate_blur(cv2_read_img(img, raise_error=True))]
+            temp_list = [[img, self.estimate_blur(read_image(img, raise_error=True))]
                          for img in
                          tqdm(self.find_images(input_dir),
                               desc="Reloading",
@@ -576,7 +576,7 @@ class Sort():
         elif group_method == 'group_hist':
             temp_list = [
                 [img,
-                 cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256])]
+                 cv2.calcHist([read_image(img, raise_error=True)], [0], None, [256], [0, 256])]
                 for img in
                 tqdm(self.find_images(input_dir),
                      desc="Reloading",
@@ -632,7 +632,7 @@ class Sort():
         Estimate the amount of blur an image has with the variance of the Laplacian.
         Normalize by pixel number to offset the effect of image size on pixel gradients & variance
         """
-        image = cv2_read_img(image_file, raise_error=True)
+        image = read_image(image_file, raise_error=True)
         if image.ndim == 3:
             image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         blur_map = cv2.Laplacian(image, cv2.CV_32F)