Training startup updates

  - Remove alignments path option from cli
  - Restructure training_data.py
  - Load alignment data from PNG header during first epoch
  - lib.image.read_image_batch - Add option to return metadata
  - lib.utils.get_image_paths - Add option for explicit extension
  - plugins.train.trainer._base - remove pre-cache alignments code
  - scripts.train - Check first image in training folders for metadata
  - Documentation

docs/full/lib/training.rst

@@ -0,0 +1,24 @@
+****************
+training package
+****************
+
+The training Package handles the processing of faces for feeding into a Faceswap model.
+
+.. contents:: Contents
+   :local:
+
+augmentation module
+===================
+
+.. automodule:: lib.training.augmentation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+generator module
+================
+
+.. automodule:: lib.training.generator
+   :members:
+   :undoc-members:
+   :show-inheritance:

lib/cli/args.py

@@ -872,17 +872,6 @@ def get_argument_list():
             help=_("Input directory. A directory containing training images for face A. This is "
                    "the original face, i.e. the face that you want to remove and replace with "
                    "face B.")))
-        argument_list.append(dict(
-            opts=("-ala", "--alignments-A"),
-            action=FileFullPaths,
-            filetypes='alignments',
-            type=str,
-            dest="alignments_path_a",
-            default=None,
-            group=_("faces"),
-            help=_("DEPRECATED - This option will be removed in a future update. Path to "
-                   "alignments file for training set A. Defaults to <input-A>/alignments.json if "
-                   "not provided.")))
         argument_list.append(dict(
             opts=("-B", "--input-B"),
             action=DirFullPaths,
@@ -892,17 +881,6 @@ def get_argument_list():
             help=_("Input directory. A directory containing training images for face B. This is "
                    "the swap face, i.e. the face that you want to place onto the head of person "
                    "A.")))
-        argument_list.append(dict(
-            opts=("-alb", "--alignments-B"),
-            action=FileFullPaths,
-            filetypes='alignments',
-            type=str,
-            dest="alignments_path_b",
-            default=None,
-            group=_("faces"),
-            help=_("DEPRECATED - This option will be removed in a future update. Path to "
-                   "alignments file for training set B. Defaults to <input-B>/alignments.json if "
-                   "not provided.")))
         argument_list.append(dict(
             opts=("-m", "--model-dir"),
             action=DirFullPaths,

lib/image.py

@@ -305,7 +305,7 @@ def read_image(filename, raise_error=False, with_metadata=False):
     return retval
 
 
-def read_image_batch(filenames):
+def read_image_batch(filenames, with_metadata=False):
     """ 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
@@ -315,6 +315,10 @@ def read_image_batch(filenames):
     ----------
     filenames: list
         A list of ``str`` full paths to the images to be loaded.
+    with_metadata: bool, optional
+        Only returns a value if the images loaded are extracted Faceswap faces. If ``True`` then
+        returns the Faceswap metadata stored with in a Face images .png exif header.
+        Default: ``False``
 
     Returns
     -------
@@ -333,20 +337,30 @@ def read_image_batch(filenames):
     logger.trace("Requested batch: '%s'", filenames)
     executor = futures.ThreadPoolExecutor()
     with executor:
-        images = {executor.submit(read_image, filename, raise_error=True): filename
+        images = {executor.submit(read_image, filename,
+                                  raise_error=True, with_metadata=with_metadata): filename
                   for filename in filenames}
         batch = [None for _ in range(len(filenames))]
+        if with_metadata:
+            meta = [None for _ in range(len(filenames))]
         # There is no guarantee that the same filename will not be passed through multiple times
         # (and when shuffle is true this can definitely happen), so we can't just call
         # filenames.index().
         return_indices = {filename: [idx for idx, fname in enumerate(filenames)
                                      if fname == filename]
                           for filename in set(filenames)}
         for future in futures.as_completed(images):
-            batch[return_indices[images[future]].pop()] = future.result()
+            return_idx = return_indices[images[future]].pop()
+            if with_metadata:
+                batch[return_idx], meta[return_idx] = future.result()
+            else:
+                batch[return_idx] = future.result()
+
     batch = np.array(batch)
-    logger.trace("Returning images: (filenames: %s, batch shape: %s)", filenames, batch.shape)
-    return batch
+    retval = (batch, meta) if with_metadata else batch
+    logger.trace("Returning images: (filenames: %s, batch shape: %s, with_metadata: %s)",
+                 filenames, batch.shape, with_metadata)
+    return retval
 
 
 def read_image_meta(filename):

lib/training/__init__.py

@@ -0,0 +1,6 @@
+#!/usr/bin/env python3
+""" Package for handling alignments files, detected faces and aligned faces along with their
+associated objects. """
+
+from .augmentation import ImageAugmentation
+from .generator import TrainingDataGenerator