Minor fixups and linting

lib/gui/display_command.py

@@ -350,7 +350,6 @@ def display_item_process(self) -> None:
             self.after(1000, self.display_item_process)
             return
 
-        logger.debug("Adding graph")
         existing = list(self.subnotebook_get_titles_ids().keys())
 
         loss_keys = self.display_item.get_loss_keys(Session.session_ids[-1])
@@ -367,6 +366,7 @@ def display_item_process(self) -> None:
             tabname = loss_key.replace("_", " ").title()
             if tabname in existing:
                 continue
+            logger.debug("Adding graph '%s'", tabname)
 
             display_keys = [key for key in loss_keys if key.startswith(loss_key)]
             data = Calculations(session_id=Session.session_ids[-1],

lib/gui/options.py

@@ -129,9 +129,9 @@ def process_options(self, command_options, command):
                 helptext=opt["help"],
                 track_modified=True,
                 command=command)
-            gui_options[title] = dict(cpanel_option=cpanel_option,
-                                      opts=opt["opts"],
-                                      nargs=opt.get("nargs", None))
+            gui_options[title] = {"cpanel_option": cpanel_option,
+                                  "opts": opt["opts"],
+                                  "nargs": opt.get("nargs", None)}
             logger.trace("Processed: %s", gui_options)
         return gui_options
 

lib/gui/project.py

@@ -90,8 +90,8 @@ def _stored_tab_name(self):
     def _selected_to_choices(self):
         """ dict: The selected value and valid choices for multi-option, radio or combo options.
         """
-        valid_choices = {cmd: {opt: dict(choices=val["cpanel_option"].choices,
-                                         is_multi=val["cpanel_option"].is_multi_option)
+        valid_choices = {cmd: {opt: {"choices": val["cpanel_option"].choices,
+                                     "is_multi": val["cpanel_option"].is_multi_option}
                                for opt, val in data.items()
                                if isinstance(val, dict) and "cpanel_option" in val
                                and val["cpanel_option"].choices is not None}
@@ -600,9 +600,9 @@ def _add_task(self, command):
             The tab that pertains to the currently active task
 
         """
-        self._tasks[command] = dict(filename=self._filename,
-                                    options=self._options,
-                                    is_project=self._is_project)
+        self._tasks[command] = {"filename": self._filename,
+                                "options": self._options,
+                                "is_project": self._is_project}
 
     def clear_tasks(self):
         """ Clears all of the stored tasks.
@@ -629,7 +629,7 @@ def add_project_task(self, filename, command, options):
         options: dict
             The options for this task loaded from the project
         """
-        self._tasks[command] = dict(filename=filename, options=options, is_project=True)
+        self._tasks[command] = {"filename": filename, "options": options, "is_project": True}
 
     def _set_active_task(self, command=None):
         """ Set the active :attr:`_filename` and :attr:`_options` to currently selected tab's

lib/gui/theme.py

@@ -370,7 +370,7 @@ def scrollbar(self, key, trough_color, border_color, control_backgrounds, contro
                     ("disabled", images[f"img_{lookup}_disabled"]),
                     ("pressed !disabled", images[f"img_{lookup}_active"]),
                     ("active !disabled", images[f"img_{lookup}_active"]))
-            kwargs = dict(border=1, sticky="ns") if element == "thumb" else {}
+            kwargs = {"border": 1, "sticky": "ns"} if element == "thumb" else {}
             self._style.element_create(*args, **kwargs)
 
         # Get a configurable trough
@@ -487,7 +487,7 @@ def _get_arrow(cls, dimensions, thickness, direction):
         crop_size = (square_size // 16) * 16
         draw_rows = int(6 * crop_size / 16)
         start_row = dimensions[1] // 2 - draw_rows // 2
-        initial_indent = (2 * (crop_size // 16) + (dimensions[0] - crop_size) // 2)
+        initial_indent = 2 * (crop_size // 16) + (dimensions[0] - crop_size) // 2
 
         retval = np.zeros((dimensions[1], dimensions[0]), dtype="uint8")
         for i in range(start_row, start_row + draw_rows):

lib/image.py

@@ -23,7 +23,7 @@
 
 from lib.multithreading import MultiThread
 from lib.queue_manager import queue_manager, QueueEmpty
-from lib.utils import convert_to_secs, FaceswapError, _video_extensions, get_image_paths
+from lib.utils import convert_to_secs, FaceswapError, VIDEO_EXTENSIONS, get_image_paths
 
 if T.TYPE_CHECKING:
     from lib.align.alignments import PNGHeaderDict
@@ -1148,7 +1148,7 @@ def _check_for_video(self):
         """
         if not isinstance(self.location, str) or os.path.isdir(self.location):
             retval = False
-        elif os.path.splitext(self.location)[1].lower() in _video_extensions:
+        elif os.path.splitext(self.location)[1].lower() in VIDEO_EXTENSIONS:
             retval = True
         else:
             raise FaceswapError("The input file '{}' is not a valid video".format(self.location))

lib/keypress.py

@@ -43,7 +43,7 @@ def __init__(self, is_gui=False):
             self.old_term = termios.tcgetattr(self.file_desc)
 
             # New terminal setting unbuffered
-            self.new_term[3] = (self.new_term[3] & ~termios.ICANON & ~termios.ECHO)
+            self.new_term[3] = self.new_term[3] & ~termios.ICANON & ~termios.ECHO
             termios.tcsetattr(self.file_desc, termios.TCSAFLUSH, self.new_term)
 
             # Support normal-terminal reset at exit

lib/utils.py

@@ -24,9 +24,9 @@
     from http.client import HTTPResponse
 
 # Global variables
-_image_extensions = [  # pylint:disable=invalid-name
+IMAGE_EXTENSIONS = [  # pylint:disable=invalid-name
     ".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff"]
-_video_extensions = [  # pylint:disable=invalid-name
+VIDEO_EXTENSIONS = [  # pylint:disable=invalid-name
     ".avi", ".flv", ".mkv", ".mov", ".mp4", ".mpeg", ".mpg", ".webm", ".wmv",
     ".ts", ".vob"]
 _TF_VERS: tuple[int, int] | None = None
@@ -249,7 +249,7 @@ def get_image_paths(directory: str, extension: str | None = None) -> list[str]:
     ['/path/to/directory/image1.jpg']
     """
     logger = logging.getLogger(__name__)
-    image_extensions = _image_extensions if extension is None else [extension]
+    image_extensions = IMAGE_EXTENSIONS if extension is None else [extension]
     dir_contents = []
 
     if not os.path.exists(directory):

plugins/convert/mask/mask_blend.py

@@ -13,7 +13,7 @@
 logger = logging.getLogger(__name__)
 
 
-class Mask():  # pylint:disable=too-few-public-methods
+class Mask():
     """ Manipulations to perform to the mask that is to be applied to the output of the Faceswap
     model.
 

plugins/extract/align/cv2_dnn.py

@@ -46,6 +46,7 @@ def __init__(self, **kwargs) -> None:
         super().__init__(git_model_id=git_model_id, model_filename=model_filename, **kwargs)
 
         self.model: cv2.dnn.Net
+        self.model_path: str
         self.name = "cv2-DNN Aligner"
         self.input_size = 128
         self.color_format = "RGB"
@@ -56,8 +57,8 @@ def __init__(self, **kwargs) -> None:
 
     def init_model(self) -> None:
         """ Initialize CV2 DNN Detector Model"""
-        self.model = cv2.dnn.readNetFromTensorflow(self.model_path)  # pylint:disable=no-member
-        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint:disable=no-member
+        self.model = cv2.dnn.readNetFromTensorflow(self.model_path)
+        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
 
     def faces_to_feed(self, faces: np.ndarray) -> np.ndarray:
         """ Convert a batch of face images from UINT8 (0-255) to fp32 (0.0-255.0)
@@ -136,7 +137,7 @@ def align_image(self, batch: AlignerBatch) -> tuple[list[np.ndarray],
         offsets: list
             List of offsets for the faces
         """
-        logger.trace("Aligning image around center")  # type:ignore
+        logger.trace("Aligning image around center")  # type:ignore[attr-defined]
         sizes = (self.input_size, self.input_size)
         rois = []
         faces = []
@@ -247,7 +248,7 @@ def pad_image(cls, box: list[int], image: np.ndarray) -> tuple[np.ndarray, tuple
         pad_t = 1 - box[1] if box[1] < 0 else 0
         pad_r = box[2] - width if box[2] > width else 0
         pad_b = box[3] - height if box[3] > height else 0
-        logger.trace("Padding: (l: %s, t: %s, r: %s, b: %s)",  # type:ignore
+        logger.trace("Padding: (l: %s, t: %s, r: %s, b: %s)",  # type:ignore[attr-defined]
                      pad_l, pad_t, pad_r, pad_b)
         padded_image = cv2.copyMakeBorder(image.copy(),
                                           pad_t,
@@ -257,7 +258,8 @@ def pad_image(cls, box: list[int], image: np.ndarray) -> tuple[np.ndarray, tuple
                                           cv2.BORDER_CONSTANT,
                                           value=(0, 0, 0))
         offsets = (pad_l - pad_r, pad_t - pad_b)
-        logger.trace("image_shape: %s, Padded shape: %s, box: %s, offsets: %s",  # type:ignore
+        logger.trace("image_shape: %s, Padded shape: %s, box: %s, "  # type:ignore[attr-defined]
+                     "offsets: %s",
                      image.shape, padded_image.shape, box, offsets)
         return padded_image, offsets
 
@@ -311,4 +313,4 @@ def get_pts_from_predict(self, batch: AlignerBatch):
                 points[:, 1] += (roi[1] - offset[1])
                 landmarks.append(points)
             batch.landmarks = np.array(landmarks)
-        logger.trace("Predicted Landmarks: %s", batch.landmarks)  # type:ignore
+        logger.trace("Predicted Landmarks: %s", batch.landmarks)  # type:ignore[attr-defined]

plugins/extract/align/fan_defaults.py

@@ -50,19 +50,19 @@
 
 
 _DEFAULTS = {
-    "batch-size": dict(
-        default=12,
-        info="The batch size to use. To a point, higher batch sizes equal better performance, "
-             "but setting it too high can harm performance.\n"
-             "\n\tNvidia users: If the batchsize is set higher than the your GPU can "
-             "accomodate then this will automatically be lowered."
-             "\n\tAMD users: A batchsize of 8 requires about 4 GB vram.",
-        datatype=int,
-        rounding=1,
-        min_max=(1, 64),
-        choices=[],
-        group="settings",
-        gui_radio=False,
-        fixed=True,
-    )
+    "batch-size": {
+        "default": 12,
+        "info": "The batch size to use. To a point, higher batch sizes equal better performance, "
+                "but setting it too high can harm performance.\n"
+                "\n\tNvidia users: If the batchsize is set higher than the your GPU can "
+                "accomodate then this will automatically be lowered."
+                "\n\tAMD users: A batchsize of 8 requires about 4 GB vram.",
+        "datatype": int,
+        "rounding": 1,
+        "min_max": (1, 64),
+        "choices": [],
+        "group": "settings",
+        "gui_radio": False,
+        "fixed": True,
+    }
 }

plugins/extract/detect/cv2_dnn.py

@@ -26,14 +26,14 @@ def __init__(self, **kwargs) -> None:
     def init_model(self) -> None:
         """ Initialize CV2 DNN Detector Model"""
         assert isinstance(self.model_path, list)
-        self.model = cv2.dnn.readNetFromCaffe(self.model_path[1],  # pylint:disable=no-member
+        self.model = cv2.dnn.readNetFromCaffe(self.model_path[1],
                                               self.model_path[0])
-        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint:disable=no-member
+        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
 
     def process_input(self, batch: BatchType) -> None:
         """ Compile the detection image(s) for prediction """
         assert isinstance(batch, DetectorBatch)
-        batch.feed = cv2.dnn.blobFromImages(batch.image,  # pylint:disable=no-member
+        batch.feed = cv2.dnn.blobFromImages(batch.image,
                                             scalefactor=1.0,
                                             size=(self.input_size, self.input_size),
                                             mean=[104, 117, 123],
@@ -53,13 +53,13 @@ def finalize_predictions(self, predictions: np.ndarray) -> np.ndarray:
         for i in range(predictions.shape[2]):
             confidence = predictions[0, 0, i, 2]
             if confidence >= self.confidence:
-                logger.trace("Accepting due to confidence %s >= %s",  # type:ignore
+                logger.trace("Accepting due to confidence %s >= %s",  # type:ignore[attr-defined]
                              confidence, self.confidence)
                 faces.append([(predictions[0, 0, i, 3] * self.input_size),
                               (predictions[0, 0, i, 4] * self.input_size),
                               (predictions[0, 0, i, 5] * self.input_size),
                               (predictions[0, 0, i, 6] * self.input_size)])
-        logger.trace("faces: %s", faces)  # type:ignore
+        logger.trace("faces: %s", faces)  # type:ignore[attr-defined]
         return np.array(faces)[None, ...]
 
     def process_output(self, batch: BatchType) -> None:

plugins/extract/detect/cv2_dnn_defaults.py

@@ -50,17 +50,17 @@
 )
 
 
-_DEFAULTS = dict(
-    confidence=dict(
-        default=50,
-        info="The confidence level at which the detector has succesfully found a face.\nHigher "
-             "levels will be more discriminating, lower levels will have more false positives.",
-        datatype=int,
-        rounding=5,
-        min_max=(25, 100),
-        choices=[],
-        group="settings",
-        gui_radio=False,
-        fixed=True,
-    ),
-)
+_DEFAULTS = {
+    "confidence": {
+        "default": 50,
+        "info": "The confidence level at which the detector has succesfully found a face.\nHigher "
+                "levels will be more discriminating, lower levels will have more false positives.",
+        "datatype": int,
+        "rounding": 5,
+        "min_max": (25, 100),
+        "choices": [],
+        "group": "settings",
+        "gui_radio": False,
+        "fixed": True,
+    },
+}