Enforce naming conventions (#1968)

backend/src/packages/chaiNNer_ncnn/ncnn/batch_processing/model_file_iterator.py

@@ -40,7 +40,7 @@
     node_type="iteratorHelper",
     side_effects=True,
 )
-def ModelFileIteratorLoadModelNode(
+def iterator_helper_load_model_node(
     param_path: str, bin_path: str, root_dir: str, index: int
 ) -> Tuple[NcnnModelWrapper, str, str, str, int]:
     model, _, model_name = load_model_node(param_path, bin_path)
@@ -71,7 +71,7 @@ def ModelFileIteratorLoadModelNode(
     node_type="iterator",
     side_effects=True,
 )
-async def ModelFileIteratorNode(directory: str, context: IteratorContext) -> None:
+async def model_file_iterator_node(directory: str, context: IteratorContext) -> None:
     logger.debug(f"Iterating over models in directory: {directory}")
 
     model_path_node_id = context.get_helper(NCNN_ITERATOR_NODE_ID).id

backend/src/packages/chaiNNer_ncnn/ncnn/utility/get_scale.py → backend/src/packages/chaiNNer_ncnn/ncnn/utility/get_model_scale.py

@@ -15,5 +15,5 @@
     inputs=[NcnnModelInput()],
     outputs=[NumberOutput("Scale", output_type="Input0.scale")],
 )
-def model_dim_node(model: NcnnModelWrapper) -> int:
+def get_model_scale_node(model: NcnnModelWrapper) -> int:
     return model.scale

backend/src/packages/chaiNNer_onnx/onnx/batch_processing/model_file_iterator.py

@@ -40,7 +40,7 @@
     node_type="iteratorHelper",
     side_effects=True,
 )
-def ModelFileIteratorLoadModelNode(
+def iterator_helper_load_model_node(
     path: str, root_dir: str, index: int
 ) -> Tuple[OnnxModel, str, str, str, int]:
     model, dirname, basename = load_model_node(path)
@@ -69,7 +69,7 @@ def ModelFileIteratorLoadModelNode(
     node_type="iterator",
     side_effects=True,
 )
-async def ModelFileIteratorNode(directory: str, context: IteratorContext) -> None:
+async def model_file_iterator_node(directory: str, context: IteratorContext) -> None:
     logger.debug(f"Iterating over models in directory: {directory}")
 
     model_path_node_id = context.get_helper(ONNX_ITERATOR_NODE_ID).id

backend/src/packages/chaiNNer_onnx/onnx/processing/rembg.py → backend/src/packages/chaiNNer_onnx/onnx/processing/remove_background.py

@@ -43,7 +43,7 @@
         ImageOutput("Mask", image_type=navi.Image(size_as="Input0"), channels=1),
     ],
 )
-def rembg_node(
+def remove_background_node(
     img: np.ndarray,
     model: OnnxRemBg,
     post_process_mask: bool,

backend/src/packages/chaiNNer_pytorch/pytorch/iteration/model_file_iterator.py

@@ -40,7 +40,7 @@
     node_type="iteratorHelper",
     side_effects=True,
 )
-def ModelFileIteratorLoadModelNode(
+def iterator_helper_load_model_node(
     path: str, root_dir: str, index: int
 ) -> Tuple[PyTorchModel, str, str, str, int]:
     model, dirname, basename = load_model_node(path)
@@ -69,7 +69,7 @@ def ModelFileIteratorLoadModelNode(
     node_type="iterator",
     side_effects=True,
 )
-async def ModelFileIteratorNode(directory: str, context: IteratorContext) -> None:
+async def model_file_iterator_node(directory: str, context: IteratorContext) -> None:
     logger.debug(f"Iterating over models in directory: {directory}")
 
     model_path_node_id = context.get_helper(PYTORCH_ITERATOR_NODE_ID).id

backend/src/packages/chaiNNer_pytorch/pytorch/restoration/upscale_face.py

@@ -147,7 +147,7 @@ def upscale(
         )
     ],
 )
-def face_upscale_node(
+def upscale_face_node(
     img: np.ndarray,
     face_model: PyTorchFaceModel,
     background_img: Union[np.ndarray, None],

backend/src/packages/chaiNNer_pytorch/pytorch/utility/get_scale.py → backend/src/packages/chaiNNer_pytorch/pytorch/utility/get_model_scale.py

@@ -15,5 +15,5 @@
     inputs=[ModelInput()],
     outputs=[NumberOutput("Scale", output_type="Input0.scale")],
 )
-def model_dim_node(model: PyTorchModel) -> int:
+def get_model_scale_node(model: PyTorchModel) -> int:
     return model.scale

backend/src/packages/chaiNNer_standard/image/batch_processing/image_file_iterator.py

@@ -62,7 +62,7 @@ def list_glob(directory: str, globexpr: str, ext_filter: List[str]) -> List[str]
     ],
     side_effects=True,
 )
-def ImageFileIteratorLoadImageNode(
+def iterator_helper_load_image_node(
     path: str, root_dir: str, index: int
 ) -> Tuple[np.ndarray, str, str, str, int]:
     img, img_dir, basename = load_image_node(path)
@@ -106,7 +106,7 @@ def ImageFileIteratorLoadImageNode(
         "chainner:image:load",
     ],
 )
-async def ImageFileIteratorNode(
+async def image_file_iterator_node(
     directory: str,
     use_glob: bool,
     is_recursive: bool,

backend/src/packages/chaiNNer_standard/image/batch_processing/paired_image_file_iterator.py → backend/src/packages/chaiNNer_standard/image/batch_processing/image_pairs_iterator.py

@@ -45,7 +45,7 @@
     ],
     side_effects=True,
 )
-def ImageFileIteratorLoadImageNodeA(
+def iterator_helper_load_image_node(
     path_a: str, path_b: str, root_dir_a: str, root_dir_b: str, index: int
 ) -> Tuple[np.ndarray, np.ndarray, str, str, str, str, str, str, int]:
     img_a, img_dir_a, basename_a = load_image_node(path_a)
@@ -90,7 +90,7 @@ def ImageFileIteratorLoadImageNodeA(
         "chainner:image:file_iterator",
     ],
 )
-async def PairedImageFileIteratorNode(
+async def image_pairs_iterator_node(
     directory_a: str, directory_b: str, context: IteratorContext
 ) -> None:
     logger.debug(f"Iterating over images in directories: {directory_a}, {directory_b}")

backend/src/packages/chaiNNer_standard/image/batch_processing/sprite_sheet_iterator.py → backend/src/packages/chaiNNer_standard/image/batch_processing/spritesheet_iterator.py

@@ -30,7 +30,7 @@
     ],
     side_effects=True,
 )
-def ImageSpriteSheetIteratorLoadImageNode(
+def iterator_helper_load_image_node(
     img: np.ndarray, index: int
 ) -> Tuple[np.ndarray, int]:
     return img, index
@@ -46,7 +46,7 @@ def ImageSpriteSheetIteratorLoadImageNode(
     outputs=[],
     side_effects=True,
 )
-def ImageSpriteSheetIteratorAppendImageNode(
+def iterator_helper_append_image_node(
     img: np.ndarray, results: List[np.ndarray]
 ) -> None:
     results.append(img)
@@ -92,7 +92,7 @@ def ImageSpriteSheetIteratorAppendImageNode(
     ],
     side_effects=True,
 )
-async def ImageSpriteSheetIteratorNode(
+async def spritesheet_iterator_node(
     sprite_sheet: np.ndarray,
     rows: int,
     columns: int,

backend/src/packages/chaiNNer_standard/image/batch_processing/video_frame_iterator.py

@@ -73,7 +73,7 @@ class Writer:
     ],
     side_effects=True,
 )
-def VideoFrameIteratorFrameLoaderNode(
+def iterator_helper_load_frame_as_image_node(
     img: np.ndarray, idx: int, video_dir: str, video_name: str
 ) -> Tuple[np.ndarray, int, str, str]:
     return img, idx, video_dir, video_name
@@ -113,7 +113,7 @@ def VideoFrameIteratorFrameLoaderNode(
     outputs=[],
     side_effects=True,
 )
-def VideoFrameIteratorFrameWriterNode(
+def iterator_helper_write_output_frame_node(
     img: np.ndarray,
     save_dir: str,
     video_name: str,
@@ -198,7 +198,7 @@ def VideoFrameIteratorFrameWriterNode(
     ],
     side_effects=True,
 )
-async def SimpleVideoFrameIteratorNode(path: str, context: IteratorContext) -> None:
+async def video_frame_iterator_node(path: str, context: IteratorContext) -> None:
     logger.debug(f"{ffmpeg_path=}, {ffprobe_path=}")
     logger.debug(f"Iterating over frames in video file: {path}")
 

backend/src/packages/chaiNNer_standard/image/io/view_image_external.py

@@ -25,7 +25,7 @@
     outputs=[],
     side_effects=True,
 )
-def open_image_node(img: np.ndarray) -> None:
+def view_image_external_node(img: np.ndarray) -> None:
     tempdir = mkdtemp(prefix="chaiNNer-")
     logger.debug(f"Writing image to temp path: {tempdir}")
     im_name = f"{time.time()}.png"

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/hue_and_saturation.py

@@ -65,7 +65,7 @@ def with_lightness(img: np.ndarray, lightness: float) -> np.ndarray:
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def hue_and_sat(
+def hue_and_saturation_node(
     img: np.ndarray,
     hue: float,
     saturation: float,

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/invert.py → backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/invert_color.py

@@ -17,7 +17,7 @@
     inputs=[ImageInput()],
     outputs=[ImageOutput(image_type="Input0", assume_normalized=True)],
 )
-def invert_node(img: np.ndarray) -> np.ndarray:
+def invert_color_node(img: np.ndarray) -> np.ndarray:
     c = get_h_w_c(img)[2]
 
     # invert the first 3 channels

backend/src/packages/chaiNNer_standard/image_adjustment/adjustments/threshold_adaptive.py

@@ -37,7 +37,7 @@
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def adaptive_threshold_node(
+def threshold_adaptive_node(
     img: np.ndarray,
     maxval: float,
     adaptive_method: int,

backend/src/packages/chaiNNer_standard/image_channel/all/rgba_merge.py → backend/src/packages/chaiNNer_standard/image_channel/all/merge_channels.py

@@ -48,7 +48,7 @@
     ],
     deprecated=True,
 )
-def merge_rgba_node(
+def merge_channels_node(
     im1: np.ndarray,
     im2: Union[np.ndarray, None],
     im3: Union[np.ndarray, None],

backend/src/packages/chaiNNer_standard/image_channel/all/rgba_separate.py → backend/src/packages/chaiNNer_standard/image_channel/all/separate_rgba.py

@@ -48,7 +48,7 @@
         ),
     ],
 )
-def separate_rgba(
+def separate_rgba_node(
     img: np.ndarray,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     h, w, c = get_h_w_c(img)

backend/src/packages/chaiNNer_standard/image_channel/transparency/transparency_split.py → backend/src/packages/chaiNNer_standard/image_channel/transparency/split_transparency.py

@@ -33,7 +33,7 @@
         ),
     ],
 )
-def split_transparency(img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+def split_transparency_node(img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
     """Split a multi-channel image into separate channels"""
 
     img = as_target_channels(img, 4)

backend/src/packages/chaiNNer_standard/image_dimension/crop/crop_border.py

@@ -34,7 +34,7 @@
         )
     ],
 )
-def border_crop_node(img: np.ndarray, amount: int) -> np.ndarray:
+def crop_border_node(img: np.ndarray, amount: int) -> np.ndarray:
     h, w, _ = get_h_w_c(img)
 
     assert 2 * amount < h, "Cropped area would result in an image with no height"

backend/src/packages/chaiNNer_standard/image_dimension/crop/crop_edges.py

@@ -35,7 +35,7 @@
         )
     ],
 )
-def edge_crop_node(
+def crop_edges_node(
     img: np.ndarray, top: int, left: int, right: int, bottom: int
 ) -> np.ndarray:
     h, w, _ = get_h_w_c(img)

backend/src/packages/chaiNNer_standard/image_filter/blur/surface_blur.py

@@ -37,7 +37,7 @@
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def bilateral_blur_node(
+def surface_blur_node(
     img: np.ndarray,
     diameter: int,
     sigma_color: int,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/convolve.py

@@ -37,7 +37,7 @@
         )
     ],
 )
-def image_convolve_node(
+def convolve_node(
     img: np.ndarray,
     kernel_in: str,
     padding: int,

backend/src/packages/chaiNNer_standard/image_filter/miscellaneous/high_pass.py

@@ -33,7 +33,7 @@
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def high_pass_filter_node(
+def high_pass_node(
     img: np.ndarray,
     radius: float,
     contrast: float,

backend/src/packages/chaiNNer_standard/image_filter/noise/fast_nlmeans.py → backend/src/packages/chaiNNer_standard/image_filter/noise/fast_nl_means.py

@@ -44,7 +44,7 @@
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def fast_nlmeans_node(
+def fast_nl_means_node(
     img: np.ndarray,
     h: float,
     h_color: float,

backend/src/packages/chaiNNer_standard/image_filter/quantize/dither_palette.py

@@ -63,7 +63,7 @@ class PaletteDitherAlgorithm(Enum):
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def palette_dither_node(
+def dither_palette_node(
     img: np.ndarray,
     palette: np.ndarray,
     dither_algorithm: PaletteDitherAlgorithm,

backend/src/packages/chaiNNer_standard/image_filter/sharpen/high_boost_sharpen.py → backend/src/packages/chaiNNer_standard/image_filter/sharpen/high_boost_filter.py

@@ -59,7 +59,7 @@ class KernelType(Enum):
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def sharpen_hbf_node(
+def high_boost_filter_node(
     img: np.ndarray,
     kernel_type: KernelType,
     amount: float,

backend/src/packages/chaiNNer_standard/image_filter/sharpen/sharpen.py → backend/src/packages/chaiNNer_standard/image_filter/sharpen/unsharp_mask.py

@@ -39,7 +39,7 @@
     ],
     outputs=[ImageOutput(image_type="Input0")],
 )
-def sharpen_node(
+def unsharp_mask_node(
     img: np.ndarray,
     radius: float,
     amount: float,

backend/src/packages/chaiNNer_standard/image_utility/compositing/caption.py → backend/src/packages/chaiNNer_standard/image_utility/compositing/add_caption.py

@@ -35,7 +35,7 @@
         )
     ],
 )
-def caption_node(
+def add_caption_node(
     img: np.ndarray, caption: str, size: int, position: CaptionPosition
 ) -> np.ndarray:
     """Add caption an image"""

backend/src/packages/chaiNNer_standard/image_utility/compositing/blend.py → backend/src/packages/chaiNNer_standard/image_utility/compositing/blend_images.py

@@ -40,7 +40,7 @@ def getHeight(i: any) = match i { Image => i.height, _ => -inf };
         ).with_never_reason("At least one layer must be an image"),
     ],
 )
-def blend_node(
+def blend_images_node(
     base: np.ndarray | Color,
     ov: np.ndarray | Color,
     blend_mode: BlendMode,

backend/src/packages/chaiNNer_standard/image_utility/compositing/stack.py → backend/src/packages/chaiNNer_standard/image_utility/compositing/stack_images.py

@@ -77,7 +77,7 @@ def getAdjustedHeight(img: Image | null) {
         )
     ],
 )
-def stack_node(
+def stack_images_node(
     im1: np.ndarray,
     im2: np.ndarray | None,
     im3: np.ndarray | None,

backend/src/packages/chaiNNer_standard/image_utility/compositing/z_stack.py → backend/src/packages/chaiNNer_standard/image_utility/compositing/z_stack_images.py

@@ -59,7 +59,7 @@ def conv(i: Image | null) = match i { Image => i, _ => any };
         ),
     ],
 )
-def z_stack(
+def z_stack_images_node(
     expression: Expression,
     *inputs: np.ndarray | None,
 ) -> np.ndarray:

backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/lut.py → backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/apply_lut.py

@@ -47,7 +47,7 @@ def quantize(img: np.ndarray, levels: int) -> np.ndarray:
         ImageOutput(image_type=navi.Image(size_as="Input0", channels_as="Input1"))
     ],
 )
-def lut_node(
+def apply_lut_node(
     img: np.ndarray,
     lut: np.ndarray,
 ) -> np.ndarray:

backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/convert_color.py → backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/change_colorspace.py

@@ -60,7 +60,7 @@
         )
     ],
 )
-def convert_color_node(
+def change_colorspace_node(
     img: np.ndarray, input_: int, output: int, alpha: bool
 ) -> np.ndarray:
     """Takes an image and changes the color mode it"""

backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/generate_hash.py

@@ -34,7 +34,7 @@
         TextOutput("Base64"),
     ],
 )
-def run(img: np.ndarray, size: int) -> Tuple[str, str]:
+def generate_hash_node(img: np.ndarray, size: int) -> Tuple[str, str]:
     """Generate a hash from the input image. The digest size determines the length of the hash that is output."""
     img = np.ascontiguousarray(to_uint8(img))
     h = hashlib.blake2b(img, digest_size=size)  # type: ignore

backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/image_stat.py → backend/src/packages/chaiNNer_standard/image_utility/miscellaneous/image_statistics.py

@@ -35,7 +35,7 @@
         NumberOutput("Percentile", output_type="0..255"),
     ],
 )
-def image_statistic(
+def image_statistics_node(
     img: np.ndarray,
     percentile: float,
 ) -> Tuple[float, float, float, float]: