Feat/inference

luxonis_train/__main__.py

@@ -2,7 +2,7 @@
 from enum import Enum
 from importlib.metadata import version
 from pathlib import Path
-from typing import Annotated, Optional
+from typing import Annotated
 
 import typer
 import yaml
@@ -25,7 +25,7 @@ class _ViewType(str, Enum):
 
 
 ConfigType = Annotated[
-    Optional[str],
+    str | None,
     typer.Option(
         help="Path to the configuration file.",
         show_default=False,
@@ -34,7 +34,7 @@ class _ViewType(str, Enum):
 ]
 
 OptsType = Annotated[
-    Optional[list[str]],
+    list[str] | None,
     typer.Argument(
         help="A list of optional CLI overrides of the config file.",
         show_default=False,
@@ -46,16 +46,23 @@ class _ViewType(str, Enum):
 ]
 
 SaveDirType = Annotated[
-    Optional[Path],
+    Path | None,
     typer.Option(help="Where to save the inference results."),
 ]
 
+ImgPathType = Annotated[
+    str | None,
+    typer.Option(
+        help="Path to an image file or a directory containing images for inference."
+    ),
+]
+
 
 @app.command()
 def train(
     config: ConfigType = None,
     resume: Annotated[
-        Optional[str],
+        str | None,
         typer.Option(help="Resume training from this checkpoint."),
     ] = None,
     opts: OptsType = None,
@@ -99,12 +106,15 @@ def infer(
     config: ConfigType = None,
     view: ViewType = _ViewType.VAL,
     save_dir: SaveDirType = None,
+    img_path: ImgPathType = None,
     opts: OptsType = None,
 ):
     """Run inference."""
     from luxonis_train.core import LuxonisModel
 
-    LuxonisModel(config, opts).infer(view=view.value, save_dir=save_dir)
+    LuxonisModel(config, opts).infer(
+        view=view.value, save_dir=save_dir, img_path=img_path
+    )
 
 
 @app.command()
@@ -200,7 +210,7 @@ def common(
         ),
     ] = False,
     source: Annotated[
-        Optional[Path],
+        Path | None,
         typer.Option(
             help="Path to a python file with custom components. "
             "Will be sourced before running the command.",

luxonis_train/core/core.py

@@ -18,7 +18,6 @@
 from luxonis_ml.utils import LuxonisFileSystem, reset_logging, setup_logging
 from typeguard import typechecked
 
-from luxonis_train.attached_modules.visualizers import get_unnormalized_images
 from luxonis_train.callbacks import (
     LuxonisRichProgressBar,
     LuxonisTQDMProgressBar,
@@ -35,7 +34,10 @@
     replace_weights,
     try_onnx_simplify,
 )
-from .utils.infer_utils import render_visualizations
+from .utils.infer_utils import (
+    process_dataset_images,
+    process_images,
+)
 from .utils.train_utils import create_trainer
 
 logger = getLogger(__name__)
@@ -419,6 +421,7 @@
         self,
         view: Literal["train", "val", "test"] = "val",
         save_dir: str | Path | None = None,
+        img_path: str | None = None,
     ) -> None:
         """Runs inference.
 
@@ -429,15 +432,25 @@
         @param save_dir: Directory where to save the visualizations. If
             not specified, visualizations will be rendered on the
             screen.
+        @type img_path: str | None
+        @param img_path: Path to the image file or directory for inference.
+            If None, defaults to using dataset images.
         """
         self.lightning_module.eval()
 
-        for inputs, labels in self.pytorch_loaders[view]:
-            images = get_unnormalized_images(self.cfg, inputs)
-            outputs = self.lightning_module.forward(
-                inputs, labels, images=images, compute_visualizations=True
-            )
-            render_visualizations(outputs.visualizations, save_dir)
+        if img_path:
+            img_path_obj = Path(img_path)
+            if img_path_obj.is_file():
+                process_images(self, [img_path_obj], view, save_dir)
+            elif img_path_obj.is_dir():
+                image_files = [
+                    f
+                    for f in img_path_obj.iterdir()
+                    if f.suffix.lower() in {".png", ".jpg", ".jpeg"}
+                ]
+                process_images(self, image_files, view, save_dir)
+        else:
+            process_dataset_images(self, view, save_dir)
 
     def tune(self) -> None:
         """Runs Optuna tunning of hyperparameters."""

luxonis_train/core/utils/infer_utils.py

@@ -1,12 +1,17 @@
 from pathlib import Path
 
 import cv2
+import torch
 from torch import Tensor
 
+from luxonis_train.attached_modules.visualizers import get_unnormalized_images
+from luxonis_train.enums import TaskType
+
 
 def render_visualizations(
     visualizations: dict[str, dict[str, Tensor]], save_dir: str | Path | None
 ) -> None:
+    """Render or save visualizations."""
     save_dir = Path(save_dir) if save_dir is not None else None
     if save_dir is not None:
         save_dir.mkdir(exist_ok=True, parents=True)
@@ -28,3 +33,66 @@
     if save_dir is None:
         if cv2.waitKey(0) == ord("q"):
             exit()
+
+
+def process_images(
+    model, img_paths: list[Path], view: str, save_dir: str | Path | None
+) -> None:
+    """Handles inference on one or more images."""
+    first_image = cv2.cvtColor(
+        cv2.imread(str(img_paths[0])), cv2.COLOR_BGR2RGB
+    )
+    labels = create_dummy_labels(model, view, first_image.shape)
+    for img_path in img_paths:
+        img = cv2.cvtColor(cv2.imread(str(img_path)), cv2.COLOR_BGR2RGB)
+        img, _ = (
+            model.train_augmentations([(img, {})])
+            if view == "train"
+            else model.val_augmentations([(img, {})])
+        )
+
+        inputs = {
+            "image": torch.tensor(img).unsqueeze(0).permute(0, 3, 1, 2).float()
+        }
+        images = get_unnormalized_images(model.cfg, inputs)
+
+        outputs = model.lightning_module.forward(
+            inputs, labels, images=images, compute_visualizations=True
+        )
+        render_visualizations(outputs.visualizations, save_dir)
+
+
+def process_dataset_images(
+    model, view: str, save_dir: str | Path | None
+) -> None:
+    """Handles the inference on dataset images."""
+    for inputs, labels in model.pytorch_loaders[view]:
+        images = get_unnormalized_images(model.cfg, inputs)
+        outputs = model.lightning_module.forward(
+            inputs, labels, images=images, compute_visualizations=True
+        )
+        render_visualizations(outputs.visualizations, save_dir)
+
+
+def create_dummy_labels(model, view: str, img_shape: tuple) -> dict:
+    """Prepares the labels for different tasks (classification,
+    keypoints, etc.)."""
+    tasks = list(model.loaders["train"].get_classes().keys())
+    h, w, _ = img_shape
+    labels = {}
+    nk = model.loaders[view].get_n_keypoints()["keypoints"]
+
+    for task in tasks:
+        if task == "classification":
+            labels[task] = [-1, TaskType.CLASSIFICATION]
+        elif task == "keypoints":
+            labels[task] = [torch.zeros((1, nk * 3 + 2)), TaskType.KEYPOINTS]
+        elif task == "segmentation":
+            labels[task] = [torch.zeros((1, h, w)), TaskType.SEGMENTATION]
+        elif task == "boundingbox":
+            labels[task] = [
+                torch.tensor([[-1, 0, 0, 0, 0, 0]]),
+                TaskType.BOUNDINGBOX,
+            ]
+
+    return labels