Backport changes to release

.github/workflows/ci.yml

@@ -46,9 +46,6 @@ jobs:
       run: npm run eslint
 
   unit_tests:
-    needs:
-      - linters_python
-      - linters_vue
     runs-on: ubuntu-latest
     name: ubuntu-latest-tests-python${{ matrix.python-version}}
     strategy:
@@ -81,7 +78,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip setuptools wheel "build<0.10.0" python-semantic-release
+          python -m pip install --upgrade pip setuptools wheel "build<1.3.0" python-semantic-release
 
       - name: Python Semantic Release
         id: release

.github/workflows/create_release.yaml

@@ -25,7 +25,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip setuptools wheel "build<0.10.0" python-semantic-release
+          python -m pip install --upgrade pip setuptools wheel "build<1.3.0" python-semantic-release
 
       - name: Python Semantic Release
         id: release

README.md

@@ -6,7 +6,8 @@ insights of a image dataset in [COCO][3] format and it evaluate image
 transformation and perturbation resilience of object recognition DL models. It
 is built using [trame][1] by the [kitware][2] team.
 
-![nrtk explorer](https://raw.githubusercontent.com/Kitware/nrtk-explorer/main/screenshot.png)
+![nrtk explorer screenshot](https://github.com/user-attachments/assets/85c95836-3490-40ec-813d-e6841c540d51)
+
 
 Features
 --------

captain-definition

@@ -0,0 +1,4 @@
+{
+  "schemaVersion": 2,
+  "dockerfilePath": "./docker/Dockerfile"
+}

docker/Dockerfile

@@ -0,0 +1,3 @@
+FROM kitware/trame:py3.10-glvnd-2024-09
+COPY --chown=trame-user:trame-user ./docker /deploy
+RUN /opt/trame/entrypoint.sh build

docker/setup/apps.yml

@@ -0,0 +1,17 @@
+trame:
+    www_modules:
+      - nrtk_explorer.module
+    cmd:
+        - python
+        - -m
+        - nrtk_explorer.app.main
+        - --host
+        - ${host}
+        - --port
+        - ${port}
+        - --authKey
+        - ${secret}
+        - --server
+        - --banner
+        - --dataset
+        - /data/OIRDS_v1_0/oirds.json

docker/setup/initialize.sh

@@ -0,0 +1 @@
+pip install /deploy/nrtk_explorer-0.3.1-py2.py3-none-any.whl

docker/setup/requirements.txt

@@ -0,0 +1,2 @@
+nrtk[headless]
+# nrtk-explorer

pyproject.toml

@@ -32,16 +32,15 @@ dependencies = [
     "numpy",
     "Pillow",
     "pybsm>=0.6",
-    "scikit-learn==1.5.1",
+    "scikit-learn==1.5.2",
     "smqtk_image_io",
     "tabulate",
     "timm>=1.0.3",
     "torch",
     "torchvision",
-    "trame",
-    "trame-client>=2.15.0",
+    "trame>=3.6",
     "trame-quasar",
-    "trame-server>=2.15.0",
+    "trame-server>=3.2",
     "transformers",
     "umap-learn",
 ]
@@ -60,7 +59,7 @@ dev = [
 ]
 
 package = [
-  "build<0.10.0",
+  "build<1.3.0",
   "python-semantic-release",
   "setuptools",
   "wheel",
@@ -78,10 +77,10 @@ build-backend = "hatchling.build"
 [tool.hatch.build.hooks.custom]
 
 [project.scripts]
-nrtk-explorer = "nrtk_explorer.app:main"
-nrtk-explorer-embeddings = "nrtk_explorer.app.embeddings:embeddings"
-nrtk-explorer-transforms = "nrtk_explorer.app.transforms:transforms"
-nrtk-explorer-filtering = "nrtk_explorer.app.filtering:filtering"
+nrtk-explorer = "nrtk_explorer.app.main:main"
+nrtk-explorer-embeddings = "nrtk_explorer.app.embeddings:main"
+nrtk-explorer-transforms = "nrtk_explorer.app.transforms:main"
+nrtk-explorer-filtering = "nrtk_explorer.app.filtering:main"
 
 [tool.black]
 line-length = 99
@@ -126,7 +125,7 @@ build_command = """
   python -m venv .venv
   source .venv/bin/activate
   pip install -U pip
-  python -m pip install "build<0.10.0" python-semantic-release setuptools wheel
+  python -m pip install "build<1.3.0" python-semantic-release setuptools wheel
   python -m build .
 """
 

src/nrtk_explorer/__main__.py

@@ -0,0 +1,4 @@
+from .app.main import main
+
+if __name__ == "__main__":
+    main()

src/nrtk_explorer/app/__init__.py

@@ -1,5 +0,0 @@
-from .main import main
-
-__all__ = [
-    "main",
-]

src/nrtk_explorer/app/core.py

@@ -1,13 +1,13 @@
 import logging
 from typing import Iterable
-from pathlib import Path
 
 from trame.widgets import html
 from trame_server.utils.namespace import Translator
-from nrtk_explorer.library import images_manager
 from nrtk_explorer.library.filtering import FilterProtocol
 from nrtk_explorer.library.dataset import get_dataset, get_image_fpath
+from nrtk_explorer.library.debounce import debounce
 
+from nrtk_explorer.app.images.images import Images
 from nrtk_explorer.app.embeddings import EmbeddingsApp
 from nrtk_explorer.app.transforms import TransformsApp
 from nrtk_explorer.app.filtering import FilteringApp
@@ -25,13 +25,13 @@
 html.Template.slot_names.add("before")
 html.Template.slot_names.add("after")
 
-HORIZONTAL_SPLIT_DEFAULT_VALUE = 17
-VERTICAL_SPLIT_DEFAULT_VALUE = 40
 
 DIR_NAME = os.path.dirname(nrtk_explorer.test_data.__file__)
 DEFAULT_DATASETS = [
     f"{DIR_NAME}/coco-od-2017/test_val2017.json",
 ]
+NUM_IMAGES_DEFAULT = 500
+NUM_IMAGES_DEBOUNCE_TIME = 0.3  # seconds
 
 
 # ---------------------------------------------------------
@@ -52,41 +52,18 @@ def __init__(self, server=None):
 
         known_args, _ = self.server.cli.parse_known_args()
         self.input_paths = known_args.dataset
-        self.state.current_dataset = str(Path(self.input_paths[0]).resolve())
+        self.state.current_dataset = self.input_paths[0]
 
         self.ctrl.get_image_fpath = lambda i: get_image_fpath(i, self.state.current_dataset)
-
-        self.context["image_objects"] = {}
-        self.context["images_manager"] = images_manager.ImagesManager()
-
-        self.state.collapse_dataset = False
-        self.state.collapse_embeddings = False
-        self.state.collapse_filter = False
-        self.state.collapse_transforms = False
-        self.state.client_only(
-            "collapse_dataset", "collapse_embeddings", "collapse_filter", "collapse_transforms"
-        )
-
-        self.state.horizontal_split = HORIZONTAL_SPLIT_DEFAULT_VALUE
-        self.state.vertical_split = VERTICAL_SPLIT_DEFAULT_VALUE
-        self.state.client_only("horizontal_split", "vertical_split")
-
-        transforms_translator = Translator()
-        transforms_translator.add_translation(
-            "feature_extraction_model", "current_transforms_model"
-        )
+        images = Images(server=self.server)
 
         self._transforms_app = TransformsApp(
-            server=self.server.create_child_server(translator=transforms_translator)
-        )
-
-        embeddings_translator = Translator()
-        embeddings_translator.add_translation(
-            "feature_extraction_model", "current_embeddings_model"
+            server=self.server.create_child_server(), images=images
         )
 
         self._embeddings_app = EmbeddingsApp(
-            server=self.server.create_child_server(translator=embeddings_translator),
+            server=self.server.create_child_server(),
+            images=images,
         )
 
         filtering_translator = Translator()
@@ -95,15 +72,11 @@ def __init__(self, server=None):
             server=self.server.create_child_server(translator=filtering_translator),
         )
 
-        self._embeddings_app.set_on_select(self._transforms_app.set_selected_dataset_ids)
         self._transforms_app.set_on_transform(self._embeddings_app.on_run_transformations)
         self._embeddings_app.set_on_hover(self._transforms_app.on_image_hovered)
         self._transforms_app.set_on_hover(self._embeddings_app.on_image_hovered)
         self._filtering_app.set_on_apply_filter(self.on_filter_apply)
 
-        # Set state variable
-        self.state.trame__title = "nrtk_explorer"
-
         # Bind instance methods to controller
         self.ctrl.on_server_reload = self._build_ui
         self.ctrl.add("on_server_ready")(self.on_server_ready)
@@ -112,84 +85,74 @@ def __init__(self, server=None):
         self.state.num_images_disabled = True
         self.state.random_sampling = False
         self.state.random_sampling_disabled = True
-        self.state.images_id = []
+        self.state.dataset_ids = []
+        self.state.hovered_id = None
+
+        def clear_hovered(**kwargs):
+            self.state.hovered_id = None
+
+        self.state.change("dataset_ids")(clear_hovered)
 
         self._build_ui()
 
     def on_server_ready(self, *args, **kwargs):
         # Bind instance methods to state change
         self.state.change("current_dataset")(self.on_dataset_change)
-        self.state.change("num_images")(self.on_num_images_change)
-        self.state.change("random_sampling")(self.on_random_sampling_change)
+        self.state.change("num_images")(
+            debounce(NUM_IMAGES_DEBOUNCE_TIME, self.state)(self.resample_images)
+        )
+        self.state.change("random_sampling")(self.resample_images)
 
         self.on_dataset_change()
 
     def on_dataset_change(self, **kwargs):
-        # Reset cache
-        self.context.images_manager = images_manager.ImagesManager()
+        self.state.dataset_ids = []  # sampled images
         self.context.dataset = get_dataset(self.state.current_dataset, force_reload=True)
         self.state.num_images_max = len(self.context.dataset.imgs)
+        self.state.num_images = min(self.state.num_images_max, NUM_IMAGES_DEFAULT)
+        self.state.dirty("num_images")  # Trigger resample_images()
         self.state.random_sampling_disabled = False
         self.state.num_images_disabled = False
 
-        self.reload_images()
+        self.state.annotation_categories = {
+            category["id"]: category for category in self.context.dataset.cats.values()
+        }
 
     def on_filter_apply(self, filter: FilterProtocol[Iterable[int]], **kwargs):
-        selected_indices = []
-        for index, image_id in enumerate(self.state.images_ids):
+        selected_ids = []
+        for dataset_id in self.state.dataset_ids:
             image_annotations_categories = [
                 annotation["category_id"]
                 for annotation in self.context.dataset.anns.values()
-                if annotation["image_id"] == image_id
+                if annotation["image_id"] == int(dataset_id)
             ]
             include = filter.evaluate(image_annotations_categories)
             if include:
-                selected_indices.append(index)
+                selected_ids.append(dataset_id)
 
-        self._embeddings_app.on_select(selected_indices)
-
-    def on_num_images_change(self, **kwargs):
-        self.reload_images()
-
-    def on_random_sampling_change(self, **kwargs):
-        self.reload_images()
-
-    def reload_images(self):
-        categories = {}
-        for category in self.context.dataset.cats.values():
-            categories[category["id"]] = category
+        self._embeddings_app.on_select(selected_ids)
 
+    def resample_images(self, **kwargs):
         images = list(self.context.dataset.imgs.values())
 
         selected_images = []
         if self.state.num_images:
             if self.state.random_sampling:
-                selected_images = random.sample(images, self.state.num_images)
+                selected_images = random.sample(images, min(len(images), self.state.num_images))
             else:
                 selected_images = images[: self.state.num_images]
         else:
             selected_images = images
 
-        paths = list()
-        for image in selected_images:
-            paths.append(
-                os.path.join(
-                    os.path.dirname(self.state.current_dataset),
-                    image["file_name"],
-                )
-            )
-
-        self.context.paths = paths
-        self.state.annotation_categories = categories
-        self.state.images_ids = [img["id"] for img in selected_images]
+        self.state.dataset_ids = [str(img["id"]) for img in selected_images]
 
     def _build_ui(self):
         extra_args = {}
         if self.server.hot_reload:
             ui.reload(ui)
             extra_args["reload"] = self._build_ui
 
-        self.ui = ui.build_layout(
+        self.ui = ui.NrtkExplorerLayout(
             server=self.server,
             dataset_paths=self.input_paths,
             embeddings_app=self._embeddings_app,