diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index 108c2a65e..6a92c2e3b 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -25,15 +25,15 @@ Tell us a little about the system you're using.
Please include information about how you installed.
-->
-- OS:
+- OS:
<!-- [e.g. ubuntu 20.04, macOS 11.0] -->
-- Version(s):
-<!-- e.g. [SLEAP v1.4.1a2, python 3.8] --->
-- SLEAP installation method (listed [here](https://sleap.ai/installation.html#)):
- - [ ] [Conda from package](https://sleap.ai/installation.html#conda-package)
- - [ ] [Conda from source](https://sleap.ai/installation.html#conda-from-source)
- - [ ] [pip package](https://sleap.ai/installation.html#pip-package)
- - [ ] [Apple Silicon Macs](https://sleap.ai/installation.html#apple-silicon-macs)
+- Version(s):
+<!-- e.g. [SLEAP v1.4.1, python 3.8] --->
+- SLEAP installation method (listed [here](https://sleap.ai/installation.html#)):
+ - [ ] [Conda from package](https://sleap.ai/installation.html#conda-package)
+ - [ ] [Conda from source](https://sleap.ai/installation.html#conda-from-source)
+ - [ ] [pip package](https://sleap.ai/installation.html#pip-package)
+ - [ ] [Apple Silicon Macs](https://sleap.ai/installation.html#apple-silicon-macs)
<details><summary>Environment packages</summary>
<!-- For reproduction, it's useful to have the full environment. For example, the output of `pip freeze` or `conda list` --->
diff --git a/.github/workflows/build_conda_ci.yml b/.github/workflows/build_conda_ci.yml
index 0d5980730..3fd3d2b92 100644
--- a/.github/workflows/build_conda_ci.yml
+++ b/.github/workflows/build_conda_ci.yml
@@ -11,7 +11,7 @@ on:
- "requirements.txt"
- "dev_requirements.txt"
- "environment_build.yml"
- - ".github/workflows/build_conda_ci.yml"
+ - ".github/workflows/build_conda_ci.yml" # Run!
# If RUN_BUILD_JOB is set to true, then RUN_ID will be overwritten to the current run id
env:
diff --git a/.github/workflows/build_pypi_ci.yml b/.github/workflows/build_pypi_ci.yml
index c22cc5a69..68142b288 100644
--- a/.github/workflows/build_pypi_ci.yml
+++ b/.github/workflows/build_pypi_ci.yml
@@ -11,7 +11,7 @@ on:
- "jupyter_requirements.txt"
- "pypi_requirements.txt"
- "environment_build.yml"
- - ".github/workflows/build_pypi_ci.yml"
+ - ".github/workflows/build_pypi_ci.yml" # Run!
jobs:
build:
diff --git a/.github/workflows/website.yml b/.github/workflows/website.yml
index ede9eef9f..36c1d6ad7 100644
--- a/.github/workflows/website.yml
+++ b/.github/workflows/website.yml
@@ -7,8 +7,8 @@ on:
branches:
# 'main' triggers updates to 'sleap.ai', all others to 'sleap.ai/develop'
- main
- - develop
- - liezl/update-intallation-docs-1.4.1 # again!
+ - develop # Run
+ - liezl/bump-to-1.4.1
paths:
- "docs/**"
- "README.rst"
diff --git a/README.rst b/README.rst
index 7cc9b27a3..f7a5acd6c 100644
--- a/README.rst
+++ b/README.rst
@@ -69,7 +69,7 @@ Quick install
.. code-block:: bash
- conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap
+ conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap
`pip` **(any OS except Apple silicon)**:
diff --git a/docs/_static/bonsai-connection.jpg b/docs/_static/bonsai-connection.jpg
new file mode 100644
index 000000000..32b725416
Binary files /dev/null and b/docs/_static/bonsai-connection.jpg differ
diff --git a/docs/_static/bonsai-filecapture.jpg b/docs/_static/bonsai-filecapture.jpg
new file mode 100644
index 000000000..7a809d67a
Binary files /dev/null and b/docs/_static/bonsai-filecapture.jpg differ
diff --git a/docs/_static/bonsai-predictcentroids.jpg b/docs/_static/bonsai-predictcentroids.jpg
new file mode 100644
index 000000000..e284f2338
Binary files /dev/null and b/docs/_static/bonsai-predictcentroids.jpg differ
diff --git a/docs/_static/bonsai-predictposeidentities.jpg b/docs/_static/bonsai-predictposeidentities.jpg
new file mode 100644
index 000000000..8582fd707
Binary files /dev/null and b/docs/_static/bonsai-predictposeidentities.jpg differ
diff --git a/docs/_static/bonsai-predictposes.jpg b/docs/_static/bonsai-predictposes.jpg
new file mode 100644
index 000000000..2e4f04a22
Binary files /dev/null and b/docs/_static/bonsai-predictposes.jpg differ
diff --git a/docs/_static/bonsai-workflow.jpg b/docs/_static/bonsai-workflow.jpg
new file mode 100644
index 000000000..0481c3dcf
Binary files /dev/null and b/docs/_static/bonsai-workflow.jpg differ
diff --git a/docs/conf.py b/docs/conf.py
index 796497f6b..074869903 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -28,7 +28,7 @@
copyright = f"2019–{date.today().year}, Talmo Lab"
# The short X.Y version
-version = "1.4.1a2"
+version = "1.4.1"
# Get the sleap version
# with open("../sleap/version.py") as f:
@@ -36,7 +36,7 @@
# version = re.search("\d.+(?=['\"])", version_file).group(0)
# Release should be the full branch name
-release = "v1.4.1a2"
+release = "v1.4.1"
html_title = f"SLEAP ({release})"
html_short_title = "SLEAP"
diff --git a/docs/guides/bonsai.md b/docs/guides/bonsai.md
new file mode 100644
index 000000000..d262873b6
--- /dev/null
+++ b/docs/guides/bonsai.md
@@ -0,0 +1,75 @@
+(bonsai)=
+
+# Using Bonsai with SLEAP
+
+Bonsai is a visual language for reactive programming and currently supports SLEAP models.
+
+:::{note}
+Currently Bonsai supports only single instance, top-down and top-down-id SLEAP models.
+:::
+
+### Exporting a SLEAP trained model
+
+Before we can import a trained model into Bonsai, we need to use the {code}`sleap-export` command to convert the model to a format supported by Bonsai. For example, to export a top-down-id model, the command is as follows:
+
+```bash
+sleap-export -m centroid/model/folder/path -m top_down_id/model/folder/path -e exported/model/path
+```
+
+Please refer to the {ref}`sleap-export` docs for more details on using the command.
+
+This will generate the necessary `.pb` file and other information files required by Bonsai. In this example, these files were saved to the specified `exported/model/path` folder.
+
+The `exported/model/path` folder will have a structure like the following:
+
+```plaintext
+exported/model/path
+├── centroid_config.json
+├── confmap_config.json
+├── frozen_graph.pb
+└── info.json
+```
+
+### Installing Bonsai and necessary packages
+
+1. Install Bonsai. See the [Bonsai installation instructions](https://bonsai-rx.org/docs/articles/installation.html).
+
+2. Download and add the necessary packages for Bonsai to run with SLEAP. See the official [Bonsai SLEAP documentation](https://github.com/bonsai-rx/sleap?tab=readme-ov-file#bonsai---sleap) for more information.
+
+### Using Bonsai SLEAP modules
+
+Once you have Bonsai installed with the required packages, you should be able to open the Bonsai application. The workflow must have a source module `FileCapture` which can be found in the toolbox search in the workflow editor. Provide the path to the video that was used to train the SLEAP model in the `FileName` field of the module.
+
+
+
+#### Top-down model
+The top-down model requires both the `PredictCentroids` and the `PredictPoses` modules.
+
+The `PredictCentroids` module will predict the centroids of detections. There are two fields inside the `PredictCentroids` module: the `ModelFileName` field and the `TrainingConfig` field. The `TrainingConfig` field expects the path to the training config JSON file for the centroid model. The `ModelFileName` field expects the path to the `frozen_graph.pb` file in the `exported/model/path` folder.
+
+
+
+The `PredictPoses` module will predict the instances of detections. Similar to the `PredictCentroid` module, there are two fields inside the `PredictPoses` module: the `ModelFileName` field and the `TrainingConfig` field. The `TrainingConfig` field expects the path to the training config JSON file for the centered instance model. The `ModelFileName` field expects the path to the `frozen_graph.pb` file in the `exported/model/path` folder.
+
+
+
+#### Top-Down-ID model
+The `PredictPoseIdentities` module will predict the instances with identities. This module has two fields: the `ModelFileName` field and the `TrainingConfig` field. The `TrainingConfig` field expects the path to the training config JSON file for the top-down-id model. The `ModelFileName` field expects the path to the `frozen_graph.pb` file in the `exported/model/path` folder.
+
+
+
+#### Single instance model
+The `PredictSinglePose` module will predict the poses for single instance models. This module also has two fields: the `ModelFileName` field and the `TrainingConfig` field. The `TrainingConfig` field expects the path to the training config JSON file for the single instance model. The `ModelFileName` field expects the path to the `frozen_graph.pb` file in the `exported/model/path` folder.
+
+### Connecting the modules
+Right-click on the `FileCapture` module and select **Create Connection**. Now click on the required SLEAP module to complete the connection.
+
+
+
+Once it is done, the workflow in Bonsai will look something like the following:
+
+
+
+Now you can click the green start button to run the workflow and you can add more modules to analyze and visualize the results in Bonsai.
+
+For more documentation on various modules and workflows, please refer to the [official Bonsai docs](https://bonsai-rx.org/docs/articles/editor.html).
diff --git a/docs/guides/cli.md b/docs/guides/cli.md
index 134461c60..339c5405b 100644
--- a/docs/guides/cli.md
+++ b/docs/guides/cli.md
@@ -230,7 +230,7 @@ optional arguments:
--tracking.kf_node_indices TRACKING.KF_NODE_INDICES
For Kalman filter: Indices of nodes to track. (default: )
--tracking.kf_init_frame_count TRACKING.KF_INIT_FRAME_COUNT
- For Kalman filter: Number of frames to track with other tracker. 0 means no Kalman filters will be used. (default: 0)
+ For Kalman filter: Number of frames to track with other tracker. 0 means no Kalman filters will be used. (default: 0) Kalman filters require TRACKING.KF_NODE_INDICES, TRACKING.MAX_TRACKING and TRACKING.MAX_TRACKS or TRACKING.TARGET_INSTANCE_COUNT, TRACKING.TRACKER to be simple or simplemaxtracks, and TRACKING.SIMILARITY to not be normalized_instance.
```
#### Examples:
@@ -285,6 +285,12 @@ sleap-track --gpu 1 ...
sleap-track -m "models/my_model" --frames 1000-2000 "input_video.mp4"
```
+**9. Use Kalman tracker (not recommended since flow is preferred):**
+
+```none
+sleap-track -m "models/my_model" --tracking.similarity instance --tracking.tracker simplemaxtracks --tracking.max_tracking 1 --tracking.max_tracks 4 --tracking.kf_init_frame_count 10 --tracking.kf_node_indices 0,1 -o "output_predictions.slp" "input_video.mp4"
+```
+
## Dataset files
(sleap-convert)=
diff --git a/docs/guides/index.md b/docs/guides/index.md
index 7eb55b2b2..6d773d9de 100644
--- a/docs/guides/index.md
+++ b/docs/guides/index.md
@@ -30,6 +30,10 @@
{ref}`remote-inference` when you trained models and you want to run inference on a different machine using a **command-line interface**.
+## SLEAP with Bonsai
+
+{ref}`bonsai` when you want to analyze the trained SLEAP model to visualize the poses, centroids and identities for further visual analysis.
+
```{toctree}
:hidden: true
:maxdepth: 2
@@ -44,4 +48,5 @@ proofreading
colab
custom-training
remote
+bonsai
```
diff --git a/docs/installation.md b/docs/installation.md
index 4799a0893..2c1ef41be 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,12 +5,12 @@ SLEAP can be installed as a Python package on Windows, Linux, and Mac OS. For qu
````{tabs}
```{group-tab} Windows and Linux
```bash
- conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap=1.4.1a2
+ conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.4.1
```
```
```{group-tab} Mac OS
```bash
- conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1a2
+ conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1
```
```
````
@@ -27,7 +27,7 @@ local:
Installation requires entering commands in a terminal. To open one:
````{tabs}
```{tab} Windows
- Open the *Start menu* and search for the *Anaconda Prompt* (if using Miniconda) or the *Command Prompt* if not.
+ Open the *Start menu* and search for the *Anaconda Prompt* (if using Miniconda) or the *Command Prompt* if not.
```{note}
On Windows, our personal preference is to use alternative terminal apps like [Cmder](https://cmder.net) or [Windows Terminal](https://aka.ms/terminal).
```
@@ -66,7 +66,6 @@ If you don't have a `conda` package manager installation, here are some quick in
Miniforge is a minimal installer for conda that includes the `conda` package manager and is maintained by the [conda-forge](https://conda-forge.org) community. The only difference between Miniforge and Miniconda is that Miniforge uses the `conda-forge` channel by default, which provides a much wider selection of community-maintained packages.
-
````{tabs}
```{group-tab} Windows
Open a new PowerShell terminal (does not need to be admin) and enter:
@@ -135,20 +134,20 @@ This is a minimal installer for conda that includes the `conda` package manager
See the [Miniconda website](https://docs.anaconda.com/free/miniconda/) for up-to-date installation instructions if the above instructions don't work for your system.
-
(installation-methods)=
+
## Installation methods
SLEAP can be installed three different ways: via {ref}`conda package<condapackage>`, {ref}`conda from source<condasource>`, or {ref}`pip package<pippackage>`. Select one of the methods below to install SLEAP. We recommend {ref}`conda package<condapackage>`.
-````{tabs}
+`````{tabs}
```{tab} conda package
**This is the recommended installation method**.
````{tabs}
```{group-tab} Windows and Linux
```bash
- conda create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap=1.4.1a2
- ```
+ conda create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.4.1
+ ```
```{note}
- This comes with CUDA to enable GPU support. All you need is to have an NVIDIA GPU and [updated drivers](https://nvidia.com/drivers).
- If you already have CUDA installed on your system, this will not conflict with it.
@@ -157,7 +156,7 @@ SLEAP can be installed three different ways: via {ref}`conda package<condapackag
```
```{group-tab} Mac OS
```bash
- conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1a2
+ conda create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1
```
```{note}
This will also work in CPU mode if you don't have a GPU on your machine.
@@ -222,7 +221,7 @@ SLEAP can be installed three different ways: via {ref}`conda package<condapackag
````{tabs}
```{group-tab} NVIDIA GPU
```bash
- conda create --name sleap pip python=3.7.12 cudatoolkit=11.3 cudnn=8.2
+ conda create --name sleap pip python=3.7.12 cudatoolkit=11.3 cudnn=8.2 -c conda-forge -c nvidia
```
```
```{group-tab} CPU or other GPU
@@ -240,7 +239,7 @@ SLEAP can be installed three different ways: via {ref}`conda package<condapackag
```
3. Finally, we can perform the `pip install`:
```bash
- pip install sleap[pypi]==1.4.1a2
+ pip install sleap[pypi]==1.4.1
```
```{note}
The pypi distributed package of SLEAP ships with the following extras:
@@ -256,7 +255,7 @@ SLEAP can be installed three different ways: via {ref}`conda package<condapackag
```
````
```
-````
+`````
## Testing that things are working
diff --git a/sleap/config/frame_range_form.yaml b/sleap/config/frame_range_form.yaml
new file mode 100644
index 000000000..3f01eade4
--- /dev/null
+++ b/sleap/config/frame_range_form.yaml
@@ -0,0 +1,13 @@
+main:
+
+ - name: min_frame_idx
+ label: Minimum frame index
+ type: int
+ range: 1,1000000
+ default: 1
+
+ - name: max_frame_idx
+ label: Maximum frame index
+ type: int
+ range: 1,1000000
+ default: 1000
\ No newline at end of file
diff --git a/sleap/gui/app.py b/sleap/gui/app.py
index 2dbceb3b7..8b711c806 100644
--- a/sleap/gui/app.py
+++ b/sleap/gui/app.py
@@ -656,17 +656,18 @@ def prev_vid():
key="edge style",
)
+ # XXX
add_submenu_choices(
menu=viewMenu,
title="Node Marker Size",
- options=(1, 2, 4, 6, 8, 12),
+ options=prefs["node marker sizes"],
key="marker size",
)
add_submenu_choices(
menu=viewMenu,
title="Node Label Size",
- options=(6, 12, 18, 24, 36),
+ options=prefs["node label sizes"],
key="node label size",
)
@@ -804,6 +805,12 @@ def new_instance_menu_action():
"Delete Predictions beyond Max Instances...",
self.commands.deleteInstanceLimitPredictions,
)
+ add_menu_item(
+ labelMenu,
+ "delete frame limit predictions",
+ "Delete Predictions beyond Frame Limit...",
+ self.commands.deleteFrameLimitPredictions,
+ )
### Tracks Menu ###
@@ -873,6 +880,8 @@ def new_instance_menu_action():
"Point Displacement (max)",
"Primary Point Displacement (sum)",
"Primary Point Displacement (max)",
+ "Tracking Score (mean)",
+ "Tracking Score (min)",
"Instance Score (sum)",
"Instance Score (min)",
"Point Score (sum)",
@@ -1331,7 +1340,7 @@ def updateStatusMessage(self, message: Optional[str] = None):
message += f" [Hidden] Press '{hide_key}' to toggle."
self.statusBar().setStyleSheet("color: red")
else:
- self.statusBar().setStyleSheet("color: black")
+ self.statusBar().setStyleSheet("")
self.statusBar().showMessage(message)
@@ -1406,6 +1415,8 @@ def _set_seekbar_header(self, graph_name: str):
"Point Displacement (max)": data_obj.get_point_displacement_series,
"Primary Point Displacement (sum)": data_obj.get_primary_point_displacement_series,
"Primary Point Displacement (max)": data_obj.get_primary_point_displacement_series,
+ "Tracking Score (mean)": data_obj.get_tracking_score_series,
+ "Tracking Score (min)": data_obj.get_tracking_score_series,
"Instance Score (sum)": data_obj.get_instance_score_series,
"Instance Score (min)": data_obj.get_instance_score_series,
"Point Score (sum)": data_obj.get_point_score_series,
@@ -1419,7 +1430,7 @@ def _set_seekbar_header(self, graph_name: str):
else:
if graph_name in header_functions:
kwargs = dict(video=self.state["video"])
- reduction_name = re.search("\\((sum|max|min)\\)", graph_name)
+ reduction_name = re.search("\\((sum|max|min|mean)\\)", graph_name)
if reduction_name is not None:
kwargs["reduction"] = reduction_name.group(1)
series = header_functions[graph_name](**kwargs)
diff --git a/sleap/gui/commands.py b/sleap/gui/commands.py
index dfc0dbad8..fca982327 100644
--- a/sleap/gui/commands.py
+++ b/sleap/gui/commands.py
@@ -49,6 +49,7 @@ class which inherits from `AppCommand` (or a more specialized class such as
from sleap.gui.dialogs.merge import MergeDialog, ReplaceSkeletonTableDialog
from sleap.gui.dialogs.message import MessageDialog
from sleap.gui.dialogs.missingfiles import MissingFilesDialog
+from sleap.gui.dialogs.frame_range import FrameRangeDialog
from sleap.gui.state import GuiState
from sleap.gui.suggestions import VideoFrameSuggestions
from sleap.instance import Instance, LabeledFrame, Point, PredictedInstance, Track
@@ -494,6 +495,10 @@ def deleteInstanceLimitPredictions(self):
"""Gui for deleting instances beyond some number in each frame."""
self.execute(DeleteInstanceLimitPredictions)
+ def deleteFrameLimitPredictions(self):
+ """Gui for deleting instances beyond some frame number."""
+ self.execute(DeleteFrameLimitPredictions)
+
def completeInstanceNodes(self, instance: Instance):
"""Adds missing nodes to given instance."""
self.execute(AddMissingInstanceNodes, instance=instance)
@@ -2472,6 +2477,36 @@ def ask(cls, context: CommandContext, params: dict) -> bool:
return super().ask(context, params)
+class DeleteFrameLimitPredictions(InstanceDeleteCommand):
+ @staticmethod
+ def get_frame_instance_list(context: CommandContext, params: Dict):
+ """Called from the parent `InstanceDeleteCommand.ask` method.
+
+ Returns:
+ List of instances to be deleted.
+ """
+ instances = []
+ # Select the instances to be deleted
+ for lf in context.labels.labeled_frames:
+ if lf.frame_idx < (params["min_frame_idx"] - 1) or lf.frame_idx > (
+ params["max_frame_idx"] - 1
+ ):
+ instances.extend([(lf, inst) for inst in lf.instances])
+ return instances
+
+ @classmethod
+ def ask(cls, context: CommandContext, params: Dict) -> bool:
+ current_video = context.state["video"]
+ dialog = FrameRangeDialog(
+ title="Delete Instances in Frame Range...", max_frame_idx=len(current_video)
+ )
+ results = dialog.get_results()
+ if results:
+ params["min_frame_idx"] = results["min_frame_idx"]
+ params["max_frame_idx"] = results["max_frame_idx"]
+ return super().ask(context, params)
+
+
class TransposeInstances(EditCommand):
topics = [UpdateTopic.project_instances, UpdateTopic.tracks]
diff --git a/sleap/gui/dataviews.py b/sleap/gui/dataviews.py
index f68dc0180..721bdc321 100644
--- a/sleap/gui/dataviews.py
+++ b/sleap/gui/dataviews.py
@@ -15,20 +15,17 @@
"""
-from qtpy import QtCore, QtWidgets, QtGui
-
-import numpy as np
import os
-
from operator import itemgetter
+from pathlib import Path
+from typing import Any, Callable, List, Optional
-from typing import Any, Callable, Dict, List, Optional, Type
+import numpy as np
+from qtpy import QtCore, QtGui, QtWidgets
-from sleap.gui.state import GuiState
from sleap.gui.commands import CommandContext
-from sleap.gui.color import ColorManager
-from sleap.io.dataset import Labels
-from sleap.instance import LabeledFrame, Instance
+from sleap.gui.state import GuiState
+from sleap.instance import LabeledFrame
from sleap.skeleton import Skeleton
@@ -386,10 +383,25 @@ def getSelectedRowItem(self) -> Any:
class VideosTableModel(GenericTableModel):
- properties = ("filename", "frames", "height", "width", "channels")
-
- def item_to_data(self, obj, item):
- return {key: getattr(item, key) for key in self.properties}
+ properties = (
+ "name",
+ "filepath",
+ "frames",
+ "height",
+ "width",
+ "channels",
+ )
+
+ def item_to_data(self, obj, item: "Video"):
+ data = {}
+ for property in self.properties:
+ if property == "name":
+ data[property] = Path(item.filename).name
+ elif property == "filepath":
+ data[property] = str(Path(item.filename).parent)
+ else:
+ data[property] = getattr(item, property)
+ return data
class SkeletonNodesTableModel(GenericTableModel):
diff --git a/sleap/gui/dialogs/frame_range.py b/sleap/gui/dialogs/frame_range.py
new file mode 100644
index 000000000..7165dd939
--- /dev/null
+++ b/sleap/gui/dialogs/frame_range.py
@@ -0,0 +1,42 @@
+"""Frame range dialog."""
+from qtpy import QtWidgets
+from sleap.gui.dialogs.formbuilder import FormBuilderModalDialog
+from typing import Optional
+
+
+class FrameRangeDialog(FormBuilderModalDialog):
+ def __init__(self, max_frame_idx: Optional[int] = None, title: str = "Frame Range"):
+
+ super().__init__(form_name="frame_range_form")
+ min_frame_idx_field = self.form_widget.fields["min_frame_idx"]
+ max_frame_idx_field = self.form_widget.fields["max_frame_idx"]
+
+ if max_frame_idx is not None:
+ min_frame_idx_field.setRange(1, max_frame_idx)
+ min_frame_idx_field.setValue(1)
+
+ max_frame_idx_field.setRange(1, max_frame_idx)
+ max_frame_idx_field.setValue(max_frame_idx)
+
+ min_frame_idx_field.valueChanged.connect(self._update_max_frame_range)
+ max_frame_idx_field.valueChanged.connect(self._update_min_frame_range)
+
+ self.setWindowTitle(title)
+
+ def _update_max_frame_range(self, value):
+ min_frame_idx_field = self.form_widget.fields["min_frame_idx"]
+ max_frame_idx_field = self.form_widget.fields["max_frame_idx"]
+
+ max_frame_idx_field.setRange(value, max_frame_idx_field.maximum())
+
+ def _update_min_frame_range(self, value):
+ min_frame_idx_field = self.form_widget.fields["min_frame_idx"]
+ max_frame_idx_field = self.form_widget.fields["max_frame_idx"]
+
+ min_frame_idx_field.setRange(min_frame_idx_field.minimum(), value)
+
+
+if __name__ == "__main__":
+ app = QtWidgets.QApplication([])
+ dialog = FrameRangeDialog(max_frame_idx=100)
+ print(dialog.get_results())
diff --git a/sleap/gui/learning/dialog.py b/sleap/gui/learning/dialog.py
index 2c2617036..bc26d826c 100644
--- a/sleap/gui/learning/dialog.py
+++ b/sleap/gui/learning/dialog.py
@@ -637,6 +637,20 @@ def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInferen
)
return items_for_inference
+ def _validate_id_model(self) -> bool:
+ """Make sure we have instances with tracks set for ID models."""
+ if not self.labels.tracks:
+ message = "Cannot run ID model training without tracks."
+ return False
+
+ found_tracks = False
+ for inst in self.labels.instances():
+ if type(inst) == sleap.Instance and inst.track is not None:
+ found_tracks = True
+ break
+
+ return found_tracks
+
def _validate_pipeline(self):
can_run = True
message = ""
@@ -655,6 +669,15 @@ def _validate_pipeline(self):
f"({', '.join(untrained)})."
)
+ # Make sure we have instances with tracks set for ID models.
+ if self.mode == "training" and self.current_pipeline in (
+ "top-down-id",
+ "bottom-up-id",
+ ):
+ can_run = self.validate_id_model()
+ if not can_run:
+ message = "Cannot run ID model training without tracks."
+
# Make sure skeleton will be valid for bottom-up inference.
if self.mode == "training" and self.current_pipeline == "bottom-up":
skeleton = self.labels.skeletons[0]
diff --git a/sleap/gui/widgets/video.py b/sleap/gui/widgets/video.py
index 949703020..08ee5bf36 100644
--- a/sleap/gui/widgets/video.py
+++ b/sleap/gui/widgets/video.py
@@ -62,6 +62,7 @@
QShortcut,
QVBoxLayout,
QWidget,
+ QPinchGesture,
)
import sleap
@@ -823,6 +824,8 @@ def __init__(self, state=None, player=None, *args, **kwargs):
# Set icon as default background.
self.setImage(QImage(sleap.util.get_package_file("gui/background.png")))
+ self.grabGesture(Qt.GestureType.PinchGesture)
+
def dragEnterEvent(self, event):
if self.parentWidget():
self.parentWidget().dragEnterEvent(event)
@@ -1189,6 +1192,23 @@ def keyReleaseEvent(self, event):
"""Custom event hander, disables default QGraphicsView behavior."""
event.ignore() # Kicks the event up to parent
+ def event(self, event):
+ if event.type() == QtCore.QEvent.Gesture:
+ return self.handleGestureEvent(event)
+ return super().event(event)
+
+ def handleGestureEvent(self, event):
+ gesture = event.gesture(Qt.GestureType.PinchGesture)
+ if gesture:
+ self.handlePinchGesture(gesture)
+ return True
+
+ def handlePinchGesture(self, gesture: QPinchGesture):
+ if gesture.state() == Qt.GestureState.GestureUpdated:
+ factor = gesture.scaleFactor()
+ self.zoomFactor = max(factor * self.zoomFactor, 1)
+ self.updateViewer()
+
class QtNodeLabel(QGraphicsTextItem):
"""
@@ -1570,7 +1590,6 @@ def mousePressEvent(self, event):
def mouseMoveEvent(self, event):
"""Custom event handler for mouse move."""
- # print(event)
if self.dragParent:
self.parentObject().mouseMoveEvent(event)
else:
@@ -1581,7 +1600,6 @@ def mouseMoveEvent(self, event):
def mouseReleaseEvent(self, event):
"""Custom event handler for mouse release."""
- # print(event)
self.unsetCursor()
if self.dragParent:
self.parentObject().mouseReleaseEvent(event)
@@ -1610,6 +1628,10 @@ def mouseDoubleClickEvent(self, event: QMouseEvent):
view = scene.views()[0]
view.instanceDoubleClicked.emit(self.parentObject().instance, event)
+ def hoverEnterEvent(self, event):
+ """Custom event handler for mouse hover enter."""
+ return super().hoverEnterEvent(event)
+
class QtEdge(QGraphicsPolygonItem):
"""
@@ -1809,6 +1831,7 @@ def __init__(
self.labels = {}
self.labels_shown = True
self._selected = False
+ self._is_hovering = False
self._bounding_rect = QRectF()
# Show predicted instances behind non-predicted ones
@@ -1830,6 +1853,7 @@ def __init__(
box_pen.setStyle(Qt.DashLine)
box_pen.setCosmetic(True)
self.box.setPen(box_pen)
+ self.setAcceptHoverEvents(True)
# Add label for highlighted instance
self.highlight_label = QtTextWithBackground(parent=self)
@@ -1991,7 +2015,12 @@ def updateBox(self, *args, **kwargs):
select this instance.
"""
# Only show box if instance is selected
- op = 0.7 if self._selected else 0
+ op = 0
+ if self._selected:
+ op = 0.8
+ elif self._is_hovering:
+ op = 0.4
+
self.box.setOpacity(op)
# Update the position for the box
rect = self.getPointsBoundingRect()
@@ -2085,6 +2114,16 @@ def paint(self, painter, option, widget=None):
"""Method required by Qt."""
pass
+ def hoverEnterEvent(self, event):
+ self._is_hovering = True
+ self.updateBox()
+ return super().hoverEnterEvent(event)
+
+ def hoverLeaveEvent(self, event):
+ self._is_hovering = False
+ self.updateBox()
+ return super().hoverLeaveEvent(event)
+
class VisibleBoundingBox(QtWidgets.QGraphicsRectItem):
"""QGraphicsRectItem for user instance bounding boxes.
@@ -2275,7 +2314,7 @@ def mouseReleaseEvent(self, event):
self.parent.nodes[node_key].setPos(new_x, new_y)
# Update the instance
- self.parent.updatePoints(complete=True, user_change=True)
+ self.parent.updatePoints(complete=False, user_change=True)
self.resizing = None
diff --git a/sleap/info/summary.py b/sleap/info/summary.py
index c6a6af60e..0cad1617e 100644
--- a/sleap/info/summary.py
+++ b/sleap/info/summary.py
@@ -21,7 +21,7 @@ class StatisticSeries:
are frame index and value are some numerical value for the frame.
Args:
- labels: The :class:`Labels` for which to calculate series.
+ labels: The `Labels` for which to calculate series.
"""
labels: Labels
@@ -41,7 +41,7 @@ def get_point_score_series(
"""Get series with statistic of point scores in each frame.
Args:
- video: The :class:`Video` for which to calculate statistic.
+ video: The `Video` for which to calculate statistic.
reduction: name of function applied to scores:
* sum
* min
@@ -67,7 +67,7 @@ def get_instance_score_series(self, video, reduction="sum") -> Dict[int, float]:
"""Get series with statistic of instance scores in each frame.
Args:
- video: The :class:`Video` for which to calculate statistic.
+ video: The `Video` for which to calculate statistic.
reduction: name of function applied to scores:
* sum
* min
@@ -93,7 +93,7 @@ def get_point_displacement_series(self, video, reduction="sum") -> Dict[int, flo
same track) from the closest earlier labeled frame.
Args:
- video: The :class:`Video` for which to calculate statistic.
+ video: The `Video` for which to calculate statistic.
reduction: name of function applied to point scores:
* sum
* mean
@@ -121,7 +121,7 @@ def get_primary_point_displacement_series(
Get sum of displacement for single node of each instance per frame.
Args:
- video: The :class:`Video` for which to calculate statistic.
+ video: The `Video` for which to calculate statistic.
reduction: name of function applied to point scores:
* sum
* mean
@@ -226,7 +226,7 @@ def _calculate_frame_velocity(
Calculate total point displacement between two given frames.
Args:
- lf: The :class:`LabeledFrame` for which we want velocity
+ lf: The `LabeledFrame` for which we want velocity
last_lf: The frame from which to calculate displacement.
reduce_function: Numpy function (e.g., np.sum, np.nanmean)
is applied to *point* displacement, and then those
@@ -246,3 +246,35 @@ def _calculate_frame_velocity(
inst_dist = reduce_function(point_dist)
val += inst_dist if not np.isnan(inst_dist) else 0
return val
+
+ def get_tracking_score_series(
+ self, video: Video, reduction: str = "min"
+ ) -> Dict[int, float]:
+ """Get series with statistic of tracking scores in each frame.
+
+ Args:
+ video: The `Video` for which to calculate statistic.
+ reduction: name of function applied to scores:
+ * mean
+ * min
+
+ Returns:
+ The series dictionary (see class docs for details)
+ """
+ reduce_fn = {
+ "min": np.nanmin,
+ "mean": np.nanmean,
+ }[reduction]
+
+ series = dict()
+
+ for lf in self.labels.find(video):
+ vals = [
+ inst.tracking_score for inst in lf if hasattr(inst, "tracking_score")
+ ]
+ if vals:
+ val = reduce_fn(vals)
+ if not np.isnan(val):
+ series[lf.frame_idx] = val
+
+ return series
diff --git a/sleap/instance.py b/sleap/instance.py
index 08a5c6ae6..382ececf2 100644
--- a/sleap/instance.py
+++ b/sleap/instance.py
@@ -1049,7 +1049,9 @@ def scores(self) -> np.ndarray:
return self.points_and_scores_array[:, 2]
@classmethod
- def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance":
+ def from_instance(
+ cls, instance: Instance, score: float, tracking_score: float = 0.0
+ ) -> "PredictedInstance":
"""Create a `PredictedInstance` from an `Instance`.
The fields are copied in a shallow manner with the exception of points. For each
@@ -1059,6 +1061,7 @@ def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance":
Args:
instance: The `Instance` object to shallow copy data from.
score: The score for this instance.
+ tracking_score: The tracking score for this instance.
Returns:
A `PredictedInstance` for the given `Instance`.
@@ -1070,6 +1073,7 @@ def from_instance(cls, instance: Instance, score: float) -> "PredictedInstance":
)
kw_args["points"] = PredictedPointArray.from_array(instance._points)
kw_args["score"] = score
+ kw_args["tracking_score"] = tracking_score
return cls(**kw_args)
@classmethod
@@ -1080,6 +1084,7 @@ def from_arrays(
instance_score: float,
skeleton: Skeleton,
track: Optional[Track] = None,
+ tracking_score: float = 0.0,
) -> "PredictedInstance":
"""Create a predicted instance from data arrays.
@@ -1094,6 +1099,7 @@ def from_arrays(
skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the
predicted instance.
track: Optional `sleap.Track` to associate with the instance.
+ tracking_score: Optional float representing the track matching score.
Returns:
A new `PredictedInstance`.
@@ -1114,6 +1120,7 @@ def from_arrays(
skeleton=skeleton,
score=instance_score,
track=track,
+ tracking_score=tracking_score,
)
@classmethod
@@ -1124,6 +1131,7 @@ def from_pointsarray(
instance_score: float,
skeleton: Skeleton,
track: Optional[Track] = None,
+ tracking_score: float = 0.0,
) -> "PredictedInstance":
"""Create a predicted instance from data arrays.
@@ -1138,12 +1146,18 @@ def from_pointsarray(
skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the
predicted instance.
track: Optional `sleap.Track` to associate with the instance.
+ tracking_score: Optional float representing the track matching score.
Returns:
A new `PredictedInstance`.
"""
return cls.from_arrays(
- points, point_confidences, instance_score, skeleton, track=track
+ points,
+ point_confidences,
+ instance_score,
+ skeleton,
+ track=track,
+ tracking_score=tracking_score,
)
@classmethod
@@ -1154,6 +1168,7 @@ def from_numpy(
instance_score: float,
skeleton: Skeleton,
track: Optional[Track] = None,
+ tracking_score: float = 0.0,
) -> "PredictedInstance":
"""Create a predicted instance from data arrays.
@@ -1168,12 +1183,18 @@ def from_numpy(
skeleton: A sleap.Skeleton instance with n_nodes nodes to associate with the
predicted instance.
track: Optional `sleap.Track` to associate with the instance.
+ tracking_score: Optional float representing the track matching score.
Returns:
A new `PredictedInstance`.
"""
return cls.from_arrays(
- points, point_confidences, instance_score, skeleton, track=track
+ points,
+ point_confidences,
+ instance_score,
+ skeleton,
+ track=track,
+ tracking_score=tracking_score,
)
diff --git a/sleap/io/format/coco.py b/sleap/io/format/coco.py
index 25122e4d0..44e7fb84a 100644
--- a/sleap/io/format/coco.py
+++ b/sleap/io/format/coco.py
@@ -180,6 +180,9 @@ def read(
if flag == 0:
# node not labeled for this instance
+ if (x, y) != (0, 0):
+ # If labeled but invisible, place the node at the coord
+ points[node] = Point(x, y, False)
continue
is_visible = flag == 2
diff --git a/sleap/nn/inference.py b/sleap/nn/inference.py
index df061a289..f33af8e73 100644
--- a/sleap/nn/inference.py
+++ b/sleap/nn/inference.py
@@ -1140,9 +1140,11 @@ def export_model(
info["predicted_tensors"] = tensors
full_model = tf.function(
- lambda x: sleap.nn.data.utils.unrag_example(model(x), numpy=False)
- if unrag_outputs
- else model(x)
+ lambda x: (
+ sleap.nn.data.utils.unrag_example(model(x), numpy=False)
+ if unrag_outputs
+ else model(x)
+ )
)
full_model = full_model.get_concrete_function(
@@ -3818,9 +3820,10 @@ def _object_builder():
PredictedInstance.from_numpy(
points=pts,
point_confidences=confs,
- instance_score=np.nanmean(score),
+ instance_score=np.nanmean(confs),
skeleton=skeleton,
track=track,
+ tracking_score=np.nanmean(score),
)
)
@@ -4502,18 +4505,27 @@ def _object_builder():
break
# Loop over frames.
- for image, video_ind, frame_ind, points, confidences, scores in zip(
+ for (
+ image,
+ video_ind,
+ frame_ind,
+ centroid_vals,
+ points,
+ confidences,
+ scores,
+ ) in zip(
ex["image"],
ex["video_ind"],
ex["frame_ind"],
+ ex["centroid_vals"],
ex["instance_peaks"],
ex["instance_peak_vals"],
ex["instance_scores"],
):
# Loop over instances.
predicted_instances = []
- for i, (pts, confs, score) in enumerate(
- zip(points, confidences, scores)
+ for i, (pts, centroid_val, confs, score) in enumerate(
+ zip(points, centroid_vals, confidences, scores)
):
if np.isnan(pts).all():
continue
@@ -4524,9 +4536,10 @@ def _object_builder():
PredictedInstance.from_numpy(
points=pts,
point_confidences=confs,
- instance_score=np.nanmean(score),
+ instance_score=centroid_val,
skeleton=skeleton,
track=track,
+ tracking_score=score,
)
)
@@ -5756,3 +5769,7 @@ def main(args: Optional[list] = None):
"To retrack on predictions, must specify tracker. "
"Use \"sleap-track --tracking.tracker ...' to specify tracker to use."
)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/sleap/nn/tracker/kalman.py b/sleap/nn/tracker/kalman.py
index 2b0343927..774a4634e 100644
--- a/sleap/nn/tracker/kalman.py
+++ b/sleap/nn/tracker/kalman.py
@@ -608,7 +608,7 @@ def remove_second_bests_from_cost_matrix(
cost matrix with invalid matches set to specified invalid value.
"""
- valid_match_mask = np.full_like(cost_matrix, True, dtype=np.bool)
+ valid_match_mask = np.full_like(cost_matrix, True, dtype=bool)
rows, columns = cost_matrix.shape
diff --git a/sleap/nn/tracking.py b/sleap/nn/tracking.py
index 558aa9309..231b004f5 100644
--- a/sleap/nn/tracking.py
+++ b/sleap/nn/tracking.py
@@ -574,7 +574,7 @@ class Tracker(BaseTracker):
max_tracking: bool = False # To enable maximum tracking.
cleaner: Optional[Callable] = None # TODO: deprecate
- target_instance_count: int = 0
+ target_instance_count: int = 0 # TODO: deprecate
pre_cull_function: Optional[Callable] = None
post_connect_single_breaks: bool = False
robust_best_instance: float = 1.0
@@ -824,8 +824,15 @@ def final_pass(self, frames: List[LabeledFrame]):
# "tracking."
# )
self.cleaner.run(frames)
- elif self.target_instance_count and self.post_connect_single_breaks:
+ elif (
+ self.target_instance_count or self.max_tracks
+ ) and self.post_connect_single_breaks:
+ if not self.target_instance_count:
+ # If target_instance_count is not set, use max_tracks instead
+ # target_instance_count not available in the GUI
+ self.target_instance_count = self.max_tracks
connect_single_track_breaks(frames, self.target_instance_count)
+ print("Connecting single track breaks.")
def get_name(self):
tracker_name = self.candidate_maker.__class__.__name__
@@ -850,7 +857,7 @@ def make_tracker_by_name(
of_max_levels: int = 3,
save_shifted_instances: bool = False,
# Pre-tracking options to cull instances
- target_instance_count: int = 0,
+ target_instance_count: int = 0, # TODO: deprecate target_instance_count
pre_cull_to_target: bool = False,
pre_cull_iou_threshold: Optional[float] = None,
# Post-tracking options to connect broken tracks
@@ -921,6 +928,7 @@ def make_tracker_by_name(
pre_cull_function = None
if target_instance_count and pre_cull_to_target:
+ # Right now this is not accessible from the GUI
def pre_cull_function(inst_list):
cull_frame_instances(
@@ -940,11 +948,34 @@ def pre_cull_function(inst_list):
pre_cull_function=pre_cull_function,
max_tracking=max_tracking,
max_tracks=max_tracks,
- target_instance_count=target_instance_count,
+ target_instance_count=target_instance_count, # TODO: deprecate target_instance_count
post_connect_single_breaks=post_connect_single_breaks,
)
- if target_instance_count and kf_init_frame_count:
+ # Kalman filter requires deprecated target_instance_count
+ if (max_tracks or target_instance_count) and kf_init_frame_count:
+ if not kf_node_indices:
+ raise ValueError(
+ "Kalman filter requires node indices for instance tracking."
+ )
+
+ if tracker == "flow" or tracker == "flowmaxtracks":
+ # Tracking with Kalman filter requires initial tracker object to be simple
+ raise ValueError(
+ "Kalman filter requires simple tracker for initial tracking."
+ )
+
+ if similarity == "normalized_instance":
+ # Kalman filter doesnot support normalized_instance_similarity
+ raise ValueError(
+ "Kalman filter does not support normalized_instance_similarity."
+ )
+
+ if not target_instance_count:
+ # If target_instance_count is not set, use max_tracks instead
+ # target_instance_count not available in the GUI
+ target_instance_count = max_tracks
+
kalman_obj = KalmanTracker.make_tracker(
init_tracker=tracker_obj,
init_frame_count=kf_init_frame_count,
@@ -954,8 +985,10 @@ def pre_cull_function(inst_list):
)
return kalman_obj
- elif kf_init_frame_count and not target_instance_count:
- raise ValueError("Kalman filter requires target instance count.")
+ elif kf_init_frame_count and not (max_tracks or target_instance_count):
+ raise ValueError(
+ "Kalman filter requires max tracks or target instance count."
+ )
else:
return tracker_obj
@@ -1369,6 +1402,10 @@ def cull_function(inst_list):
if init_tracker.pre_cull_function is None:
init_tracker.pre_cull_function = cull_function
+ print(
+ f"Using {init_tracker.get_name()} to track {init_frame_count} frames for Kalman filters."
+ )
+
return cls(
init_tracker=init_tracker,
kalman_tracker=kalman_tracker,
@@ -1386,6 +1423,7 @@ def track(
untracked_instances: List[InstanceType],
img: Optional[np.ndarray] = None,
t: int = None,
+ **kwargs,
) -> List[InstanceType]:
"""Tracks individual frame, using Kalman filters if possible."""
@@ -1420,7 +1458,7 @@ def track(
# Initialize the Kalman filters
self.kalman_tracker.init_filters(self.init_set.instances)
- # print(f"Kalman filters initialized (frame {t})")
+ print(f"Kalman filters initialized (frame {t})")
# Clear the data used to init filters, so that if the filters
# stop tracking and we need to re-init, we won't re-use the
diff --git a/sleap/prefs.py b/sleap/prefs.py
index 8790f1d3f..e043afc44 100644
--- a/sleap/prefs.py
+++ b/sleap/prefs.py
@@ -28,6 +28,8 @@ class Preferences(object):
"node label size": 12,
"show non-visible nodes": True,
"share usage data": True,
+ "node marker sizes": (1, 2, 3, 4, 6, 8, 12),
+ "node label sizes": (6, 9, 12, 18, 24, 36),
}
_filename = "preferences.yaml"
@@ -43,14 +45,14 @@ def load_(self):
"""Load preferences from file (regardless of whether loaded already)."""
try:
self._prefs = util.get_config_yaml(self._filename)
- if not hasattr(self._prefs, "get"):
- self._prefs = self._defaults
- else:
- self._prefs["trail length"] = self._prefs.get(
- "trail length", self._defaults["trail length"]
- )
except FileNotFoundError:
- self._prefs = self._defaults
+ pass
+
+ self._prefs = self._prefs or {}
+
+ for k, v in self._defaults.items():
+ if k not in self._prefs:
+ self._prefs[k] = v
def save(self):
"""Save preferences to file."""
diff --git a/sleap/version.py b/sleap/version.py
index 7711477cb..698710132 100644
--- a/sleap/version.py
+++ b/sleap/version.py
@@ -11,7 +11,7 @@
Must be a semver string, "aN" should be appended for alpha releases.
"""
-__version__ = "1.4.1a2"
+__version__ = "1.4.1"
def versions():
diff --git a/tests/data/tracks/clip.predictions.slp b/tests/data/tracks/clip.predictions.slp
new file mode 100644
index 000000000..652e21302
Binary files /dev/null and b/tests/data/tracks/clip.predictions.slp differ
diff --git a/tests/fixtures/datasets.py b/tests/fixtures/datasets.py
index ec5dfbc29..c6507caec 100644
--- a/tests/fixtures/datasets.py
+++ b/tests/fixtures/datasets.py
@@ -97,6 +97,20 @@ def min_tracks_2node_labels():
)
+@pytest.fixture
+def min_tracks_2node_predictions():
+ """
+ Generated with:
+ ```
+ sleap-track -m "tests/data/models/min_tracks_2node.UNet.bottomup_multiclass" "tests/data/tracks/clip.mp4"
+ ```
+ """
+ return Labels.load_file(
+ "tests/data/tracks/clip.predictions.slp",
+ video_search=["tests/data/tracks/clip.mp4"],
+ )
+
+
@pytest.fixture
def min_tracks_13node_labels():
return Labels.load_file(
diff --git a/tests/gui/learning/test_dialog.py b/tests/gui/learning/test_dialog.py
index 3d77c891f..389bb48a3 100644
--- a/tests/gui/learning/test_dialog.py
+++ b/tests/gui/learning/test_dialog.py
@@ -7,6 +7,7 @@
import pytest
from qtpy import QtWidgets
+import sleap
from sleap.gui.learning.dialog import LearningDialog, TrainingEditorWidget
from sleap.gui.learning.configs import (
TrainingConfigFilesWidget,
@@ -429,3 +430,22 @@ def test_immutablilty_of_trained_config_info(
# saving multiple configs from one config info.
ld.save(output_dir=tmpdir)
ld.save(output_dir=tmpdir)
+
+
+def test_validate_id_model(qtbot, min_labels_slp, min_labels_slp_path):
+ app = MainWindow(no_usage_data=True)
+ ld = LearningDialog(
+ mode="training",
+ labels_filename=Path(min_labels_slp_path),
+ labels=min_labels_slp,
+ )
+ assert not ld._validate_id_model()
+
+ # Add track but don't assign it to instances
+ new_track = sleap.Track(name="new_track")
+ min_labels_slp.tracks.append(new_track)
+ assert not ld._validate_id_model()
+
+ # Assign track to instances
+ min_labels_slp[0][0].track = new_track
+ assert ld._validate_id_model()
diff --git a/tests/gui/test_commands.py b/tests/gui/test_commands.py
index ffd382ab1..e19e00236 100644
--- a/tests/gui/test_commands.py
+++ b/tests/gui/test_commands.py
@@ -20,6 +20,7 @@
ReplaceVideo,
OpenSkeleton,
SaveProjectAs,
+ DeleteFrameLimitPredictions,
get_new_version_filename,
)
from sleap.instance import Instance, LabeledFrame
@@ -851,6 +852,26 @@ def load_and_assert_changes(new_video_path: Path):
shutil.move(new_video_path, expected_video_path)
+def test_DeleteFrameLimitPredictions(
+ centered_pair_predictions: Labels, centered_pair_vid: Video
+):
+ """Test deleting instances beyond a certain frame limit."""
+ labels = centered_pair_predictions
+
+ # Set-up command context
+ context = CommandContext.from_labels(labels)
+ context.state["video"] = centered_pair_vid
+
+ # Set-up params for the command
+ params = {"min_frame_idx": 900, "max_frame_idx": 1000}
+
+ instances_to_delete = DeleteFrameLimitPredictions.get_frame_instance_list(
+ context, params
+ )
+
+ assert len(instances_to_delete) == 2070
+
+
@pytest.mark.parametrize("export_extension", [".json.zip", ".slp"])
def test_exportLabelsPackage(export_extension, centered_pair_labels: Labels, tmpdir):
def assert_loaded_package_similar(path_to_pkg: Path, sugg=False, pred=False):
diff --git a/tests/info/test_summary.py b/tests/info/test_summary.py
index 2cf76c166..672d97e63 100644
--- a/tests/info/test_summary.py
+++ b/tests/info/test_summary.py
@@ -37,6 +37,19 @@ def test_frame_statistics(simple_predictions):
x = stats.get_point_displacement_series(video, "max")
assert len(x) == 2
- assert len(x) == 2
assert x[0] == 0
assert x[1] == 18.0
+
+
+def test_get_tracking_score_series(min_tracks_2node_predictions):
+
+ stats = StatisticSeries(min_tracks_2node_predictions)
+ x = stats.get_tracking_score_series(min_tracks_2node_predictions.video, "min")
+ assert len(x) == 1500
+ assert x[0] == 0.9999966621398926
+ assert x[1000] == 0.9998022317886353
+
+ x = stats.get_tracking_score_series(min_tracks_2node_predictions.video, "mean")
+ assert len(x) == 1500
+ assert x[0] == 0.9999983310699463
+ assert x[1000] == 0.9999011158943176
diff --git a/tests/nn/test_tracking_integration.py b/tests/nn/test_tracking_integration.py
index 625302fd0..4a601ac00 100644
--- a/tests/nn/test_tracking_integration.py
+++ b/tests/nn/test_tracking_integration.py
@@ -2,13 +2,205 @@
import operator
import os
import time
-
+import pytest
import sleap
from sleap.nn.inference import main as inference_cli
import sleap.nn.tracker.components
from sleap.io.dataset import Labels, LabeledFrame
+similarity_args = [
+ "instance",
+ "normalized_instance",
+ "object_keypoint",
+ "centroid",
+ "iou",
+]
+match_args = ["hungarian", "greedy"]
+
+
+@pytest.mark.parametrize(
+ "tracker_name", ["simple", "simplemaxtracks", "flow", "flowmaxtracks"]
+)
+@pytest.mark.parametrize("similarity", similarity_args)
+@pytest.mark.parametrize("match", match_args)
+def test_kalman_tracker(
+ tmpdir, centered_pair_predictions_slp_path, tracker_name, similarity, match
+):
+
+ if tracker_name == "flow" or tracker_name == "flowmaxtracks":
+ # Expecting ValueError for "flow" or "flowmaxtracks" due to Kalman filter requiring a simple tracker
+ with pytest.raises(
+ ValueError,
+ match="Kalman filter requires simple tracker for initial tracking.",
+ ):
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ f"-o {tmpdir}/{tracker_name}.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+ else:
+ # For simple or simplemaxtracks, continue with other tests
+ # Check for ValueError when similarity is "normalized_instance"
+ if similarity == "normalized_instance":
+ with pytest.raises(
+ ValueError,
+ match="Kalman filter does not support normalized_instance_similarity.",
+ ):
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ f"-o {tmpdir}/{tracker_name}.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+ return
+
+ # Check for ValueError when kf_node_indices is None which is the default
+ with pytest.raises(
+ ValueError,
+ match="Kalman filter requires node indices for instance tracking.",
+ ):
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ f"-o {tmpdir}/{tracker_name}.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+
+ # Test for missing max_tracks and target_instance_count with kf_init_frame_count
+ with pytest.raises(
+ ValueError,
+ match="Kalman filter requires max tracks or target instance count.",
+ ):
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ f"-o {tmpdir}/{tracker_name}.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+
+ # Test with target_instance_count and without max_tracks
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ "--tracking.target_instance_count 2 "
+ f"-o {tmpdir}/{tracker_name}_target_instance_count.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+
+ labels = sleap.load_file(f"{tmpdir}/{tracker_name}_target_instance_count.slp")
+ assert len(labels.tracks) == 2
+
+ # Test with target_instance_count and with max_tracks
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ "--tracking.target_instance_count 2 "
+ f"-o {tmpdir}/{tracker_name}_max_tracks_target_instance_count.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+
+ labels = sleap.load_file(
+ f"{tmpdir}/{tracker_name}_max_tracks_target_instance_count.slp"
+ )
+ assert len(labels.tracks) == 2
+
+ # Test with "--tracking.pre_cull_iou_threshold", "0.8"
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ "--tracking.target_instance_count 2 "
+ "--tracking.pre_cull_iou_threshold 0.8 "
+ f"-o {tmpdir}/{tracker_name}_max_tracks_target_instance_count_iou.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+
+ labels = sleap.load_file(
+ f"{tmpdir}/{tracker_name}_max_tracks_target_instance_count_iou.slp"
+ )
+ assert len(labels.tracks) == 2
+
+ # Test with "--tracking.pre_cull_to_target", "1"
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ "--tracking.target_instance_count 2 "
+ "--tracking.pre_cull_to_target 1 "
+ f"-o {tmpdir}/{tracker_name}_max_tracks_target_instance_count_to_target.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+ labels = sleap.load_file(
+ f"{tmpdir}/{tracker_name}_max_tracks_target_instance_count_to_target.slp"
+ )
+ assert len(labels.tracks) == 2
+
+ # Test with 'tracking.post_connect_single_breaks': 0
+ cli = (
+ f"--tracking.tracker {tracker_name} "
+ "--tracking.max_tracking 1 --tracking.max_tracks 2 "
+ f"--tracking.similarity {similarity} "
+ f"--tracking.match {match} "
+ "--tracking.track_window 5 "
+ "--tracking.kf_init_frame_count 10 "
+ "--tracking.kf_node_indices 0,1 "
+ "--tracking.target_instance_count 2 "
+ "--tracking.post_connect_single_breaks 0 "
+ f"-o {tmpdir}/{tracker_name}_max_tracks_target_instance_count_single_breaks.slp "
+ f"{centered_pair_predictions_slp_path}"
+ )
+ inference_cli(cli.split(" "))
+ labels = sleap.load_file(
+ f"{tmpdir}/{tracker_name}_max_tracks_target_instance_count_single_breaks.slp"
+ )
+ assert len(labels.tracks) == 2
+
+
def test_simple_tracker(tmpdir, centered_pair_predictions_slp_path):
cli = (
"--tracking.tracker simple "