Merge branch 'develop' into divya/fix-scaling-topdown

.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` --->

.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:

.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:

.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"

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)**:
 

docs/conf.py

@@ -28,15 +28,15 @@
 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:
 #     version_file = f.read()
 #     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"

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.
+
+![Bonsai FileCapture module](../_static/bonsai-filecapture.jpg)
+
+#### 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.
+
+![Bonsai PredictCentroids module](../_static/bonsai-predictcentroids.jpg)
+
+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.
+
+![Bonsai PredictPoses module](../_static/bonsai-predictposes.jpg)
+
+#### 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.
+
+![Bonsai PredictPoseIdentities module](../_static/bonsai-predictposeidentities.jpg)
+
+#### 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. 
+
+![Bonsai module connection ](../_static/bonsai-connection.jpg)
+
+Once it is done, the workflow in Bonsai will look something like the following:
+
+![Bonsai.SLEAP workflow](../_static/bonsai-workflow.jpg)
+
+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).

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)=

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
 ```

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
 

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

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)