Thank you, @brukew, for the updates. I have a few suggestions and points to address based on our previous discussions:

1. Reorganize Code Structure
   Please re-organize your code by separating data structures from functionalities (what we refer to as tasks in Senselab).

   • The skeleton should be treated as a data structure. It should define the skeleton and optionally include utilities for visualization. Alternatively, visualization can be handled as a standalone task.
   • Pose estimation should be a task (generating the skeleton as an output). All human pose estimation models (e.g., MediaPipe, AlphaPose) should conform to a consistent data structure.
   • To encourage generalizability, I suggest integrating a second pose estimation tool, such as YOLO due to its simplicity.

2. Model Inclusion
   The current approach of including a model within the source code (e.g., src/senselab/video/tasks/pose_estimation/models/pose_landmarker.task) makes the package unnecessarily heavy. Instead, please ensure models are downloaded as needed. You can take inspiration from this example.

3. Documentation
   Please add a dedicated documentation page:

   • Explain human pose estimation as a task, its purpose, and supported models.
   • For instance, you can reference this documentation for MediaPipe.
   • Feel free to draw inspiration from the existing audio task documentation (though some sections are incomplete).

4. Tutorial
   Create a Jupyter Notebook tutorial to demonstrate:

   • How to use the interface.
   • What functionalities are available.
     Add this under a video folder in the tutorial/ directory.

5. Failing Tests
   I noticed two tests are failing:

   • test_valid_image_single_person
     • AssertionError: "Input and output image shapes should match."
   • test_visualization_single_person
     • ValueError: "Input image must contain three-channel BGR data."
       Please double-check these tests to ensure they pass.

Nice, thank you for the feedback @fabiocat93. I will address your comments and ask questions as I go.

hi @brukew , did you have any time to work further on this?

hey @fabiocat93, slowed down on development towards the end of the semester. I will continue this month.

@fabiocat93 lmk what you think about having separate estimator classes for different models. also, I kept the landmark names as they are originally listed but wondering if I should homogenize them at all.

@fabiocat93 lmk what you think about having separate estimator classes for different models. also, I kept the landmark names as they are originally listed but wondering if I should homogenize them at all.

Good question. You can have multiple estimators, each being a child of the same abstract class and producing a skeleton following the same universal structure

addressed model download by downloading models to video/tasks/pose_estimation/models upon use. Let me know if there is a better alternative to this that is more in line with senselab use.

also wondering how I can ensure tutorial works (and cells are run in tutorial) if changes haven't been pushed to main branch @fabiocat93. should I wait until after?

addressed model download by downloading models to video/tasks/pose_estimation/models upon use. Let me know if there is a better alternative to this that is more in line with senselab use.

Good for now. But you raised a good point. We will need to implement a customizable cache folder for the package (of course with a default value)

also wondering how I can ensure tutorial works (and cells are run in tutorial) if changes haven't been pushed to main branch @fabiocat93. should I wait until after?

When testing the tutorial locally, you can install the package directly from a specific branch instead of relying on pip install senselab. This allows you to ensure the tutorial runs correctly with the latest changes before they are merged into the main branch