Merge pull request #121 from ttngu207/main

add new table `LabeledVideo` to generate/store labeled video data after PoseEstimation

CHANGELOG.md

@@ -3,6 +3,15 @@
 Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and 
 [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention.
 
+
+## [0.3.1] - 2024-08-16
+
++ Add - add new table `LabeledVideo` to generate/store labeled video data after PoseEstimation
+
+## [0.3.0] - 2024-08-08
+
++ Add - add support for inference (PoseEstimation) using pytorch model
+
 ## [0.2.14] - 2024-08-02
 
 + Fix - improve imports, avoid circular dependencies

element_deeplabcut/model.py

@@ -19,6 +19,8 @@
 from .readers import dlc_reader
 
 schema = dj.schema()
+logger = dj.logger
+
 _linking_module = None
 
 
@@ -733,10 +735,16 @@ def make(self, key):
                 find_full_path(get_dlc_root_data_dir(), fp).as_posix()
                 for fp in video_relpaths
             ]
-            analyze_video_params = (PoseEstimationTask & key).fetch1(
+            pose_estimation_params = (PoseEstimationTask & key).fetch1(
                 "pose_estimation_params"
             ) or {}
 
+            # expect a nested dictionary with "analyze_videos" params
+            # if not, assume "pose_estimation_params" as a flat dictionary that include relevant "analyze_videos" params
+            analyze_video_params = (
+                pose_estimation_params.get("analyze_videos") or pose_estimation_params
+            )
+
             @memoized_result(
                 uniqueness_dict={
                     **analyze_video_params,
@@ -867,6 +875,102 @@ def get_trajectory(cls, key: dict, body_parts: list = "all") -> pd.DataFrame:
         return df
 
 
+@schema
+class LabeledVideo(dj.Computed):
+    definition = """
+    -> PoseEstimation
+    """
+
+    class File(dj.Part):
+        definition = """
+        -> master
+        -> VideoRecording.File
+        ---
+        labeled_video_path: varchar(255)  # relative path to labeled video
+        """
+
+    @property
+    def key_source(self):
+        return PoseEstimation & RecordingInfo
+
+    def make(self, key):
+        import deeplabcut
+
+        pose_estimation_params = (PoseEstimationTask & key).fetch1(
+            "pose_estimation_params"
+        ) or {}
+
+        # expect a nested dictionary with "create_labeled_video" and "extract_outlier_frames" params
+        # if not, assume "pose_estimation_params" as a flat dictionary
+        create_labeled_video_params = (
+            pose_estimation_params.get("create_labeled_video") or pose_estimation_params
+        )
+
+        outputframerate = create_labeled_video_params.pop(
+            "outputframerate", 5
+        )  # final labeled video FPS defaults to 5 Hz
+
+        dlc_model_ = (Model & key).fetch1()
+        fps, nframes = (RecordingInfo & key).fetch1("fps", "nframes")
+        output_dir = (PoseEstimationTask & key).fetch1("pose_estimation_output_dir")
+        output_dir = find_full_path(get_dlc_root_data_dir(), output_dir)
+
+        project_path = find_full_path(
+            get_dlc_root_data_dir(), dlc_model_["project_path"]
+        )
+
+        try:
+            dlc_config = next(output_dir.glob("dj_dlc_config*.yaml"))
+            dlc_config = project_path / dlc_config.name
+            assert dlc_config.exists()
+        except (StopIteration, AssertionError):
+            dlc_config = next(project_path.glob("dj_dlc_config*.yaml"))
+            logger.warning(
+                f"No dj_dlc_config*.yaml file found in {output_dir} - this is unexpected.\nUsing {dlc_config}"
+            )
+
+        entries = []
+        for vkey in (VideoRecording.File & key).fetch("KEY"):
+            video_file = (VideoRecording.File & vkey).fetch1("file_path")
+            video_file = find_full_path(get_dlc_root_data_dir(), video_file)
+
+            # -- create labeled video --
+            create_labeled_video_kwargs = {
+                k: v
+                for k, v in create_labeled_video_params.items()
+                if k in inspect.signature(deeplabcut.create_labeled_video).parameters
+            }
+            create_labeled_video_kwargs.update(
+                dict(
+                    config=dlc_config.as_posix(),
+                    videos=[video_file.as_posix()],
+                    shuffle=dlc_model_["shuffle"],
+                    trainingsetindex=dlc_model_["trainingsetindex"],
+                    modelprefix=dlc_model_["model_prefix"],
+                    destfolder=output_dir.as_posix(),
+                    Frames2plot=np.arange(0, nframes, int(fps / outputframerate)),
+                    outputframerate=outputframerate,
+                )
+            )
+            deeplabcut.create_labeled_video(**create_labeled_video_kwargs)
+
+            labeled_video_path = next(
+                output_dir.glob(f"{video_file.stem}*_labeled.mp4")
+            )
+            entries.append(
+                {
+                    **key,
+                    **vkey,
+                    "labeled_video_path": labeled_video_path.relative_to(
+                        get_dlc_processed_data_dir()
+                    ).as_posix(),
+                }
+            )
+
+        self.insert1(key)
+        self.File.insert(entries)
+
+
 def str_to_bool(value) -> bool:
     """Return whether the provided string represents true. Otherwise false.
 

element_deeplabcut/version.py

@@ -2,4 +2,4 @@
 Package metadata
 """
 
-__version__ = "0.3.0"
+__version__ = "0.3.1"