Multicam DLC project support

src/spyglass/common/common_behav.py

@@ -1,4 +1,5 @@
 import pathlib
+import re
 from functools import reduce
 from typing import Dict
 
@@ -372,7 +373,7 @@ def _no_transaction_make(self, key, verbose=True):
                 "interval_list_name": interval_list_name,
             }
         ).fetch1("valid_times")
-
+        cam_device_str = r"camera_device (\d+)"
         is_found = False
         for ind, video in enumerate(videos.values()):
             if isinstance(video, pynwb.image.ImageSeries):
@@ -385,7 +386,11 @@ def _no_transaction_make(self, key, verbose=True):
                     interval_list_contains(valid_times, video_obj.timestamps)
                     > 0.9 * len(video_obj.timestamps)
                 ):
-                    key["video_file_num"] = ind
+                    nwb_cam_device = video_obj.device.name
+                    # returns whatever was captured in the first group (within the parentheses) of the regular expression -- in this case, 0
+                    key["video_file_num"] = int(
+                        re.match(cam_device_str, nwb_cam_device)[1]
+                    )
                     camera_name = video_obj.device.camera_name
                     if CameraDevice & {"camera_name": camera_name}:
                         key["camera_name"] = video_obj.device.camera_name

src/spyglass/position/v1/dlc_utils.py

@@ -18,6 +18,7 @@
 import pandas as pd
 from tqdm import tqdm as tqdm
 
+from spyglass.common.common_behav import VideoFile
 from spyglass.settings import dlc_output_dir, dlc_video_dir, raw_dir
 
 
@@ -418,8 +419,6 @@ def get_video_path(key):
     """
     import pynwb
 
-    from ...common.common_behav import VideoFile
-
     vf_key = {"nwb_file_name": key["nwb_file_name"], "epoch": key["epoch"]}
     VideoFile()._no_transaction_make(vf_key, verbose=False)
     video_query = VideoFile & vf_key
@@ -434,9 +433,7 @@ def get_video_path(key):
     with pynwb.NWBHDF5IO(path=nwb_path, mode="r") as in_out:
         nwb_file = in_out.read()
         nwb_video = nwb_file.objects[video_info["video_file_object_id"]]
-        video_filepath = VideoFile.get_abs_path(
-            {"nwb_file_name": key["nwb_file_name"], "epoch": key["epoch"]}
-        )
+        video_filepath = VideoFile.get_abs_path(vf_key)
         video_dir = os.path.dirname(video_filepath) + "/"
         video_filename = video_filepath.split(video_dir)[-1]
         meters_per_pixel = nwb_video.device.meters_per_pixel

src/spyglass/position/v1/position_dlc_pose_estimation.py

@@ -10,12 +10,11 @@
 
 from spyglass.common.common_behav import (  # noqa: F401
     RawPosition,
-    VideoFile,
     convert_epoch_interval_name_to_position_interval_name,
 )
+from spyglass.common.common_nwbfile import AnalysisNwbfile
+from spyglass.utils.dj_mixin import SpyglassMixin
 
-from ...common.common_nwbfile import AnalysisNwbfile
-from ...utils.dj_mixin import SpyglassMixin
 from .dlc_utils import OutputLogger, infer_output_dir
 from .position_dlc_model import DLCModel
 
@@ -87,10 +86,11 @@ def insert_estimation_task(
         Parameters
         ----------
         key: DataJoint key specifying a pairing of VideoRecording and Model.
-        task_mode (bool): Default 'trigger' computation. Or 'load' existing results.
+        task_mode (bool): Default 'trigger' computation.
+        Or 'load' existing results.
         params (dict): Optional. Parameters passed to DLC's analyze_videos:
-            videotype, gputouse, save_as_csv, batchsize, cropping, TFGPUinference,
-            dynamic, robust_nframes, allow_growth, use_shelve
+            videotype, gputouse, save_as_csv, batchsize, cropping,
+            TFGPUinference, dynamic, robust_nframes, allow_growth, use_shelve
         """
         from .dlc_utils import check_videofile, get_video_path
 
@@ -261,7 +261,8 @@ def make(self, key):
             del key["meters_per_pixel"]
             body_parts = dlc_result.df.columns.levels[0]
             body_parts_df = {}
-            # Insert dlc pose estimation into analysis NWB file for each body part.
+            # Insert dlc pose estimation into analysis NWB file for
+            # each body part.
             for body_part in bodyparts:
                 if body_part in body_parts:
                     body_parts_df[body_part] = pd.DataFrame.from_dict(

src/spyglass/position/v1/position_dlc_project.py

@@ -340,6 +340,9 @@ def insert_new_project(
         }
         # TODO: make permissions setting more flexible.
         if set_permissions:
+            raise NotImplementedError(
+                "permission-setting is not functional at this time"
+            )
             permissions = (
                 stat.S_IRUSR
                 | stat.S_IWUSR
@@ -375,9 +378,84 @@ def insert_new_project(
             config_path = config_path.as_posix()
         return {"project_name": project_name, "config_path": config_path}
 
+    @classmethod
+    def add_video_files(
+        cls,
+        video_list,
+        config_path=None,
+        key=None,
+        output_path: str = os.getenv("DLC_VIDEO_PATH"),
+        add_new=False,
+        add_to_files=True,
+        **kwargs,
+    ):
+        if add_new & (not config_path):
+            if not key:
+                raise ValueError(
+                    "at least one of config_path or key have to be passed if add_new=True"
+                )
+            else:
+                config_path = (cls & key).fetch1("config_path")
+        if (not key) & add_to_files:
+            if config_path:
+                if len(cls & {"config_path": config_path}) == 1:
+                    pass
+                else:
+                    raise ValueError(
+                        "Cannot set add_to_files=True without passing key"
+                    )
+            else:
+                raise ValueError(
+                    "Cannot set add_to_files=True without passing key"
+                )
+
+        if all(isinstance(n, Dict) for n in video_list):
+            videos_to_convert = [
+                get_video_path(video_key) for video_key in video_list
+            ]
+            videos = [
+                check_videofile(
+                    video_path=video[0],
+                    output_path=output_path,
+                    video_filename=video[1],
+                )[0].as_posix()
+                for video in videos_to_convert
+            ]
+        # If not dict, assume list of video file paths
+        # that may or may not need to be converted
+        else:
+            videos = []
+            if not all([Path(video).exists() for video in video_list]):
+                raise OSError("at least one file in video_list does not exist")
+            for video in video_list:
+                video_path = Path(video).parent
+                video_filename = video.rsplit(
+                    video_path.as_posix(), maxsplit=1
+                )[-1].split("/")[-1]
+                videos.extend(
+                    [
+                        check_videofile(
+                            video_path=video_path,
+                            output_path=output_path,
+                            video_filename=video_filename,
+                        )[0].as_posix()
+                    ]
+                )
+            if len(videos) < 1:
+                raise ValueError(f"no .mp4 videos found in{video_path}")
+        if add_new:
+            from deeplabcut import add_new_videos
+
+            add_new_videos(config=config_path, videos=videos, copy_videos=True)
+        if add_to_files:
+            # Add videos to training files
+            cls.add_training_files(key, **kwargs)
+        return videos
+
     @classmethod
     def add_training_files(cls, key, **kwargs):
-        """Add training videos and labeled frames .h5 and .csv to DLCProject.File"""
+        """Add training videos and labeled frames .h5
+        and .csv to DLCProject.File"""
         config_path = (cls & {"project_name": key["project_name"]}).fetch1(
             "config_path"
         )
@@ -394,7 +472,8 @@ def add_training_files(cls, key, **kwargs):
             )[0]
             training_files.extend(
                 glob.glob(
-                    f"{cfg['project_path']}/labeled-data/{video_name}/*Collected*"
+                    f"{cfg['project_path']}/"
+                    f"labeled-data/{video_name}/*Collected*"
                 )
             )
         for video in video_names:
@@ -457,16 +536,19 @@ def import_labeled_frames(
         video_filenames: Union[str, List],
         **kwargs,
     ):
-        """Function to import pre-labeled frames from an existing project into a new project
+        """Function to import pre-labeled frames from an existing project
+        into a new project
 
         Parameters
         ----------
         key : Dict
             key to specify entry in DLCProject table to add labeled frames to
         import_project_path : str
-            absolute path to project directory containing labeled frames to import
+            absolute path to project directory containing
+            labeled frames to import
         video_filenames : str or List
-            filename or list of filenames of video(s) from which to import frames.
+            filename or list of filenames of video(s)
+            from which to import frames.
             without file extension
         """
         project_entry = (cls & key).fetch1()
@@ -476,9 +558,10 @@ def import_labeled_frames(
             f"{current_project_path.as_posix()}/labeled-data"
         )
         if isinstance(import_project_path, PosixPath):
-            assert (
-                import_project_path.exists()
-            ), f"import_project_path: {import_project_path.as_posix()} does not exist"
+            assert import_project_path.exists(), (
+                "import_project_path: "
+                f"{import_project_path.as_posix()} does not exist"
+            )
             import_labeled_data_path = Path(
                 f"{import_project_path.as_posix()}/labeled-data"
             )
@@ -504,7 +587,8 @@ def import_labeled_frames(
             dlc_df.columns = dlc_df.columns.set_levels([team_name], level=0)
             dlc_df.to_hdf(
                 Path(
-                    f"{current_labeled_data_path.as_posix()}/{video_file}/CollectedData_{team_name}.h5"
+                    f"{current_labeled_data_path.as_posix()}/"
+                    f"{video_file}/CollectedData_{team_name}.h5"
                 ).as_posix(),
                 "df_with_missing",
             )