Add CLI sleap-render command to render videos

docs/guides/cli.md

@@ -0,0 +1,320 @@
+(cli)=
+
+# Command line interfaces
+
+SLEAP provides several types of functionality accessible through a command prompt.
+
+## GUI
+
+(sleap-label)=
+
+### `sleap-label`
+
+{code}`sleap-label` runs the GUI application for labeling and viewing {code}`.slp` files.
+
+```none
+usage: sleap-label [-h] [--nonnative] [--profiling] [--reset] [labels_path]
+
+positional arguments:
+  labels_path  Path to labels file
+
+optional arguments:
+  -h, --help   show this help message and exit
+  --nonnative  Don't use native file dialogs
+  --profiling  Enable performance profiling
+  --reset      Reset GUI state and preferences. Use this flag if the GUI
+               appears incorrectly or fails to open.
+```
+
+## Training
+
+(sleap-train)=
+
+### `sleap-train`
+
+{code}`sleap-train` is the command-line interface for training. Use this for training on a remote machine/cluster/colab notebook instead of through the GUI.
+
+```none
+usage: sleap-train [-h] [--video-paths VIDEO_PATHS] [--val_labels VAL_LABELS]
+                   [--test_labels TEST_LABELS] [--tensorboard] [--save_viz]
+                   [--zmq] [--run_name RUN_NAME] [--prefix PREFIX]
+                   [--suffix SUFFIX]
+                   training_job_path [labels_path]
+
+positional arguments:
+  training_job_path     Path to training job profile JSON file.
+  labels_path           Path to labels file to use for training. If specified,
+                        overrides the path specified in the training job
+                        config.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --video-paths VIDEO_PATHS
+                        List of paths for finding videos in case paths inside
+                        labels file are not accessible.
+  --val_labels VAL_LABELS, --val VAL_LABELS
+                        Path to labels file to use for validation. If
+                        specified, overrides the path specified in the
+                        training job config.
+  --test_labels TEST_LABELS, --test TEST_LABELS
+                        Path to labels file to use for test. If specified,
+                        overrides the path specified in the training job
+                        config.
+  --tensorboard         Enable TensorBoard logging to the run path if not
+                        already specified in the training job config.
+  --save_viz            Enable saving of prediction visualizations to the run
+                        folder if not already specified in the training job
+                        config.
+  --zmq                 Enable ZMQ logging (for GUI) if not already specified
+                        in the training job config.
+  --run_name RUN_NAME   Run name to use when saving file, overrides other run
+                        name settings.
+  --prefix PREFIX       Prefix to prepend to run name.
+  --suffix SUFFIX       Suffix to append to run name.
+  --cpu                 Run training only on CPU. If not specified, will use
+                        available GPU.
+  --first-gpu           Run training on the first GPU, if available.
+  --last-gpu            Run training on the last GPU, if available.
+  --gpu GPU             Run training on the i-th GPU on the system.
+```
+
+## Inference and Tracking
+
+(sleap-track)=
+
+### `sleap-track`
+
+{code}`sleap-track` is the command-line interface for running inference using models which have already been trained. Use this for running inference on a remote machine such as an HPC cluster or Colab notebook.
+
+If you specify how many identities there should be in a frame (i.e., the number of animals) with the {code}`--tracking.clean_instance_count` argument, then we will use a heuristic method to connect "breaks" in the track identities where we lose one identity and spawn another. This can be used as part of the inference pipeline (if models are specified), as part of the tracking-only pipeline (if the predictions file is specified and no models are specified), or by itself on predictions with pre-tracked identities (if you specify {code}`--tracking.tracker none`). See {ref}`proofreading` for more details on tracking.
+
+```none
+usage: sleap-track [-h] [-m MODELS] [--frames FRAMES] [--only-labeled-frames]
+                   [--only-suggested-frames] [-o OUTPUT] [--no-empty-frames]
+                   [--verbosity {none,rich,json}]
+                   [--video.dataset VIDEO.DATASET]
+                   [--video.input_format VIDEO.INPUT_FORMAT]
+                   [--cpu | --first-gpu | --last-gpu | --gpu GPU]
+                   [--peak_threshold PEAK_THRESHOLD] [--batch_size BATCH_SIZE]
+                   [--open-in-gui] [--tracking.tracker TRACKING.TRACKER]
+                   [--tracking.target_instance_count TRACKING.TARGET_INSTANCE_COUNT]
+                   [--tracking.pre_cull_to_target TRACKING.PRE_CULL_TO_TARGET]
+                   [--tracking.pre_cull_iou_threshold TRACKING.PRE_CULL_IOU_THRESHOLD]
+                   [--tracking.post_connect_single_breaks TRACKING.POST_CONNECT_SINGLE_BREAKS]
+                   [--tracking.clean_instance_count TRACKING.CLEAN_INSTANCE_COUNT]
+                   [--tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD]
+                   [--tracking.similarity TRACKING.SIMILARITY]
+                   [--tracking.match TRACKING.MATCH]
+                   [--tracking.track_window TRACKING.TRACK_WINDOW]
+                   [--tracking.min_new_track_points TRACKING.MIN_NEW_TRACK_POINTS]
+                   [--tracking.min_match_points TRACKING.MIN_MATCH_POINTS]
+                   [--tracking.img_scale TRACKING.IMG_SCALE]
+                   [--tracking.of_window_size TRACKING.OF_WINDOW_SIZE]
+                   [--tracking.of_max_levels TRACKING.OF_MAX_LEVELS]
+                   [--tracking.kf_node_indices TRACKING.KF_NODE_INDICES]
+                   [--tracking.kf_init_frame_count TRACKING.KF_INIT_FRAME_COUNT]
+                   [data_path]
+
+positional arguments:
+  data_path             Path to data to predict on. This can be a labels
+                        (.slp) file or any supported video format.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -m MODELS, --model MODELS
+                        Path to trained model directory (with
+                        training_config.json). Multiple models can be
+                        specified, each preceded by --model.
+  --frames FRAMES       List of frames to predict when running on a video. Can
+                        be specified as a comma separated list (e.g. 1,2,3) or
+                        a range separated by hyphen (e.g., 1-3, for 1,2,3). If
+                        not provided, defaults to predicting on the entire
+                        video.
+  --only-labeled-frames
+                        Only run inference on user labeled frames when running
+                        on labels dataset. This is useful for generating
+                        predictions to compare against ground truth.
+  --only-suggested-frames
+                        Only run inference on unlabeled suggested frames when
+                        running on labels dataset. This is useful for
+                        generating predictions for initialization during
+                        labeling.
+  -o OUTPUT, --output OUTPUT
+                        The output filename to use for the predicted data. If
+                        not provided, defaults to
+                        '[data_path].predictions.slp'.
+  --no-empty-frames     Clear any empty frames that did not have any detected
+                        instances before saving to output.
+  --verbosity {none,rich,json}
+                        Verbosity of inference progress reporting. 'none' does
+                        not output anything during inference, 'rich' displays
+                        an updating progress bar, and 'json' outputs the
+                        progress as a JSON encoded response to the console.
+  --video.dataset VIDEO.DATASET
+                        The dataset for HDF5 videos.
+  --video.input_format VIDEO.INPUT_FORMAT
+                        The input_format for HDF5 videos.
+  --cpu                 Run inference only on CPU. If not specified, will use
+                        available GPU.
+  --first-gpu           Run inference on the first GPU, if available.
+  --last-gpu            Run inference on the last GPU, if available.
+  --gpu GPU             Run inference on the i-th GPU specified.
+  --peak_threshold PEAK_THRESHOLD
+                        Minimum confidence map value to consider a peak as
+                        valid.
+  --batch_size BATCH_SIZE
+                        Number of frames to predict at a time. Larger values
+                        result in faster inference speeds, but require more
+                        memory.
+  --open-in-gui         Open the resulting predictions in the GUI when
+                        finished.
+  --tracking.tracker TRACKING.TRACKER
+                        Options: simple, flow, None (default: None)
+  --tracking.target_instance_count TRACKING.TARGET_INSTANCE_COUNT
+                        Target number of instances to track per frame.
+                        (default: 0)
+  --tracking.pre_cull_to_target TRACKING.PRE_CULL_TO_TARGET
+                        If non-zero and target_instance_count is also non-
+                        zero, then cull instances over target count per frame
+                        *before* tracking. (default: 0)
+  --tracking.pre_cull_iou_threshold TRACKING.PRE_CULL_IOU_THRESHOLD
+                        If non-zero and pre_cull_to_target also set, then use
+                        IOU threshold to remove overlapping instances over
+                        count *before* tracking. (default: 0)
+  --tracking.post_connect_single_breaks TRACKING.POST_CONNECT_SINGLE_BREAKS
+                        If non-zero and target_instance_count is also non-
+                        zero, then connect track breaks when exactly one track
+                        is lost and exactly one track is spawned in frame.
+                        (default: 0)
+  --tracking.clean_instance_count TRACKING.CLEAN_INSTANCE_COUNT
+                        Target number of instances to clean *after* tracking.
+                        (default: 0)
+  --tracking.clean_iou_threshold TRACKING.CLEAN_IOU_THRESHOLD
+                        IOU to use when culling instances *after* tracking.
+                        (default: 0)
+  --tracking.similarity TRACKING.SIMILARITY
+                        Options: instance, centroid, iou (default: instance)
+  --tracking.match TRACKING.MATCH
+                        Options: hungarian, greedy (default: greedy)
+  --tracking.track_window TRACKING.TRACK_WINDOW
+                        How many frames back to look for matches (default: 5)
+  --tracking.min_new_track_points TRACKING.MIN_NEW_TRACK_POINTS
+                        Minimum number of instance points for spawning new
+                        track (default: 0)
+  --tracking.min_match_points TRACKING.MIN_MATCH_POINTS
+                        Minimum points for match candidates (default: 0)
+  --tracking.img_scale TRACKING.IMG_SCALE
+                        For optical-flow: Image scale (default: 1.0)
+  --tracking.of_window_size TRACKING.OF_WINDOW_SIZE
+                        For optical-flow: Optical flow window size to consider
+                        at each pyramid (default: 21)
+  --tracking.of_max_levels TRACKING.OF_MAX_LEVELS
+                        For optical-flow: Number of pyramid scale levels to
+                        consider (default: 3)
+  --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)
+```
+
+## Dataset files
+
+(sleap-convert)=
+
+### `sleap-convert`
+
+{code}`sleap-convert` allows you to convert between various dataset file formats. Amongst other things, it can be used to export data from a SLEAP dataset into an HDF5 file that can be easily used for analysis (e.g., read from MATLAB). See {py:mod}`sleap.io.convert` for more information.
+
+```none
+usage: sleap-convert [-h] [-o OUTPUT] [--format FORMAT] [--video VIDEO]
+                     input_path
+
+positional arguments:
+  input_path            Path to input file.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -o OUTPUT, --output OUTPUT
+                        Path to output file (optional).
+  --format FORMAT       Output format. Default ('slp') is SLEAP dataset;
+                        'analysis' results in analysis.h5 file; 'h5' or 'json'
+                        results in SLEAP dataset with specified file format.
+  --video VIDEO         Path to video (if needed for conversion).
+```
+
+For example, to convert a predictions SLP file to an analysis HDF5 file:
+
+```
+sleap-convert --format analysis -o "session1.predictions.analysis.h5" "session1.predictions.slp"
+```
+
+See [Analysis examples](../notebooks/Analysis_examples.html) for how to work with these outputs.
+
+(sleap-inspect)=
+
+### `sleap-inspect`
+
+{code}`sleap-inspect` gives you various information about a SLEAP dataset file such as a list of videos and a count of the frames with labels. If you're inspecting a predictions dataset (i.e., the output from running {code}`sleap-track` or inference in the GUI) it will also include details about how those predictions were created (i.e., the models, the version of SLEAP, and any inference parameters).
+
+You can also specify a model folder to get a quick summary of the configuration and metrics (if available).
+
+```none
+usage: sleap-inspect [-h] [--verbose] data_path
+
+positional arguments:
+  data_path   Path to labels file (.slp) or model folder
+
+optional arguments:
+  -h, --help  show this help message and exit
+  --verbose
+```
+
+## Rendering
+
+(sleap-render)=
+
+### `sleap-render`
+
+{code}`sleap-render` allows you to render videos directly from the CLI. It is used to render video clips with Instances.
+
+```none
+usage: sleap-render [-h] [-o OUTPUT] [-f FPS] [--scale SCALE] [--crop CROP] [--frames FRAMES] [--video-index VIDEO_INDEX] data_path
+positional arguments:
+  data_path             Path to labels json file
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -o OUTPUT, --output OUTPUT
+                        Path for saving output
+  -f FPS, --fps FPS     Frames per second for output video
+  --scale SCALE         Output image scale
+  --crop CROP           Crop size as <width>,<height>
+  --frames FRAMES       List of frames to predict. Either comma separated list (e.g. 1,2,3) or a range separated by hyphen (e.g. 1-3). (default is entire video)
+  -video-index VIDEO_INDEX
+                        Index of video in labels dataset
+```
+
+## Debugging
+
+(sleap-diagnostic)=
+
+### `sleap-diagnostic`
+
+There's also a script to output diagnostic information which may help us if you need to contact us about problems installing or running SLEAP. If you were able to install the SLEAP Python package, you can run this script with {code}`sleap-diagnostic`. Otherwise, you can download [diagnostic.py](https://raw.githubusercontent.com/talmolab/sleap/main/sleap/diagnostic.py) and run {code}`python diagnostic.py`.
+
+```none
+usage: sleap-diagnostic [-h] [-o OUTPUT] [--gui-check]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -o OUTPUT, --output OUTPUT
+                        Path for saving output
+  --gui-check           Check if Qt GUI widgets can be used
+```
+
+:::{note}
+For more details about any command, run with the {code}`--help` argument (e.g., {code}`sleap-track --help`).
+:::

setup.py

@@ -49,9 +49,10 @@ def get_requirements(require_name=None):
     license="BSD 3-Clause License",
     packages=find_packages(exclude=["tensorflow"]),
     include_package_data=True,
-    entry_points={
+    entry_points={ 
         "console_scripts": [
             "sleap-convert=sleap.io.convert:main",
+            "sleap-render=sleap.io.visuals:main",
             "sleap-label=sleap.gui.app:main",
             "sleap-train=sleap.nn.training:main",
             "sleap-track=sleap.nn.inference:main",

sleap/io/visuals.py

@@ -563,7 +563,7 @@ def resize_images(images: np.ndarray, scale: float) -> np.ndarray:
     return np.stack([resize_image(img, scale) for img in images])
 
 
-def main_cli():
+def main(args: list = None):
     import argparse
     from sleap.util import frame_list
 
@@ -591,8 +591,7 @@ def main_cli():
     parser.add_argument(
         "--video-index", type=int, default=0, help="Index of video in labels dataset"
     )
-    args = parser.parse_args()
-
+    args = parser.parse_args(args=args)
     labels = Labels.load_file(
         args.data_path, video_search=[os.path.dirname(args.data_path)]
     )
@@ -627,5 +626,5 @@ def main_cli():
     print(f"Video saved as: {filename}")
 
 
-if __name__ == "__main__":
-    main_cli()
+# if __name__ == "__main__":
+#    main()

tests/io/test_visuals.py

@@ -1,6 +1,11 @@
 import numpy as np
 import os
-from sleap.io.visuals import save_labeled_video, resize_images, VideoMarkerThread
+from sleap.io.visuals import (
+    save_labeled_video,
+    resize_images,
+    VideoMarkerThread,
+    main as sleap_render,
+)
 
 
 def test_resize(small_robot_mp4_vid):
@@ -56,6 +61,12 @@ def test_serial_pipeline(centered_pair_predictions, tmpdir):
     )
 
 
+def test_sleap_render(centered_pair_predictions):
+    args = f"-o testvis.avi -f 2 --scale 1.2 --frames 1,2 --video-index 0 tests/data/json_format_v2/centered_pair_predictions.json".split()
+    sleap_render(args)
+    assert os.path.exists("testvis.avi")
+
+
 def test_write_visuals(tmpdir, centered_pair_predictions):
     path = os.path.join(tmpdir, "clip.avi")
     save_labeled_video(