diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..af423ef
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,50 @@
+name: New Release
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+jobs:
+  semantic_release:
+    runs-on: ubuntu-latest
+    concurrency: semantic_release
+    permissions:
+      id-token: write
+      contents: write
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.11'
+
+      - name: Install Poetry
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+          echo "${HOME}/.local/bin" >> $GITHUB_PATH
+
+      - name: Install dependencies
+        run: poetry install
+
+      - name: Set PYTHONPATH to subdirectory sdcat
+        run: echo "PYTHONPATH=." >> $GITHUB_ENV
+      - name: Check release status
+        id: release-status
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          pip install python-semantic-release
+          ##########################################################################
+          # Add a --noop flag to check without releasing if adding pypi to next steps
+          ##########################################################################
+          if semantic-release --strict version 
+          then
+            echo "Releasing new version."
+          else
+            echo "Skipping release steps."
+          fi
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ddf5f0a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,59 @@
+# Checkpoings
+checkpoints/
+
+# CoTracker source
+co-tracker/
+
+# Diagrams
+*.graffle
+
+# Log files
+*.log
+
+# Secrets
+.env
+
+# Model weights
+*.pt
+
+# test data
+examples/data/
+*.tar.gz
+
+# Pycharm
+.idea/
+
+# Node artifact files
+node_modules/
+dist/
+
+# Compiled Python bytecode
+*.py[cod]
+
+# JetBrains IDE
+.idea/
+
+# Unit test reports
+TEST*.xml
+
+# Generated by MacOS
+.DS_Store
+
+# Generated by Windows
+Thumbs.db
+
+# Applications
+*.app
+*.exe
+*.war
+
+# Large media files
+*.mp4
+*.tiff
+*.avi
+*.flv
+*.mov
+*.wmv
+
+#Mkdocs
+docs/site
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..a0cf709
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1 @@
+# CHANGELOG
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..e395ca3
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,399 @@
+Attribution-NonCommercial 4.0 International
+
+=======================================================================
+
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+does not provide legal services or legal advice. Distribution of
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+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
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+Section 6 -- Term and Termination.
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+  a. This Public License applies for the term of the Copyright and
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+       2. upon express reinstatement by the Licensor.
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+     For the avoidance of doubt, this Section 6(b) does not affect any
+     right the Licensor may have to seek remedies for Your violations
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+  c. For the avoidance of doubt, the Licensor may also offer the
+     Licensed Material under separate terms or conditions or stop
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+     will not terminate this Public License.
+
+  d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
+     License.
+
+Section 7 -- Other Terms and Conditions.
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+     independent of the terms and conditions of this Public License.
+
+Section 8 -- Interpretation.
+
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+     shall not be interpreted to, reduce, limit, restrict, or impose
+     conditions on any use of the Licensed Material that could lawfully
+     be made without permission under this Public License.
+
+  b. To the extent possible, if any provision of this Public License is
+     deemed unenforceable, it shall be automatically reformed to the
+     minimum extent necessary to make it enforceable. If the provision
+     cannot be reformed, it shall be severed from this Public License
+     without affecting the enforceability of the remaining terms and
+     conditions.
+
+  c. No term or condition of this Public License will be waived and no
+     failure to comply consented to unless expressly agreed to by the
+     Licensor.
+
+  d. Nothing in this Public License constitutes or may be interpreted
+     as a limitation upon, or waiver of, any privileges and immunities
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+     processes of any jurisdiction or authority.
+
+=======================================================================
+
+Creative Commons is not a party to its public
+licenses. Notwithstanding, Creative Commons may elect to apply one of
+its public licenses to material it publishes and in those instances
+will be considered the “Licensor.” The text of the Creative Commons
+public licenses is dedicated to the public domain under the CC0 Public
+Domain Dedication. Except for the limited purpose of indicating that
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+public licenses.
+
+Creative Commons may be contacted at creativecommons.org.
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..f6709b4
--- /dev/null
+++ b/README.md
@@ -0,0 +1,23 @@
+# Tracker based on BioClip and CoTracker3 
+
+# Installation
+
+```bash
+poetry install
+```
+
+# Run the tracker on a sample video
+
+```bash
+export PYTHONPATH=. && poetry run python examples/video.py
+```
+
+Should see a window with the video and the tracked points, something simiar to this:
+
+![video](https://github.com/mbari-org/biotrack/blob/main/examples/data/video/V4318_20201208T203419Z_h264_tl_3_biotrack.gif)
+
+
+# References
+
+- [BioClip](https://imageomics.github.io/bioclip/)
+- [CoTracker3](https://cotracker3.github.io/)
\ No newline at end of file
diff --git a/biotrack/__init__.py b/biotrack/__init__.py
new file mode 100644
index 0000000..8b3b843
--- /dev/null
+++ b/biotrack/__init__.py
@@ -0,0 +1,2 @@
+# biotrack/__init__.py
+__version__ = "0.1.0"
\ No newline at end of file
diff --git a/biotrack/__main__.py b/biotrack/__main__.py
new file mode 100644
index 0000000..3140390
--- /dev/null
+++ b/biotrack/__main__.py
@@ -0,0 +1,37 @@
+# biotrack, Apache-2.0 license
+# Filename: __main__.py
+# Description: Main entry point for the biotrack command line interface
+from datetime import datetime
+from pathlib import Path
+
+import click
+import sys
+
+from biotrack.logger import create_logger_file, info, err
+from biotrack import __version__
+
+sys.path.insert(0, str(Path(__file__).parent.parent)) 
+
+
+create_logger_file("biotrack")
+default_data_path = Path(__file__).parent / "testdata"
+
+
+@click.group(context_settings={"help_option_names": ["-h", "--help"]})
+@click.version_option(__version__, "-V", "--version", message="%(prog)s, version %(version)s")
+def cli():
+    """
+    Track video from a command line.
+    """
+    pass
+
+
+if __name__ == "__main__":
+    try:
+        start = datetime.now()
+        cli()
+        end = datetime.now()
+        info(f"Done. Elapsed time: {end - start} seconds")
+    except Exception as e:
+        err(f"Exiting. Error: {e}")
+        exit(-1)
diff --git a/biotrack/assoc.py b/biotrack/assoc.py
new file mode 100644
index 0000000..156130b
--- /dev/null
+++ b/biotrack/assoc.py
@@ -0,0 +1,29 @@
+# biotrack, Apache-2.0 license
+# Filename: biotrack/tracker/assoc.py
+# Description:  Track association functions
+
+import numpy as np
+from scipy.spatial.distance import cdist
+
+from biotrack.logger import debug
+
+def associate(detection_pts, detection_emb, tracker_pts, tracker_emb, alpha: float = 0.8) -> np.ndarray:
+    if len(detection_pts) == 0 or len(tracker_pts) == 0:
+        return [100.0 * np.ones(len(detection_pts), dtype=float)]
+
+    debug("=======================")
+    debug(f"detection_pts: {detection_pts}")
+    debug("=======================")
+    debug(f"tracker_pts: {tracker_pts}")
+    debug("=======================")
+    debug(f"detection_emb: {detection_emb.shape}")
+    debug("=======================")
+    debug(f"tracker_emb: {tracker_emb.shape}")
+    # Calculate Euclidean distance and Cosine distance
+    D_E = np.round(100 * cdist(detection_pts, tracker_pts, metric="euclidean"))
+    D_C = cdist(detection_emb, tracker_emb, metric="cosine")
+    np.nan_to_num(D_C, copy=False, nan=0.0)
+    D_C = np.round(100 * D_C)
+    D_combined = alpha * D_E + (1 - alpha) * D_C
+    debug(f"D_E: {D_E}  - D_C: {D_C} D_combined: {D_combined}")
+    return D_combined
diff --git a/biotrack/batch_utils.py b/biotrack/batch_utils.py
new file mode 100644
index 0000000..99ca30d
--- /dev/null
+++ b/biotrack/batch_utils.py
@@ -0,0 +1,65 @@
+from pathlib import Path
+
+
+def find_files(directory: Path, extensions: list):
+    files = list(directory.rglob("*"))
+    valid_files = [f for f in files if f.suffix in extensions]
+    return valid_files
+
+
+def media_to_stack(media_path: Path, frame_count=None, resize=None):
+    import imageio
+    import numpy as np
+    import cv2
+
+    frames = []
+
+    if not media_path.exists():
+        print(f"{media_path} does not exist")
+        return None, 0
+
+    if media_path.is_dir():  # Assumes images
+        valid_extensions = [".jpg", ".jpeg", ".png"]
+        files = list(media_path.rglob("*"))
+        valid_files = [f for f in files if f.suffix in valid_extensions]
+        if len(valid_files) == 0:
+            print(f"No valid files found in {media_path}")
+            return None, 0
+
+        for f in valid_files:
+            print(f"Processing {f}")
+            im = cv2.imread(str(f))
+            if resize:
+                im = cv2.resize(im, resize)
+            frames.append(np.array(im))
+    else:  # Assumes video file
+        try:
+            reader = imageio.get_reader(media_path.as_posix())
+        except Exception as e:
+            print(f"Error opening video file: {e}")
+            return None, 0
+
+        for i, im in enumerate(reader):
+            if frame_count is not None and i >= frame_count:
+                break
+            if resize:
+                im = cv2.resize(im, resize)
+            frames.append(np.array(im))
+
+    num_frames = len(frames)
+    video_chunk = np.stack(frames)
+    # frames = torch.tensor(frames, dtype=torch.float32).permute(0, 3, 1, 2) #/ 255.0
+    # video_chunk = frames.unsqueeze(0) # Shape: (B, T, C, H, W)
+    # video_chunk = video_chunk
+
+    return video_chunk, num_frames
+
+
+if __name__ == "__main__":
+    from pathlib import Path
+
+    # Get the path to the parent directory of this file
+    parent_dir = Path(__file__).parent.parent.parent / "data" / "video"
+    media_path = parent_dir / "V4318_20201208T203419Z_h264_tl_3.mp4"
+
+    video, num_frames = media_to_stack(media_path, frame_count=3, resize=(640, 360))
diff --git a/biotrack/embedding.py b/biotrack/embedding.py
new file mode 100644
index 0000000..1d3a7eb
--- /dev/null
+++ b/biotrack/embedding.py
@@ -0,0 +1,79 @@
+# biotrack, Apache-2.0 license
+# Filename: biotrack/tracker/embedding.py
+# Description:  Miscellaneous functions for computing VIT embeddings and caching them.
+
+from PIL import Image
+import numpy as np
+import torch
+from typing import List
+import open_clip
+from open_clip import tokenizer
+
+from biotrack.logger import info, err
+
+
+class ViTWrapper:
+    MODEL_NAME = "ViT-B-16"
+    PRETRAINED = "openai"
+    VECTOR_DIMENSIONS = 1024  # 512 for image, 512 for text
+
+    def __init__(self, device: str = "cpu", batch_size: int = 8):
+        self.batch_size = batch_size
+        self.model, _, self.process = open_clip.create_model_and_transforms(self.MODEL_NAME, pretrained=self.PRETRAINED)
+        self.tokenizer = tokenizer
+
+        # Load the model and processor
+        if "cuda" in device and torch.cuda.is_available():
+            device_num = int(device.split(":")[-1])
+            info(f"Using GPU device {device_num}")
+            torch.cuda.set_device(device_num)
+            self.device = "cuda"
+            self.model.to("cuda")
+        else:
+            self.device = "cpu"
+
+    @property
+    def model_name(self) -> str:
+        return self.model.config._name_or_path
+
+
+def compute_embedding_vits(vit_wrapper: ViTWrapper, images: List[str], text: List[str]):
+    """
+    Compute the embedding for the given images
+    :param vit_wrapper: Wrapper for the ViT model
+    :param images: List of image filenames
+    :param text:  List of text strings to embed
+    :param device: Device to use for the computation (cpu or cuda:0, cuda:1, etc.)
+    :return: List of embeddings in the same order as the input images
+    """
+    batch_size = 8
+
+    # Batch process the images
+    batches = [images[i : i + batch_size] for i in range(0, len(images), batch_size)]
+
+    # Store the embeddings in a list
+    all_embeddings = []
+    for batch in batches:
+        try:
+            images = [Image.open(filename).convert("RGB") for filename in batch]
+            inputs = [vit_wrapper.process(image) for image in images]
+            image_input = torch.tensor(np.stack(inputs))
+            text_tokens = vit_wrapper.tokenizer.tokenize(["This is " + t for t in text])
+
+            with torch.no_grad():
+                image_features = vit_wrapper.model.encode_image(image_input).float()
+                text_features = vit_wrapper.model.encode_text(text_tokens).float()
+
+            # Concatenate the image and text features
+            batch_embeddings = torch.cat([image_features, text_features], dim=1).cpu().numpy()
+
+            # Save the embeddings
+            for emb, filename in zip(batch_embeddings, batch):
+                emb = emb.astype(np.float32)
+                all_embeddings.append(emb)
+        except Exception as e:
+            err(f"Error processing {batch}: {e}")
+            raise e
+
+    # Return the embeddings
+    return all_embeddings
diff --git a/biotrack/logger/__init__.py b/biotrack/logger/__init__.py
new file mode 100644
index 0000000..0a33c45
--- /dev/null
+++ b/biotrack/logger/__init__.py
@@ -0,0 +1,123 @@
+# biotrack, Apache-2.0 license
+# Filename: biotrack/logger/__init__.py
+# Description:  Logs to both a file and the console. The file is named with the current date.
+
+import logging
+import os
+from pathlib import Path
+from datetime import datetime as dt, timezone
+
+LOGGER_NAME = "biotrack"
+DEBUG = True
+
+
+class _Singleton(type):
+    """A metaclass that creates a Singleton base class when called."""
+
+    _instances = {}  # type: ignore
+
+    def __call__(cls, *args, **kwargs):
+        if cls not in cls._instances:
+            cls._instances[cls] = super(_Singleton, cls).__call__(*args, **kwargs)
+        return cls._instances[cls]
+
+
+class Singleton(_Singleton("SingletonMeta", (object,), {})):
+    pass
+
+
+class CustomLogger(Singleton):
+    logger = None
+    if os.getenv("ENVIRONMENT") and os.getenv("ENVIRONMENT").upper() == "TESTING":
+        output_path = Path("logs")
+    else:
+        output_path = Path.home() / "biotrack" / "logs"
+
+    def __init__(self, output_path: Path = Path.cwd(), output_prefix: str = LOGGER_NAME):
+        """
+        Initialize the logger
+        """
+        self.logger = logging.getLogger(LOGGER_NAME)
+        self.logger.setLevel(logging.DEBUG)
+        self.output_path = output_path
+        self.output_path.mkdir(parents=True, exist_ok=True)
+        formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s")
+
+        # default log file date to today
+        now = dt.now(timezone.utc)
+
+        # log to file
+        self.log_filename = output_path / f"{output_prefix}_{now:%Y%m%d}.log"
+        handler = logging.FileHandler(self.log_filename, mode="w")
+        handler.setFormatter(formatter)
+        handler.setLevel(logging.INFO)
+        self.logger.addHandler(handler)
+
+        # also log to console
+        console = logging.StreamHandler()
+        console.setLevel(logging.DEBUG)
+        console.setFormatter(formatter)
+        self.logger.addHandler(console)
+
+        self.logger.info(f"Logging to {self.log_filename}")
+
+    def loggers(self) -> logging.Logger:
+        return self.logger
+
+
+def create_logger_file(prefix: str = "biotrack"):
+    """
+    Create a logger file
+    :param log_path: Path to the log file
+    """
+    ENVIRONMENT = str(os.getenv("ENVIRONMENT"))
+    if ENVIRONMENT and ENVIRONMENT.upper() == "TESTING":
+        log_path = Path("logs")
+    else:
+        log_path = Path.home() / "biotrack" / "logs"
+        # Check if can write to the log path, and if not revert to system temp
+        try:
+            log_path.mkdir(parents=True, exist_ok=True)
+            test_file = log_path / "test.txt"
+            test_file.touch()
+            test_file.unlink()
+        except PermissionError:
+            import tempfile
+
+            temp_dir = tempfile.gettempdir()
+            log_path = Path(temp_dir) / "biotrack" / "logs"
+
+    # create the log directory if it doesn't exist
+    log_path.mkdir(parents=True, exist_ok=True)
+    return CustomLogger(log_path, prefix)
+
+
+def custom_logger() -> logging.Logger:
+    """
+    Get the logger
+    """
+    return logging.getLogger(LOGGER_NAME)
+
+
+def err(s: str):
+    custom_logger().error(s)
+
+
+def info(s: str):
+    custom_logger().info(s)
+
+
+def debug(s: str):
+    custom_logger().debug(s)
+
+
+def warn(s: str):
+    custom_logger().warning(s)
+
+
+def exception(s: str):
+    custom_logger().exception(s)
+
+
+def critical(s: str):
+    custom_logger().critical(s)
diff --git a/biotrack/track.py b/biotrack/track.py
new file mode 100644
index 0000000..84507e1
--- /dev/null
+++ b/biotrack/track.py
@@ -0,0 +1,76 @@
+# biotrack, Apache-2.0 license
+# Filename: biotrack/tracker/track.py
+# Description:  Basic track object to contain and update tracks
+from biotrack.logger import info, debug
+import numpy as np
+
+
+class Track:
+    def __init__(self, label: str, pt: np.array, emb: np.array, frame: int, x_scale: float, y_scale: float, max_empty_frame: int = 5, max_frames: int = 60, id: int = 0):
+        info(f"Creating tracker {id} at frame {frame} with point {pt} and emb {emb.shape}. Max empty frame {max_empty_frame} Max frames {max_frames}")
+        self.max_empty_frame = max_empty_frame
+        self.max_frames = max_frames
+        self.id = id
+        self.pt = {frame: pt}
+        self.label = {frame: label}
+        self.emb = emb
+        self.start_frame = frame
+        self.last_updated_frame = frame
+        self.x_scale = x_scale
+        self.y_scale = y_scale
+
+    @property
+    def track_id(self):
+        return self.id
+
+    @property
+    def embedding(self):
+        return self.emb
+
+    def is_closed(self, frame_num: int) -> bool:
+        is_closed = (frame_num - self.last_updated_frame + 1) >= self.max_empty_frame or len(self.pt) >= self.max_frames
+        debug(f"Tracker {self.id} is_closed {is_closed} frame_num {frame_num} last_updated_frame {self.last_updated_frame} max_empty_frame {self.max_empty_frame} max_frames {self.max_frames}")
+        return is_closed
+
+    @property
+    def last_update_frame(self):
+        return self.last_updated_frame
+
+    def get_point_label(self, frame_num: int, rescale=True) -> (np.array, str):
+        if frame_num not in self.pt.keys():
+            return None, None
+        pt = self.pt[frame_num]
+        label = self.label[frame_num]
+        if rescale:
+            pt[0] *= self.x_scale
+            pt[1] *= self.y_scale
+        return pt, label
+
+    def predict(self) -> np.array:
+        return self.pt[self.last_updated_frame]
+
+    def update(self, label: str, pt: np.array, emb: np.array, frame_num: int) -> None:
+        if self.is_closed(frame_num):
+            debug(f"Tracker {self.id} has a gap from {self.last_updated_frame} to {frame_num} or more than max_frames {self.max_frames}")
+            return
+
+        # If updating the same last_updated_frame, replace the point
+        if frame_num == self.last_updated_frame:
+            info(f"Updating tracker {self.id} at frame {frame_num} with point {pt}")
+            self.pt[frame_num] = pt
+            self.label[frame_num] = label
+            # If there is a valid embedding, update it
+            if len(emb) > 0:
+                self.emb = emb
+            return
+
+        # If adding in a new last_updated_frame, add the point
+        self.pt[frame_num] = pt
+        self.label[frame_num] = label
+        if len(emb) > 0:
+            self.emb = emb
+        self.last_updated_frame = frame_num
+        pts_pretty = [f"{pt[0]:.2f},{pt[1]:.2f},{label}" for pt, label in zip(self.pt.values(), self.label.values())]
+        best_label = max(set(self.label.values()), key=list(self.label.values()).count)
+        total_frames = len(self.pt)
+        info(f"Updating tracker {self.id} total_frames {total_frames} updated start {self.start_frame} to {self.last_updated_frame} {pts_pretty} with label {best_label}")
diff --git a/biotrack/tracker.py b/biotrack/tracker.py
new file mode 100644
index 0000000..75cc9f2
--- /dev/null
+++ b/biotrack/tracker.py
@@ -0,0 +1,247 @@
+# biotrack, Apache-2.0 license
+# Filename: biotrack/tracker/tracker.py
+# Description:  Main tracker class for tracking objects with points, label and embeddings using the CoTracker model and BioClip ViT embeddings
+import cv2
+from pathlib import Path
+
+from PIL import Image
+
+import torch
+import piexif
+from typing import List, Dict, Tuple
+import numpy as np
+from cotracker.predictor import CoTrackerPredictor
+from biotrack.assoc import associate
+from biotrack.embedding import ViTWrapper, compute_embedding_vits
+from biotrack.track import Track
+from biotrack.logger import create_logger_file, info, debug
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+
+class BioTracker:
+    def __init__(self, image_width: int, image_height: int):
+        self.logger = create_logger_file()
+        self.image_width = image_width
+        self.image_height = image_height
+        self.model_width = 640
+        self.model_height = 360
+        self.x_scale = image_width / self.model_width
+        self.y_scale = image_height / self.model_height
+        self.open_trackers: List[Track] = []
+        self.closed_trackers: List[Track] = []
+        self.next_track_id = 0  # Unique ID for new tracks
+        model = torch.hub.load("facebookresearch/co-tracker", "cotracker3_online")
+        self.offline_model = CoTrackerPredictor(checkpoint=None)
+        self.offline_model.model = model.model
+        self.offline_model.step = model.model.window_len // 2
+        if DEFAULT_DEVICE == "cuda":
+            self.offline_model.model = self.offline_model.model.cuda()
+
+        # Initialize the model for computing crop embeddings
+        self.vit_wrapper = ViTWrapper(device=DEFAULT_DEVICE)
+
+    def update_trackers(self, frame_num: int, points: List[np.array], embeddings: List[np.array], **kwargs):
+        """
+        Update the tracker with new detections and crops
+        :param frame_num: the starting frame number of the batch
+        :param points: points in the format [[x1,y1],[x2,y2],[[x1,y1],[x2,y2],[x3,y3...]
+        a collection of detections detected in each last_updated_frame.
+        :param embeddings: a numpy array of embeddings in the format [[emb1],[emb2],[emb3]...]
+        :param kwargs:
+        :return:
+        """
+        info(f"Updating tracks with {len(points)} points in frames batch starting at {frame_num}")
+
+        labels = kwargs.get("labels", [])
+        max_cost = kwargs.get("max_cost", 50)
+        t_pts = np.zeros((len(self.open_trackers), 2))
+        t_emb = np.zeros((len(self.open_trackers), ViTWrapper.VECTOR_DIMENSIONS))
+        d_emb = embeddings
+
+        # If there are no detections, return
+        if len(points) == 0:
+            return
+
+        open_tracks = [t for t in self.open_trackers if not t.is_closed(frame_num)]
+        info(f"Updating {len(open_tracks)} open tracks")
+
+        # Get predicted detections and embeddings from existing trackers
+        for i, t in enumerate(open_tracks):
+            t_pts[i] = t.predict()
+            # Normalize the detections 0-1 based on the model size
+            t_pts[i][0] = t_pts[i][0] / self.model_width
+            t_pts[i][1] = t_pts[i][1] / self.model_height
+            t_emb[i] = t.embedding
+
+        # Normalize the detections 0-1 based on the model size, and convert to numpy array
+        points = np.array(points)
+        points[:, 0] = points[:, 0] / self.model_width
+        points[:, 1] = points[:, 1] / self.model_height
+        d_emb = np.array(d_emb)
+
+        # Associate the new detections with the existing tracks
+        costs = associate(detection_pts=points, detection_emb=d_emb, tracker_pts=t_pts, tracker_emb=t_emb)
+
+        for cost, point, emb, label in zip(costs, points, d_emb, labels):
+            match = np.argmin(cost, axis=0)
+            # Denormalize the detections
+            point[0] = point[0] * self.model_width
+            point[1] = point[1] * self.model_height
+            best_cost = cost[match]
+            if len(open_tracks) > 0:
+                info(f"Match {match} all costs {cost} for point {point} num trackers {len(open_tracks)}")
+                if cost[match] < max_cost:
+                    if len(open_tracks) > 0 and match < len(open_tracks):
+                        info(f"Found match {match} with cost {best_cost} for point {point}")
+                        info(f"Found match to track id {open_tracks[match].track_id} cost {best_cost} for point {point}")
+                        open_tracks[match].update(label, point, emb, frame_num)
+                else:
+                    info(f"Match too high {best_cost} > {max_cost}; creating new track {self.next_track_id} for point {point}")
+                    self.open_trackers.append(Track(label, point, emb, frame_num, self.x_scale, self.y_scale, id=self.next_track_id))
+                    self.next_track_id += 1
+            else:
+                self.open_trackers.append(Track(label, point, emb, frame_num, self.x_scale, self.y_scale, id=self.next_track_id))
+                self.next_track_id += 1
+
+        # Close any tracks ready to close
+        i = len(self.open_trackers)
+        for t in reversed(self.open_trackers):
+            i -= 1
+            if t.is_closed(frame_num):
+                info(f"Closing track {t.track_id}")
+                self.closed_trackers.append(t)
+                self.open_trackers.pop(i)
+
+    def update_batch(self, frame_range: Tuple[int, int], frames: List[np.array], detections: Dict, **kwargs):
+        """
+        Update the tracker with new frames and detections
+        :param frame_range: a tuple of the starting and ending frame numbers
+        :param frames:  a list of frames in the format [frame1, frame2, frame3...]
+        :param detections: dictionary of detections in the format {["x": x, "y": y, "crop_path": crop_path, "frame": frame]}
+        :param kwargs:
+        :return:
+        """
+        det_query = {}
+        crop_query = {}
+        for d in detections:
+            if not Path(d["crop_path"]).exists():
+                continue
+
+            image = cv2.imread(d["crop_path"])
+            gray_crop = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            (min_val, max_val, min_loc, max_loc) = cv2.minMaxLoc(gray_crop)
+
+            # Extract originating bounding box from EXIF UserComment tag
+            # This is the box the crop was extracted from in the raw image
+            bbox = None
+            with Image.open(d["crop_path"]) as img:
+                exif_dict = img._getexif()
+                if exif_dict is not None:
+                    user_comment = exif_dict[piexif.ExifIFD.UserComment]
+                    user_comment = user_comment.decode("utf-8")
+                    if user_comment.startswith("bbox:"):
+                        bbox_str = user_comment.split("bbox:")[1]
+                        bbox = eval(bbox_str)
+
+            # Should never get here unless something is wrong
+            if bbox is None:
+                raise ValueError(f"No bounding box found in {d['crop_path']}")
+
+            x, y = max_loc
+            # Actual size of the crop which is rescaled to 224x224 - this is used to find
+            # the top left x, y in the original image for the query
+            scale = (bbox[2] - bbox[0]) / 224
+            x = x * scale
+            y = y * scale
+            x += bbox[0]
+            y += bbox[1]
+            # Adjust the x, y to the model scale
+            x /= self.x_scale
+            y /= self.y_scale
+            frame_num = d["frame"]
+            label = d["class_name"]
+
+            # Add the brightest spot to the query
+            info(f"Adding query for {x}, {y} {label} in frame {frame_num}")
+            if frame_num not in det_query:
+                det_query[frame_num] = []
+                crop_query[frame_num] = []
+            det_query[frame_num].append([x, y, label])
+            crop_query[frame_num].append(d["crop_path"])
+
+        return self._update_batch(frame_range, frames, det_query, crop_query, **kwargs)
+
+    def _update_batch(self, frame_range: Tuple[int, int], frames: List[np.array], detections: Dict, crop_paths: Dict, **kwargs):
+        """
+        Update the tracker with the new frames, detections and crops of the detections
+        :param frame_range: a tuple of the starting and ending frame numbers
+        :param frames: a list of frames in the format [frame1, frame2, frame3...]
+        :param detections: a dictionary of detections in the format {frame_num: [[x1,y1,label],[x2,y2,label],[[x1,y1,label]]...]}
+        :param crop_paths: a dictionary of crop paths in the format {frame_num: [crop_path1, crop_path2, crop_path3...]}
+        :param kwargs:
+        :return:
+        """
+        if len(detections) == 0 or len(crop_paths) == 0:
+            info("No data for frame")
+            return []
+
+        if len(detections) != len(crop_paths):
+            info(f"Number of detections {len(detections)} and crop paths {len(crop_paths)} do not match")
+            return []
+
+        # Compute the embeddings for the new query detection crops
+        # Format the queries for the model, each query is [frame_number, x, y]
+        q_emb = {}
+        queries = []
+        for f, d in detections.items():
+            # TODO: replace with parallel processing
+            info(f"Computing embeddings for frame {f} {crop_paths[f]}")
+            labels = [det[2] for det in d]
+            q_emb[f] = compute_embedding_vits(self.vit_wrapper, crop_paths[f], labels)
+            for det in d:
+                queries.append([f - frame_range[0], det[0], det[1]])
+
+        if len(queries) == 0:
+            return []
+
+        # Put the queries and frames into tensors and run the model with the backward tracking option which is
+        # more accurate than the forward/online only tracking
+        queries_t = torch.tensor(queries, dtype=torch.float32)
+        frames = torch.tensor(frames, dtype=torch.float32).permute(0, 3, 1, 2)
+        if DEFAULT_DEVICE == "cuda":
+            queries_t = queries_t.cuda()
+            frames = frames.cuda()  # / 255.0
+
+        video_chunk = frames.unsqueeze(0)  # Shape: (B, T, C, H, W)
+        info(f"Running co-tracker model with {len(queries)} queries frames {frame_range}")
+        pred_pts, pred_visibilities = self.offline_model(video_chunk, queries=queries_t[None], backward_tracking=True)
+        pred_pts, pred_visibilities = pred_pts.numpy(), pred_visibilities.numpy()
+
+        # Update with the queries - these seed new tracks and update existing tracks
+        for f in range(frame_range[0], frame_range[1]):
+            if f not in detections:
+                continue
+            labels_in_frame = [d[2] for d in detections[f]]
+            queries_in_frame = [d[:2] for d in detections[f]]
+            debug(f"Updating with queries {queries_in_frame} in frame {f}")
+            self.update_trackers(f, queries_in_frame, q_emb[f], labels=labels_in_frame, **kwargs)
+
+        # Update with predictions
+        for f in range(frame_range[0], frame_range[1], 1):
+            if len(pred_pts) == 0:
+                continue
+            pts = pred_pts[:, f - frame_range[0], :, :]
+            if len(pts) == 0:
+                continue
+
+            # Filter out the detections that are not visible
+            pred_visibilities_in_frame = pred_visibilities[:, f - frame_range[0], :]
+            filtered_pts = pts[pred_visibilities_in_frame]
+
+            # Create empty embeddings for the predicted detections since this is just pt tracking
+            empty_emb = np.zeros((len(filtered_pts), ViTWrapper.VECTOR_DIMENSIONS))
+            self.update_trackers(f, filtered_pts, empty_emb, **kwargs)
+
+        return self.open_trackers + self.closed_trackers
+
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..0f1d0c3
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,8 @@
+name: biotrack
+channels:
+  - conda-forge
+dependencies:
+  - python >=3.10,<3.12
+  - pip
+  - pip:
+    - -r requirements.txt
\ No newline at end of file
diff --git a/examples/data/detections/16.json b/examples/data/detections/16.json
new file mode 100644
index 0000000..ab4ebd6
--- /dev/null
+++ b/examples/data/detections/16.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "16.jpg",
+        "crop_path": "5e4c4a83-0c73-568f-9bef-52c02a866e25.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.41716485023498534,
+        "y": 0.4242680019802517,
+        "xx": 0.42656664848327636,
+        "xy": 0.45543085734049477,
+        "confidence": 0.36808881163597107,
+        "track_id": -1,
+        "class_name": "Amphipoda"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/22.json b/examples/data/detections/22.json
new file mode 100644
index 0000000..600b892
--- /dev/null
+++ b/examples/data/detections/22.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "22.jpg",
+        "crop_path": "04bc219b-c3ab-578a-9d3a-378a8a7d9a51.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.4958321571350098,
+        "y": 0.41406428019205727,
+        "xx": 0.504338550567627,
+        "xy": 0.44863052368164064,
+        "confidence": 0.2969337999820709,
+        "track_id": -1,
+        "class_name": "Gonatus"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/24.json b/examples/data/detections/24.json
new file mode 100644
index 0000000..ef22936
--- /dev/null
+++ b/examples/data/detections/24.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "24.jpg",
+        "crop_path": "ee138b43-19aa-55e8-bc7a-47ae1cc64f30.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.4254740238189697,
+        "y": 0.4393697102864583,
+        "xx": 0.43489699363708495,
+        "xy": 0.4767725626627604,
+        "confidence": 0.5122886896133423,
+        "track_id": -1,
+        "class_name": "Gonatus"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/26.json b/examples/data/detections/26.json
new file mode 100644
index 0000000..c7522bd
--- /dev/null
+++ b/examples/data/detections/26.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "26.jpg",
+        "crop_path": "0899d3ac-db85-5df5-8025-607b6ab82eff.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.30465238094329833,
+        "y": 0.44328172471788196,
+        "xx": 0.3165201425552368,
+        "xy": 0.4920756022135417,
+        "confidence": 0.6075283885002136,
+        "track_id": -1,
+        "class_name": "Gonatus"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/28.json b/examples/data/detections/28.json
new file mode 100644
index 0000000..a2fc01f
--- /dev/null
+++ b/examples/data/detections/28.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "28.jpg",
+        "crop_path": "a471d784-5931-5460-9ba4-6d807eaaebb0.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.041498565673828126,
+        "y": 0.4566148969862196,
+        "xx": 0.06337025165557861,
+        "xy": 0.5323814815945096,
+        "confidence": 0.7562034130096436,
+        "track_id": -1,
+        "class_name": "Gonatus"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/48.json b/examples/data/detections/48.json
new file mode 100644
index 0000000..803d63b
--- /dev/null
+++ b/examples/data/detections/48.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "48.jpg",
+        "crop_path": "4305194a-58b0-5ad9-b27b-9d07c60453a9.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.25748796463012696,
+        "y": 0.25620443556043837,
+        "xx": 0.2893531799316406,
+        "xy": 0.4372981177435981,
+        "confidence": 0.7091726064682007,
+        "track_id": -1,
+        "class_name": "Pseudosagitta maxima"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/54.json b/examples/data/detections/54.json
new file mode 100644
index 0000000..5fd4218
--- /dev/null
+++ b/examples/data/detections/54.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "54.jpg",
+        "crop_path": "87440a38-90c7-52f9-b11e-5f04731b2ca9.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.32279086112976074,
+        "y": 0.015367741054958767,
+        "xx": 0.38132777214050295,
+        "xy": 0.27827684614393444,
+        "confidence": 0.8360358476638794,
+        "track_id": -1,
+        "class_name": "Galiteuthis"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/56.json b/examples/data/detections/56.json
new file mode 100644
index 0000000..3e80b6a
--- /dev/null
+++ b/examples/data/detections/56.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "56.jpg",
+        "crop_path": "2620fd27-0d4c-5964-8391-e3e266dd3daf.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.30018765926361085,
+        "y": 0.31578790876600477,
+        "xx": 0.39138624668121336,
+        "xy": 0.5256228976779513,
+        "confidence": 0.8554758429527283,
+        "track_id": -1,
+        "class_name": "Galiteuthis phyllura"
+    }
+]
\ No newline at end of file
diff --git a/examples/data/detections/58.json b/examples/data/detections/58.json
new file mode 100644
index 0000000..5d81844
--- /dev/null
+++ b/examples/data/detections/58.json
@@ -0,0 +1,15 @@
+[
+    {
+        "image_path": "58.jpg",
+        "crop_path": "fcb5ed50-c95f-580f-8a01-d6e2692ce992.jpg",
+        "image_width": 1920,
+        "image_height": 1080,
+        "x": 0.34359190464019773,
+        "y": 0.39694730970594616,
+        "xx": 0.46210994720458987,
+        "xy": 0.6625360276963975,
+        "confidence": 0.9029698967933655,
+        "track_id": -1,
+        "class_name": "Galiteuthis phyllura"
+    }
+]
\ No newline at end of file
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diff --git a/examples/data/video/V4318_20201208T203419Z_h264_tl_3.mp4 b/examples/data/video/V4318_20201208T203419Z_h264_tl_3.mp4
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diff --git a/examples/data/video/V4318_20201208T203419Z_h264_tl_3_biotrack.gif b/examples/data/video/V4318_20201208T203419Z_h264_tl_3_biotrack.gif
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diff --git a/examples/video.py b/examples/video.py
new file mode 100644
index 0000000..47c4351
--- /dev/null
+++ b/examples/video.py
@@ -0,0 +1,81 @@
+from biotrack.tracker import BioTracker
+from biotrack.batch_utils import media_to_stack
+
+if __name__ == "__main__":
+    import json
+    import cv2
+    from pathlib import Path
+
+    parent_dir = Path(__file__).parent
+    media_path = parent_dir / "data" / "video" / "V4318_20201208T203419Z_h264_tl_3.mp4"
+    out_video_path = parent_dir / "data" / "video" / "V4318_20201208T203419Z_h264_tl_3_biotrack.mp4"
+    detections_path = parent_dir / "data" / "detections"
+    crops_path = parent_dir / "data" / "images" / "crops"
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+
+    # Load the video and convert it to a stack of frames
+    frame_stack, num_frames = media_to_stack(media_path, frame_count=60, resize=(640, 360))
+    out_video = cv2.VideoWriter(out_video_path, fourcc, 1, (1920, 1080))
+
+    # Initialize the tracker
+    tracker = BioTracker(1920, 1080)
+    window_len = 8
+
+    tracks = None
+
+    # Run the tracker for all the frames in windows of window_len
+    for i in range(0, num_frames, window_len):
+        frames = frame_stack[i : i + window_len]
+
+        # Get all the detections in the window
+        detections = []
+        for j in range(len(frames)):
+            frame_num = i + j
+            # Load the detections for the last_updated_frame
+            detections_file = detections_path / f"{frame_num}.json"
+            if not detections_file.exists():
+                print(f"No detections for frame {frame_num}")
+                continue
+
+            data = json.loads(detections_file.read_text())
+            for loc in data:
+                if j >= num_frames:
+                    continue
+
+                # Convert the x, y to the image coordinates and adjust crop path to the full path
+                loc["x"] = loc["x"] * loc["image_width"]
+                loc["y"] = loc["y"] * loc["image_height"]
+                loc["xx"] = loc["xx"] * loc["image_width"]
+                loc["xy"] = loc["xy"] * loc["image_height"]
+                loc["crop_path"] = (crops_path / loc["crop_path"]).as_posix()
+                loc["frame"] = frame_num
+                detections.append(loc)
+
+        i_e = min(i + window_len - 1, num_frames - 1)  # handle the end of the video
+        print(f"Tracking frames {i} to {i_e}")
+        tracks = tracker.update_batch((i, i_e), frames, detections=detections)
+
+        # Display the tracks for the window
+        for j in range(len(frames)):
+            frame_num = i + j
+            print(f"Displaying last_updated_frame {frame_num}")
+            image = parent_dir / "data" / "images" / f"{frame_num}.jpg"
+            frame = cv2.imread(image.as_posix())
+            for track in tracks:
+                pt, label = track.get_point_label(frame_num)
+                if pt is not None:
+                    print(f"Drawing point {pt},{label}")
+                    center = (int(pt[0]), int(pt[1]))
+                    radius = 10
+                    color = (255, 255, 255)
+                    thickness = 1
+                    frame = cv2.circle(frame, center, radius, color, thickness)
+                    # Draw the track id with the label, e.g. 1:Unknown
+                    font = cv2.FONT_HERSHEY_SIMPLEX
+                    fontScale = 1
+                    frame = cv2.putText(frame, f"{track.id}:{label}", center, font, fontScale, color, thickness, cv2.LINE_AA)
+
+            if frame is not None:
+                cv2.imshow("Frame", frame)
+                cv2.waitKey(250)
+                out_video.write(frame)
diff --git a/justfile b/justfile
new file mode 100755
index 0000000..7c7446d
--- /dev/null
+++ b/justfile
@@ -0,0 +1,32 @@
+#!/usr/bin/env just --justfile
+
+# List recipes
+list:
+    @just --list --unsorted
+
+# Install dependencies
+setup:
+    poetry install
+
+# Check source formatting
+format-check:
+    poetry run ruff format --check
+
+format:
+    poetry run ruff format
+
+# Lint source code
+lint:
+    poetry run ruff check --fix
+
+# Check linting of source code
+lint-check:
+    poetry run ruff check
+
+# Export poetry dependencies to requirements.txt
+export-requirements:
+    poetry export -f requirements.txt --output requirements.txt --without-hashes
+
+# Run an example video
+track-video:
+    export PYTHONPATH=. && poetry run python examples/video.py
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..7a72397
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1469 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "certifi"
+version = "2024.8.30"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.0"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"},
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+    {file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
+    {file = "triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dadaca7fc24de34e180271b5cf864c16755702e9f63a16f62df714a8099126a"},
+    {file = "triton-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aafa9a20cd0d9fee523cd4504aa7131807a864cd77dcf6efe7e981f18b8c6c11"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.2.3"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
+    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.12"
+content-hash = "e4926ace8893fc6dd03b929162ceeeb13545692e4cdef920d79cd151e4ef981a"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..088d843
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,63 @@
+[tool.poetry]
+name = "biotrack"
+version = "0.1.1"
+description = "Tracker based on BioClip and CoTracker3 developed by MBARI"
+authors = ["danellecline <dcline@mbari.org>"]
+license = "Apache"
+readme = "README.md"
+repository = "https://github.com/mbari-org/biotrack"
+packages = [{include = "biotrack"}]
+exclude = ["tests"]
+
+[tool.poetry.scripts]
+sdcat = "biotrack.__main__:cli"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+imageio = {extras = ["ffmpeg"], version = "^2.36.0"}
+torch = "^2.3.1"
+click = "8.1.7"
+cotracker = {git = "https://github.com/facebookresearch/co-tracker.git"}
+opencv-python-headless = "4.10.0.84"
+piexif = "^1.1.3"
+scipy = "^1.14.1"
+open-clip-torch = "^2.29.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+line-length = 200
+exclude = ["co-tracker"]
+
+[tool.semantic_release]
+branch = "main"
+commit_message = "{version}\n\nAutomatically generated by python-semantic-release"
+tag_format = "v{version}"
+commit_version_number = true
+allow_zero_version = true
+commit_parser = "angular"
+logging_use_named_masks = true
+major_on_zero = true
+assets = []
+version_variables = ["biotrack/__init__.py:__version__"]
+version_toml = ["pyproject.toml:tool.poetry.version"]
+build_command = "pip install poetry && poetry build"
+
+[tool.semantic_release.commit_parser_options]
+allowed_tags = [
+    "build",
+    "chore",
+    "ci",
+    "docs",
+    "feat",
+    "fix",
+    "perf",
+    "style",
+    "refactor",
+    "test"
+]
+minor_tags = ["feat"]
+patch_tags = ["fix", "perf"]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6683d12
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,50 @@
+certifi==2024.8.30 ; python_version >= "3.10" and python_version < "3.12"
+charset-normalizer==3.4.0 ; python_version >= "3.10" and python_version < "3.12"
+click==8.1.7 ; python_version >= "3.10" and python_version < "3.12"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.12" and platform_system == "Windows"
+cotracker @ git+https://github.com/facebookresearch/co-tracker.git@b00a83b66a2e72c7136bdc91e258988f42ebf6bc ; python_version >= "3.10" and python_version < "3.12"
+filelock==3.16.1 ; python_version >= "3.10" and python_version < "3.12"
+fsspec==2024.10.0 ; python_version >= "3.10" and python_version < "3.12"
+ftfy==6.3.1 ; python_version >= "3.10" and python_version < "3.12"
+huggingface-hub==0.26.2 ; python_version >= "3.10" and python_version < "3.12"
+idna==3.10 ; python_version >= "3.10" and python_version < "3.12"
+imageio-ffmpeg==0.5.1 ; python_version >= "3.10" and python_version < "3.12"
+imageio[ffmpeg]==2.36.0 ; python_version >= "3.10" and python_version < "3.12"
+jinja2==3.1.4 ; python_version >= "3.10" and python_version < "3.12"
+markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "3.12"
+mpmath==1.3.0 ; python_version >= "3.10" and python_version < "3.12"
+networkx==3.4.2 ; python_version >= "3.10" and python_version < "3.12"
+numpy==2.1.3 ; python_version >= "3.10" and python_version < "3.12"
+nvidia-cublas-cu12==12.4.5.8 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cuda-cupti-cu12==12.4.127 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cuda-nvrtc-cu12==12.4.127 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cuda-runtime-cu12==12.4.127 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cudnn-cu12==9.1.0.70 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cufft-cu12==11.2.1.3 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-curand-cu12==10.3.5.147 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cusolver-cu12==11.6.1.9 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-cusparse-cu12==12.3.1.170 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-nccl-cu12==2.21.5 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-nvjitlink-cu12==12.4.127 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+nvidia-nvtx-cu12==12.4.127 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.10" and python_version < "3.12"
+open-clip-torch==2.29.0 ; python_version >= "3.10" and python_version < "3.12"
+opencv-python-headless==4.10.0.84 ; python_version >= "3.10" and python_version < "3.12"
+packaging==24.2 ; python_version >= "3.10" and python_version < "3.12"
+piexif==1.1.3 ; python_version >= "3.10" and python_version < "3.12"
+pillow==11.0.0 ; python_version >= "3.10" and python_version < "3.12"
+psutil==6.1.0 ; python_version >= "3.10" and python_version < "3.12"
+pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "3.12"
+regex==2024.11.6 ; python_version >= "3.10" and python_version < "3.12"
+requests==2.32.3 ; python_version >= "3.10" and python_version < "3.12"
+safetensors==0.4.5 ; python_version >= "3.10" and python_version < "3.12"
+scipy==1.14.1 ; python_version >= "3.10" and python_version < "3.12"
+setuptools==75.4.0 ; python_version >= "3.10" and python_version < "3.12"
+sympy==1.13.1 ; python_version >= "3.10" and python_version < "3.12"
+timm==1.0.11 ; python_version >= "3.10" and python_version < "3.12"
+torch==2.5.1 ; python_version >= "3.10" and python_version < "3.12"
+torchvision==0.20.1 ; python_version >= "3.10" and python_version < "3.12"
+tqdm==4.67.0 ; python_version >= "3.10" and python_version < "3.12"
+triton==3.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.12" and python_version >= "3.10"
+typing-extensions==4.12.2 ; python_version >= "3.10" and python_version < "3.12"
+urllib3==2.2.3 ; python_version >= "3.10" and python_version < "3.12"
+wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "3.12"