Release 1.5.2

README.md

@@ -1,12 +1,12 @@
 <p align="center">
-  <img width="350" src=https://github.com/softmatterlab/DeepTrack-2.0/blob/develop/assets/logo.png?raw=true>
+  <img width="350" src=https://github.com/softmatterlab/DeepTrack2/blob/develop/assets/logo.png?raw=true>
 </p>
 
 <h3 align="center">A comprehensive deep learning framework for digital microscopy.</h3>
 <p align="center">
   <a href="/LICENSE" alt="licence"><img src="https://img.shields.io/github/license/softmatterlab/DeepTrack-2.0"></a>
   <a href="https://badge.fury.io/py/deeptrack"><img src="https://badge.fury.io/py/deeptrack.svg" alt="PyPI version"></a>
-  <a href="https://softmatterlab.github.io/DeepTrack-2.0/deeptrack.html"><img src="https://img.shields.io/badge/docs-passing-green" alt="PyPI version"></a>
+  <a href="https://softmatterlab.github.io/DeepTrack2/deeptrack.html"><img src="https://img.shields.io/badge/docs-passing-green" alt="PyPI version"></a>
   <a href="https://badge.fury.io/py/deeptrack"><img src="https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue" alt="Python version"></a>
   <a href="https://doi.org/10.1063/5.0034891" alt="DOI">
     <img src="https://img.shields.io/badge/DOI-10.1063%2F5.0034891-blue">
@@ -54,8 +54,8 @@ DeepTrack is a general purpose deep learning framework for microscopy, meaning y
   <br/>
   <a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/paper-examples/2-single_particle_tracking.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training a CNN-based single particle tracker using simulated data </a>
   <br/>
-  <a href="https://doi.org/10.48550/arXiv.2202.13546" alt="DOI lodestar">
-    <img src="https://img.shields.io/badge/DOI-10.48550%2FarXiv.2202.13546-blue">
+  <a href="https://doi.org/10.1038/s41467-022-35004-y" alt="DOI lodestar">
+    <img src="https://img.shields.io/badge/DOI-10.1038%2Fs41467--022--35004--y-blue">
   </a> 
   <a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/LodeSTAR/02.%20tracking_particles_of_various_shapes.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Unsupervised training of a single particle tracker using LodeSTAR </a> 
 
@@ -67,8 +67,8 @@ DeepTrack is a general purpose deep learning framework for microscopy, meaning y
 <p align="left">
   <img width="600" src=/assets/MPT-packed.gif?raw=true>
   <br/>
-  <a href="https://doi.org/10.48550/arXiv.2202.13546" alt="DOI lodestar">
-    <img src="https://img.shields.io/badge/DOI-10.48550%2FarXiv.2202.13546-blue">
+  <a href="https://doi.org/10.1038/s41467-022-35004-y" alt="DOI lodestar">
+    <img src="https://img.shields.io/badge/DOI-10.1038%2Fs41467--022--35004--y-blue">
   </a> <a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/LodeSTAR/03.track_BF-C2DL-HSC.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training LodeSTAR to detect multiple cells from a single image </a>
   <br/>
   <a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/paper-examples/4-multi-molecule-tracking.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training a UNet-based multi-particle tracker using simulated data </a> 
@@ -144,6 +144,13 @@ https://doi.org/10.1063/5.0034891
 
 See also:
 
+<https://www.nature.com/articles/s41467-022-35004-y>:
+```
+Midtvedt, B., Pineda, J., Skärberg, F. et al. 
+"Single-shot self-supervised object detection in microscopy." 
+Nat Commun 13, 7492 (2022).
+```
+
 <https://arxiv.org/abs/2202.06355>:
 ```
 Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel  Midtvedt, Giovanni Volpe,1 and  Carlo  Manzo

deeptrack/datasets/__init__.py

@@ -4,4 +4,8 @@
     regression_holography_nanoparticles,
     segmentation_fluorescence_u2os,
     detection_holography_nanoparticles,
+    detection_linking_hela,
+    dmdataset,
+    regression_diffusion_landscape,
+    endothelial_vs,
 )

deeptrack/datasets/detection_linking_hela/__init__.py

@@ -0,0 +1,3 @@
+"""detection_linking_hela dataset."""
+
+from .detection_linking_hela import DetectionLinkingHela

deeptrack/datasets/detection_linking_hela/checksums.tsv

@@ -0,0 +1,3 @@
+# TODO(detection_linking_hela): If your dataset downloads files, then the checksums
+# will be automatically added here when running
+# `tfds build --register_checksums`.

deeptrack/datasets/detection_linking_hela/detection_linking_hela.py

@@ -0,0 +1,121 @@
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+import numpy as np
+import pandas as pd
+
+_DESCRIPTION = """
+This dataset includes tracking data from DIC-C2DH-HELA (provided by the sixth edition of the Cell Tracking Challenge).
+It consists of two dataframes: ``nodes`` and ``parenthood``. ``nodes`` contains information about the individual 
+cells, while "parenthood" includes information on the lineage of the cells.
+"""
+
+_CITATION = """
+@article{pineda2022geometric,
+  title={Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion},
+  author={Pineda, Jes{\'u}s and Midtvedt, Benjamin and Bachimanchi, Harshith and No{\'e}, Sergio and Midtvedt, Daniel and Volpe, Giovanni and Manzo, Carlo},
+  journal={arXiv preprint arXiv:2202.06355},
+  year={2022}
+}
+"""
+
+
+class DetectionLinkingHela(tfds.core.GeneratorBasedBuilder):
+    """DatasetBuilder for detection_linking_Hela dataset."""
+
+    VERSION = tfds.core.Version("1.0.0")
+    RELEASE_NOTES = {
+        "1.0.0": "Initial release.",
+    }
+
+    def _info(self) -> tfds.core.DatasetInfo:
+        """Returns the dataset metadata."""
+        NODE_FEATURES = self.get_node_features()
+        return tfds.core.DatasetInfo(
+            builder=self,
+            description=_DESCRIPTION,
+            features=tfds.features.FeaturesDict(
+                {
+                    "nodes": tfds.features.FeaturesDict(
+                        {
+                            **{
+                                key: tfds.features.Tensor(
+                                    shape=(None,), dtype=NODE_FEATURES[key]
+                                )
+                                for key in NODE_FEATURES.keys()
+                            },
+                        }
+                    ),
+                    "parenthood": tfds.features.FeaturesDict(
+                        {
+                            "child": tfds.features.Tensor(
+                                shape=(None,), dtype=tf.int32
+                            ),
+                            "parent": tfds.features.Tensor(
+                                shape=(None,), dtype=tf.int32
+                            ),
+                        }
+                    ),
+                    "images": tfds.features.Tensor(
+                        shape=(84, 512, 512, 1), dtype=tf.float64
+                    ),
+                    "masks": tfds.features.Tensor(
+                        shape=(84, 512, 512, 1), dtype=tf.float64
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://dataset-homepage/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: tfds.download.DownloadManager):
+        """Returns SplitGenerators."""
+        # Downloads the data and defines the splits
+        path = dl_manager.download_and_extract(
+            "https://drive.google.com/u/1/uc?id=1itHz4KmrUqDCKpGNyHUiHE4AFhwiJ5XR&export=download"
+        )
+
+        # Returns the Dict[split names, Iterator[Key, Example]]
+        return {
+            "train": self._generate_examples(
+                path / "detection_linking_hela", "train"
+            ),
+            "test": self._generate_examples(
+                path / "detection_linking_hela", "test"
+            ),
+        }
+
+    def _generate_examples(self, path, split):
+        """Yields examples."""
+
+        # Load data
+        nodes, parenthood, images, masks = (
+            pd.read_csv(path / split / "nodesdf.csv"),
+            pd.read_csv(path / split / "parenthood.csv"),
+            np.load(path / split / "images.npy"),
+            np.load(path / split / "masks.npy"),
+        )
+
+        yield "_", {
+            "nodes": {**nodes.to_dict("list")},
+            "parenthood": {**parenthood.to_dict("list")},
+            "images": images * 1.0,
+            "masks": masks * 1.0,
+        }
+
+    def get_node_features(self):
+        return {
+            "frame": tf.int32,
+            "label": tf.int32,
+            "centroid-0": tf.float32,
+            "centroid-1": tf.float32,
+            "area": tf.float32,
+            "mean_intensity": tf.float32,
+            "perimeter": tf.float32,
+            "eccentricity": tf.float32,
+            "solidity": tf.float32,
+            "set": tf.float32,
+            "parent": tf.int32,
+            "solution": tf.float32,
+        }

deeptrack/datasets/dmdataset/__init__.py

@@ -0,0 +1,3 @@
+"""dmdataset dataset."""
+
+from .dmdataset import Dmdataset

deeptrack/datasets/dmdataset/checksums.tsv

@@ -0,0 +1,3 @@
+# TODO(dmdataset): If your dataset downloads files, then the checksums
+# will be automatically added here when running
+# `tfds build --register_checksums`.

deeptrack/datasets/dmdataset/dmdataset.py

@@ -0,0 +1,135 @@
+"""dmdataset dataset."""
+
+import tensorflow_datasets as tfds
+import tensorflow as tf
+
+import os
+import scipy
+
+_DESCRIPTION = """
+"""
+
+_CITATION = """
+"""
+
+
+class Dmdataset(tfds.core.GeneratorBasedBuilder):
+    """DatasetBuilder for dmdataset dataset."""
+
+    VERSION = tfds.core.Version("1.0.0")
+    RELEASE_NOTES = {
+        "1.0.0": "Initial release.",
+    }
+
+    def _info(self) -> tfds.core.DatasetInfo:
+        """Returns the dataset metadata."""
+
+        NODE_FEATURES = self.get_features()
+
+        return tfds.core.DatasetInfo(
+            builder=self,
+            description=_DESCRIPTION,
+            features=tfds.features.FeaturesDict(
+                {
+                    "graph": tfds.features.FeaturesDict(
+                        {
+                            **{
+                                key: tfds.features.Tensor(
+                                    shape=feature[0],
+                                    dtype=feature[1],
+                                )
+                                for key, feature in NODE_FEATURES[
+                                    "graph"
+                                ].items()
+                            },
+                        }
+                    ),
+                    "labels": tfds.features.FeaturesDict(
+                        {
+                            **{
+                                key: tfds.features.Tensor(
+                                    shape=feature[0],
+                                    dtype=feature[1],
+                                )
+                                for key, feature in NODE_FEATURES[
+                                    "labels"
+                                ].items()
+                            },
+                        }
+                    ),
+                    "sets": tfds.features.FeaturesDict(
+                        {
+                            **{
+                                key: tfds.features.Tensor(
+                                    shape=feature[0],
+                                    dtype=feature[1],
+                                )
+                                for key, feature in NODE_FEATURES[
+                                    "sets"
+                                ].items()
+                            },
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://dataset-homepage/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: tfds.download.DownloadManager):
+        """Returns SplitGenerators."""
+        path = dl_manager.download_and_extract(
+            "https://drive.google.com/u/1/uc?id=19vplN2lbKo4KAmv4NRU2qr3NSlzxFzrx&export=download"
+        )
+
+        return {
+            "train": self._generate_examples(
+                os.path.join(path, "dmdataset", "training")
+            ),
+            "test": self._generate_examples(
+                os.path.join(path, "dmdataset", "validation")
+            ),
+        }
+
+    def _generate_examples(self, path):
+        """Yields examples."""
+        data = [{}, {}, {}]
+        for i, subdict in enumerate(self.get_features().values()):
+            files = (*subdict.keys(),)
+
+            for file in files:
+                data_elem = scipy.sparse.load_npz(
+                    os.path.join(path, file + ".npz")
+                ).toarray()
+                data_elem = (
+                    data_elem[0] if data_elem.shape[0] == 1 else data_elem
+                )
+
+                data[i][file] = data_elem
+
+        yield "key", {
+            "graph": data[0],
+            "labels": data[1],
+            "sets": data[2],
+        }
+
+    def get_features(self):
+        return {
+            "graph": {
+                "frame": [(None, 1), tf.float64],
+                "node_features": [(None, 3), tf.float64],
+                "edge_features": [(None, 1), tf.float64],
+                "edge_indices": [(None, 2), tf.int64],
+                "edge_dropouts": [(None, 2), tf.float64],
+            },
+            "labels": {
+                "node_labels": [(None,), tf.float64],
+                "edge_labels": [(None,), tf.float64],
+                "global_labels": [(None, 3), tf.float64],
+            },
+            "sets": {
+                "node_sets": [(None, 2), tf.int64],
+                "edge_sets": [(None, 3), tf.int64],
+            },
+        }

deeptrack/datasets/endothelial_vs/__init__.py

@@ -0,0 +1,3 @@
+"""endothelial_vs dataset."""
+
+from .endothelial_vs import EndothelialVs

deeptrack/datasets/endothelial_vs/checksums.tsv

@@ -0,0 +1 @@
+https://drive.google.com/u/1/uc?id=10gqn0MwuxgyfJVWDZ6a8xduQuGAke4K3&export=download	878669723	7059c1e1245ed9127f207d41efc7d53187d8e4c46d75bea8c90dce630cabe6b4	LiveDeadDataset.zip