API Reference¶

class spotiflow.model.spotiflow.Spotiflow(config: SpotiflowModelConfig | None = None)¶

Supervised spot detector using a multi-stage neural network as a backbone for feature extraction followed by resolution-dependent post-processing modules to allow loss computation and optimization at different resolution levels.

Train a Spotiflow model.

Parameters:¶

train_images : Sequence[np.ndarray]¶: training images
train_spots : Sequence[np.ndarray]¶: training spots
val_images : Sequence[np.ndarray]¶: validation images
val_spots : Sequence[np.ndarray]¶: validation spots
augment_train : Union[bool, AugmentationPipeline]¶: whether to augment the training data. Defaults to True.
save_dir : Optional[str], optional¶: directory to save the model to. Must be given if no checkpoint logger is given as a callback. Defaults to None.
train_config : Optional[SpotiflowTrainingConfig], optional¶: training config. If not given, will use the default config. Defaults to None.
device : Literal["cpu", "cuda", "mps"], optional¶: computing device to use. Can be “cpu”, “cuda”, “mps”. Defaults to “cpu”.
logger : Optional[pl.loggers.Logger], optional¶: logger to use. Defaults to “tensorboard”.
number_of_devices : Optional[int], optional¶: number of accelerating devices to use. Only applicable to “cuda” acceleration. Defaults to 1.
num_workers : Optional[int], optional¶: number of workers to use for data loading. Defaults to 0 (main process only).
callbacks : Optional[Sequence[pl.callbacks.Callback]], optional¶: callbacks to use during training. Defaults to no callbacks.
deterministic : Optional[bool], optional¶: whether to use deterministic training. Set to True for deterministic behaviour at a cost of performance. Defaults to True.
benchmark : Optional[bool], optional¶: whether to use benchmarking. Set to False for deterministic behaviour at a cost of performance. Defaults to False.
**dataset_kwargs¶: additional arguments to pass to the SpotsDataset class. Defaults to no additional arguments.

classmethod from_folder(pretrained_path: str, inference_mode=True, which: str = 'best', map_location: 'auto' | 'cpu' | 'cuda' | 'mps' = 'auto', verbose: bool = False) → Self¶

Load a pretrained model.

Parameters:¶

pretrained_path : str¶: path to the model folder
inference_mode : bool, optional¶: whether to set the model in eval mode. Defaults to True.
which : str, optional¶: which checkpoint to load. Defaults to “best”.
map_location : str, optional¶: device string to load the model to. Defaults to ‘auto’ (hardware-based).

Returns:¶

loaded model

Return type:¶

Self

Load a pretrained model with given name

Parameters:¶

pretrained_name : str¶: name of the pretrained model to be loaded
inference_mode : bool, optional¶: whether to set the model in eval mode. Defaults to True.
which : str, optional¶: which checkpoint to load. Defaults to “best”.
map_location : str, optional¶: device string to load the model to. Defaults to ‘auto’ (hardware-based).
cache_dir : Optional[Union[Path, str]], optional¶: directory to cache the model. Defaults to None. If None, will use the default cache directory (given by the env var SPOTIFLOW_CACHE_DIR if set, otherwise ~/.spotiflow).

Returns:¶

loaded model

Return type:¶

Self

load(path: str, which: 'best' | 'last' = 'best', inference_mode: bool = True, map_location: str = 'cuda') → None¶

Load a model from disk.

Parameters:¶

path : str¶: folder to load the model from
which : Literal['best', 'last'], optional¶: which checkpoint to load. Defaults to “best”.
inference_mode : bool, optional¶: whether to set the model in eval mode. Defaults to True.
map_location : str, optional¶: device string to load the model to. Defaults to ‘cuda’.

optimize_threshold(val_ds: Dataset, cutoff_distance: int = 3, min_distance: int = 1, exclude_border: bool = False, threshold_range: tuple[float, float] = (0.3, 0.7), niter: int = 11, batch_size: int = 1, device: device | 'auto' | 'cpu' | 'cuda' | 'mps' | None = None, subpix: bool | None = None) → None¶

Optimize the probability threshold on an annotated dataset.: The metric used to optimize is the F1 score.

Parameters:¶

val_ds : torch.utils.data.Dataset¶: dataset to optimize on
cutoff_distance : int, optional¶: distance tolerance considered for points matching. Defaults to 3.
min_distance : int, optional¶: Minimum distance between spots for NMS. Defaults to 1.. Defaults to 2.
exclude_border : bool, optional¶: Whether to exclude spots at the border. Defaults to False.
threshold_range : Tuple[float, float], optional¶: Range of thresholds to consider. Defaults to (.3, .7).
niter : int, optional¶: number of iterations for both coarse- and fine-grained search. Defaults to 11.
batch_size : int, optional¶: batch size to use. Defaults to 2.
device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional¶: computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.
subpix : Optional[bool], optional¶: whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.

Predict spots in an image.

Parameters:¶

img : Union[np.ndarray, da.Array]¶: input image
prob_thresh : Optional[float], optional¶: Probability threshold for peak detection. If None, will load the optimal one. Defaults to None.
n_tiles : Tuple[int, int], optional¶: Number of tiles to split the image into. Defaults to (1,1).
min_distance : int, optional¶: Minimum distance between spots for NMS. Defaults to 1.
exclude_border : bool, optional¶: Whether to exclude spots at the border. Defaults to False.
scale : Optional[int], optional¶: Scale factor to apply to the image. Defaults to None.
subpix : bool, optional¶: Whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.
peak_mode : str, optional¶: Peak detection mode (can be either “skimage” or “fast”, which is a faster custom C++ implementation). Defaults to “fast”.
normalizer : Optional[Union[Literal["auto"], callable]], optional¶: Normalizer to use. If None, will use the default normalizer. Defaults to “auto” (percentile-based normalization with p_min=1, p_max=99.8).
verbose : bool, optional¶: Whether to print logs and progress. Defaults to True.
progress_bar_wrapper : Optional[callable], optional¶: Progress bar wrapper to use. Defaults to None.
device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional¶: computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.
fit_params : bool, optional¶: Whether to fit the model parameters to the input image. Defaults to False.

Returns:¶

Tuple of (points, details). Points are the coordinates of the spots. Details is a namespace containing the spot-wise probabilities (prob), the heatmap (heatmap), the stereographic flow (flow), the 2D local offset vector field (subpix) and the spot intensities (intens).

Return type:¶

Tuple[np.ndarray, SimpleNamespace]

save(path: str, which: 'best' | 'last' = 'best', update_thresholds: bool = False) → None¶

Save the model to disk.

Parameters:¶

path : str¶: folder to save the model to
which : Literal["best", "last"]¶: which checkpoint to save. Should be either “best” or “last”.
update_thresholds : bool, optional¶: whether to update the thresholds file. Defaults to False.

class spotiflow.model.config.SpotiflowModelConfig(backbone: 'resnet' | 'unet' | 'unet_res' = 'unet', in_channels: int = 1, out_channels: int = 1, initial_fmaps: int = 32, fmap_inc_factor: Number = 2, n_convs_per_level: int = 3, levels: int = 4, downsample_factor: int = 2, kernel_size: int = 3, padding: int | str = 'same', mode: 'direct' | 'fpn' | 'slim' = 'slim', background_remover: bool = False, compute_flow: bool = True, batch_norm: bool = True, downsample_factors: tuple[tuple[int, int]] | None = None, kernel_sizes: tuple[tuple[int, int]] | None = None, dropout: float = 0.0, sigma: Number = 1.0, is_3d: bool = False, grid: int | tuple[int, int, int] = (1, 1, 1), **kwargs)¶

class spotiflow.model.config.SpotiflowTrainingConfig(crop_size: int | tuple[int, int] | tuple[int, int, int] = 512, smart_crop: bool = False, heatmap_loss_f: str = 'bce', flow_loss_f: str = 'l1', loss_levels: int | None = None, num_train_samples: int | None = None, pos_weight: Number = 10.0, lr: float = 0.0003, optimizer: str = 'adamw', batch_size: int = 4, lr_reduce_patience: int = 10, num_epochs: int = 200, finetuned_from: str | None = None, early_stopping_patience: int = 0, crop_size_depth: int = 32, **kwargs)¶

class spotiflow.data.spots.SpotsDataset(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, mode: str = 'max', compute_flow: bool = False, image_files: Sequence[str] | None = None, normalizer: 'auto' | Callable | None = 'auto', add_class_label: bool = True, grid: Sequence[int] | None = None)¶

Base spot dataset class instantiated with loaded images and centers.

__init__(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, mode: str = 'max', compute_flow: bool = False, image_files: Sequence[str] | None = None, normalizer: 'auto' | Callable | None = 'auto', add_class_label: bool = True, grid: Sequence[int] | None = None)¶

Constructor

Parameters:¶

images : Sequence[np.ndarray]¶: Sequence of images.
centers : Sequence[np.ndarray]¶: Sequence of center coordinates.
augmenter : Optional[Callable], optional¶: Augmenter function. If given, function arguments should two (first image, second spots). Defaults to None.
downsample_factors : Sequence[int], optional¶: Downsample factors. Defaults to (1,).
sigma : float, optional¶: Sigma of Gaussian kernel to generate heatmap. Defaults to 1.
mode : str, optional¶: Mode of heatmap generation. Defaults to “max”.
compute_flow : bool, optional¶: Whether to compute flow from centers. Defaults to False.
image_files : Optional[Sequence[str]], optional¶: Sequence of image filenames. If the dataset was not constructed from a folder, this will be None. Defaults to None.
normalizer : Union[Literal["auto"], Callable, None], optional¶: Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).

property augmenter : Callable¶

Return augmenter function.

Returns:¶: Augmenter function. It should take image and centers as input.
Return type:¶: Callable

property centers : Sequence[ndarray]¶

Return centers of spots in dataset.

Returns:¶: Sequence of center coordinates.
Return type:¶: Sequence[np.ndarray]

classmethod from_folder(path: Path | str, augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, image_extensions: Sequence[str] = ('tif', 'tiff', 'png', 'jpg', 'jpeg'), mode: str = 'max', max_files: int | None = None, compute_flow: bool = False, normalizer: Callable | 'auto' | None = 'auto', random_state: int | None = None, add_class_label: bool = True, grid: Sequence[int] | None = None) → Self¶

Build dataset from folder. Images and centers are loaded from disk and normalized.

Parameters:¶

path : Union[Path, str]¶: Path to folder containing images (with given extensions) and centers.
augmenter : Callable¶: Augmenter function.
downsample_factors : Sequence[int], optional¶: Downsample factors. Defaults to (1,).
sigma : float, optional¶: Sigma of Gaussian kernel to generate heatmap. Defaults to 1.
image_extensions : Sequence[str], optional¶: Image extensions to look for in images. Defaults to (“tif”, “tiff”, “png”, “jpg”, “jpeg”).
mode : str, optional¶: Mode of heatmap generation. Defaults to “max”.
max_files : Optional[int], optional¶: Maximum number of files to load. Defaults to None (all of them).
compute_flow : bool, optional¶: Whether to compute flow from centers. Defaults to False.
normalizer : Optional[Union[Callable, Literal["auto"]]], optional¶: Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).
random_state : Optional[int], optional¶: Random state used when shuffling file names when “max_files” is not None. Defaults to None.

Returns:¶

Dataset instance.

Return type:¶

Self

property image_files : Sequence[str]¶

Return image filenames with the same order as in the dataset.

Returns:¶: Sequence of image filenames. If the dataset was not constructed from a folder, this will be None.
Return type:¶: Union[Sequence[str], None]

property images : Sequence[ndarray]¶

Return images in dataset.

Returns:¶: Sequence of images.
Return type:¶: Sequence[np.ndarray]

property n_classes : int¶

Return number of classes in the dataset.

Returns:¶: number of spot classes.
Return type:¶: int

spotiflow.utils.get_data(path: Path | str, normalize: bool = True, include_test: bool = False, is_3d: bool = False) → tuple[ndarray, ndarray, ndarray, ndarray]¶

Get data from a given path. The path should contain a ‘train’ and ‘val’ folder.

Parameters:¶

path : Union[Path, str]¶: Path to the data.
normalize : bool, optional¶: Whether to normalize the data. Defaults to True.

Returns:¶

A 4-length tuple of arrays corresponding to the training images, training spots, validation images and validation spots.

Return type:¶

Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]

spotiflow.utils.normalize(x: ndarray, pmin: float = 1.0, pmax: float = 99.8, subsample: int = 1, clip: bool = False, ignore_val: int | float | None = None) → ndarray¶

Normalizes (percentile-based) a 2d image with the additional option to ignore a value. The normalization is done as follows:

x = (x - I_{p_{min}}) / (I_{p_{max}} - I_{p_{min}})

where I_{p_{min}} and I_{p_{max}} are the pmin and pmax percentiles of the image intensity, respectively.

Parameters:¶

x : np.ndarray¶: Image to be normalized
pmin : float, optional¶: Minimum percentile. Defaults to 1..
pmax : float, optional¶: Maximum percentile. Defaults to 99.8.
subsample : int, optional¶: Subsampling factor for percentile calculation. Defaults to 1.
clip : bool, optional¶: Whether to clip the normalized image. Defaults to False.
ignore_val : Optional[Union[int, float]], optional¶: Value to be ignored. Defaults to None.

Returns:¶

Normalized image

Return type:¶

np.ndarray

spotiflow.utils.read_coords_csv(fname: str, add_class_column: bool = False) → ndarray¶

Parses a csv file and returns correctly ordered points array

Parameters:¶

fname : str¶: Path to the csv file
add_class_column : bool, optional¶: Whether to add a class column to the points array. Defaults to False.

Returns:¶

A 2D array of spot coordinates. If add_class_column is True, then the array will have shape (N, 3) where N is the number of points. Otherwise, the array will have shape (N, 2)

Return type:¶

np.ndarray

spotiflow.utils.write_coords_csv(pts: ndarray, fname: Path | str) → None¶

Writes points in a NumPy array to a CSV file

Parameters:¶

pts : np.ndarray¶: 2D or 3D array of points
fname : Union[Path, str]¶: Path to the CSV file

spotiflow.sample_data.test_image_hybiss_2d()¶: Single test HybISS image from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)

spotiflow.sample_data.test_image_synth_3d()¶: Single synthetic volumetric stack from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)

spotiflow.sample_data.test_image_terra_2d()¶: Single test Terra frame from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)