diff --git a/arcos4py/tools/_detect_events.py b/arcos4py/tools/_detect_events.py index fb9853f..17163ab 100644 --- a/arcos4py/tools/_detect_events.py +++ b/arcos4py/tools/_detect_events.py @@ -16,10 +16,10 @@ import matplotlib.pyplot as plt import numpy as np +import ot import pandas as pd -import pulp from kneed import KneeLocator -from scipy.spatial.distance import cdist +from numba import njit, prange from skimage.transform import rescale from sklearn.cluster import DBSCAN, HDBSCAN from sklearn.neighbors import KDTree @@ -147,82 +147,6 @@ def _hdbscan( return np.empty((0, 0)) -def solve_transportation_problem( - sources: np.ndarray, - destinations: np.ndarray, - supply_range: List[int], - demand_range: List[int], - max_distance: float, -) -> Tuple[Dict[Tuple[int, int], float], List[int], List[int]]: - """Solves the transportation problem using the PuLP linear programming solver. - - Arguments: - sources (np.ndarray): An array representing the source locations. - destinations (np.ndarray): An array representing the destination locations. - supply_range (List[int]): The range of the supply (minimum, maximum). - demand_range (List[int]): The range of the demand (minimum, maximum). - max_distance (float): The maximum allowable distance between a source and a destination. - - Returns: - Tuple containing the assignments, supply, and demand as calculated by the linear programming solver. - """ - # Calculate the costs (distances) - costs = cdist(sources, destinations, metric='euclidean') - - # Set costs that exceed the max distance to a high value - costs[costs > max_distance] = 1e6 # you can adjust this value as needed - - # Create the problem - problem = pulp.LpProblem("TransportationProblem", pulp.LpMinimize) - - # Create decision variables for the transportation amounts - x = pulp.LpVariable.dicts( - "x", ((i, j) for i in range(len(sources)) for j in range(len(destinations))), lowBound=0, cat='Integer' - ) - - # Create decision variables for the supply and demand - supply = [ - pulp.LpVariable(f"supply_{i}", lowBound=supply_range[0], upBound=supply_range[1], cat='Integer') - for i in range(len(sources)) - ] - demand = [ - pulp.LpVariable(f"demand_{j}", lowBound=demand_range[0], upBound=demand_range[1], cat='Integer') - for j in range(len(destinations)) - ] - - # Objective function - problem += pulp.lpSum(costs[i, j] * x[i, j] for i in range(len(sources)) for j in range(len(destinations))) - - # Supply constraints - for i in range(len(sources)): - problem += pulp.lpSum(x[i, j] for j in range(len(destinations))) <= supply[i] - - # Demand constraints - for j in range(len(destinations)): - problem += pulp.lpSum(x[i, j] for i in range(len(sources))) == demand[j] - - # Solve the problem - problem.solve() - - # Get the assignments - assignments = { - (i, j): pulp.value(x[i, j]) - for i in range(len(sources)) - for j in range(len(destinations)) - if pulp.value(x[i, j]) > 0 - } - - # # Filter out assignments that exceed the maximum distance - assignments = {(i, j): np.linalg.norm(sources[i] - destinations[j]) for (i, j) in assignments} - - # Return the results - return ( - assignments, - [pulp.value(supply[i]) for i in range(len(sources))], - [pulp.value(demand[j]) for j in range(len(destinations))], - ) - - def brute_force_linking( cluster_labels: np.ndarray, cluster_coordinates: np.ndarray, @@ -269,46 +193,100 @@ def brute_force_linking( return cluster_labels, max_cluster_label +@njit(parallel=True) +def _compute_filtered_distances(current_coords, memory_coords): + n, m = len(current_coords), len(memory_coords) + distances = np.empty((n, m)) + for i in prange(n): + for j in prange(m): + distances[i, j] = np.sum((current_coords[i] - memory_coords[j]) ** 2) + return np.sqrt(distances) + + +@njit +def _assign_labels(matches, current_indices, memory_indices, memory_cluster_labels, cluster_labels_size): + new_cluster_labels = np.full(cluster_labels_size, -1) + for i, m in enumerate(matches): + if m != -1: + new_cluster_labels[current_indices[i]] = memory_cluster_labels[memory_indices[m]] + return new_cluster_labels + + def transportation_linking( cluster_labels: np.ndarray, cluster_coordinates: np.ndarray, memory_cluster_labels: np.ndarray, memory_coordinates: np.ndarray, + memory_kdtree: KDTree, epsPrev: float, max_cluster_label: int, - supply_range: List[int], - demand_range: List[int], + reg: float = 1, + reg_m: float = 10, + cost_threshold: float = 0, + **kwargs: Dict[str, Any], ) -> Tuple[np.ndarray, int]: - """Transportation linking of clusters across frames. + """Optimized transportation linking of clusters across frames, using a pre-constructed sklearn KDTree. - Arguments: + Args: cluster_labels (np.ndarray): The cluster labels for the current frame. cluster_coordinates (np.ndarray): The cluster coordinates for the current frame. memory_cluster_labels (np.ndarray): The cluster labels for previous frames. memory_coordinates (np.ndarray): The coordinates for previous frames. + memory_kdtree (KDTree): Pre-constructed sklearn KDTree for memory coordinates. epsPrev (float): Frame-to-frame distance, used to connect clusters across frames. max_cluster_label (int): The maximum label for clusters. - supply_range (List[int]): The range of the supply (minimum, maximum). - demand_range (List[int]): The range of the demand (minimum, maximum). + reg (float): Entropy regularization parameter for Sinkhorn algorithm. + reg_m (float): Marginal relaxation parameter for unbalanced OT. + cost_threshold (float): Threshold for filtering low-probability matches. + **kwargs: Additional keyword arguments. Returns: - Tuple containing the updated cluster labels and the maximum cluster label. + Tuple[np.ndarray, int]: Updated cluster labels and the maximum cluster label. """ - sources = memory_coordinates - destinations = cluster_coordinates - assignments, supply, demand = solve_transportation_problem( - sources, destinations, supply_range, demand_range, epsPrev + # Find neighbors within the maximum allowed distance (epsPrev) + indices = memory_kdtree.query_radius(cluster_coordinates, r=epsPrev) + + if all(len(ind) == 0 for ind in indices): + max_cluster_label += 1 + return np.full_like(cluster_labels, max_cluster_label), max_cluster_label + + # Prepare indices of valid points + valid_mask = np.array([len(ind) > 0 for ind in indices]) + current_indices = np.arange(len(indices))[valid_mask] + memory_indices = np.array([ind[0] for ind in indices if len(ind) > 0]) + + if len(current_indices) == 0: + max_cluster_label += 1 + return np.full_like(cluster_labels, max_cluster_label), max_cluster_label + + # Compute distance matrix for valid pairs + filtered_distances = _compute_filtered_distances( + cluster_coordinates[current_indices], memory_coordinates[memory_indices] ) - cluster_labels = np.zeros(cluster_coordinates.shape[0]) - for (i, j), dist in assignments.items(): - if dist <= epsPrev: - cluster_labels[j] = memory_cluster_labels[i] - else: - cluster_labels[j] = -1 - if any(cluster_labels == -1): + + # Uniform distribution on the valid points + a = np.ones(len(current_indices)) / len(current_indices) + b = np.ones(len(memory_indices)) / len(memory_indices) + + # Solve the unbalanced OT problem + ot_plan = ot.unbalanced.sinkhorn_unbalanced(a, b, filtered_distances, reg, reg_m) + + # Propagate cluster id from previous frame + matches = np.argmax(ot_plan, axis=1) + + # Set matches to -1 if the cost is too high + matches[ot_plan[np.arange(len(matches)), matches] < cost_threshold] = -1 + + new_cluster_labels = _assign_labels( + matches, current_indices, memory_indices, memory_cluster_labels, cluster_labels.size + ) + + # Assign new labels to unmatched clusters + if np.any(new_cluster_labels == -1): max_cluster_label += 1 - cluster_labels[cluster_labels == -1] = max_cluster_label - return cluster_labels, max_cluster_label + new_cluster_labels[new_cluster_labels == -1] = max_cluster_label + + return new_cluster_labels, max_cluster_label @dataclass @@ -538,6 +516,9 @@ def __init__( linking_method: str = "nearest", predictor: bool | Callable = True, n_prev: int = 1, + cost_threshold: float = 0, + reg: float = 1, + reg_m: float = 10, n_jobs: int = 1, **kwargs, ): @@ -560,6 +541,9 @@ def __init__( n_prev (int): Number of previous frames the tracking algorithm looks back to connect collective events. n_jobs (int): Number of jobs to run in parallel (only for clustering algorithm). + cost_threshold (int): Threshold for filtering low-probability matches (only for transportation linking). + reg (float): Entropy regularization parameter for unbalanced OT algorithm (only for transportation linking). + reg_m (float): Marginal relaxation parameter for unbalanced OT (only for transportation linking). kwargs (Any): Additional keyword arguments. Includes deprecated parameters for backwards compatibility. - epsPrev: Deprecated parameter for eps_prev. Use eps_prev instead. - minClSz: Deprecated parameter for min_clustersize. Use min_clustersize instead. @@ -609,6 +593,11 @@ def __init__( self._eps_prev = eps else: self._eps_prev = eps_prev + + self._reg = reg + self._reg_m = reg_m + self._cost_threshold = cost_threshold + self._n_jobs = n_jobs self._validate_input(eps, eps_prev, min_clustersize, min_samples, clustering_method, n_prev, n_jobs) @@ -679,10 +668,12 @@ def _link_next_cluster(self, cluster: np.ndarray, cluster_coordinates: np.ndarra cluster_coordinates=cluster_coordinates, memory_cluster_labels=self._memory.all_cluster_ids, memory_coordinates=self._memory.all_coordinates, + memory_kdtree=self._nn_tree, epsPrev=self._eps_prev, max_cluster_label=self._memory.max_prev_cluster_id, - supply_range=[1, 10], - demand_range=[1, 10], + reg=self._reg, + reg_m=self._reg_m, + cost_threshold=self._cost_threshold, ) else: raise ValueError(f'Linking method must be (for now) in {AVAILABLE_LINKING_METHODS}') @@ -1218,6 +1209,9 @@ def track_events_image( n_prev: int = 1, predictor: bool | Callable = False, linking_method: str = 'nearest', + reg: float = 1, + reg_m: float = 10, + cost_threshold: float = 0, dims: str = "TXY", downsample: int = 1, n_jobs: int = 1, @@ -1240,6 +1234,9 @@ def track_events_image( True uses the default predictor. A callable can be passed to use a custom predictor. See default predictor method for details. linking_method (str): The method used for linking. Default is 'nearest'. + reg (float): Entropy regularization parameter for unbalanced OT algorithm (only for transportation linking). + reg_m (float): Marginal relaxation parameter for unbalanced OT (only for transportation linking). + cost_threshold (float): Threshold for filtering low-probability matches (only for transportation linking). dims (str): String of dimensions in order, such as. Default is "TXY". Possible values are "T", "X", "Y", "Z". downsample (int): Factor by which to downsample the image. Default is 1. n_jobs (int): Number of jobs to run in parallel. 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version = ">=0.20.0", python = "< 3.12" }, { version = ">=0.22.0", python = ">= 3.12" } @@ -57,6 +56,8 @@ pre-commit = {version = "^2.12.0", optional = true} toml = {version = "^0.10.2", optional = true} bump2version = {version = "^1.0.1", optional = true} PyYAML = { version = "^6.0", optional = true} +pot = "^0.9.4" +numba = "^0.60.0" [tool.poetry.extras] test = [ diff --git a/tests/test_event_detection_pix.py b/tests/test_event_detection_pix.py index a7f2cae..fe2dea3 100644 --- a/tests/test_event_detection_pix.py +++ b/tests/test_event_detection_pix.py @@ -95,7 +95,15 @@ def test_4_colliding_transportaion(): true_img = imread('tests/testdata/pix/4_colliding_transportation.tif') test_img = np.where(test_img == 255, 0, 1) tracked_img = track_events_image( - test_img, eps=2, eps_prev=2, min_clustersize=4, n_prev=2, dims="TXY", linking_method="transportation" + test_img, + eps=2, + eps_prev=2, + min_clustersize=4, + n_prev=2, + dims="TXY", + linking_method="transportation", + reg=10, + reg_m=100, ) assert_array_equal(tracked_img, true_img) diff --git a/tests/testdata/pix/4_colliding_transportation.tif b/tests/testdata/pix/4_colliding_transportation.tif index 66f741c..9f47a22 100644 Binary files a/tests/testdata/pix/4_colliding_transportation.tif and b/tests/testdata/pix/4_colliding_transportation.tif differ