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851 lines
34 KiB
851 lines
34 KiB
"""
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Adapted from:
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[1] https://github.com/JunMa11/NeurIPS-CellSeg/blob/main/baseline/compute_metric.py
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[2] https://github.com/stardist/stardist/blob/master/stardist/matching.py
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"""
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import numpy as np
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from numpy.typing import NDArray
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from numba import jit
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from skimage import segmentation
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from scipy.optimize import linear_sum_assignment
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from typing import Dict, List, Tuple, Any, Union
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__all__ = [
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"compute_batch_segmentation_f1_metrics", "compute_batch_segmentation_average_precision_metrics",
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"compute_batch_segmentation_tp_fp_fn",
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"compute_segmentation_f1_metrics", "compute_segmentation_average_precision_metrics",
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"compute_segmentation_tp_fp_fn",
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"compute_confusion_matrix", "compute_f1_score", "compute_average_precision_score"
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]
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def compute_f1_score(
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true_positives: int,
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false_positives: int,
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false_negatives: int
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) -> Tuple[float, float, float]:
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"""
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Computes the precision, recall, and F1-score given the numbers of
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true positives, false positives, and false negatives.
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Args:
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true_positives: Number of true positive detections.
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false_positives: Number of false positive detections.
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false_negatives: Number of false negative detections.
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Returns:
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A tuple (precision, recall, f1_score).
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"""
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if true_positives == 0:
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return 0.0, 0.0, 0.0
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precision = true_positives / (true_positives + false_positives)
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recall = true_positives / (true_positives + false_negatives)
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f1_score = 2 * (precision * recall) / (precision + recall)
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return precision, recall, f1_score
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def compute_average_precision_score(
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true_positives: int,
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false_positives: int,
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false_negatives: int
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) -> float:
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"""
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Computes the average precision score using the formula:
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Average Precision = TP / (TP + FP + FN)
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If the denominator is zero, returns 0.
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Args:
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true_positives: Number of true positive detections.
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false_positives: Number of false positive detections.
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false_negatives: Number of false negative detections.
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Returns:
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Average precision score as a float.
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"""
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denominator = true_positives + false_positives + false_negatives
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return 0.0 if denominator == 0 else true_positives / denominator
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def compute_confusion_matrix(
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ground_truth_mask: np.ndarray,
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predicted_mask: np.ndarray,
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iou_threshold: float = 0.5
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) -> Tuple[int, int, int]:
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"""
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Computes the confusion matrix elements (true positives, false positives, false negatives)
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for a single image given the ground truth and predicted masks.
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Args:
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ground_truth_mask: Ground truth segmentation mask.
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predicted_mask: Predicted segmentation mask.
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iou_threshold: IoU threshold for matching objects.
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Returns:
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A tuple (TP, FP, FN).
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"""
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# Determine the number of objects in the ground truth and prediction.
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num_ground_truth = np.max(ground_truth_mask)
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num_predictions = np.max(predicted_mask)
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# If no predictions were made, return zeros (with a printout for debugging).
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if num_predictions == 0:
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print("No segmentation results!")
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return 0, 0, 0
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# Compute the IoU matrix and ignore the background (first row and column).
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iou_matrix = _calculate_iou(ground_truth_mask, predicted_mask)
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# Count true positives based on optimal matching.
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true_positive_count = _calculate_true_positive(iou_matrix, iou_threshold)
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# Derive false positives and false negatives.
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false_positive_count = num_predictions - true_positive_count
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false_negative_count = num_ground_truth - true_positive_count
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return true_positive_count, false_positive_count, false_negative_count
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def compute_segmentation_tp_fp_fn(
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ground_truth_mask: np.ndarray,
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predicted_mask: np.ndarray,
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iou_threshold: float = 0.5,
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return_error_masks: bool = False,
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remove_boundary_objects: bool = True
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) -> Dict[str, np.ndarray]:
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"""
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Computes TP, FP and FN for segmentation on a single image.
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If the input masks have shape (H, W), they are expanded to (1, H, W).
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For multi-channel inputs (C, H, W), each channel is processed independently, and the returned
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metrics (TP, FP, FN) are provided as NumPy arrays with shape (C,).
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Optionally, if return_error_masks is True, binary error masks for true positives, false positives,
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and false negatives are also returned with shape (C, H, W).
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Args:
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ground_truth_mask: Ground truth segmentation mask (HxW or CxHxW).
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predicted_mask: Predicted segmentation mask (HxW or CxHxW).
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iou_threshold: IoU threshold for matching objects.
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return_error_masks: Whether to also return binary error masks.
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remove_boundary_objects: Whether to remove objects that touch the image boundary.
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Returns:
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A dictionary with the following keys:
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- 'tp', 'fp', 'fn': arrays of shape (C,)
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- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask' with shape (C, H, W)
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"""
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# If the masks are 2D, add a singleton channel dimension.
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ground_truth_mask = _ensure_ndim(ground_truth_mask, 3, insert_position=0)
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predicted_mask = _ensure_ndim(predicted_mask, 3, insert_position=0)
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num_channels = ground_truth_mask.shape[0]
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true_positive_list = []
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false_positive_list = []
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false_negative_list = []
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if return_error_masks:
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true_positive_mask_list = []
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false_positive_mask_list = []
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false_negative_mask_list = []
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# Process each channel independently.
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for channel in range(num_channels):
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channel_ground_truth = ground_truth_mask[channel, ...]
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channel_prediction = predicted_mask[channel, ...]
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if np.prod(channel_ground_truth.shape) < (5000 * 5000):
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results = _process_instance_matching(
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channel_ground_truth, channel_prediction, iou_threshold,
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return_masks=return_error_masks, without_boundary_objects=remove_boundary_objects
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)
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else:
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results = _compute_patch_based_metrics(
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channel_ground_truth, channel_prediction, iou_threshold,
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return_masks=return_error_masks, without_boundary_objects=remove_boundary_objects
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)
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tp = results['tp']
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fp = results['fp']
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fn = results['fn']
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true_positive_list.append(tp)
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false_positive_list.append(fp)
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false_negative_list.append(fn)
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if return_error_masks:
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true_positive_mask_list.append(results.get('tp_mask')) # type: ignore
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false_positive_mask_list.append(results.get('fp_mask')) # type: ignore
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false_negative_mask_list.append(results.get('fn_mask')) # type: ignore
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output: Dict[str, np.ndarray] = {
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'tp': np.array(true_positive_list),
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'fp': np.array(false_positive_list),
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'fn': np.array(false_negative_list)
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}
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if return_error_masks:
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output['tp_mask'] = np.stack(true_positive_mask_list, axis=0) # type: ignore
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output['fp_mask'] = np.stack(false_positive_mask_list, axis=0) # type: ignore
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output['fn_mask'] = np.stack(false_negative_mask_list, axis=0) # type: ignore
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return output
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def compute_segmentation_f1_metrics(
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ground_truth_mask: np.ndarray,
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predicted_mask: np.ndarray,
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iou_threshold: float = 0.5,
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return_error_masks: bool = False,
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remove_boundary_objects: bool = True
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) -> Dict[str, np.ndarray]:
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"""
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Computes F1 metrics (precision, recall, F1-score) for segmentation on a single image.
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If the input masks have shape (H, W), they are expanded to (1, H, W).
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For multi-channel inputs (C, H, W), each channel is processed independently, and the returned
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metrics (precision, recall, f1_score, TP, FP, FN) are provided as NumPy arrays with shape (C,).
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Optionally, if return_error_masks is True, binary error masks for true positives, false positives,
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and false negatives are also returned with shape (C, H, W).
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Args:
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ground_truth_mask: Ground truth segmentation mask (HxW or CxHxW).
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predicted_mask: Predicted segmentation mask (HxW or CxHxW).
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iou_threshold: IoU threshold for matching objects.
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return_error_masks: Whether to also return binary error masks.
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remove_boundary_objects: Whether to remove objects that touch the image boundary.
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Returns:
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A dictionary with the following keys:
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- 'precision', 'recall', 'f1_score': arrays of shape (C,)
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- 'tp', 'fp', 'fn': arrays of shape (C,)
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- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask' with shape (C, H, W)
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"""
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num_channels = ground_truth_mask.shape[0]
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precision_list = []
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recall_list = []
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f1_score_list = []
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results = compute_segmentation_tp_fp_fn(
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ground_truth_mask, predicted_mask,
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iou_threshold, return_error_masks,
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remove_boundary_objects
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)
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# Process each channel independently.
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for channel in range(num_channels):
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tp = results['tp'][channel]
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fp = results['fp'][channel]
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fn = results['fn'][channel]
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precision, recall, f1_score = compute_f1_score(
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tp, fp, fn # type: ignore
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)
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precision_list.append(precision)
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recall_list.append(recall)
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f1_score_list.append(f1_score)
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output: Dict[str, np.ndarray] = {
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'precision': np.array(precision_list),
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'recall': np.array(recall_list),
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'f1_score': np.array(f1_score_list),
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}
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output.update(results)
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return output
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def compute_segmentation_average_precision_metrics(
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ground_truth_mask: np.ndarray,
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predicted_mask: np.ndarray,
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iou_threshold: float = 0.5,
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return_error_masks: bool = False,
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remove_boundary_objects: bool = True
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) -> Dict[str, np.ndarray]:
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"""
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Computes the average precision (AP) for segmentation on a single image.
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If the input masks have shape (H, W), they are expanded to (1, H, W).
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For multi-channel inputs (C, H, W), each channel is processed independently and the returned
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metrics (average precision, TP, FP, FN) are provided as NumPy arrays with shape (C,).
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Optionally, if return_error_masks is True, binary error masks for true positives, false positives,
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and false negatives are also returned with shape (C, H, W).
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Args:
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ground_truth_mask: Ground truth segmentation mask (HxW or CxHxW).
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predicted_mask: Predicted segmentation mask (HxW or CxHxW).
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iou_threshold: IoU threshold for matching objects.
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return_error_masks: Whether to also return binary error masks.
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remove_boundary_objects: Whether to remove objects that touch the image boundary.
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Returns:
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A dictionary with the following keys:
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- 'avg_precision': array of shape (C,)
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- 'tp', 'fp', 'fn': arrays of shape (C,)
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- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask' with shape (C, H, W)
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"""
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num_channels = ground_truth_mask.shape[0]
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avg_precision_list = []
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results = compute_segmentation_tp_fp_fn(
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ground_truth_mask, predicted_mask,
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iou_threshold, return_error_masks,
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remove_boundary_objects
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)
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# Process each channel independently.
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for channel in range(num_channels):
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tp = results['tp'][channel]
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fp = results['fp'][channel]
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fn = results['fn'][channel]
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avg_precision = compute_average_precision_score(
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tp, fp, fn # type: ignore
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)
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avg_precision_list.append(avg_precision)
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output: Dict[str, np.ndarray] = {
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'avg_precision': np.array(avg_precision_list)
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}
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output.update(results)
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return output
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def compute_batch_segmentation_tp_fp_fn(
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batch_ground_truth: np.ndarray,
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batch_prediction: np.ndarray,
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iou_threshold: float = 0.5,
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return_error_masks: bool = False,
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remove_boundary_objects: bool = True
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) -> Dict[str, np.ndarray]:
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"""
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Computes segmentation TP, FP and FN for a batch of images.
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Expects inputs with shape (B, C, H, W). For each image in the batch, the data is extracted
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into (C, H, W) and then processed using compute_segmentation_tp_fp_fn. The results are stacked
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so that each metric has shape (B, C). If error masks are returned, their shape will be (B, C, H, W).
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Args:
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batch_ground_truth: Batch of ground truth masks (BxCxHxW).
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batch_prediction: Batch of predicted masks (BxCxHxW).
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iou_threshold: IoU threshold for matching objects.
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return_error_masks: Whether to also return binary error masks.
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remove_boundary_objects: Whether to remove objects that touch the image boundary.
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Returns:
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A dictionary with keys:
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- 'tp', 'fp', 'fn': arrays of shape (B, C)
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- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask': arrays of shape (B, C, H, W)
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"""
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batch_ground_truth = _ensure_ndim(batch_ground_truth, 4, insert_position=0)
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batch_prediction = _ensure_ndim(batch_prediction, 4, insert_position=0)
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batch_size = batch_ground_truth.shape[0]
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tp_list = []
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fp_list = []
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fn_list = []
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if return_error_masks:
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tp_mask_list = []
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fp_mask_list = []
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fn_mask_list = []
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for i in range(batch_size):
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image_ground_truth = batch_ground_truth[i]
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image_prediction = batch_prediction[i]
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result = compute_segmentation_tp_fp_fn(
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image_ground_truth,
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image_prediction,
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iou_threshold,
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return_error_masks,
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remove_boundary_objects
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)
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tp_list.append(result['tp'])
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fp_list.append(result['fp'])
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fn_list.append(result['fn'])
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if return_error_masks:
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tp_mask_list.append(result.get('tp_mask')) # type: ignore
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fp_mask_list.append(result.get('fp_mask')) # type: ignore
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fn_mask_list.append(result.get('fn_mask')) # type: ignore
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output: Dict[str, np.ndarray] = {
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'tp': np.stack(tp_list, axis=0),
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'fp': np.stack(fp_list, axis=0),
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'fn': np.stack(fn_list, axis=0)
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}
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if return_error_masks:
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output['tp_mask'] = np.stack(tp_mask_list, axis=0) # type: ignore
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output['fp_mask'] = np.stack(fp_mask_list, axis=0) # type: ignore
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output['fn_mask'] = np.stack(fn_mask_list, axis=0) # type: ignore
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return output
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|
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def compute_batch_segmentation_f1_metrics(
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batch_ground_truth: np.ndarray,
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batch_prediction: np.ndarray,
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iou_threshold: float = 0.5,
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return_error_masks: bool = False,
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remove_boundary_objects: bool = True
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) -> Dict[str, np.ndarray]:
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|
"""
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|
Computes segmentation F1 metrics for a batch of images.
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Expects inputs with shape (B, C, H, W). For each image in the batch, the data is extracted
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into (C, H, W) and then processed using compute_segmentation_f1_metrics. The results are stacked
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|
so that each metric has shape (B, C). If error masks are returned, their shape will be (B, C, H, W).
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|
|
Args:
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batch_ground_truth: Batch of ground truth masks (BxCxHxW).
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|
batch_prediction: Batch of predicted masks (BxCxHxW).
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iou_threshold: IoU threshold for matching objects.
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|
return_error_masks: Whether to also return binary error masks.
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|
remove_boundary_objects: Whether to remove objects that touch the image boundary.
|
|
|
|
Returns:
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A dictionary with keys:
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- 'precision', 'recall', 'f1_score', 'tp', 'fp', 'fn': arrays of shape (B, C)
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- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask': arrays of shape (B, C, H, W)
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"""
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batch_ground_truth = _ensure_ndim(batch_ground_truth, 4, insert_position=0)
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batch_prediction = _ensure_ndim(batch_prediction, 4, insert_position=0)
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batch_size = batch_ground_truth.shape[0]
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precision_list = []
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recall_list = []
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f1_score_list = []
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tp_list = []
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fp_list = []
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fn_list = []
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if return_error_masks:
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tp_mask_list = []
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fp_mask_list = []
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fn_mask_list = []
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for i in range(batch_size):
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image_ground_truth = batch_ground_truth[i]
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image_prediction = batch_prediction[i]
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result = compute_segmentation_f1_metrics(
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image_ground_truth,
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image_prediction,
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iou_threshold,
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return_error_masks,
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remove_boundary_objects
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)
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precision_list.append(result['precision'])
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recall_list.append(result['recall'])
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f1_score_list.append(result['f1_score'])
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tp_list.append(result['tp'])
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fp_list.append(result['fp'])
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fn_list.append(result['fn'])
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if return_error_masks:
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tp_mask_list.append(result.get('tp_mask')) # type: ignore
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fp_mask_list.append(result.get('fp_mask')) # type: ignore
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fn_mask_list.append(result.get('fn_mask')) # type: ignore
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output: Dict[str, np.ndarray] = {
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'precision': np.stack(precision_list, axis=0),
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'recall': np.stack(recall_list, axis=0),
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'f1_score': np.stack(f1_score_list, axis=0),
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'tp': np.stack(tp_list, axis=0),
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'fp': np.stack(fp_list, axis=0),
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'fn': np.stack(fn_list, axis=0)
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}
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if return_error_masks:
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output['tp_mask'] = np.stack(tp_mask_list, axis=0) # type: ignore
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output['fp_mask'] = np.stack(fp_mask_list, axis=0) # type: ignore
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output['fn_mask'] = np.stack(fn_mask_list, axis=0) # type: ignore
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return output
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|
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|
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def compute_batch_segmentation_average_precision_metrics(
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|
batch_ground_truth: np.ndarray,
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|
batch_prediction: np.ndarray,
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|
iou_threshold: float = 0.5,
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|
return_error_masks: bool = False,
|
|
remove_boundary_objects: bool = True
|
|
) -> Dict[str, NDArray]:
|
|
"""
|
|
Computes segmentation average precision metrics for a batch of images.
|
|
|
|
Expects inputs with shape (B, C, H, W). For each image in the batch, the data is extracted
|
|
into (C, H, W) and then processed using compute_segmentation_average_precision_metrics. The results are stacked
|
|
so that each metric has shape (B, C). If error masks are returned, their shape will be (B, C, H, W).
|
|
|
|
Args:
|
|
batch_ground_truth: Batch of ground truth masks (BxCxHxW).
|
|
batch_prediction: Batch of predicted masks (BxCxHxW).
|
|
iou_threshold: IoU threshold for matching objects.
|
|
return_error_masks: Whether to also return binary error masks.
|
|
remove_boundary_objects: Whether to remove objects that touch the image boundary.
|
|
|
|
Returns:
|
|
A dictionary with keys:
|
|
- 'avg_precision', 'tp', 'fp', 'fn': arrays of shape (B, C)
|
|
- If return_error_masks is True: 'tp_mask', 'fp_mask', 'fn_mask': arrays of shape (B, C, H, W)
|
|
"""
|
|
batch_ground_truth = _ensure_ndim(batch_ground_truth, 4, insert_position=0)
|
|
batch_prediction = _ensure_ndim(batch_prediction, 4, insert_position=0)
|
|
|
|
batch_size = batch_ground_truth.shape[0]
|
|
avg_precision_list = []
|
|
tp_list = []
|
|
fp_list = []
|
|
fn_list = []
|
|
if return_error_masks:
|
|
tp_mask_list = []
|
|
fp_mask_list = []
|
|
fn_mask_list = []
|
|
|
|
for i in range(batch_size):
|
|
ground_truth_mask = batch_ground_truth[i]
|
|
prediction_mask = batch_prediction[i]
|
|
result = compute_segmentation_average_precision_metrics(
|
|
ground_truth_mask,
|
|
prediction_mask,
|
|
iou_threshold,
|
|
return_error_masks,
|
|
remove_boundary_objects
|
|
)
|
|
avg_precision_list.append(result['avg_precision'])
|
|
tp_list.append(result['tp'])
|
|
fp_list.append(result['fp'])
|
|
fn_list.append(result['fn'])
|
|
if return_error_masks:
|
|
tp_mask_list.append(result.get('tp_mask')) # type: ignore
|
|
fp_mask_list.append(result.get('fp_mask')) # type: ignore
|
|
fn_mask_list.append(result.get('fn_mask')) # type: ignore
|
|
|
|
output: Dict[str, NDArray] = {
|
|
'avg_precision': np.stack(avg_precision_list, axis=0),
|
|
'tp': np.stack(tp_list, axis=0),
|
|
'fp': np.stack(fp_list, axis=0),
|
|
'fn': np.stack(fn_list, axis=0)
|
|
}
|
|
if return_error_masks:
|
|
output['tp_mask'] = np.stack(tp_mask_list, axis=0) # type: ignore
|
|
output['fp_mask'] = np.stack(fp_mask_list, axis=0) # type: ignore
|
|
output['fn_mask'] = np.stack(fn_mask_list, axis=0) # type: ignore
|
|
return output
|
|
|
|
|
|
# ===================== INTERNAL HELPER FUNCTIONS =====================
|
|
|
|
def _ensure_ndim(array: np.ndarray, target_ndim: int, insert_position: int = 0) -> np.ndarray:
|
|
"""
|
|
Makes sure that the array has the right dimension by adding axes in front if necessary.
|
|
|
|
Args:
|
|
array (np.ndarray): Input array.
|
|
target_new (int): The expected number of axes.
|
|
insert_position (int): Where to add axes.
|
|
|
|
Returns:
|
|
np.ndarray: An array with the desired dimension.
|
|
|
|
Raises:
|
|
ValueError: If the array cannot be cast to target_ndim in a valid way.
|
|
"""
|
|
while array.ndim < target_ndim:
|
|
array = np.expand_dims(array, axis=insert_position)
|
|
|
|
if array.ndim != target_ndim:
|
|
raise ValueError(
|
|
f"Expected ndim={target_ndim}, but got ndim={array.ndim} and shape={array.shape}"
|
|
)
|
|
|
|
return array
|
|
|
|
|
|
def _process_instance_matching(
|
|
ground_truth_mask: np.ndarray,
|
|
predicted_mask: np.ndarray,
|
|
iou_threshold: float = 0.5,
|
|
return_masks: bool = False,
|
|
without_boundary_objects: bool = True
|
|
) -> Dict[str, Union[int, NDArray[np.uint8]]]:
|
|
"""
|
|
Processes instance matching on a full image by performing the following steps:
|
|
- Removes objects that touch the image boundary and reindexes the masks.
|
|
- Computes the IoU matrix between instances (ignoring background).
|
|
- Computes optimal matching via linear assignment based on the IoU matrix.
|
|
|
|
If return_masks is True, binary error masks (TP, FP, FN) are also generated.
|
|
|
|
Args:
|
|
ground_truth_mask: Ground truth instance mask.
|
|
predicted_mask: Predicted instance mask.
|
|
iou_threshold: IoU threshold for matching.
|
|
return_masks: Whether to generate binary error masks.
|
|
without_boundary_objects: Whether to remove objects touching the image boundary.
|
|
|
|
Returns:
|
|
A dictionary with keys:
|
|
- 'tp', 'fp', 'fn': integer counts.
|
|
- If return_masks is True, also 'tp_mask', 'fp_mask', and 'fn_mask'.
|
|
"""
|
|
# Optionally remove boundary objects.
|
|
if without_boundary_objects:
|
|
processed_ground_truth = _remove_boundary_objects(ground_truth_mask.astype(np.int32))
|
|
processed_prediction = _remove_boundary_objects(predicted_mask.astype(np.int32))
|
|
else:
|
|
processed_ground_truth = ground_truth_mask.astype(np.int32)
|
|
processed_prediction = predicted_mask.astype(np.int32)
|
|
|
|
num_ground_truth = np.max(processed_ground_truth)
|
|
num_prediction = np.max(processed_prediction)
|
|
|
|
# If no predictions are found, return with all ground truth as false negatives.
|
|
if num_prediction == 0:
|
|
print("No segmentation results!")
|
|
result = {'tp': 0, 'fp': 0, 'fn': num_ground_truth}
|
|
if return_masks:
|
|
tp_mask = np.zeros_like(ground_truth_mask, dtype=np.uint8)
|
|
fp_mask = np.zeros_like(ground_truth_mask, dtype=np.uint8)
|
|
fn_mask = np.zeros_like(ground_truth_mask, dtype=np.uint8)
|
|
# Mark all ground truth objects as false negatives.
|
|
fn_mask[ground_truth_mask > 0] = 1
|
|
result.update({'tp_mask': tp_mask, 'fp_mask': fp_mask, 'fn_mask': fn_mask})
|
|
return result
|
|
|
|
# Compute the IoU matrix for the processed masks.
|
|
iou_matrix = _calculate_iou(processed_ground_truth, processed_prediction)
|
|
# Compute optimal matching pairs using linear assignment.
|
|
matching_pairs = _compute_optimal_matching_pairs(iou_matrix, iou_threshold)
|
|
|
|
true_positive_count = len(matching_pairs)
|
|
false_positive_count = num_prediction - true_positive_count
|
|
false_negative_count = num_ground_truth - true_positive_count
|
|
result = {'tp': true_positive_count, 'fp': false_positive_count, 'fn': false_negative_count}
|
|
|
|
if return_masks:
|
|
# Initialize binary masks for error visualization.
|
|
tp_mask = np.zeros_like(processed_ground_truth, dtype=np.uint8)
|
|
fp_mask = np.zeros_like(processed_ground_truth, dtype=np.uint8)
|
|
fn_mask = np.zeros_like(processed_ground_truth, dtype=np.uint8)
|
|
|
|
# Record which labels were matched.
|
|
matched_ground_truth_labels = {gt for gt, _ in matching_pairs}
|
|
matched_prediction_labels = {pred for _, pred in matching_pairs}
|
|
|
|
# For each matching pair, mark the intersection as true positive.
|
|
for gt_label, pred_label in matching_pairs:
|
|
gt_region = (processed_ground_truth == gt_label)
|
|
prediction_region = (processed_prediction == pred_label)
|
|
intersection = gt_region & prediction_region
|
|
tp_mask[intersection] = 1
|
|
# Mark parts of the ground truth not in the intersection as false negatives.
|
|
fn_mask[gt_region & ~intersection] = 1
|
|
# Mark parts of the prediction not in the intersection as false positives.
|
|
fp_mask[prediction_region & ~intersection] = 1
|
|
|
|
# Mark entire regions for objects with no match.
|
|
all_ground_truth_labels = set(np.unique(processed_ground_truth)) - {0}
|
|
for gt_label in (all_ground_truth_labels - matched_ground_truth_labels):
|
|
fn_mask[processed_ground_truth == gt_label] = 1
|
|
|
|
all_prediction_labels = set(np.unique(processed_prediction)) - {0}
|
|
for pred_label in (all_prediction_labels - matched_prediction_labels):
|
|
fp_mask[processed_prediction == pred_label] = 1
|
|
|
|
result.update({'tp_mask': tp_mask, 'fp_mask': fp_mask, 'fn_mask': fn_mask})
|
|
return result
|
|
|
|
|
|
def _compute_optimal_matching_pairs(iou_matrix: np.ndarray, iou_threshold: float) -> List[Any]:
|
|
"""
|
|
Computes the optimal matching pairs between ground truth and predicted masks using the IoU matrix.
|
|
|
|
Args:
|
|
iou_matrix: The IoU matrix between ground truth and predicted masks.
|
|
iou_threshold: The IoU threshold for considering a valid match.
|
|
|
|
Returns:
|
|
A list of tuples (ground_truth_label, predicted_label) representing matched pairs.
|
|
"""
|
|
# Exclude the background (first row and column).
|
|
iou_without_background = iou_matrix[1:, 1:]
|
|
|
|
if iou_without_background.size == 0:
|
|
return []
|
|
|
|
# Determine the number of possible matches.
|
|
num_possible_matches = min(iou_without_background.shape[0], iou_without_background.shape[1])
|
|
|
|
# Create a cost matrix where lower costs indicate better matches.
|
|
cost_matrix = -(iou_without_background >= iou_threshold).astype(np.float64) - iou_without_background / (2 * num_possible_matches)
|
|
|
|
# Solve the assignment problem using the Hungarian algorithm.
|
|
matched_ground_truth_indices, matched_prediction_indices = linear_sum_assignment(cost_matrix)
|
|
|
|
# Only accept matches that meet the IoU threshold.
|
|
matched_pairs_arr = np.stack([matched_ground_truth_indices, matched_prediction_indices], axis=1)
|
|
|
|
# Filtering by IoU
|
|
ious = iou_without_background[matched_ground_truth_indices, matched_prediction_indices]
|
|
valid_mask = ious >= iou_threshold
|
|
|
|
# Apply a filter and index shift (the background is missing, so +1)
|
|
return (matched_pairs_arr[valid_mask] + 1).tolist()
|
|
|
|
|
|
def _compute_patch_based_metrics(
|
|
ground_truth_mask: np.ndarray,
|
|
predicted_mask: np.ndarray,
|
|
iou_threshold: float = 0.5,
|
|
return_masks: bool = False,
|
|
without_boundary_objects: bool = True
|
|
) -> Dict[str, Union[int, NDArray[np.uint8]]]:
|
|
"""
|
|
Computes segmentation metrics using a patch-based approach for very large images.
|
|
|
|
The image is divided into fixed-size patches (e.g., 2000x2000 pixels). For each patch,
|
|
instance matching is performed and the statistics (TP, FP, FN) are accumulated.
|
|
If error masks are requested, they are also collected and cropped to the original image size.
|
|
|
|
Args:
|
|
ground_truth_mask: Ground truth segmentation mask.
|
|
predicted_mask: Predicted segmentation mask.
|
|
iou_threshold: IoU threshold for matching objects.
|
|
return_masks: Whether to generate binary error masks.
|
|
without_boundary_objects: Whether to remove objects that touch the image boundary.
|
|
|
|
Returns:
|
|
A dictionary with accumulated 'tp', 'fp', 'fn'. If return_masks is True,
|
|
also includes 'tp_mask', 'fp_mask', and 'fn_mask'.
|
|
"""
|
|
H, W = ground_truth_mask.shape
|
|
patch_size = 2000
|
|
|
|
# Calculate number of patches needed in height and width.
|
|
num_patches_height = H // patch_size + (H % patch_size != 0)
|
|
num_patches_width = W // patch_size + (W % patch_size != 0)
|
|
padded_height, padded_width = patch_size * num_patches_height, patch_size * num_patches_width
|
|
|
|
# Create padded images to ensure full patches.
|
|
padded_ground_truth = np.zeros((padded_height, padded_width), dtype=ground_truth_mask.dtype)
|
|
padded_prediction = np.zeros((padded_height, padded_width), dtype=ground_truth_mask.dtype)
|
|
padded_ground_truth[:H, :W] = ground_truth_mask
|
|
padded_prediction[:H, :W] = predicted_mask
|
|
|
|
total_tp, total_fp, total_fn = 0, 0, 0
|
|
if return_masks:
|
|
padded_tp_mask = np.zeros((padded_height, padded_width), dtype=np.uint8)
|
|
padded_fp_mask = np.zeros((padded_height, padded_width), dtype=np.uint8)
|
|
padded_fn_mask = np.zeros((padded_height, padded_width), dtype=np.uint8)
|
|
|
|
# Loop over all patches.
|
|
for i in range(num_patches_height):
|
|
for j in range(num_patches_width):
|
|
y_start, y_end = patch_size * i, patch_size * (i + 1)
|
|
x_start, x_end = patch_size * j, patch_size * (j + 1)
|
|
# Extract the patch from both ground truth and prediction.
|
|
patch_ground_truth = padded_ground_truth[y_start:y_end, x_start:x_end]
|
|
patch_prediction = padded_prediction[y_start:y_end, x_start:x_end]
|
|
# Process the patch and accumulate the results.
|
|
patch_results = _process_instance_matching(
|
|
patch_ground_truth, patch_prediction, iou_threshold,
|
|
return_masks=return_masks, without_boundary_objects=without_boundary_objects
|
|
)
|
|
total_tp += patch_results['tp']
|
|
total_fp += patch_results['fp']
|
|
total_fn += patch_results['fn']
|
|
if return_masks:
|
|
padded_tp_mask[y_start:y_end, x_start:x_end] = patch_results.get('tp_mask', 0) # type: ignore
|
|
padded_fp_mask[y_start:y_end, x_start:x_end] = patch_results.get('fp_mask', 0) # type: ignore
|
|
padded_fn_mask[y_start:y_end, x_start:x_end] = patch_results.get('fn_mask', 0) # type: ignore
|
|
|
|
results: Dict[str, Union[int, np.ndarray]] = {'tp': total_tp, 'fp': total_fp, 'fn': total_fn}
|
|
if return_masks:
|
|
# Crop the padded masks back to the original image size.
|
|
results.update({
|
|
'tp_mask': padded_tp_mask[:H, :W], # type: ignore
|
|
'fp_mask': padded_fp_mask[:H, :W], # type: ignore
|
|
'fn_mask': padded_fn_mask[:H, :W] # type: ignore
|
|
})
|
|
return results
|
|
|
|
|
|
def _remove_boundary_objects(mask: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Removes objects that touch the image boundary and reindexes the mask.
|
|
|
|
A border of 2 pixels is defined around the image; any object that touches this border is removed.
|
|
|
|
Args:
|
|
mask: Segmentation mask where 0 represents the background and positive integers represent object labels.
|
|
|
|
Returns:
|
|
A reindexed mask with objects touching the boundary removed.
|
|
"""
|
|
H, W = mask.shape
|
|
# Create a mask with a border (value 1 in border, 0 in interior).
|
|
border_mask = np.ones((H, W), dtype=np.uint8)
|
|
border_mask[2:H - 2, 2:W - 2] = 0
|
|
# Multiply the mask with the border mask to identify boundary labels.
|
|
border_labels = np.unique(mask * border_mask)
|
|
|
|
# Remove objects (set to 0) that appear in the border.
|
|
mask[np.isin(mask, border_labels[1:])] = 0
|
|
|
|
# Reindex the mask so that labels are sequential.
|
|
new_mask, _, _ = segmentation.relabel_sequential(mask)
|
|
return new_mask
|
|
|
|
|
|
def _calculate_true_positive(iou_matrix: np.ndarray, iou_threshold: float = 0.5) -> int:
|
|
"""
|
|
Calculates the number of true positive instances based on the IoU matrix.
|
|
|
|
Args:
|
|
iou_matrix: IoU matrix between ground truth and predicted masks (excluding background).
|
|
iou_threshold: IoU threshold for matching.
|
|
|
|
Returns:
|
|
The number of true positive matches.
|
|
"""
|
|
matching_pairs = _compute_optimal_matching_pairs(iou_matrix, iou_threshold)
|
|
return len(matching_pairs)
|
|
|
|
|
|
def _calculate_iou(ground_truth_mask: np.ndarray, predicted_mask: np.ndarray) -> NDArray[np.float32]:
|
|
"""
|
|
Computes the Intersection over Union (IoU) matrix between ground truth and predicted masks.
|
|
|
|
Args:
|
|
ground_truth_mask: Ground truth mask with integer labels.
|
|
predicted_mask: Predicted mask with integer labels.
|
|
|
|
Returns:
|
|
An IoU matrix of shape (num_ground_truth+1, num_prediction+1).
|
|
"""
|
|
# Compute the overlap matrix between the two masks.
|
|
overlap_matrix = _calculate_label_overlap(ground_truth_mask, predicted_mask)
|
|
|
|
# Total number of pixels in each predicted object (sum over columns).
|
|
pixels_per_prediction = np.sum(overlap_matrix, axis=0, keepdims=True)
|
|
# Total number of pixels in each ground truth object (sum over rows).
|
|
pixels_per_ground_truth = np.sum(overlap_matrix, axis=1, keepdims=True)
|
|
|
|
# Compute the union for each pair.
|
|
union_matrix = pixels_per_prediction + pixels_per_ground_truth - overlap_matrix
|
|
|
|
# Avoid division by zero.
|
|
iou = np.zeros_like(union_matrix, dtype=np.float32)
|
|
valid = union_matrix > 0
|
|
iou[valid] = overlap_matrix[valid] / union_matrix[valid]
|
|
return iou
|
|
|
|
|
|
@jit(nopython=True)
|
|
def _calculate_label_overlap(mask_x: np.ndarray, mask_y: np.ndarray) -> NDArray[np.uint32]:
|
|
"""
|
|
Computes the overlap (number of common pixels) between labels in two masks.
|
|
|
|
Args:
|
|
mask_x: First mask (integer labels with 0 as background).
|
|
mask_y: Second mask (integer labels with 0 as background).
|
|
|
|
Returns:
|
|
An overlap matrix of shape [mask_x.max()+1, mask_y.max()+1].
|
|
"""
|
|
flat_x = mask_x.ravel()
|
|
flat_y = mask_y.ravel()
|
|
|
|
# Create an empty overlap matrix with size based on the maximum label in each mask.
|
|
overlap = np.zeros((1 + flat_x.max(), 1 + flat_y.max()), dtype=np.uint32)
|
|
|
|
# Count overlaps for each pixel pair.
|
|
for i in range(flat_x.shape[0]):
|
|
overlap[flat_x[i], flat_y[i]] += 1
|
|
return overlap
|