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import numpy as np
import cv2
import metrics
def eval_mask(gt_masks: np.ndarray, fake_masks: np.ndarray):
"""TODO: Docstring for eval_mask.
Args:
gt_masks (np.ndarray): The
fake_masks (np.ndarray): TODO
Returns: TODO
"""
iou = metrics.db_eval_iou(gt_masks, fake_masks)
boundary = metrics.db_eval_boundary(gt_masks, fake_masks)
return iou, boundary
def existence_accuracy(gt_mask: np.ndarray, pred_mask: np.ndarray):
gt_zeros = np.all(gt_mask == 0)
pred_zeros = np.all(pred_mask == 0)
return gt_zeros == pred_zeros
def location_score(gt_mask, pred_mask, size=(480, 480)):
H, W = size
(gt_size, pred_size), (centroid_gt, centroid_pred), (gt_compact_mask, pred_compact_mask) = metrics.crop_mask(gt_mask, pred_mask)
centroid_distance = np.sqrt((centroid_gt[0] - centroid_pred[0])**2 + (centroid_gt[1] - centroid_pred[1])**2)
lscore = centroid_distance / np.sqrt(H**2 + W**2)
return lscore |