| import numpy as np |
| import cv2 |
|
|
| import metrics |
|
|
| def reshape_img_nopad(img, max_dim=480): |
| H, W = img.shape[:2] |
| if H > W: |
| ratio = 1. / H * max_dim |
| else: |
| ratio = 1. / W * max_dim |
| newH, newW = int(H * ratio), int(W * ratio) |
| img = cv2.resize(img, (newW, newH), interpolation=cv2.INTER_NEAREST) |
| return img |
|
|
| def remove_pad(img, orig_size): |
| cur_H, cur_W = img.shape[:2] |
| orig_H, orig_W = orig_size |
| if orig_W > orig_H: |
| ratio = 1. / orig_W * cur_W |
| else: |
| ratio = 1. / orig_H * cur_H |
| new_H, new_W = int(orig_H * ratio), int(orig_W * ratio) |
| if new_W > new_H: |
| diff_H = (cur_H - new_H) // 2 |
| img = img[diff_H:-diff_H] |
| else: |
| diff_W = (cur_W - new_W) // 2 |
| img = img[:, diff_W:-diff_W] |
| return img |
|
|
| 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 |
