| """ |
| Utils for Dataset |
| Extended from ADNet code by Hansen et al. |
| """ |
| import random |
| import torch |
| import numpy as np |
| import operator |
| import os |
| import logging |
|
|
|
|
| def set_seed(seed): |
| """ |
| Set the random seed |
| """ |
| random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
|
|
|
|
| CLASS_LABELS = { |
| 'CHAOST2': { |
| 'pa_all': set(range(1, 5)), |
| 0: set([1, 4]), |
| 1: set([2, 3]), |
| }, |
| } |
|
|
|
|
| def get_bbox(fg_mask, inst_mask): |
| """ |
| Get the ground truth bounding boxes |
| """ |
|
|
| fg_bbox = torch.zeros_like(fg_mask, device=fg_mask.device) |
| bg_bbox = torch.ones_like(fg_mask, device=fg_mask.device) |
|
|
| inst_mask[fg_mask == 0] = 0 |
| area = torch.bincount(inst_mask.view(-1)) |
| cls_id = area[1:].argmax() + 1 |
| cls_ids = np.unique(inst_mask)[1:] |
|
|
| mask_idx = np.where(inst_mask[0] == cls_id) |
| y_min = mask_idx[0].min() |
| y_max = mask_idx[0].max() |
| x_min = mask_idx[1].min() |
| x_max = mask_idx[1].max() |
| fg_bbox[0, y_min:y_max + 1, x_min:x_max + 1] = 1 |
|
|
| for i in cls_ids: |
| mask_idx = np.where(inst_mask[0] == i) |
| y_min = max(mask_idx[0].min(), 0) |
| y_max = min(mask_idx[0].max(), fg_mask.shape[1] - 1) |
| x_min = max(mask_idx[1].min(), 0) |
| x_max = min(mask_idx[1].max(), fg_mask.shape[2] - 1) |
| bg_bbox[0, y_min:y_max + 1, x_min:x_max + 1] = 0 |
| return fg_bbox, bg_bbox |
|
|
|
|
| def t2n(img_t): |
| """ |
| torch to numpy regardless of whether tensor is on gpu or memory |
| """ |
| if img_t.is_cuda: |
| return img_t.data.cpu().numpy() |
| else: |
| return img_t.data.numpy() |
|
|
|
|
| def to01(x_np): |
| """ |
| normalize a numpy to 0-1 for visualize |
| """ |
| return (x_np - x_np.min()) / (x_np.max() - x_np.min() + 1e-5) |
|
|
|
|
| class Scores(): |
|
|
| def __init__(self): |
| self.TP = 0 |
| self.TN = 0 |
| self.FP = 0 |
| self.FN = 0 |
|
|
| self.patient_dice = [] |
| self.patient_iou = [] |
|
|
| def record(self, preds, label): |
| assert len(torch.unique(preds)) < 3 |
|
|
| tp = torch.sum((label == 1) * (preds == 1)) |
| tn = torch.sum((label == 0) * (preds == 0)) |
| fp = torch.sum((label == 0) * (preds == 1)) |
| fn = torch.sum((label == 1) * (preds == 0)) |
|
|
| self.patient_dice.append(2 * tp / (2 * tp + fp + fn)) |
| self.patient_iou.append(tp / (tp + fp + fn)) |
|
|
| self.TP += tp |
| self.TN += tn |
| self.FP += fp |
| self.FN += fn |
|
|
| def compute_dice(self): |
| return 2 * self.TP / (2 * self.TP + self.FP + self.FN) |
|
|
| def compute_iou(self): |
| return self.TP / (self.TP + self.FP + self.FN) |
|
|
|
|
| def set_logger(path): |
| logger = logging.getLogger() |
| logger.handlers = [] |
| formatter = logging.Formatter('[%(levelname)] - %(name)s - %(message)s') |
| logger.setLevel("INFO") |
|
|
| |
| file_handler = logging.FileHandler(path) |
| file_handler.setFormatter(formatter) |
| logger.addHandler(file_handler) |
|
|
| |
| stream_handler = logging.StreamHandler() |
| stream_handler.setFormatter(formatter) |
| logger.addHandler(stream_handler) |
| return logger |
|
|
