def evaluate(self, res_path, metric=('J', 'F'), debug=False): metric = metric if isinstance(metric, tuple) or isinstance( metric, list) else [metric] if 'T' in metric: raise ValueError('Temporal metric not supported!') if 'J' not in metric and 'F' not in metric: raise ValueError( 'Metric possible values are J for IoU or F for Boundary') # Containers metrics_res = {} if 'J' in metric: metrics_res['J'] = {"M": [], "R": [], "D": [], "M_per_object": {}} if 'F' in metric: metrics_res['F'] = {"M": [], "R": [], "D": [], "M_per_object": {}} # Sweep all sequences separate_objects_masks = self.year != '2016' results = Results(root_dir=res_path) for seq in tqdm(list(self.dataset.get_sequences())): all_gt_masks, all_void_masks, all_masks_id = self.dataset.get_all_masks( seq, separate_objects_masks) # all_gt_masks, all_void_masks, all_masks_id = self.dataset.get_all_masks(seq, True) if all_gt_masks.ndim == 3: all_gt_masks = np.expand_dims(all_gt_masks, axis=0) if self.task == 'semi-supervised': all_gt_masks, all_masks_id = all_gt_masks[:, 1: -1, :, :], all_masks_id[ 1:-1] all_res_masks = results.read_masks(seq, all_masks_id) if self.task == 'unsupervised': j_metrics_res, f_metrics_res = self._evaluate_unsupervised( all_gt_masks, all_res_masks, all_void_masks, metric) elif self.task == 'semi-supervised': j_metrics_res, f_metrics_res = self._evaluate_semisupervised( all_gt_masks, all_res_masks, None, metric) for ii in range(all_gt_masks.shape[0]): seq_name = f'{seq}_{ii+1}' if 'J' in metric: [JM, JR, JD] = utils.db_statistics(j_metrics_res[ii]) metrics_res['J']["M"].append(JM) metrics_res['J']["R"].append(JR) metrics_res['J']["D"].append(JD) metrics_res['J']["M_per_object"][seq_name] = JM if 'F' in metric: [FM, FR, FD] = utils.db_statistics(f_metrics_res[ii]) metrics_res['F']["M"].append(FM) metrics_res['F']["R"].append(FR) metrics_res['F']["D"].append(FD) metrics_res['F']["M_per_object"][seq_name] = FM # Show progress if debug: sys.stdout.write(seq + '\n') sys.stdout.flush() return metrics_res
bmap = np.zeros((height, width)) for x in range(w): for y in range(h): if b[y, x]: j = 1 + math.floor((y - 1) + height / h) i = 1 + math.floor((x - 1) + width / h) bmap[j, i] = 1 return bmap if __name__ == '__main__': from davis2017.davis import DAVIS from davis2017.results import Results dataset = DAVIS(root='input_dir/ref', subset='val', sequences='aerobatics') results = Results(root_dir='examples/osvos') # Test timing F measure for seq in dataset.get_sequences(): all_gt_masks, _, all_masks_id = dataset.get_all_masks(seq, True) all_gt_masks, all_masks_id = all_gt_masks[:, 1:-1, :, :], all_masks_id[ 1:-1] all_res_masks = results.read_masks(seq, all_masks_id) f_metrics_res = np.zeros(all_gt_masks.shape[:2]) for ii in range(all_gt_masks.shape[0]): f_metrics_res[ii, :] = db_eval_boundary(all_gt_masks[ii, ...], all_res_masks[ii, ...]) # Run using to profile code: python -m cProfile -o f_measure.prof metrics.py # snakeviz f_measure.prof