| |
| from collections.abc import Sequence |
|
|
| import numpy as np |
| from mmengine.logging import print_log |
| from terminaltables import AsciiTable |
|
|
| from .bbox_overlaps import bbox_overlaps |
|
|
|
|
| def _recalls(all_ious, proposal_nums, thrs): |
|
|
| img_num = all_ious.shape[0] |
| total_gt_num = sum([ious.shape[0] for ious in all_ious]) |
|
|
| _ious = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32) |
| for k, proposal_num in enumerate(proposal_nums): |
| tmp_ious = np.zeros(0) |
| for i in range(img_num): |
| ious = all_ious[i][:, :proposal_num].copy() |
| gt_ious = np.zeros((ious.shape[0])) |
| if ious.size == 0: |
| tmp_ious = np.hstack((tmp_ious, gt_ious)) |
| continue |
| for j in range(ious.shape[0]): |
| gt_max_overlaps = ious.argmax(axis=1) |
| max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps] |
| gt_idx = max_ious.argmax() |
| gt_ious[j] = max_ious[gt_idx] |
| box_idx = gt_max_overlaps[gt_idx] |
| ious[gt_idx, :] = -1 |
| ious[:, box_idx] = -1 |
| tmp_ious = np.hstack((tmp_ious, gt_ious)) |
| _ious[k, :] = tmp_ious |
|
|
| _ious = np.fliplr(np.sort(_ious, axis=1)) |
| recalls = np.zeros((proposal_nums.size, thrs.size)) |
| for i, thr in enumerate(thrs): |
| recalls[:, i] = (_ious >= thr).sum(axis=1) / float(total_gt_num) |
|
|
| return recalls |
|
|
|
|
| def set_recall_param(proposal_nums, iou_thrs): |
| """Check proposal_nums and iou_thrs and set correct format.""" |
| if isinstance(proposal_nums, Sequence): |
| _proposal_nums = np.array(proposal_nums) |
| elif isinstance(proposal_nums, int): |
| _proposal_nums = np.array([proposal_nums]) |
| else: |
| _proposal_nums = proposal_nums |
|
|
| if iou_thrs is None: |
| _iou_thrs = np.array([0.5]) |
| elif isinstance(iou_thrs, Sequence): |
| _iou_thrs = np.array(iou_thrs) |
| elif isinstance(iou_thrs, float): |
| _iou_thrs = np.array([iou_thrs]) |
| else: |
| _iou_thrs = iou_thrs |
|
|
| return _proposal_nums, _iou_thrs |
|
|
|
|
| def eval_recalls(gts, |
| proposals, |
| proposal_nums=None, |
| iou_thrs=0.5, |
| logger=None, |
| use_legacy_coordinate=False): |
| """Calculate recalls. |
| |
| Args: |
| gts (list[ndarray]): a list of arrays of shape (n, 4) |
| proposals (list[ndarray]): a list of arrays of shape (k, 4) or (k, 5) |
| proposal_nums (int | Sequence[int]): Top N proposals to be evaluated. |
| iou_thrs (float | Sequence[float]): IoU thresholds. Default: 0.5. |
| logger (logging.Logger | str | None): The way to print the recall |
| summary. See `mmengine.logging.print_log()` for details. |
| Default: None. |
| use_legacy_coordinate (bool): Whether use coordinate system |
| in mmdet v1.x. "1" was added to both height and width |
| which means w, h should be |
| computed as 'x2 - x1 + 1` and 'y2 - y1 + 1'. Default: False. |
| |
| |
| Returns: |
| ndarray: recalls of different ious and proposal nums |
| """ |
|
|
| img_num = len(gts) |
| assert img_num == len(proposals) |
| proposal_nums, iou_thrs = set_recall_param(proposal_nums, iou_thrs) |
| all_ious = [] |
| for i in range(img_num): |
| if proposals[i].ndim == 2 and proposals[i].shape[1] == 5: |
| scores = proposals[i][:, 4] |
| sort_idx = np.argsort(scores)[::-1] |
| img_proposal = proposals[i][sort_idx, :] |
| else: |
| img_proposal = proposals[i] |
| prop_num = min(img_proposal.shape[0], proposal_nums[-1]) |
| if gts[i] is None or gts[i].shape[0] == 0: |
| ious = np.zeros((0, img_proposal.shape[0]), dtype=np.float32) |
| else: |
| ious = bbox_overlaps( |
| gts[i], |
| img_proposal[:prop_num, :4], |
| use_legacy_coordinate=use_legacy_coordinate) |
| all_ious.append(ious) |
| all_ious = np.array(all_ious) |
| recalls = _recalls(all_ious, proposal_nums, iou_thrs) |
|
|
| print_recall_summary(recalls, proposal_nums, iou_thrs, logger=logger) |
| return recalls |
|
|
|
|
| def print_recall_summary(recalls, |
| proposal_nums, |
| iou_thrs, |
| row_idxs=None, |
| col_idxs=None, |
| logger=None): |
| """Print recalls in a table. |
| |
| Args: |
| recalls (ndarray): calculated from `bbox_recalls` |
| proposal_nums (ndarray or list): top N proposals |
| iou_thrs (ndarray or list): iou thresholds |
| row_idxs (ndarray): which rows(proposal nums) to print |
| col_idxs (ndarray): which cols(iou thresholds) to print |
| logger (logging.Logger | str | None): The way to print the recall |
| summary. See `mmengine.logging.print_log()` for details. |
| Default: None. |
| """ |
| proposal_nums = np.array(proposal_nums, dtype=np.int32) |
| iou_thrs = np.array(iou_thrs) |
| if row_idxs is None: |
| row_idxs = np.arange(proposal_nums.size) |
| if col_idxs is None: |
| col_idxs = np.arange(iou_thrs.size) |
| row_header = [''] + iou_thrs[col_idxs].tolist() |
| table_data = [row_header] |
| for i, num in enumerate(proposal_nums[row_idxs]): |
| row = [f'{val:.3f}' for val in recalls[row_idxs[i], col_idxs].tolist()] |
| row.insert(0, num) |
| table_data.append(row) |
| table = AsciiTable(table_data) |
| print_log('\n' + table.table, logger=logger) |
|
|
|
|
| def plot_num_recall(recalls, proposal_nums): |
| """Plot Proposal_num-Recalls curve. |
| |
| Args: |
| recalls(ndarray or list): shape (k,) |
| proposal_nums(ndarray or list): same shape as `recalls` |
| """ |
| if isinstance(proposal_nums, np.ndarray): |
| _proposal_nums = proposal_nums.tolist() |
| else: |
| _proposal_nums = proposal_nums |
| if isinstance(recalls, np.ndarray): |
| _recalls = recalls.tolist() |
| else: |
| _recalls = recalls |
|
|
| import matplotlib.pyplot as plt |
| f = plt.figure() |
| plt.plot([0] + _proposal_nums, [0] + _recalls) |
| plt.xlabel('Proposal num') |
| plt.ylabel('Recall') |
| plt.axis([0, proposal_nums.max(), 0, 1]) |
| f.show() |
|
|
|
|
| def plot_iou_recall(recalls, iou_thrs): |
| """Plot IoU-Recalls curve. |
| |
| Args: |
| recalls(ndarray or list): shape (k,) |
| iou_thrs(ndarray or list): same shape as `recalls` |
| """ |
| if isinstance(iou_thrs, np.ndarray): |
| _iou_thrs = iou_thrs.tolist() |
| else: |
| _iou_thrs = iou_thrs |
| if isinstance(recalls, np.ndarray): |
| _recalls = recalls.tolist() |
| else: |
| _recalls = recalls |
|
|
| import matplotlib.pyplot as plt |
| f = plt.figure() |
| plt.plot(_iou_thrs + [1.0], _recalls + [0.]) |
| plt.xlabel('IoU') |
| plt.ylabel('Recall') |
| plt.axis([iou_thrs.min(), 1, 0, 1]) |
| f.show() |
|
|
