# Copyright (c) OpenMMLab. All rights reserved. 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()