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