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import numpy as np |
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import warnings |
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import torch |
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def bbox_xyxy_to_cxcywh(bboxes: np.ndarray, scale=1.0, device=None): |
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w = bboxes[..., 2] - bboxes[..., 0] |
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h = bboxes[..., 3] - bboxes[..., 1] |
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cx = (bboxes[..., 0] + bboxes[..., 2]) / 2.0 |
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cy = (bboxes[..., 1] + bboxes[..., 3]) / 2.0 |
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new_bboxes = torch.stack([cx, cy, w * scale, h * scale], dim=-1) |
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if device is not None: |
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new_bboxes = torch.tensor(new_bboxes, device=device) |
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return new_bboxes |
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def compute_iou(bboxA, bboxB): |
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"""Compute the Intersection over Union (IoU) between two boxes . |
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Args: |
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bboxA (list): The first bbox info (left, top, right, bottom, score). |
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bboxB (list): The second bbox info (left, top, right, bottom, score). |
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Returns: |
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float: The IoU value. |
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""" |
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x1 = max(bboxA[0], bboxB[0]) |
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y1 = max(bboxA[1], bboxB[1]) |
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x2 = min(bboxA[2], bboxB[2]) |
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y2 = min(bboxA[3], bboxB[3]) |
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inter_area = max(0, x2 - x1) * max(0, y2 - y1) |
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bboxA_area = (bboxA[2] - bboxA[0]) * (bboxA[3] - bboxA[1]) |
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bboxB_area = (bboxB[2] - bboxB[0]) * (bboxB[3] - bboxB[1]) |
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union_area = float(bboxA_area + bboxB_area - inter_area) |
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if union_area == 0: |
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union_area = 1e-8 |
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warnings.warn("union_area=0 is unexpected") |
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iou = inter_area / union_area |
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return iou |
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def track_by_iou(res, results_last, thr): |
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"""Get track id using IoU tracking greedily. |
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Args: |
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res (dict): The bbox & pose results of the person instance. |
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results_last (list[dict]): The bbox & pose & track_id info of the |
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last frame (bbox_result, pose_result, track_id). |
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thr (float): The threshold for iou tracking. |
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Returns: |
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int: The track id for the new person instance. |
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list[dict]: The bbox & pose & track_id info of the persons |
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that have not been matched on the last frame. |
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dict: The matched person instance on the last frame. |
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""" |
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bbox = list(res["bbox"]) |
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max_iou_score = -1 |
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max_index = -1 |
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match_result = {} |
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for index, res_last in enumerate(results_last): |
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bbox_last = list(res_last["bbox"]) |
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iou_score = _compute_iou(bbox, bbox_last) |
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if iou_score > max_iou_score: |
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max_iou_score = iou_score |
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max_index = index |
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if max_iou_score > thr: |
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track_id = results_last[max_index]["track_id"] |
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match_result = results_last[max_index] |
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del results_last[max_index] |
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else: |
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track_id = -1 |
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return track_id, results_last, match_result |
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def track_by_area(humans, target_img_size, threshold=0.3): |
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if len(humans) < 1: |
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return None |
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IMAGE_AREA = target_img_size**2 |
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target_human = None |
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max_area = -1 |
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for human in humans: |
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j2d_coco = human["j2d"].to(torch.float) |
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j2d_area = (j2d_coco[..., 0].max() - j2d_coco[..., 0].min()) * ( |
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j2d_coco[..., 1].max() - j2d_coco[..., 1].min() |
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) |
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if max_area < j2d_area: |
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max_area = j2d_area |
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target_human = human |
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return target_human |
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