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