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Running
on
Zero
File size: 3,195 Bytes
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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) # [joints_smplx_to_coco()].to(torch.float)
# compute bbox
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
# if max_area / IMAGE_AREA < threshold:
# return None
return target_human
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