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# -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import numpy as np
import torch
from scipy.optimize import linear_sum_assignment
sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89])
def candidate_reselect(bboxes, bboxes_scores, pose_preds):
'''
Grouping
'''
# Group same keypointns together
kp_groups = grouping(bboxes, bboxes_scores, pose_preds)
'''
Re-select
'''
# Generate Matrix
human_num = len(pose_preds.keys())
costMatrix = []
for k in range(17):
kp_group = kp_groups[k]
joint_num = len(kp_group.keys())
costMatrix.append(np.zeros((human_num, joint_num)))
group_size = {k: {} for k in range(17)}
for n, person in pose_preds.items():
h_id = n
assert 0 <= h_id < human_num
for k in range(17):
g_id = person['group_id'][k]
if g_id is not None:
if g_id not in group_size[k].keys():
group_size[k][g_id] = 0
group_size[k][g_id] += 1
g_id = int(g_id) - 1
_, _, score = person[k][0]
h_score = person['human_score']
if score < 0.05:
costMatrix[k][h_id][g_id] = 0
else:
costMatrix[k][h_id][g_id] = -(h_score * score)
pose_preds = matching(pose_preds, costMatrix, kp_groups)
# To JSON
final_result = []
for n, person in pose_preds.items():
final_pose = torch.zeros(17, 2)
final_score = torch.zeros(17, 1)
max_score = 0
mean_score = 0
xmax, xmin = 0, 1e5
ymax, ymin = 0, 1e5
for k in range(17):
assert len(person[k]) > 0
x, y, s = person[k][0]
xmax = max(xmax, x)
xmin = min(xmin, x)
ymax = max(ymax, y)
ymin = min(ymin, y)
final_pose[k][0] = x.item() - 0.3
final_pose[k][1] = y.item() - 0.3
final_score[k] = s.item()
mean_score += (s.item() / 17)
max_score = max(max_score, s.item())
if torch.max(final_score).item() < 0.1:
continue
if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40):
continue
final_result.append({
'keypoints': final_pose,
'kp_score': final_score,
'proposal_score': mean_score + max_score + person['bbox_score']
})
return final_result
def grouping(bboxes, bboxes_scores, pose_preds):
kp_groups = {}
for k in range(17):
kp_groups[k] = {}
ids = np.zeros(17)
for n, person in pose_preds.items():
pose_preds[n]['bbox'] = bboxes[n]
pose_preds[n]['bbox_score'] = bboxes_scores[n]
pose_preds[n]['group_id'] = {}
s = 0
for k in range(17):
pose_preds[n]['group_id'][k] = None
pose_preds[n][k] = np.array(pose_preds[n][k])
assert len(pose_preds[n][k]) > 0
s += pose_preds[n][k][0][-1]
s = s / 17
pose_preds[n]['human_score'] = s
for k in range(17):
latest_id = ids[k]
kp_group = kp_groups[k]
assert len(person[k]) > 0
x0, y0, s0 = person[k][0]
if s0 < 0.05:
continue
for g_id, g in kp_group.items():
x_c, y_c = kp_group[g_id]['group_center']
'''
Get Average Box Size
'''
group_area = kp_group[g_id]['group_area']
group_area = group_area[0] * group_area[1] / (group_area[2] ** 2)
'''
Groupingn Criterion
'''
# Joint Group
dist = np.sqrt(
((x_c - x0) ** 2 + (y_c - y0) ** 2) / group_area)
if dist <= 0.1 * sigmas[k]: # Small Distance
if s0 >= 0.3:
kp_group[g_id]['kp_list'][0] += x0 * s0
kp_group[g_id]['kp_list'][1] += y0 * s0
kp_group[g_id]['kp_list'][2] += s0
kp_group[g_id]['group_area'][0] += (person['bbox'][2] - person['bbox'][0]) * person['human_score']
kp_group[g_id]['group_area'][1] += (person['bbox'][3] - person['bbox'][1]) * person['human_score']
kp_group[g_id]['group_area'][2] += person['human_score']
x_c = kp_group[g_id]['kp_list'][0] / kp_group[g_id]['kp_list'][2]
y_c = kp_group[g_id]['kp_list'][1] / kp_group[g_id]['kp_list'][2]
kp_group[g_id]['group_center'] = (x_c, y_c)
pose_preds[n]['group_id'][k] = g_id
break
else:
# A new keypoint group
latest_id += 1
kp_group[latest_id] = {
'kp_list': None,
'group_center': person[k][0].copy()[:2],
'group_area': None
}
x, y, s = person[k][0]
kp_group[latest_id]['kp_list'] = np.array((x * s, y * s, s))
# Ref Area
ref_width = person['bbox'][2] - person['bbox'][0]
ref_height = person['bbox'][3] - person['bbox'][1]
ref_score = person['human_score']
kp_group[latest_id]['group_area'] = np.array((
ref_width * ref_score, ref_height * ref_score, ref_score))
pose_preds[n]['group_id'][k] = latest_id
ids[k] = latest_id
return kp_groups
def matching(pose_preds, matrix, kp_groups):
index = []
for k in range(17):
human_ind, joint_ind = linear_sum_assignment(matrix[k])
# human_ind, joint_ind = greedy_matching(matrix[k])
index.append(list(zip(human_ind, joint_ind)))
for n, person in pose_preds.items():
for k in range(17):
g_id = person['group_id'][k]
if g_id is not None:
g_id = int(g_id) - 1
h_id = n
x, y, s = pose_preds[n][k][0]
if ((h_id, g_id) not in index[k]) and len(pose_preds[n][k]) > 1:
pose_preds[n][k] = np.delete(pose_preds[n][k], 0, 0)
elif ((h_id, g_id) not in index[k]) and len(person[k]) == 1:
x, y, _ = pose_preds[n][k][0]
pose_preds[n][k][0] = (x, y, 1e-5)
pass
elif ((h_id, g_id) in index[k]):
x, y = kp_groups[k][g_id + 1]['group_center']
s = pose_preds[n][k][0][2]
pose_preds[n][k][0] = (x, y, s)
return pose_preds
def greedy_matching(matrix):
num_human, num_joint = matrix.shape
if num_joint <= num_human or True:
human_ind = np.argmin(matrix, axis=0)
joint_ind = np.arange(num_joint)
else:
pass
return human_ind.tolist(), joint_ind.tolist()
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