-
-
Notifications
You must be signed in to change notification settings - Fork 90
/
utils.py
238 lines (186 loc) · 8.6 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
# coding: utf-8
'''
File: utils.py
Project: AlphaPose
File Created: Thursday, 1st March 2018 5:32:34 pm
Author: Yuliang Xiu (yuliangxiu@sjtu.edu.cn)
-----
Last Modified: Thursday, 20th March 2018 1:18:17 am
Modified By: Yuliang Xiu (yuliangxiu@sjtu.edu.cn>)
-----
Copyright 2018 - 2018 Shanghai Jiao Tong University, Machine Vision and Intelligence Group
'''
import numpy as np
import cv2 as cv
import os
import json
import copy
import heapq
from munkres import Munkres, print_matrix
from PIL import Image
from tqdm import tqdm
# keypoint penalty weight
delta = 2*np.array([0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144, \
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081, \
0.01291456, 0.01236173,0.01291456, 0.01236173])
# get expand bbox surrounding single person's keypoints
def get_box(pose, imgpath):
pose = np.array(pose).reshape(-1,3)
xmin = np.min(pose[:,0])
xmax = np.max(pose[:,0])
ymin = np.min(pose[:,1])
ymax = np.max(pose[:,1])
img_height, img_width, _ = cv.imread(imgpath).shape
return expand_bbox(xmin, xmax, ymin, ymax, img_width, img_height)
# expand bbox for containing more background
def expand_bbox(left, right, top, bottom, img_width, img_height):
width = right - left
height = bottom - top
ratio = 0.1 # expand ratio
new_left = np.clip(left - ratio * width, 0, img_width)
new_right = np.clip(right + ratio * width, 0, img_width)
new_top = np.clip(top - ratio * height, 0, img_height)
new_bottom = np.clip(bottom + ratio * height, 0, img_height)
return [int(new_left), int(new_right), int(new_top), int(new_bottom)]
# calculate final matching grade
def cal_grade(l, w):
return sum(np.array(l)*np.array(w))
# calculate IoU of two boxes(thanks @ZongweiZhou1)
def cal_bbox_iou(boxA, boxB):
xA = max(boxA[0], boxB[0]) #xmin
yA = max(boxA[2], boxB[2]) #ymin
xB = min(boxA[1], boxB[1]) #xmax
yB = min(boxA[3], boxB[3]) #ymax
if xA < xB and yA < yB:
interArea = (xB - xA + 1) * (yB - yA + 1)
boxAArea = (boxA[1] - boxA[0] + 1) * (boxA[3] - boxA[2] + 1)
boxBArea = (boxB[1] - boxB[0] + 1) * (boxB[3] - boxB[2] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea+0.00001)
else:
iou=0.0
return iou
# calculate OKS between two single poses
def compute_oks(anno, predict, delta):
xmax = np.max(np.vstack((anno[:, 0], predict[:, 0])))
xmin = np.min(np.vstack((anno[:, 0], predict[:, 0])))
ymax = np.max(np.vstack((anno[:, 1], predict[:, 1])))
ymin = np.min(np.vstack((anno[:, 1], predict[:, 1])))
scale = (xmax - xmin) * (ymax - ymin)
dis = np.sum((anno - predict)**2, axis=1)
oks = np.mean(np.exp(-dis / 2 / delta**2 / scale))
return oks
# stack all already tracked people's info together(thanks @ZongweiZhou1)
def stack_all_pids(track_vid, frame_list, idxs, max_pid_id, link_len):
#track_vid contains track_vid[<=idx]
all_pids_info = []
all_pids_fff = [] # boolean list, 'fff' means From Former Frame
all_pids_ids = [(item+1) for item in range(max_pid_id)]
for idx in np.arange(idxs,max(idxs-link_len,-1),-1):
for pid in range(1, track_vid[frame_list[idx]]['num_boxes']+1):
if len(all_pids_ids) == 0:
return all_pids_info, all_pids_fff
elif track_vid[frame_list[idx]][pid]['new_pid'] in all_pids_ids:
all_pids_ids.remove(track_vid[frame_list[idx]][pid]['new_pid'])
all_pids_info.append(track_vid[frame_list[idx]][pid])
if idx == idxs:
all_pids_fff.append(True)
else:
all_pids_fff.append(False)
return all_pids_info, all_pids_fff
# calculate DeepMatching Pose IoU given two boxes
def find_two_pose_box_iou(pose1_box, pose2_box, all_cors):
x1, y1, x2, y2 = [all_cors[:, col] for col in range(4)]
x_min, x_max, y_min, y_max = pose1_box
x1_region_ids = set(np.where((x1 >= x_min) & (x1 <= x_max))[0].tolist())
y1_region_ids = set(np.where((y1 >= y_min) & (y1 <= y_max))[0].tolist())
region_ids1 = x1_region_ids & y1_region_ids
x_min, x_max, y_min, y_max = pose2_box
x2_region_ids = set(np.where((x2 >= x_min) & (x2 <= x_max))[0].tolist())
y2_region_ids = set(np.where((y2 >= y_min) & (y2 <= y_max))[0].tolist())
region_ids2 = x2_region_ids & y2_region_ids
inter = region_ids1 & region_ids2
union = region_ids1 | region_ids2
pose_box_iou = len(inter) / (len(union) + 0.00001)
return pose_box_iou
# calculate general Pose IoU(only consider top NUM matched keypoints)
def cal_pose_iou(pose1_box,pose2_box, num,mag):
pose_iou = []
for row in range(len(pose1_box)):
x1,y1 = pose1_box[row]
x2,y2 = pose2_box[row]
box1 = [x1-mag,x1+mag,y1-mag,y1+mag]
box2 = [x2-mag,x2+mag,y2-mag,y2+mag]
pose_iou.append(cal_bbox_iou(box1,box2))
return np.mean(heapq.nlargest(num, pose_iou))
# calculate DeepMatching based Pose IoU(only consider top NUM matched keypoints)
def cal_pose_iou_dm(all_cors,pose1,pose2,num,mag):
poses_iou = []
for ids in range(len(pose1)):
pose1_box = [pose1[ids][0]-mag,pose1[ids][0]+mag,pose1[ids][1]-mag,pose1[ids][1]+mag]
pose2_box = [pose2[ids][0]-mag,pose2[ids][0]+mag,pose2[ids][1]-mag,pose2[ids][1]+mag]
poses_iou.append(find_two_pose_box_iou(pose1_box, pose2_box, all_cors))
return np.mean(heapq.nlargest(num, poses_iou))
# hungarian matching algorithm(thanks @ZongweiZhou1)
def best_matching_hungarian(all_cors, all_pids_info, all_pids_fff, track_vid_next_fid, weights, weights_fff, num, mag):
x1, y1, x2, y2 = [all_cors[:, col] for col in range(4)]
all_grades_details = []
all_grades = []
box1_num = len(all_pids_info)
box2_num = track_vid_next_fid['num_boxes']
cost_matrix = np.zeros((box1_num, box2_num))
for pid1 in range(box1_num):
box1_pos = all_pids_info[pid1]['box_pos']
box1_region_ids = find_region_cors_last(box1_pos, all_cors)
box1_score = all_pids_info[pid1]['box_score']
box1_pose = all_pids_info[pid1]['box_pose_pos']
box1_fff = all_pids_fff[pid1]
for pid2 in range(1, track_vid_next_fid['num_boxes'] + 1):
box2_pos = track_vid_next_fid[pid2]['box_pos']
box2_region_ids = find_region_cors_next(box2_pos, all_cors)
box2_score = track_vid_next_fid[pid2]['box_score']
box2_pose = track_vid_next_fid[pid2]['box_pose_pos']
inter = box1_region_ids & box2_region_ids
union = box1_region_ids | box2_region_ids
dm_iou = len(inter) / (len(union) + 0.00001)
box_iou = cal_bbox_iou(box1_pos, box2_pos)
pose_iou_dm = cal_pose_iou_dm(all_cors, box1_pose, box2_pose, num,mag)
pose_iou = cal_pose_iou(box1_pose, box2_pose,num,mag)
if box1_fff:
grade = cal_grade([dm_iou, box_iou, pose_iou_dm, pose_iou, box1_score, box2_score], weights)
else:
grade = cal_grade([dm_iou, box_iou, pose_iou_dm, pose_iou, box1_score, box2_score], weights_fff)
cost_matrix[pid1, pid2 - 1] = grade
m = Munkres()
indexes = m.compute((-np.array(cost_matrix)).tolist())
return indexes, cost_matrix
# calculate number of matching points in one box from last frame
def find_region_cors_last(box_pos, all_cors):
x1, y1, x2, y2 = [all_cors[:, col] for col in range(4)]
x_min, x_max, y_min, y_max = box_pos
x1_region_ids = set(np.where((x1 >= x_min) & (x1 <= x_max))[0].tolist())
y1_region_ids = set(np.where((y1 >= y_min) & (y1 <= y_max))[0].tolist())
region_ids = x1_region_ids & y1_region_ids
return region_ids
# calculate number of matching points in one box from next frame
def find_region_cors_next(box_pos, all_cors):
x1, y1, x2, y2 = [all_cors[:, col] for col in range(4)]
x_min, x_max, y_min, y_max = box_pos
x2_region_ids = set(np.where((x2 >= x_min) & (x2 <= x_max))[0].tolist())
y2_region_ids = set(np.where((y2 >= y_min) & (y2 <= y_max))[0].tolist())
region_ids = x2_region_ids & y2_region_ids
return region_ids
# fill the nose keypoint by averaging head and neck
def add_nose(array):
if min(array.shape) == 2:
head = array[-1,:]
neck = array[-2,:]
else:
head = array[-1]
neck = array[-2]
nose = (head+neck)/2.0
return np.insert(array,-1,nose,axis=0)
# list remove operation
def remove_list(l1,vname,l2):
for item in l2:
l1.remove(os.path.join(vname,item))
return l1