Spaces:
Runtime error
Runtime error
File size: 6,202 Bytes
1cdc47e |
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 |
import torch
import numpy as np
import hashlib
import cv2
import os.path as osp
spple_keypoints = [10, 8, 0, 7]
h36m_coco_order = [9, 11, 14, 12, 15, 13, 16, 4, 1, 5, 2, 6, 3]
coco_order = [0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
joint_pairs = [(0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6), (0, 7), (7, 8), (8, 9), (9, 10),
(8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16)]
colors_kps = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
[50, 205, 50], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
[170, 0, 255], [255, 0, 255]]
def wrap(func, *args, unsqueeze=False):
"""
Wrap a torch function so it can be called with NumPy arrays.
Input and return types are seamlessly converted.
"""
args = list(args)
for i, arg in enumerate(args):
if type(arg) == np.ndarray:
args[i] = torch.from_numpy(arg)
if unsqueeze:
args[i] = args[i].unsqueeze(0)
result = func(*args)
if isinstance(result, tuple):
result = list(result)
for i, res in enumerate(result):
if type(res) == torch.Tensor:
if unsqueeze:
res = res.squeeze(0)
result[i] = res.numpy()
return tuple(result)
elif type(result) == torch.Tensor:
if unsqueeze:
result = result.squeeze(0)
result = result.numpy()
return result
else:
return result
def deterministic_random(min_value, max_value, data):
"""
Encrypted, in order to generate the same size each time
"""
digest = hashlib.sha256(data.encode()).digest()
raw_value = int.from_bytes(digest[:4], byteorder="litter", signed=False)
return int(raw_value / (2**32 - 1) * (max_value - min_value) + min_value)
def resize_img(frame, max_length=640):
H, W = frame.shape[:2]
if max(W, H) > max_length:
if W > H:
W_resize = max_length
H_resize = int(H * max_length / W)
else:
H_resize = max_length
W_resize = int(W * max_length / H)
frame = cv2.resize(frame, (W_resize, H_resize), interpolation=cv2.INTER_AREA)
return frame, W_resize, H_resize
else:
return frame, W, H
def draw_2Dimg(img, kpts, scores, display=None):
# kpts : (M, 17, 2) scores: (M, 17)
im = img.copy()
for kpt, score in zip(kpts, scores):
for i, item in enumerate(kpt):
score_val = score[i]
if score_val > 0.3:
x, y = int(item[0]), int(item[1])
cv2.circle(im, (x, y), 4, (255, 255, 255), 1)
for pair, color in zip(joint_pairs, colors_kps):
j, j_parent = pair
pt1 = (int(kpt[j][0]), int(kpt[j][1]))
pt2 = (int(kpt[j_parent][0]), int(kpt[j_parent][1]))
cv2.line(im, pt1, pt2, color, 2)
if display:
cv2.imshow('frame', im)
cv2.waitKey(1)
return im
def get_path(cur_file):
project_root = osp.dirname(osp.realpath(cur_file))
chk_root = osp.join(project_root, 'checkpoint/')
data_root = osp.join(project_root, 'data/')
lib_root = osp.join(project_root, 'lib/')
output_root = osp.join(project_root, 'output/')
return project_root, chk_root, data_root, lib_root, output_root
def coco_h36m_frame(keypoints):
keypoints_h36m = np.zeros_like(keypoints, dtype=np.float32)
htps_keypoints = np.zeros((4, 2), dtype=np.float32)
# htps_keypoints: head, thorax, pelvis, spine
htps_keypoints[0, 0] = np.mean(keypoints[1:5, 0], axis=0, dtype=np.float32)
htps_keypoints[0, 1] = np.sum(keypoints[1:3, 1], axis=0, dtype=np.float32) - keypoints[0, 1]
htps_keypoints[1, :] = np.mean(keypoints[5:7, :], axis=0, dtype=np.float32)
htps_keypoints[1, :] += (keypoints[0, :] - htps_keypoints[1, :]) / 3
htps_keypoints[2, :] = np.mean(keypoints[11:13, :], axis=0, dtype=np.float32)
htps_keypoints[3, :] = np.mean(keypoints[[5, 6, 11, 12], :], axis=0, dtype=np.float32)
keypoints_h36m[spple_keypoints, :] = htps_keypoints
keypoints_h36m[h36m_coco_order, :] = keypoints[coco_order, :]
keypoints_h36m[9, :] -= (keypoints_h36m[9, :] - np.mean(keypoints[5:7, :], axis=0, dtype=np.float32)) / 4
keypoints_h36m[7, 0] += 0.3 * (keypoints_h36m[7, 0] - np.mean(keypoints_h36m[[0, 8], 0], axis=0, dtype=np.float32))
keypoints_h36m[8, 1] -= (np.mean(keypoints[1:3, 1], axis=0, dtype=np.float32) - keypoints[0, 1]) * 2 / 3
return keypoints_h36m
def h36m_coco_kpts(keypoints, scores):
# keypoints: (M, N, C) scores:(M, N, 1)
assert len(keypoints.shape) == 3 and len(scores.shape) == 3
scores.squeeze(axis=2)
h36m_kpts = []
h36m_scores = []
for i in range(keypoints.shape[0]):
kpts = keypoints[i]
score = scores[i]
new_score = np.zeros_like(score, dtype=np.float32)
if np.sum(kpts) != 0.:
new_score[h36m_coco_order] = score[coco_order]
new_score[0] = np.mean(score[[11, 12]], axis=0, dtype=np.float32)
new_score[8] = np.mean(score[[5, 6]], axis=0, dtype=np.float32)
new_score[7] = np.mean(new_score[[0, 8]], axis=0, dtype=np.float32)
new_score[10] = np.mean(score[[1, 2, 3, 4]], axis=0, dtype=np.float32)
h36m_scores.append(new_score)
kpts = coco_h36m_frame(kpts)
less_threshold_joints = np.where(new_score < 0.3)[0]
intersect = [i for i in [2, 3, 5, 6] if i in less_threshold_joints]
if [2, 3, 5, 6] == intersect:
kpts[[2, 3, 5, 6]] = kpts[[1, 1, 4, 4]]
elif [2, 3, 6] == intersect:
kpts[[2, 3, 6]] = kpts[[1, 1, 5]]
elif [3, 5, 6] == intersect:
kpts[[3, 5, 6]] = kpts[[2, 4, 4]]
elif [3, 6] == intersect:
kpts[[3, 6]] = kpts[[2, 5]]
elif [3] == intersect:
kpts[3] = kpts[2]
elif [6] == intersect:
kpts[6] = kpts[5]
h36m_kpts.append(kpts)
return h36m_kpts, h36m_scores
|