Spaces:
Runtime error
Runtime error
File size: 7,074 Bytes
5bd9834 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 |
import math
import cv2
import numpy as np
IMG_SIZE = (288, 384)
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
KPS = (
"Head",
"Neck",
"Right Shoulder",
"Right Arm",
"Right Hand",
"Left Shoulder",
"Left Arm",
"Left Hand",
"Spine",
"Hips",
"Right Upper Leg",
"Right Leg",
"Right Foot",
"Left Upper Leg",
"Left Leg",
"Left Foot",
"Left Toe",
"Right Toe",
)
SKELETON = (
(0, 1),
(1, 8),
(8, 9),
(9, 10),
(9, 13),
(10, 11),
(11, 12),
(13, 14),
(14, 15),
(1, 2),
(2, 3),
(3, 4),
(1, 5),
(5, 6),
(6, 7),
(15, 16),
(12, 17),
)
OPENPOSE_TO_GESTURE = (
0, # 0 Head\n",
1, # Neck\n",
2, # 2 Right Shoulder\n",
3, # Right Arm\n",
4, # 4 Right Hand\n",
5, # Left Shoulder\n",
6, # 6 Left Arm\n",
7, # Left Hand\n",
9, # 8 Hips\n",
10, # Right Upper Leg\n",
11, # 10Right Leg\n",
12, # Right Foot\n",
13, # 12Left Upper Leg\n",
14, # Left Leg\n",
15, # 14Left Foot\n",
-1, # \n",
-1, # 16\n",
-1, # \n",
-1, # 18\n",
16, # Left Toe\n",
-1, # 20\n",
-1, # \n",
17, # 22Right Toe\n",
-1, # \n",
-1, # 24\n",
)
def transform(img):
img = img.astype("float32") / 255
img = (img - MEAN) / STD
return np.transpose(img, axes=(2, 0, 1))
def get_affine_transform(
center,
scale,
rot,
output_size,
shift=np.array([0, 0], dtype=np.float32),
inv=0,
pixel_std=200,
):
if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
scale = np.array([scale, scale])
scale_tmp = scale * pixel_std
src_w = scale_tmp[0]
dst_w = output_size[0]
dst_h = output_size[1]
rot_rad = np.pi * rot / 180
src_dir = get_dir([0, src_w * -0.5], rot_rad)
dst_dir = np.array([0, dst_w * -0.5], np.float32)
src = np.zeros((3, 2), dtype=np.float32)
dst = np.zeros((3, 2), dtype=np.float32)
src[0, :] = center + scale_tmp * shift
src[1, :] = center + src_dir + scale_tmp * shift
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
return trans
def get_3rd_point(a, b):
direct = a - b
return b + np.array([-direct[1], direct[0]], dtype=np.float32)
def get_dir(src_point, rot_rad):
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
src_result = [0, 0]
src_result[0] = src_point[0] * cs - src_point[1] * sn
src_result[1] = src_point[0] * sn + src_point[1] * cs
return src_result
def process_image(path, input_img_size, pixel_std=200):
data_numpy = cv2.imread(path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
# BUG HERE. Must be uncommented
# data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
h, w = data_numpy.shape[:2]
c = np.array([w / 2, h / 2], dtype=np.float32)
aspect_ratio = input_img_size[0] / input_img_size[1]
if w > aspect_ratio * h:
h = w * 1.0 / aspect_ratio
elif w < aspect_ratio * h:
w = h * aspect_ratio
s = np.array([w / pixel_std, h / pixel_std], dtype=np.float32) * 1.25
r = 0
trans = get_affine_transform(c, s, r, input_img_size, pixel_std=pixel_std)
input = cv2.warpAffine(data_numpy, trans, input_img_size, flags=cv2.INTER_LINEAR)
input = transform(input)
return input, data_numpy, c, s
def get_final_preds(batch_heatmaps, center, scale, post_process=False):
coords, maxvals = get_max_preds(batch_heatmaps)
heatmap_height = batch_heatmaps.shape[2]
heatmap_width = batch_heatmaps.shape[3]
# post-processing
if post_process:
for n in range(coords.shape[0]):
for p in range(coords.shape[1]):
hm = batch_heatmaps[n][p]
px = int(math.floor(coords[n][p][0] + 0.5))
py = int(math.floor(coords[n][p][1] + 0.5))
if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
diff = np.array(
[
hm[py][px + 1] - hm[py][px - 1],
hm[py + 1][px] - hm[py - 1][px],
]
)
coords[n][p] += np.sign(diff) * 0.25
preds = coords.copy()
# Transform back
for i in range(coords.shape[0]):
preds[i] = transform_preds(
coords[i], center[i], scale[i], [heatmap_width, heatmap_height]
)
return preds, maxvals
def transform_preds(coords, center, scale, output_size):
target_coords = np.zeros(coords.shape)
trans = get_affine_transform(center, scale, 0, output_size, inv=1)
for p in range(coords.shape[0]):
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
return target_coords
def affine_transform(pt, t):
new_pt = np.array([pt[0], pt[1], 1.0]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2]
def get_max_preds(batch_heatmaps):
"""
get predictions from score maps
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
"""
assert isinstance(
batch_heatmaps, np.ndarray
), "batch_heatmaps should be numpy.ndarray"
assert batch_heatmaps.ndim == 4, "batch_images should be 4-ndim"
batch_size = batch_heatmaps.shape[0]
num_joints = batch_heatmaps.shape[1]
width = batch_heatmaps.shape[3]
heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
idx = np.argmax(heatmaps_reshaped, 2)
maxvals = np.amax(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals
def infer_single_image(model, img_path, input_img_size=(288, 384), return_kps=True):
img_path = str(img_path)
pose_input, img, center, scale = process_image(
img_path, input_img_size=input_img_size
)
model.setInput(pose_input[None])
predicted_heatmap = model.forward()
if not return_kps:
return predicted_heatmap.squeeze(0)
predicted_keypoints, confidence = get_final_preds(
predicted_heatmap, center[None], scale[None], post_process=True
)
(predicted_keypoints, confidence, predicted_heatmap,) = (
predicted_keypoints.squeeze(0),
confidence.squeeze(0),
predicted_heatmap.squeeze(0),
)
return img, predicted_keypoints, confidence, predicted_heatmap
|