Spaces:
Running
Running
File size: 12,542 Bytes
590c25b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
import numpy as np
import cv2
from scipy.ndimage.filters import gaussian_filter
from .pose_utils import _get_keypoints, _pad_image
from insightface import model_zoo
from dofaker.utils import download_file, get_model_url
class PoseEstimator:
def __init__(self, name='openpose_body', root='weights/models'):
_, model_file = download_file(get_model_url(name),
save_dir=root,
overwrite=False)
providers = model_zoo.model_zoo.get_default_providers()
self.session = model_zoo.model_zoo.PickableInferenceSession(
model_file, providers=providers)
self.input_mean = 127.5
self.input_std = 255.0
inputs = self.session.get_inputs()
self.input_names = []
for inp in inputs:
self.input_names.append(inp.name)
outputs = self.session.get_outputs()
output_names = []
for out in outputs:
output_names.append(out.name)
self.output_names = output_names
assert len(
self.output_names
) == 2, "The output number of PoseEstimator model should be 2, but got {}, please check your model.".format(
len(self.output_names))
output_shape = outputs[0].shape
input_cfg = inputs[0]
input_shape = input_cfg.shape
self.input_shape = input_shape
print('pose estimator shape:', self.input_shape)
def forward(self, image, image_format='rgb'):
if isinstance(image, str):
image = cv2.imread(image, 1)
image_format = 'bgr'
elif isinstance(image, np.ndarray):
if image_format == 'bgr':
pass
elif image_format == 'rgb':
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image_format = 'bgr'
else:
raise UserWarning(
"PoseEstimator not support image format {}".format(
image_format))
else:
raise UserWarning(
"PoseEstimator input must be str or np.ndarray, but got {}.".
format(type(image)))
scales = [0.5]
stride = 8
bboxsize = 368
padvalue = 128
thresh_1 = 0.1
thresh_2 = 0.05
multipliers = [scale * bboxsize / image.shape[0] for scale in scales]
heatmap_avg = np.zeros((image.shape[0], image.shape[1], 19))
paf_avg = np.zeros((image.shape[0], image.shape[1], 38))
for scale in multipliers:
image_scaled = cv2.resize(image, (0, 0),
fx=scale,
fy=scale,
interpolation=cv2.INTER_CUBIC)
image_padded, pads = _pad_image(image_scaled, stride, padvalue)
image_tensor = np.expand_dims(np.transpose(image_padded, (2, 0, 1)),
0)
blob = (np.float32(image_tensor) - self.input_mean) / self.input_std
pred = self.session.run(self.output_names,
{self.input_names[0]: blob})
Mconv7_stage6_L1, Mconv7_stage6_L2 = pred[0], pred[1]
heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))
heatmap = cv2.resize(heatmap, (0, 0),
fx=stride,
fy=stride,
interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:image_padded.shape[0] -
pads[3], :image_padded.shape[1] - pads[2], :]
heatmap = cv2.resize(heatmap, (image.shape[1], image.shape[0]),
interpolation=cv2.INTER_CUBIC)
paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))
paf = cv2.resize(paf, (0, 0),
fx=stride,
fy=stride,
interpolation=cv2.INTER_CUBIC)
paf = paf[:image_padded.shape[0] - pads[3], :image_padded.shape[1] -
pads[2], :]
paf = cv2.resize(paf, (image.shape[1], image.shape[0]),
interpolation=cv2.INTER_CUBIC)
heatmap_avg += (heatmap / len(multipliers))
paf_avg += (paf / len(multipliers))
all_peaks = []
num_peaks = 0
for part in range(18):
map_orig = heatmap_avg[:, :, part]
map_filt = gaussian_filter(map_orig, sigma=3)
map_L = np.zeros_like(map_filt)
map_T = np.zeros_like(map_filt)
map_R = np.zeros_like(map_filt)
map_B = np.zeros_like(map_filt)
map_L[1:, :] = map_filt[:-1, :]
map_T[:, 1:] = map_filt[:, :-1]
map_R[:-1, :] = map_filt[1:, :]
map_B[:, :-1] = map_filt[:, 1:]
peaks_binary = np.logical_and.reduce(
(map_filt >= map_L, map_filt >= map_T, map_filt
>= map_R, map_filt >= map_B, map_filt > thresh_1))
peaks = list(
zip(np.nonzero(peaks_binary)[1],
np.nonzero(peaks_binary)[0]))
peaks_ids = range(num_peaks, num_peaks + len(peaks))
peaks_with_scores = [
peak + (map_orig[peak[1], peak[0]], ) for peak in peaks
]
peaks_with_scores_and_ids = [peaks_with_scores[i] + (peaks_ids[i],) \
for i in range(len(peaks_ids))]
all_peaks.append(peaks_with_scores_and_ids)
num_peaks += len(peaks)
map_idx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44],
[19, 20], [21, 22], [23, 24], [25, 26], [27, 28], [29, 30],
[47, 48], [49, 50], [53, 54], [51, 52], [55, 56], [37, 38],
[45, 46]]
limbseq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9],
[9, 10], [10, 11], [2, 12], [12, 13], [13, 14], [2, 1],
[1, 15], [15, 17], [1, 16], [16, 18], [3, 17], [6, 18]]
all_connections = []
spl_k = []
mid_n = 10
for k in range(len(map_idx)):
score_mid = paf_avg[:, :, [x - 19 for x in map_idx[k]]]
candidate_A = all_peaks[limbseq[k][0] - 1]
candidate_B = all_peaks[limbseq[k][1] - 1]
n_A = len(candidate_A)
n_B = len(candidate_B)
index_A, index_B = limbseq[k]
if n_A != 0 and n_B != 0:
connection_candidates = []
for i in range(n_A):
for j in range(n_B):
v = np.subtract(candidate_B[j][:2], candidate_A[i][:2])
n = np.sqrt(v[0] * v[0] + v[1] * v[1])
v = np.divide(v, n)
ab = list(
zip(
np.linspace(candidate_A[i][0],
candidate_B[j][0],
num=mid_n),
np.linspace(candidate_A[i][1],
candidate_B[j][1],
num=mid_n)))
vx = np.array([
score_mid[int(round(ab[x][1])),
int(round(ab[x][0])), 0]
for x in range(len(ab))
])
vy = np.array([
score_mid[int(round(ab[x][1])),
int(round(ab[x][0])), 1]
for x in range(len(ab))
])
score_midpoints = np.multiply(vx, v[0]) + np.multiply(
vy, v[1])
score_with_dist_prior = sum(
score_midpoints) / len(score_midpoints) + min(
0.5 * image.shape[0] / n - 1, 0)
criterion_1 = len(
np.nonzero(score_midpoints > thresh_2)
[0]) > 0.8 * len(score_midpoints)
criterion_2 = score_with_dist_prior > 0
if criterion_1 and criterion_2:
connection_candidate = [
i, j, score_with_dist_prior,
score_with_dist_prior + candidate_A[i][2] +
candidate_B[j][2]
]
connection_candidates.append(connection_candidate)
connection_candidates = sorted(connection_candidates,
key=lambda x: x[2],
reverse=True)
connection = np.zeros((0, 5))
for candidate in connection_candidates:
i, j, s = candidate[0:3]
if i not in connection[:, 3] and j not in connection[:, 4]:
connection = np.vstack([
connection,
[candidate_A[i][3], candidate_B[j][3], s, i, j]
])
if len(connection) >= min(n_A, n_B):
break
all_connections.append(connection)
else:
spl_k.append(k)
all_connections.append([])
candidate = np.array(
[item for sublist in all_peaks for item in sublist])
subset = np.ones((0, 20)) * -1
for k in range(len(map_idx)):
if k not in spl_k:
part_As = all_connections[k][:, 0]
part_Bs = all_connections[k][:, 1]
index_A, index_B = np.array(limbseq[k]) - 1
for i in range(len(all_connections[k])):
found = 0
subset_idx = [-1, -1]
for j in range(len(subset)):
if subset[j][index_A] == part_As[i] or subset[j][
index_B] == part_Bs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if subset[j][index_B] != part_Bs[i]:
subset[j][index_B] = part_Bs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[
part_Bs[i].astype(int),
2] + all_connections[k][i][2]
elif found == 2:
j1, j2 = subset_idx
membership = ((subset[j1] >= 0).astype(int) +
(subset[j2] >= 0).astype(int))[:-2]
if len(np.nonzero(membership == 2)[0]) == 0:
subset[j1][:-2] += (subset[j2][:-2] + 1)
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += all_connections[k][i][2]
subset = np.delete(subset, j2, 0)
else:
subset[j1][index_B] = part_Bs[i]
subset[j1][-1] += 1
subset[j1][-2] += candidate[
part_Bs[i].astype(int),
2] + all_connections[k][i][2]
elif not found and k < 17:
row = np.ones(20) * -1
row[index_A] = part_As[i]
row[index_B] = part_Bs[i]
row[-1] = 2
row[-2] = sum(
candidate[all_connections[k][i, :2].astype(int),
2]) + all_connections[k][i][2]
subset = np.vstack([subset, row])
del_idx = []
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
del_idx.append(i)
subset = np.delete(subset, del_idx, axis=0)
return _get_keypoints(candidate, subset)
def get(self, image, image_format='rgb'):
return self.forward(image, image_format)
|