def get_ground_truth(self, meta, mask_miss):
stride = self.params_transform['stride']
mode = self.params_transform['mode']
crop_size_y = self.params_transform['crop_size_y']
crop_size_x = self.params_transform['crop_size_x']
num_parts = self.params_transform['np']
nop = meta['numOtherPeople']
grid_y = crop_size_y / stride
grid_x = crop_size_x / stride
channels = (num_parts + 1) * 2
heatmaps = np.zeros((int(grid_y), int(grid_x), 37))
mask_miss = cv2.resize(mask_miss, (0, 0),
fx=1.0 / stride,
fy=1.0 / stride,
interpolation=cv2.INTER_CUBIC).astype(
np.float32)
mask_miss = mask_miss / 255.
mask_miss = np.expand_dims(mask_miss, axis=2)
heat_mask = np.repeat(mask_miss, 37, axis=2)
# confidance maps for body parts
for i in range(36):
if (meta['joint_self'][i, 2] <= 1):
center = meta['joint_self'][i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
for j in range(nop):
if (meta['joint_others'][j, i, 2] <= 1):
center = meta['joint_others'][j, i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
# background
heatmaps[:, :,
36] = np.maximum(1 - np.max(heatmaps[:, :, :36], axis=2), 0.)
return heat_mask, heatmaps, None, None
def get_ground_truth(self, meta, mask_miss):
stride = self.params_transform['stride']
crop_size_y = self.params_transform['crop_size_y']
crop_size_x = self.params_transform['crop_size_x']
nop = meta['numOtherPeople']
grid_y = crop_size_y / stride
grid_x = crop_size_x / stride
heatmaps = np.zeros((int(grid_y), int(grid_x), 16))
pafs = np.zeros((int(grid_y), int(grid_x), 32))
mask_miss = cv2.resize(mask_miss, (0, 0),
fx=1.0 / stride,
fy=1.0 / stride,
interpolation=cv2.INTER_CUBIC).astype(
np.float32)
mask_miss = mask_miss / 255.
mask_miss = np.expand_dims(mask_miss, axis=2)
heat_mask = np.repeat(mask_miss, 16, axis=2)
paf_mask = np.repeat(mask_miss, 32, axis=2)
'''
id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee,
5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck, 9 - head top,
10 - r wrist, 11 - r elbow, 12 - r shoulder, 13 - l shoulder,
14 - l elbow, 15 - l wrist)
'''
# now 6 is the center of person, and thorax was eliminated.
ran = [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15]
for i in range(15):
# the first person'
index = ran[i]
if (meta['joint_self'][index, 2] <= 1):
center = meta['joint_self'][index, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
# the other people in the image
for j in range(nop):
if (meta['joint_others'][j, index, 2] <= 1):
center = meta['joint_others'][j, index, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
mid_1 = [8, 8, 6, 3, 4, 6, 2, 1, 8, 13, 14, 8, 12, 11, 12, 13]
mid_2 = [9, 6, 3, 4, 5, 2, 1, 0, 13, 14, 15, 12, 11, 10, 9, 9]
for i in range(16):
count = np.zeros((int(grid_y), int(grid_x)), dtype=np.uint32)
if (meta['joint_self'][mid_1[i], 2] <= 1
and meta['joint_self'][mid_2[i], 2] <= 1):
centerA = meta['joint_self'][mid_1[i], :2]
centerB = meta['joint_self'][mid_2[i], :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
for j in range(nop):
if (meta['joint_others'][j, mid_1[i], 2] <= 1
and meta['joint_others'][j, mid_2[i], 2] <= 1):
centerA = meta['joint_others'][j, mid_1[i], :2]
centerB = meta['joint_others'][j, mid_2[i], :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
# background
heatmaps[:, :,
-1] = np.maximum(1 - np.max(heatmaps[:, :, :14], axis=2), 0.)
return heat_mask, heatmaps, paf_mask, pafs
def get_ground_truth(self, meta, mask_miss):
stride = self.params_transform['stride']
mode = self.params_transform['mode']
crop_size_y = self.params_transform['crop_size_y']
crop_size_x = self.params_transform['crop_size_x']
num_parts = self.params_transform['np']
nop = meta['numOtherPeople']
grid_y = crop_size_y / stride
grid_x = crop_size_x / stride
channels = (num_parts + 1) * 2
heatmaps = np.zeros((int(grid_y), int(grid_x), 19))
pafs = np.zeros((int(grid_y), int(grid_x), 38))
mask_miss = cv2.resize(mask_miss, (0, 0),
fx=1.0 / stride,
fy=1.0 / stride,
interpolation=cv2.INTER_CUBIC).astype(
np.float32)
mask_miss = mask_miss / 255.
mask_miss = np.expand_dims(mask_miss, axis=2)
heat_mask = np.repeat(mask_miss, 19, axis=2)
paf_mask = np.repeat(mask_miss, 38, axis=2)
# confidance maps for body parts
for i in range(18):
if (meta['joint_self'][i, 2] <= 1):
center = meta['joint_self'][i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
for j in range(nop):
if (meta['joint_others'][j, i, 2] <= 1):
center = meta['joint_others'][j, i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
# pafs
mid_1 = [2, 9, 10, 2, 12, 13, 2, 3, 4, 3, 2, 6, 7, 6, 2, 1, 1, 15, 16]
mid_2 = [
9, 10, 11, 12, 13, 14, 3, 4, 5, 17, 6, 7, 8, 18, 1, 15, 16, 17, 18
]
thre = 1
for i in range(19):
# limb
count = np.zeros((int(grid_y), int(grid_x)), dtype=np.uint32)
if (meta['joint_self'][mid_1[i] - 1, 2] <= 1
and meta['joint_self'][mid_2[i] - 1, 2] <= 1):
centerA = meta['joint_self'][mid_1[i] - 1, :2]
centerB = meta['joint_self'][mid_2[i] - 1, :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
# print vec_map.shape
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
for j in range(nop):
if (meta['joint_others'][j, mid_1[i] - 1, 2] <= 1
and meta['joint_others'][j, mid_2[i] - 1, 2] <= 1):
centerA = meta['joint_others'][j, mid_1[i] - 1, :2]
centerB = meta['joint_others'][j, mid_2[i] - 1, :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
# background
heatmaps[:, :,
-1] = np.maximum(1 - np.max(heatmaps[:, :, :18], axis=2), 0.)
return heat_mask, heatmaps, paf_mask, pafs
def get_ground_truth(self, meta, mask_miss):
stride = self.params_transform['stride']
mode = self.params_transform['mode']
crop_size_y = self.params_transform['crop_size_y']
crop_size_x = self.params_transform['crop_size_x']
num_parts = self.params_transform['np']
nop = meta['numOtherPeople']
grid_y = crop_size_y / stride
grid_x = crop_size_x / stride
channels = (num_parts + 1) * 2
heatmaps = np.zeros((int(grid_y), int(grid_x), 19))
pafs = np.zeros((int(grid_y), int(grid_x), 38))
mask_miss = cv2.resize(mask_miss, (0, 0),
fx=1.0 / stride,
fy=1.0 / stride,
interpolation=cv2.INTER_CUBIC).astype(
np.float32)
mask_miss = mask_miss / 255.
mask_miss = np.expand_dims(mask_miss, axis=2)
heat_mask = np.repeat(mask_miss, 19, axis=2)
paf_mask = np.repeat(mask_miss, 38, axis=2)
# print(meta['joint_self'])
# print(meta['joint_self'][0, 2])
# print(meta['joint_self'][0, 0, 2])
# confidance maps for body parts
for i in range(18):
# the first person
if (meta['joint_self'][i, 2] <= 1):
center = meta['joint_self'][i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
# the other people in the image
for j in range(nop):
# print(meta['joint_others'])
# print(meta['joint_others'][j, i, 2] <= 1)
if (meta['joint_others'][j, i, 2] <= 1):
center = meta['joint_others'][j, i, :2]
gaussian_map = heatmaps[:, :, i]
heatmaps[:, :, i] = putGaussianMaps(
center,
gaussian_map,
params_transform=self.params_transform)
'''
MS COCO annotation order:
0: nose 1: l eye 2: r eye 3: l ear 4: r ear
5: l shoulder 6: r shoulder 7: l elbow 8: r elbow
9: l wrist 10: r wrist 11: l hip 12: r hip 13: l knee
14: r knee 15: l ankle 16: r ankle
The order in this work:
(0-'nose' 1-'neck' 2-'right_shoulder' 3-'right_elbow' 4-'right_wrist'
5-'left_shoulder' 6-'left_elbow' 7-'left_wrist' 8-'right_hip'
9-'right_knee' 10-'right_ankle' 11-'left_hip' 12-'left_knee'
13-'left_ankle' 14-'right_eye' 15-'left_eye' 16-'right_ear'
17-'left_ear' )
'''
# pafs
# neck-r_shoulder neck-l_shoulder r_shoulder-r_elbow r_elbow-r_wrist l_shoulder-l_elbow l_elbow-l_wrist
# neck-r_hip r_hip-r_knee r_knee-r_ankle neck-l_hip l_hip-l_knee l_knee-l_ankle neck-nose nose-r_eye r_eye-r_ear
# nose-l_eye l_eye-l_ear
mid_1 = [2, 9, 10, 2, 12, 13, 2, 3, 4, 3, 2, 6, 7, 6, 2, 1, 1, 15, 16]
mid_2 = [
9, 10, 11, 12, 13, 14, 3, 4, 5, 17, 6, 7, 8, 18, 1, 15, 16, 17, 18
]
thre = 1
for i in range(19):
# limb
count = np.zeros((int(grid_y), int(grid_x)), dtype=np.uint32)
if (meta['joint_self'][mid_1[i] - 1, 2] <= 1
and meta['joint_self'][mid_2[i] - 1, 2] <= 1):
centerA = meta['joint_self'][mid_1[i] - 1, :2]
centerB = meta['joint_self'][mid_2[i] - 1, :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
# print vec_map.shape
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
for j in range(nop):
if (meta['joint_others'][j, mid_1[i] - 1, 2] <= 1
and meta['joint_others'][j, mid_2[i] - 1, 2] <= 1):
centerA = meta['joint_others'][j, mid_1[i] - 1, :2]
centerB = meta['joint_others'][j, mid_2[i] - 1, :2]
vec_map = pafs[:, :, 2 * i:2 * i + 2]
pafs[:, :, 2 * i:2 * i + 2], count = putVecMaps(
centerA=centerA,
centerB=centerB,
accumulate_vec_map=vec_map,
count=count,
params_transform=self.params_transform)
# background
heatmaps[:, :,
-1] = np.maximum(1 - np.max(heatmaps[:, :, :18], axis=2), 0.)
return heat_mask, heatmaps, paf_mask, pafs