def append_dmap(self, annos, container, length):
k = util.get_key_hmap((length, length), annos, self.patch,
self.patch_l // 2)
tmp, tmp_re = util.get_dir_hmap((length, length), annos, self.ones,
self.limbs, self.patch_l // 2)
d = util.weight_dir_hmap(k, tmp, tmp_re, self.limbs)
container.append(d)
def next_batch(self):
start = self.index
end = self.index + self.batch_size
if end >= self.volumn:
end = self.volumn
self.index = 0
else:
self.index = end
data_batch = self.data[start:end]
img = []
keypoint_hmap = []
direction_hmap = []
for piece in data_batch:
tmp = misc.imread(self.img_dir + piece['image_id'] + '.jpg')
tmp, rate, left, top = \
util.resize(tmp, self.length)
annos = np.array(list(piece['keypoint_annotations'].values()),
dtype=np.float16)
annos[:, ::3] = annos[:, ::3] * rate - left
annos[:, 1::3] = annos[:, 1::3] * rate - top
annos = annos.astype(np.int16)
img.append(tmp)
tmp = misc.imresize(tmp, (self.short, self.short))
# annos = np.round(annos * self.short / self.length).astype(np.int8)
rate = self.short / self.length
annos[:, ::3] = annos[:, ::3] * rate
annos[:, 1::3] = annos[:, 1::3] * rate
annos = np.round(annos).astype(np.int8)
# print(annos)
kmap = util.get_key_hmap(\
tmp.shape, annos, self.patch, self.patch_l//2)
keypoint_hmap.append(kmap)
tmp_dmap, tmp_dmap_re = util.get_dir_hmap(\
tmp.shape, annos, self.ones, self.limbs, self.patch_l//2)
dmap = util.weight_dir_hmap(kmap, tmp_dmap, tmp_dmap_re,
self.limbs)
# use forward only
dmap = dmap[:, :, len(self.limbs) * 2:]
direction_hmap.append(dmap)
img = np.array(img, dtype=np.float32)
img -= self.mean
img /= self.var
keypoint_hmap = np.array(keypoint_hmap, dtype=np.float32)
direction_hmap = np.array(direction_hmap, dtype=np.float32)
return img, keypoint_hmap, direction_hmap
def next_batch(self):
start = self.index
end = self.index + self.batch_size
if end >= self.volumn:
end = self.volumn
self.index = 0
else:
self.index = end
data_batch = self.data[start:end]
img = []
keypoint_hmap = []
affinity_hmap = []
names = []
for piece in data_batch:
tmp = misc.imread(self.img_dir + piece['image_id'] + '.jpg')
names.append(piece['image_id'])
tmp, rate, left, top = \
util.resize(tmp, self.length)
annos = np.array(list(piece['keypoint_annotations'].values()),
dtype=np.float16)
annos[:, ::3] = annos[:, ::3] * rate - left
annos[:, 1::3] = annos[:, 1::3] * rate - top
annos = annos.astype(np.int16)
img.append(tmp)
tmp = misc.imresize(tmp, (self.short, self.short))
rate = self.short / self.length
annos[:, ::3] = annos[:, ::3] * rate
annos[:, 1::3] = annos[:, 1::3] * rate
annos = np.round(annos).astype(np.int8)
keypoint_hmap.append(
util.get_key_hmap(tmp.shape,
annos,
self.patch,
self.patch_l // 2,
strict=True))
affinity_hmap.append(
util.get_aff_hmap(tmp.shape, annos, self.limbs, strict=True))
img = np.array(img, dtype=np.float32)
img -= self.mean
img /= self.var
keypoint_hmap = np.array(keypoint_hmap, dtype=np.float32)
affinity_hmap = np.array(affinity_hmap, dtype=np.float32)
return img, keypoint_hmap, affinity_hmap, names
def next_batch(self):
start = self.index
end = self.index + self.batch_size
if end >= self.volumn:
end = self.volumn
self.index = 0
else:
self.index = end
data_batch = self.data[start:end]
img = []
keypoint_hmap = []
direction_hmap = []
names = []
lables = []
link_kmaps = []
for piece in data_batch:
tmp = misc.imread(self.img_dir + piece['image_id'] + '.jpg')
names.append(piece['image_id'])
# resize the img and annotation to 368x368
tmp, rate, left, top = \
util.resize(tmp, self.length)
annos = np.array(list(piece['keypoint_annotations'].values()),
dtype=np.float16)
annos[:, ::3] = annos[:, ::3] * rate - left
annos[:, 1::3] = annos[:, 1::3] * rate - top
annos = annos.astype(np.int16)
img.append(tmp)
# resize the img and annotations to 46x46
tmp = misc.imresize(tmp, (self.short, self.short))
rate = self.short / self.length
annos[:, ::3] = annos[:, ::3] * rate
annos[:, 1::3] = annos[:, 1::3] * rate
annos = np.round(annos).astype(np.int8)
kmap = util.get_key_hmap(\
tmp.shape, annos, self.patch, self.patch_l//2)
keypoint_hmap.append(kmap)
human_num = len(annos)
failed = True
# since many img contains only one people, we manually
# reduce the possibility to get the positive sample
random.shuffle(self.limb_index)
if np.random.random_sample() < 0.15 or human_num == 1:
# extract from the same people
for limb in self.limb_index:
start = self.limbs[limb][0]
end = self.limbs[limb][1]
human = annos[np.random.randint(human_num)]
start_x = human[start * 3]
start_y = human[start * 3 + 1]
start_v = human[start * 3 + 2]
end_x = human[end * 3]
end_y = human[end * 3 + 1]
end_v = human[end * 3 + 2]
if util.validate(start_x, start_y, start_v, self.short, self.short) \
and util.validate(end_x, end_y, end_v, self.short, self.short):
kmap_start = util.get_single_kmap(
(self.short, self.short), start_x, start_y,
self.patch_l // 2, self.patch)
kmap_end = util.get_single_kmap(
(self.short, self.short), end_x, end_y,
self.patch_l // 2, self.patch)
link_kmaps.append(
np.stack((kmap_start, kmap_end), axis=-1))
failed = False
break
if failed:
link_kmaps.append(np.zeros((self.short, self.short, 2)))
print('no validate limb in %s (same)' % piece['image_id'])
lables.append(1)
else:
# extract from two different humen
for limb in self.limb_index:
start = self.limbs[limb][0]
end = self.limbs[limb][1]
index1 = np.random.randint(human_num)
human1 = annos[index1]
start_x = human1[start * 3]
start_y = human1[start * 3 + 1]
start_v = human1[start * 3 + 2]
index2 = np.random.randint(human_num)
human2 = annos[index2]
end_x = human2[end * 3]
end_y = human2[end * 3 + 1]
end_v = human2[end * 3 + 2]
if util.validate(start_x, start_y, start_v, self.short, self.short) \
and util.validate(end_x, end_y, end_v, self.short, self.short) \
and index1 != index2:
kmap_start = util.get_single_kmap(
(self.short, self.short), start_x, start_y,
self.patch_l // 2, self.patch)
kmap_end = util.get_single_kmap(
(self.short, self.short), end_x, end_y,
self.patch_l // 2, self.patch)
link_kmaps.append(
np.stack((kmap_start, kmap_end), axis=-1))
lables.append(0)
failed = False
break
if failed:
link_kmaps.append(np.zeros((self.short, self.short, 2)))
print('no validate limb in %s (differen)' %
piece['image_id'])
lables.append(0)
tmp_dmap, tmp_dmap_re = util.get_dir_hmap(\
tmp.shape, annos, self.ones, self.limbs, self.patch_l//2)
dmap = util.weight_dir_hmap(kmap, tmp_dmap, tmp_dmap_re,
self.limbs)
direction_hmap.append(dmap)
img = np.array(img, dtype=np.float32)
img -= self.mean
img /= self.var
keypoint_hmap = np.array(keypoint_hmap, dtype=np.float32)
direction_hmap = np.array(direction_hmap, dtype=np.float32)
lables = np.array(lables, dtype=np.float32)
link_kmaps = np.array(link_kmaps, dtype=np.float32)
return img, keypoint_hmap, direction_hmap, names, lables, link_kmaps
for k in range(14):
if k in h.keys():
tmp.append(int(round(h[k][0] / rate)))
tmp.append(int(round(h[k][1] / rate)))
tmp.append(1)
else:
tmp.append(0)
tmp.append(0)
tmp.append(0)
ret['keypoint_annotations']['human%d'%(cnt+1)] = tmp
return ret
if __name__ == '__main__':
dmap = np.load('dmap.npy')
kmap = util.get_kmap_from_dmap(dmap, util.get_limbs())
humans = reconstruct(dmap, kmap, 5)
annos = format(humans, 'sample', 0.05958549222797927461139896373057)
# print(annos)
for i in range(len(annos['keypoint_annotations'])):
k_rev = util.get_key_hmap((772, 950), [annos['keypoint_annotations']['human%d'%(i+1)]], util.get_patch(40,32), r=20)
src = misc.imread('ffa97d027dfc2f2fc62692a035535579c5be74e0.jpg')
util.cover_key_map(src, k_rev)
misc.imsave('vis_anno_%d.jpg'%i, src)
# outstander, layer = find_outstander(kmap, 10)
# print(layer)
# print(outstander)
batch_k, batch_a = sess.run([kmaps, amaps], feed_dict={inflow: imgs})
batch_k = batch_k[-1]
batch_a = batch_a[-1]
k = util.concat_maps(batch_k, lefts, tops, 8)
a = util.concat_maps(batch_a, lefts, tops, 8)
humans = inf_util_ka.reconstruct(a, k, 10)
annos = inf_util_ka.format(humans, name.split('.')[0], rate)
result.append(annos)
if vis_anno:
for human, ann in annos['keypoint_annotations'].items():
k_rev = util.get_key_hmap((h, w), [ann],
util.get_patch(40, 64),
r=20)
src = misc.imread(test_path + name)
util.cover_key_map(src, k_rev)
misc.imsave('%s_%s.jpg' % (annos['image_id'], human), src)
if save:
np.save(annos['image_id'] + '_k.npy', k)
np.save(annos['image_id'] + '_a.npy', a)
toc = time.time()
cnt += 1
interval = toc - tic
elapse += interval
if cnt == 2: