def __init__(self,
img_dir,
anno_path,
batch_size,
pl=10,
ps=4,
h=480,
w=272):
self.img_dir = img_dir
with open(anno_path, 'rb') as f:
self.data = pickle.load(f)
self.batch_size = batch_size
self.index = 0
self.volumn = len(self.data)
self.height = h
self.width = w
self.patch_l = pl
self.patch_s = ps
random.shuffle(self.data)
self.patch = util.get_patch(pl, ps)
self.limbs = util.get_limbs()
self.mean = np.array([122.35131039, 115.17054545, 107.60200075])
self.var = np.array([35.77071304, 35.39201422, 37.7260754])
# np.random.seed(822)
print('Reader initialized. Data volumn %d, batch size %d.' \
% (self.volumn, self.batch_size))
def __init__(self,
img_dir,
anno_path,
batch_size,
pl=10,
ps=4,
l1=368,
l2=46):
self.img_dir = img_dir
with open(anno_path, 'rb') as f:
self.data = pickle.load(f)
self.batch_size = batch_size
self.index = 0
self.volumn = len(self.data)
self.length = l1
self.short = l2
self.patch_l = pl
self.patch_s = ps
random.shuffle(self.data)
self.patch = util.get_patch(self.patch_l, self.patch_s)
self.ones = np.ones((self.patch_l, self.patch_l, 2))
self.limbs = util.get_limbs()
self.mean = np.array([122.35131039, 115.17054545, 107.60200075])
self.var = np.array([35.77071304, 35.39201422, 37.7260754])
# np.random.seed(822)
print('DirReader initialized. Data volumn %d, batch size %d.' \
% (self.volumn, self.batch_size))
def flat(k_slice, x, y, r):
'''
remove the response peak in kmap at x, y
technically, we subtract the avg
'''
h, w = k_slice.shape
left, right, top, down = util.get_square_patch(x, y, w, h, r)
patch = util.get_patch(r * 2, 16)
for i in reversed(range(1, r)):
left, right, top, down = util.get_square_patch(x, y, w, h, i)
# k_slice[top:down, left:right] -= np.mean(k_slice[top:down, left:right])
k_slice[top:down, left:right] -= patch[r - (y - top):r + (down - y),
r - (x - left):r + (right - x)]
# k_slice[top:down, left:right] = 0
k_slice = np.maximum(k_slice, 0)
inflow, dmaps = network.v10()
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(model_path)
if ckpt:
saver.restore(sess, ckpt.model_checkpoint_path)
print(ckpt.model_checkpoint_path)
else:
print('No available ckpt.')
limbs = util.get_limbs()
connections = util.get_connections()
patch = util.get_patch(10, 4)
result = []
mean = np.array([122.35131039, 115.17054545, 107.60200075])
var = np.array([35.77071304, 35.39201422, 37.7260754])
for name in names:
tic = time.time()
src = misc.imread(test_path+name)
imgs, lefts, tops, rate = util.multi_resize(src, 368, 24)
imgs -= mean
imgs /= var
rate /= 8 # due to pooling
batch_dmaps = sess.run(dmaps, feed_dict={inflow:imgs})[-1]
dmap = util.concat_dmaps(batch_dmaps, lefts, tops, 8)
def reconstruct(dmap, kmap, mask_r, pl=10, ps=4):
h, w, _ = dmap.shape
grid_h, grid_w = util.get_grid(h, w)
connections = util.get_connections()
limbs = util.get_limbs()
patch = util.get_patch(pl, ps)
humans = [] # store the annotation for each human
q = queue.Queue() # store the layers to be extended from
used = [False] * 14 # store wether the layer has been explored
starters, layer = find_outstander(kmap, mask_r, patch)
# print(layer)
# print(starters)
# print(dmap[8,41,0])
# print(dmap[8,41,1])
# print(dmap[8,41,26])
# print(dmap[8,41,27])
for p in starters:
dic = {}
dic[layer] = p
humans.append(dic)
q.put(layer)
used[layer] = True
debug_cnt = 0
while True:
if q.empty():
# print('empty')
break
layer1 = q.get()
# print('from', layer1)
starters = []
for i,h in enumerate(humans):
if layer1 in h.keys():
starters.append((h[layer1], i))
for piece in connections[layer1]:
debug_cnt += 1
# if debug_cnt == 2:
# return humans
layer2, sign, d_layer = piece
if used[layer2]:
continue
used[layer2] = True
q.put(layer2)
# print('finding', layer2)
# print('sign', sign)
# print('dmap layer', d_layer)
k_slice = kmap[:,:,layer2]
# in limbs, the vector is from START to END
# in connections, sign = -1 means layer2 is START
# forward is from layer1's view to layer2
# backward is from layer2's view to layer1
# d_layer is the layer stores vector from START to END
# so, if sign = 1, layer1 is START, layer2 is END
# and, dx_forward is from layer1 to layer2
# so
dx_forward = dmap[:,:,d_layer*2]
dy_forward = dmap[:,:,d_layer*2+1]
dx_backward = dmap[:,:,d_layer*2+26]
dy_backward = dmap[:,:,d_layer*2+26+1]
# else, if sign = -1, layer1 is END, layer2 is START
# and, dx_forward is from layer1 to layer2
# so
if sign == -1:
dx_forward, dx_backward = dx_backward, dx_forward
dy_forward, dy_backward = dy_backward, dy_forward
mask_log = group_match(starters, dx_forward, dy_forward, dx_backward, dy_backward, grid_h, grid_w, k_slice, mask_r, patch)
trans_mask_log(mask_log, layer2, humans)
# for h in humans:
# print(h)
# print('\n')
# print(humans)
# print('mask_log')
# print(mask_log)
return humans
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)
inflow, kmaps, amaps = network.a4()
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(model_path)
if ckpt:
saver.restore(sess, ckpt.model_checkpoint_path)
print(ckpt.model_checkpoint_path)
else:
print('No available ckpt.')
limbs = util.get_limbs()
connections = util.get_connections()
patch = util.get_patch(16, 8)
result = []
mean = np.array([122.35131039, 115.17054545, 107.60200075])
var = np.array([35.77071304, 35.39201422, 37.7260754])
for name in names:
tic = time.time()
src = misc.imread(test_path + name)
imgs, lefts, tops, rate = util.multi_resize(src, 368, 24)
imgs -= mean
imgs /= var
rate /= 8 # due to dowsn sampling
batch_k, batch_a = sess.run([kmaps, amaps], feed_dict={inflow: imgs})
batch_k = batch_k[-1]
print(model_path)
except Exception as e:
print(e)
exit(0)
else:
ckpt = tf.train.get_checkpoint_state(model_path)
if ckpt:
saver.restore(sess, ckpt.model_checkpoint_path)
print(ckpt.model_checkpoint_path)
else:
print('No available ckpt.')
exit(0)
limbs = util.get_limbs()
connections = util.get_connections()
patch = util.get_patch(10, 4)
result = []
mean = np.array([122.35131039, 115.17054545, 107.60200075])
var = np.array([35.77071304, 35.39201422, 37.7260754])
cnt = 0
total = len(names)
elapse = 0
for name in names:
tic = time.time()
src = misc.imread(test_path + name)
h, w, _ = src.shape
imgs, lefts, tops, rate = util.multi_resize(src, 368, 24)
# imgs, lefts, tops, rate = util.multi_rect(src, 480, 272, 24)
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__':
amap = np.load('amap.npy')
kmap = np.load('kmap.npy')
humans = reconstruct(amap, kmap, 10)
annos = format(humans, 'ffa97d027dfc2f2fc62692a035535579c5be74e0',
0.05958549222797927461139896373057)
# annos = format(humans, 'ffa97d027dfc2f2fc62692a035535579c5be74e0', 1)
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)
# k_slice = k_rev[:,:,8].copy()
# k_rev[:,:,:] = 0
# k_rev[:,:,8] = k_slice
src = misc.imread('ffa97d027dfc2f2fc62692a035535579c5be74e0.jpg')
util.cover_key_map(src, k_rev)
misc.imsave('vis_anno_%d.jpg' % i, src)
