def load_data(path, rng, epoch, batch_size, x_, y_):
""" load data into shared variables """
#global x_,t_,y_,
#global first_report2
#first_report2 = True
start_time = time()
v, p, skeleton_feature, l = load_gzip(path)
v = v[:, :, :res_shape[2]]
res_shape[0] = v.shape[0]
v_new = empty(res_shape, dtype="uint8")
for i in xrange(v.shape[0]): #batch
if p[i] < 10: p[i] = 100
ofs = p[i] * ratio
mid = v.shape[-1] / 2.
sli = None
if ofs < mid:
start = int(round(mid - ofs))
end = int(round(mid + ofs))
sli = slice(start, end)
for j in xrange(v.shape[2]): #maps
for k in xrange(v.shape[3]): #frames
#body
img = v[i, 0, j, k]
img = cut_img(img, 5)
img = misc.imresize(img, (h, h))
# if j==0: img = 255-misc.imfilter(img,"contour")
v_new[i, 0, j, k] = img
#hand
img = v[i, 1, j, k]
img = img[sli, sli]
img = misc.imresize(img, (h, h))
v_new[i, 1, j, k] = img
vid, lbl = v_new, l
#if epoch==0: print "get in",str(time()-start_time)[:3]+"s",
# shuffle data
ind = rng.permutation(l.shape[0])
ind = ind[:batch_size]
vid = vid[:, :, :, :4, :, :]
vid, skeleton_feature, lbl = vid[ind].astype(
floatX), skeleton_feature[ind].astype(floatX), lbl[ind].astype(floatX)
#vid, skeleton_feature, lbl = vid.astype(floatX), skeleton_feature.astype(floatX),lbl.astype(floatX)
# vid = vid/(255./(scaler*2.))-scaler
#traj = traj/(255./(scaler_traj*2.))-scaler_traj
# traj = traj/(255./5.)
# Wudi already made labels start from 0
#lbl -= 1
#if first_report2:
# print "data range:",vid.min(),vid.max()
# print "traj range:",skeleton_feature.min(),skeleton_feature.max()
# print "lbl range:",lbl.min(),lbl.max()
# first_report2 = False
# set value
x_.set_value(vid, borrow=True)
#t_.set_value(skeleton_feature, borrow=True)
y_.set_value(lbl, borrow=True)
def load_data(path, trans):
global rng, x_,t_,y_,first_report2
""" load data into shared variables """
# if trans and use.aug:
# transform(path) # que up the path for augmentation
# vid, traj, lbl = load_aug(path)
# else:
# vid, traj, lbl = load_normal(path)
# file = GzipFile(path, 'rb')
# vid, skel, lbl = load(file)
# file.close()
# traj,ori,pheight = skel
# print path
# import cv2
# for img in vid[0,0,0]:
# cv2.imshow("Video", img)
# cv2.waitKey(0)
# for img in vid[0,0,1]:
# cv2.imshow("Video", img)
# cv2.waitKey(0)
# new_vid = empty(in_shape,dtype="uint8")
# vid_ = vid[:,0,:2,:,::2,::2]
# vid_ = vid[:,0,:2]
# zm = 1.*90./128.
# vid_ = ndimage.zoom(vid_,(1,1,1,zm,zm),order=0)
# new_vid[:,0] = vid_
# new_vid[:,1] = vid[:,1,:2]
# print "loading..."
start_time = time()
# if not trans:
# start_load(files.valid,jobs,False)
# vid, skel, lbl = queue.get()[0]
v,t,o,p,l = load_gzip(path)
v = v[:,:,:res_shape[2]]
v_new = empty(res_shape,dtype="uint8")
for i in xrange(v.shape[0]): #batch
if p[i] < 10: p[i] = 100
ofs = p[i]*ratio
mid = v.shape[-1]/2.
sli = None
if ofs < mid:
start = int(round(mid-ofs))
end = int(round(mid+ofs))
sli = slice(start,end)
for j in xrange(v.shape[2]): #maps
for k in xrange(v.shape[3]): #frames
#body
img = v[i,0,j,k]
img = cut_img(img,5)
img = misc.imresize(img,(h,h))
# if j==0: img = 255-misc.imfilter(img,"contour")
v_new[i,0,j,k] = img
#hand
img = v[i,1,j,k]
img = img[sli,sli]
img = misc.imresize(img,(h,h))
v_new[i,1,j,k] = img
vid, skel, lbl = v_new,(t,o,p),l
traj,ori,pheight = skel
if epoch==0: print "get in",str(time()-start_time)[:3]+"s",
# shuffle data
ind = rng.permutation(batch_size)
vid, traj, lbl = vid[ind].astype(floatX), traj[ind].astype(floatX),lbl[ind].astype(floatX)
# vid = vid/(255./(scaler*2.))-scaler
traj = traj/(255./(scaler_traj*2.))-scaler_traj
# traj = traj/(255./5.)
lbl -= 1
if first_report2:
print "data range:",vid.min(),vid.max()
print "traj range:",traj.min(),traj.max()
print "lbl range:",lbl.min(),lbl.max()
first_report2 = False
# set value
x_.set_value(vid, borrow=True)
t_.set_value(traj, borrow=True)
y_.set_value(lbl, borrow=True)
def load_data(path, trans):
global rng, x_, t_, y_, first_report2
""" load data into shared variables """
# if trans and use.aug:
# transform(path) # que up the path for augmentation
# vid, traj, lbl = load_aug(path)
# else:
# vid, traj, lbl = load_normal(path)
# file = GzipFile(path, 'rb')
# vid, skel, lbl = load(file)
# file.close()
# traj,ori,pheight = skel
# print path
# import cv2
# for img in vid[0,0,0]:
# cv2.imshow("Video", img)
# cv2.waitKey(0)
# for img in vid[0,0,1]:
# cv2.imshow("Video", img)
# cv2.waitKey(0)
# new_vid = empty(in_shape,dtype="uint8")
# vid_ = vid[:,0,:2,:,::2,::2]
# vid_ = vid[:,0,:2]
# zm = 1.*90./128.
# vid_ = ndimage.zoom(vid_,(1,1,1,zm,zm),order=0)
# new_vid[:,0] = vid_
# new_vid[:,1] = vid[:,1,:2]
# print "loading..."
start_time = time()
# if not trans:
# start_load(files.valid,jobs,False)
# vid, skel, lbl = queue.get()[0]
v, t, o, p, l = load_gzip(path)
v = v[:, :, :res_shape[2]]
v_new = empty(res_shape, dtype="uint8")
for i in xrange(v.shape[0]): #batch
if p[i] < 10: p[i] = 100
ofs = p[i] * ratio
mid = v.shape[-1] / 2.
sli = None
if ofs < mid:
start = int(round(mid - ofs))
end = int(round(mid + ofs))
sli = slice(start, end)
for j in xrange(v.shape[2]): #maps
for k in xrange(v.shape[3]): #frames
#body
img = v[i, 0, j, k]
img = cut_img(img, 5)
img = misc.imresize(img, (h, h))
# if j==0: img = 255-misc.imfilter(img,"contour")
v_new[i, 0, j, k] = img
#hand
img = v[i, 1, j, k]
img = img[sli, sli]
img = misc.imresize(img, (h, h))
v_new[i, 1, j, k] = img
vid, skel, lbl = v_new, (t, o, p), l
traj, ori, pheight = skel
if epoch == 0: print "get in", str(time() - start_time)[:3] + "s",
# shuffle data
ind = rng.permutation(batch_size)
vid, traj, lbl = vid[ind].astype(floatX), traj[ind].astype(
floatX), lbl[ind].astype(floatX)
# vid = vid/(255./(scaler*2.))-scaler
traj = traj / (255. / (scaler_traj * 2.)) - scaler_traj
# traj = traj/(255./5.)
lbl -= 1
if first_report2:
print "data range:", vid.min(), vid.max()
print "traj range:", traj.min(), traj.max()
print "lbl range:", lbl.min(), lbl.max()
first_report2 = False
# set value
x_.set_value(vid, borrow=True)
t_.set_value(traj, borrow=True)
y_.set_value(lbl, borrow=True)
def load_data(path, rng, epoch, batch_size, x_,y_):
""" load data into shared variables """
#global x_,t_,y_,
#global first_report2
#first_report2 = True
start_time = time()
v,p,skeleton_feature,l = load_gzip(path)
v = v[:,:,:res_shape[2]]
res_shape[0] = v.shape[0]
v_new = empty(res_shape,dtype="uint8")
for i in xrange(v.shape[0]): #batch
if p[i] < 10: p[i] = 100
ofs = p[i]*ratio
mid = v.shape[-1]/2.
sli = None
if ofs < mid:
start = int(round(mid-ofs))
end = int(round(mid+ofs))
sli = slice(start,end)
for j in xrange(v.shape[2]): #maps
for k in xrange(v.shape[3]): #frames
#body
img = v[i,0,j,k]
img = cut_img(img,5)
img = misc.imresize(img,(h,h))
# if j==0: img = 255-misc.imfilter(img,"contour")
v_new[i,0,j,k] = img
#hand
img = v[i,1,j,k]
img = img[sli,sli]
img = misc.imresize(img,(h,h))
v_new[i,1,j,k] = img
vid, lbl = v_new,l
#if epoch==0: print "get in",str(time()-start_time)[:3]+"s",
# shuffle data
ind = rng.permutation(l.shape[0])
ind = ind[:batch_size]
vid = vid[:,:,:,:4,:,:]
vid, skeleton_feature, lbl = vid[ind].astype(floatX), skeleton_feature[ind].astype(floatX),lbl[ind].astype(floatX)
#vid, skeleton_feature, lbl = vid.astype(floatX), skeleton_feature.astype(floatX),lbl.astype(floatX)
# vid = vid/(255./(scaler*2.))-scaler
#traj = traj/(255./(scaler_traj*2.))-scaler_traj
# traj = traj/(255./5.)
# Wudi already made labels start from 0
#lbl -= 1
#if first_report2:
# print "data range:",vid.min(),vid.max()
# print "traj range:",skeleton_feature.min(),skeleton_feature.max()
# print "lbl range:",lbl.min(),lbl.max()
# first_report2 = False
# set value
x_.set_value(vid, borrow=True)
#t_.set_value(skeleton_feature, borrow=True)
y_.set_value(lbl, borrow=True)
