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Step_1_preproc_hdf5_skeleton.py
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Step_1_preproc_hdf5_skeleton.py
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from numpy import *
import h5py
import gzip
from glob import glob
from scipy import misc
from numpy.random import permutation
import cv2
raw_input("Press Enter to continue...")
pc = "wudi"
pc = "wudi_linux"
if pc=="wudi":
src = r"D:\Chalearn2014\Data_processed/"
dst = r"J:\Chalearn2014\dummy/"
elif pc=="wudi_linux":
src = '/idiap/temp/dwu/chalearn2014_data/Data_processed/'
dst = '/idiap/user/dwu/chalearn/'
elif pc=="lio":
src = "/mnt/wd/chalearn/preproc/"
def main():
if pc=="wudi":
files_train = glob(src+"train/batch_*.zip")
files_valid = glob(src+"valid/batch_*.zip")
elif pc=="wudi_linux":
files_train = glob(src+"train/batch_*.zip")
files_valid = glob(src+"valid/batch_*.zip")
elif pc=="lio":
files_train = glob(src+"train/batch_*.zip")
files_valid = glob(src+"valid/batch_*.zip")
print len(files_train), "train files"
print len(files_valid), "valid files"
f = h5py.File(dst+"data%d.hdf5", "w", driver="family", memb_size=2**32-1)
x_train = f.create_dataset("x_train", (400*1050,2,2,4,64,64), dtype='uint8', chunks=True)
x_valid = f.create_dataset("x_valid", (29*1050,2,2,4,64,64), dtype='uint8', chunks=True)
y_train = f.create_dataset("y_train", (400*1050,), dtype='uint8', chunks=True)
y_valid = f.create_dataset("y_valid", (29*1050,), dtype='uint8', chunks=True)
x_train_skeleton_feature = f.create_dataset("x_train_skeleton_feature", (400*1050,891), dtype='float32',chunks=True)
x_valid_skeleton_feature = f.create_dataset("x_valid_skeleton_feature", (29*1050,891), dtype='float32', chunks=True)
l = 0
pos = 0
for path in files_train:
print path
vid, lbl, skeleton_feature = load_data(path)
print vid.shape[0], lbl.shape[0], skeleton_feature.shape[0]
assert vid.shape[0] == lbl.shape[0] == skeleton_feature.shape[0]
l += vid.shape[0]
x_train.resize(l, axis=0)
y_train.resize(l, axis=0)
x_train_skeleton_feature.resize(l, axis=0)
x_train[pos:pos+vid.shape[0]] = vid
y_train[pos:pos+vid.shape[0]] = lbl
x_train_skeleton_feature[pos:pos+vid.shape[0]] = skeleton_feature
pos += vid.shape[0]
print x_train.shape
l = 0
pos = 0
for path in files_valid:
vid, lbl, skeleton_feature = load_data(path)
print path
print vid.shape[0], lbl.shape[0]
assert vid.shape[0] == lbl.shape[0]
l += vid.shape[0]
x_valid.resize(l, axis=0)
y_valid.resize(l, axis=0)
x_valid_skeleton_feature.resize(l, axis=0)
x_valid[pos:pos+vid.shape[0]] = vid
y_valid[pos:pos+vid.shape[0]] = lbl
x_valid_skeleton_feature[pos:pos+vid.shape[0]] = skeleton_feature
pos += vid.shape[0]
print x_valid.shape
f.close()
print "done"
def load_gzip(path):
file = gzip.GzipFile(path, 'rb')
video, skeleton_feature, label, sk_trajectory = load(file)
file.close()
#wudi made an error in Step 1 extracting skelet, so
# the following interwined, gesture frames, before frames, after frames
pheight = empty(shape=(video.shape[2]))
count = 0
for i in range(len(sk_trajectory)):
pheight[count: count+sk_trajectory[i][0].shape[1]-12] \
= ones(shape=(sk_trajectory[i][0].shape[1]-12)) \
*sk_trajectory[i][2]
count = count+sk_trajectory[i][0].shape[1]-12
#label start from 0 REMEMBER!!!
video = video.swapaxes(0,2)
video = video.swapaxes(1,2)
return video,pheight,skeleton_feature,label
def cut_img(img,s):
if s==0: return img
return img[s:-s,s:-s]
ratio = 0.25
res_shape=[100,2,2,5,64,64]
h = res_shape[-1]
def load_data(path):
""" load data into shared variables """
v,p,skeleton_feature,l = load_gzip(path)
v = v[:,:,:2]
# print v.shape[0]
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
# shuffle data
ind = permutation(l.shape[0])
# ind = ind[:1000]
vid = vid[:,:,:,:4,:,:]
vid, skeleton_feature, lbl = vid[ind].astype("float32"), skeleton_feature[ind].astype("float32"),lbl[ind].astype("float32")
# set value
# x_.set_value(vid, borrow=True)
# y_.set_value(lbl, borrow=True)
return vid, lbl, skeleton_feature
if __name__ == '__main__':
main()