def preprocess(samples, set_label="training"):
for file_count, file in enumerate(sort(samples)):
if (set_label == "training"):
condition = (file_count < 650)
if pc == "wudi":
dest = r"D:\Chalearn2014\Data_processed\train" # dir to destination processed data
elif pc == "wudi_linux":
dest = "/idiap/temp/dwu/chalearn2014_data/Train_processed"
elif pc == "lio":
dest = r"/media/lio/Elements/chalearn/preproc/train"
else:
condition = (file_count >= 650)
if pc == "wudi":
dest = r"D:\Chalearn2014\Data_processed\valid" # dir to destination processed data
elif pc == "wudi_linux":
dest = "/idiap/temp/dwu/chalearn2014_data/Valid_processed"
elif pc == "lio":
dest = r"/media/lio/Elements/chalearn/preproc/valid" # dir to destination processed data
#set == "training" ? (condition = (file_count<650)) : (condition = (file_count>=650))
if condition: #wudi only used first 650 for validation !!! Lio be careful!
print("\t Processing file " + file)
start_time = time.time()
# Create the object to access the sample
sample = GestureSample(os.path.join(data, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gestures = sample.getGestures()
# Iterate for each action in this sample
for gesture in gestures:
skelet, depth, gray, user, c = sample.get_data_wudi(gesture, vid_res, NEUTRUAL_SEG_LENGTH)
if c: print 'corrupt'; continue
# preprocess
# skelet_feature: frames * num_features? here gestures because we need netural frames
skelet_feature, Targets, c = proc_skelet_wudi(sample, used_joints, gesture, STATE_NO,
NEUTRUAL_SEG_LENGTH)
if c: print 'corrupt'; continue
user_o = user.copy()
user = proc_user(user)
skelet, c = proc_skelet(skelet)
# depth: 2(h&b) * frames * 5 (stacked frames) * vid_shape_hand[0] *vid_shape_hand[1]
user_new, depth, c = proc_depth_wudi(depth, user, user_o, skelet, NEUTRUAL_SEG_LENGTH)
if c: print 'corrupt'; continue
# gray: 2(h&b) * frames * 5 (stacked frames) * vid_shape_hand[0] *vid_shape_hand[1]
gray, c = proc_gray_wudi(gray, user, skelet, NEUTRUAL_SEG_LENGTH)
if c: print 'corrupt'; continue
if show_depth: play_vid_wudi(depth, Targets, wait=1000 / 10, norm=False)
if show_gray: play_vid_wudi(gray, Targets, wait=1000 / 10, norm=False)
if show_user: play_vid_wudi(user_new, Targets, wait=1000 / 10, norm=False)
# user_new = user_new.astype("bool")
traj2D, traj3D, ori, pheight, hand, center = skelet
skelet = traj3D, ori, pheight
assert user.dtype == gray.dtype == depth.dtype == traj3D.dtype == ori.dtype == "uint8"
assert gray.shape == depth.shape
if not gray.shape[1] == skelet_feature.shape[0] == Targets.shape[0]:
print "too early movement or too late,skip one";
continue
# we don't need user info. anyway
video = empty((2,) + gray.shape, dtype="uint8")
video[0], video[1] = gray, depth
store_preproc_wudi(video, skelet_feature, Targets.argmax(axis=1), skelet, dest)
end_time = time.time()
print "Processing one batch requires: %d second\n"% ( end_time - start_time)
if condition and file_count==(len(samples)-1):
dump_last_data(video,skelet_feature, Targets.argmax(axis=1), skelet, dest)
print 'Process',p_i,'finished'
print "Processing one batch requires: %d second\n"% ( end_time - start_time)
if condition and file_count==(len(samples)-1):
dump_last_data(video,skelet_feature, Targets.argmax(axis=1), skelet, dest)
# we should add the traning data as well
if not condition and file_count == 650-1:
dump_last_data(video,skelet_feature, Targets.argmax(axis=1), skelet, dest)
def preprocess(samples, set_label="training"):
for file_count, file in enumerate(sort(samples)):
if (set_label == "training"):
condition = (file_count < 650)
if pc == "wudi":
dest = r"D:\Chalearn2014\Data_processed\train" # dir to destination processed data
elif pc == "wudi_linux":
dest = "/idiap/temp/dwu/chalearn2014_data/Train_processed"
elif pc == "lio":
dest = r"/media/lio/Elements/chalearn/preproc/train"
else:
condition = (file_count >= 650)
if pc == "wudi":
dest = r"D:\Chalearn2014\Data_processed\valid" # dir to destination processed data
elif pc == "wudi_linux":
dest = "/idiap/temp/dwu/chalearn2014_data/Valid_processed"
elif pc == "lio":
dest = r"/media/lio/Elements/chalearn/preproc/valid" # dir to destination processed data
#set == "training" ? (condition = (file_count<650)) : (condition = (file_count>=650))
if condition: #wudi only used first 650 for validation !!! Lio be careful!
print("\t Processing file " + file)
start_time = time.time()
# Create the object to access the sample
sample = GestureSample(os.path.join(data, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gestures = sample.getGestures()
# Iterate for each action in this sample
for gesture in gestures:
skelet, depth, gray, user, c = sample.get_data_wudi(
gesture, vid_res, NEUTRUAL_SEG_LENGTH)
if c:
print 'corrupt'
continue
# preprocess
# skelet_feature: frames * num_features? here gestures because we need netural frames
skelet_feature, Targets, c = proc_skelet_wudi(
sample, used_joints, gesture, STATE_NO,
NEUTRUAL_SEG_LENGTH)
if c:
print 'corrupt'
continue
user_o = user.copy()
user = proc_user(user)
skelet, c = proc_skelet(skelet)
# depth: 2(h&b) * frames * 5 (stacked frames) * vid_shape_hand[0] *vid_shape_hand[1]
user_new, depth, c = proc_depth_wudi(depth, user, user_o,
skelet,
NEUTRUAL_SEG_LENGTH)
if c:
print 'corrupt'
continue
# gray: 2(h&b) * frames * 5 (stacked frames) * vid_shape_hand[0] *vid_shape_hand[1]
gray, c = proc_gray_wudi(gray, user, skelet,
NEUTRUAL_SEG_LENGTH)
if c:
print 'corrupt'
continue
if show_depth:
play_vid_wudi(depth, Targets, wait=1000 / 10, norm=False)
if show_gray:
play_vid_wudi(gray, Targets, wait=1000 / 10, norm=False)
if show_user:
play_vid_wudi(user_new,
Targets,
wait=1000 / 10,
norm=False)
# user_new = user_new.astype("bool")
traj2D, traj3D, ori, pheight, hand, center = skelet
skelet = traj3D, ori, pheight
assert user.dtype == gray.dtype == depth.dtype == traj3D.dtype == ori.dtype == "uint8"
assert gray.shape == depth.shape
if not gray.shape[1] == skelet_feature.shape[
0] == Targets.shape[0]:
print "too early movement or too late,skip one"
continue
# we don't need user info. anyway
video = empty((2, ) + gray.shape, dtype="uint8")
video[0], video[1] = gray, depth
store_preproc_wudi(video, skelet_feature,
Targets.argmax(axis=1), skelet, dest)
end_time = time.time()
print "Processing one batch requires: %d second\n" % (end_time -
start_time)
if condition and file_count == (len(samples) - 1):
dump_last_data(video, skelet_feature, Targets.argmax(axis=1),
skelet, dest)
print 'Process', p_i, 'finished'
print "Processing one batch requires: %d second\n" % (end_time -
start_time)
if condition and file_count == (len(samples) - 1):
dump_last_data(video, skelet_feature, Targets.argmax(axis=1),
skelet, dest)
# we should add the traning data as well
if not condition and file_count == 650 - 1:
dump_last_data(video, skelet_feature, Targets.argmax(axis=1),
skelet, dest)
def preprocess(samples):
for file_count, file in enumerate(sort(samples)):
print "This is the %d th file : " % file_count
##totally 400 training samples, use 360 for training, 40 for validating
if file_count < 360:
if pc == "linux_fancy":
dest = "/home/fancywu/Desktop/GestureRecognition/Train_files/Preprocessed_files/train"
elif pc == "win_fancy":
dest = ""
print "Processing training file ", file
elif file_count >= 360:
if pc == "linux_fancy":
dest = "/home/fancywu/Desktop/GestureRecognition/Train_files/Preprocessed_files/valid"
elif pc == "win_fancy":
dest = ""
print "Processing validating file ", file
start_time = time()
##Create the object to access the sample
sample = GestureSample(os.path.join(data, file))
# print(os.path.join(data, file))
##USE Ground Truth information to learn the model
##Get the list of gesture for this sample
gestures = sample.getGestures()
print "len gestures: ", len(gestures)
# preprocess each gesture
for gesture in gestures:
skelet, depth, gray, user, c = sample.get_data_wudi(
gesture, vid_res, NEUTRUAL_SEG_LENGTH)
if c:
print '1: corrupt'
continue
skelet_feature, Targets, c = proc_skelet_wudi(
sample, used_joints, gesture, HIDDEN_STATE,
NEUTRUAL_SEG_LENGTH)
if c:
print '2: corrupt'
continue
user_o = user.copy()
user = proc_user(user)
skelet, c = proc_skelet(skelet)
if c: print '3: corrupt'
user_new, depth, c = proc_depth_wudi(depth, user, user_o, skelet,
NEUTRUAL_SEG_LENGTH)
if c:
print '4: corrupt'
continue
gray, c = proc_gray_wudi(gray, user, skelet, NEUTRUAL_SEG_LENGTH)
if c:
print '5: corrupt'
continue
if show_depth:
play_vid_wudi(depth, Targets, wait=1000 / 10, norm=False)
if show_gray:
play_vid_wudi(gray, Targets, wait=1000 / 10, norm=False)
if show_user:
play_vid_wudi(user_new, Targets, wait=1000 / 10, norm=False)
traj2D, traj3D, ori, pheight, hand, center = skelet
skelet = traj3D, ori, pheight
assert user.dtype == gray.dtype == depth.dtype == traj3D.dtype == ori.dtype == "uint8"
assert gray.shape == depth.shape
if not gray.shape[1] == skelet_feature.shape[0] == Targets.shape[0]:
print "too early or too late movement, skip one"
continue
##we just use gray and depth videos for training, dont need user
video = empty((2, ) + gray.shape, dtype="uint8")
video[0], video[1] = gray, depth
store_preproc_video_skelet_data(video, skelet_feature,
Targets.argmax(axis=1), skelet,
dest)
print "finished"
print "Processing one batch requires : %d seconds\n" % (time() -
start_time)
if (file_count == len(samples) - 1) or (file_count == 360 - 1):
store_preproc_video_skelet_data(video,
skelet_feature,
Targets.argmax(axis=1),
skelet,
dest,
last_data=True)
print "Processing %d sample requies: %3.3f mins" % (
file_count + 1, (time() - prog_start_time) / 60.)
