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Step_1_preproc.py
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Step_1_preproc.py
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"""
This is the very first file that you should run to extract training data
This file extract the skeleton data and the depth and rgb data
author: Di Wu : stevenwudi@gmail.com
2015/05/15
"""
import cv2
import os
import sys
import shutil
import errno
import gzip
from itertools import tee, islice
from cPickle import dump
from glob import glob
from random import shuffle
import numpy
from numpy import *
from numpy import linalg
from numpy.random import RandomState
#timing
import time
from classes import GestureSample
from functions.preproc_functions import *
#data path and store path definition
pc = "wudi"
#pc = "wudi_linux"
if pc == "wudi":
data = r"F:\CHALEARN2014\Train" # dir of original data -- note that wudi has decompressed it!!!
elif pc == "wudi_linux":
data = "/idiap/temp/dwu/chalearn2014_data/Train"
elif pc == "lio":
data = r"/media/lio/Elements/chalearn/trainingset"
# global variable definition
store_result = True
bg_remove = False
norm_gray = True
show_gray = False
show_depth = False
show_user = False
vid_res = (480, 640) # 640 x 480 video resolution
vid_shape_hand = (128, 128)
vid_shape_body = (128, 128)
batch_size = 20 # number of gesture instance
used_joints = ['ElbowLeft', 'WristLeft', 'ShoulderLeft', 'HandLeft',
'ElbowRight', 'WristRight', 'ShoulderRight', 'HandRight',
'Head', 'Spine', 'HipCenter']
#globals
offset = vid_shape_hand[1] / 2
v, s, l = [], [], []
batch_idx = 0
count = 1
# Then we choose 8 frame before and after the ground true data:
# in effect it only generate 4 frames because acceleration requires 5 frames
NEUTRUAL_SEG_LENGTH = 8
# number of hidden states for each gesture class
STATE_NO = 5
def main():
os.chdir(data)
if pc == "wudi" or pc == "wudi_linux":
samples = glob("*") # because wudi unzipped all the files already!
elif pc == "lio":
samples = glob("*.zip")
#samples.sort()
print len(samples), "samples found"
#start preprocessing
preprocess(samples, "training")
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)
# 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 store_preproc_wudi(video, skelet, label, skelet_info, dest):
"""
Wudi modified how to- store the result
original code is a bit hard to understand
"""
global v, s, l, sk, count, batch_idx
if len(v) == 0:
v = video
s = skelet
l = label
sk = []
sk.append(skelet_info)
else:
v = numpy.concatenate((v, video), axis=2)
s = numpy.concatenate((s, skelet), axis=0)
l = numpy.concatenate((l, label))
sk.append(skelet_info)
if len(l) > 1000:
make_sure_path_exists(dest)
os.chdir(dest)
file_name = "batch_" + "_" + str(batch_idx) + "_" + str(len(l)) + ".zip"
if store_result:
file = gzip.GzipFile(file_name, 'wb')
dump((v, s, l, sk), file, -1)
file.close()
print file_name
batch_idx += 1
count = 1
v, s, l, sk = [], [], [], []
count += 1
def dump_last_data(video, skelet, label, skelet_info, dest):
global v, s, l, sk, count, batch_idx
v = numpy.concatenate((v, video), axis=2)
s = numpy.concatenate((s, skelet), axis=0)
l = numpy.concatenate((l, label))
sk.append(skelet_info)
os.chdir(dest)
file_name = "batch_" + "_" + str(batch_idx) + "_" + str(len(l)) + ".zip"
if store_result:
file = gzip.GzipFile(file_name, 'wb')
dump((v, s, l, sk), file, -1)
file.close()
print file_name
if __name__ == '__main__':
main()