#-------------------------------------------------------------------------------

# Name: Starting Kit for ChaLearn LAP 2014 Track3

# Purpose: Show basic functionality of provided code

#

# Author: Xavier Baro

# Author: Di Wu: stevenwudi@gmail.com

# Created: 24/03/2014

# Copyright: (c) Chalearn LAP 2014

# Licence: GPL3

#-------------------------------------------------------------------------------

import sys, os,random,numpy,zipfile

from shutil import copyfile

import matplotlib.pyplot as plt

from ChalearnLAPEvaluation import evalGesture,exportGT_Gesture

from ChalearnLAPSample import GestureSample

from utils import IsLeftDominant

from utils import Extract_feature_Realtime

from utils import Extract_feature_UNnormalized

# Data folder (Training data)

print("Extracting the training files")

data=os.path.join("I:\Kaggle_multimodal\Training\\")

# Get the list of training samples

samples=os.listdir(data)

used_joints = ['ElbowLeft', 'WristLeft', 'ShoulderLeft','HandLeft',

'ElbowRight', 'WristRight','ShoulderRight','HandRight',

'Head','Spine','HipCenter']

njoints = len(used_joints)

STATE_NO = 10

count = 0

# pre-allocating the memory

Feature_all = numpy.zeros(shape=(100000, (njoints*(njoints-1)/2 + njoints**2)*3),dtype=numpy.float32)

Targets = numpy.zeros( shape=(100000, STATE_NO*20+1), dtype=numpy.uint8)

# Access to each sample

for file_count, file in enumerate(samples):

#if not file.endswith(".zip"):

# continue;

if file_count<650:

print("\t Processing file " + file)

# Create the object to access the sample

smp=GestureSample(os.path.join(data,file))

# ###############################################

# USE Ground Truth information to learn the model

# ###############################################

# Get the list of actions for this frame

gesturesList=smp.getGestures()

# Iterate for each action in this sample

# Then we also choose 5 frame before and after the ground true data:

seg_length = 5

for gesture in gesturesList:

# Get the gesture ID, and start and end frames for the gesture

gestureID,startFrame,endFrame=gesture

# This part is to extract action data

Skeleton_matrix = numpy.zeros(shape=(5, len(used_joints)*3))

HipCentre_matrix = numpy.zeros(shape=(5, 3))

frame_num = 0

## extract first 5 frames

if startFrame-seg_length > 0:

Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, startFrame-seg_length+1, startFrame)

if not valid_skel:

print "No detected Skeleton: ", gestureID

else:

Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)

begin_frame = count

end_frame = count+seg_length-1

Feature_all[begin_frame:end_frame,:] = Feature

Targets[begin_frame:end_frame, -1] = 1

count=count+seg_length-1

## extract last 5 frames

if endFrame+seg_length < smp.getNumFrames():

Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, endFrame, endFrame+seg_length-1)

if not valid_skel:

print "No detected Skeleton: ", gestureID

else:

Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)

begin_frame = count

end_frame = count+seg_length-1

Feature_all[begin_frame:end_frame,:] = Feature

Targets[begin_frame:end_frame, -1] = 1

count=count+seg_length-1

# ###############################################

del smp

# save the skeleton file:

Feature_all_new = Feature_all[0:end_frame, :]

Targets_all_new = Targets[0:end_frame, :]

import cPickle as pickle

Feature_train = { "Feature_all_neutral": Feature_all_new, "Targets_all_new": Targets_all_new }

pickle.dump( Feature_train, open( "Feature_all_neutral_realtime.pkl", "wb" ) )

import scipy.io as sio

sio.savemat('Feature_all_neutral_realtime.mat', Feature_train)