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

# 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

import cv2

from ChalearnLAPEvaluation import evalGesture,exportGT_Gesture

from ChalearnLAPSample import GestureSample

from utils import IsLeftDominant

from utils import Extract_feature_normalized

from utils import Extract_feature

import time

import cPickle

""" Main script. Show how to perform all competition steps

Access the sample information to learn a model. """

# 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', 'ElbowRight', 'WristRight']

njoints = len(used_joints)

STATE_NO = 10

batch_num = 13

# pre-allocating the memory

Prior = numpy.zeros(shape=(201))

Transition_matrix = numpy.zeros(shape=(201,201))

for file_count, file in enumerate(samples):

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

# continue;

time_tic = time.time()

if (file_count<651):

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()

for gesture in gesturesList:

gestureID,startFrame,endFrame=gesture

for frame in range(endFrame-startFrame+1-4):

state_no_1 = numpy.floor(frame*(STATE_NO*1.0/(endFrame-startFrame+1-3)))

state_no_1 = state_no_1+STATE_NO*(gestureID-1)

state_no_2 = numpy.floor((frame+1)*(STATE_NO*1.0/(endFrame-startFrame+1-3)))

state_no_2 = state_no_2+STATE_NO*(gestureID-1)

## we allow first two states add together:

Prior [state_no_1] += 1

Transition_matrix[state_no_1, state_no_2] += 1

if frame<2:

Transition_matrix[-1, state_no_1] += 1

Prior[-1] += 1

if frame> (endFrame-startFrame+1-4-2):

Transition_matrix[state_no_2, -1] += 1

Prior[-1] += 1

del smp

import scipy.io as sio

sio.savemat('Transition_matrix.mat', {'Transition_matrix':Transition_matrix})

sio.savemat('Prior.mat', {Prior:'Prior'})

sio.savemat('Prior_Transition_matrix.mat', {'Transition_matrix':Transition_matrix, 'Prior': Prior})

img = Transition_matrix*1.0*255/Transition_matrix.max()

fig, ax = plt.subplots()

cax = ax.imshow(temp2, interpolation='nearest', cmap=cm.coolwarm)

cbar = fig.colorbar(cax, ticks=[-1, 0, 1])

cbar.ax.set_yticklabels(['< -1', '0', '> 1'])# vertically oriented colorbar