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

# 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

import time

import cPickle

from scipy.misc import imresize

""" 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)

STATE_NO = 10

batch_num = 29

# pre-allocating the memory

IM_SZ = 90

Feature_all = numpy.zeros(shape=(IM_SZ, IM_SZ, 4, 20000), dtype=numpy.uint8)

Targets = numpy.zeros(shape=(100000, 1), dtype=numpy.uint8)

cuboid_count = 0

for file_count, file in enumerate(samples):

time_tic = time.time()

if (file_count<650 and file_count!=155 and file_count!=538 and file_count>560):

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()

total_frame = smp.getNumFrames()

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

# obtain the shift and scaling according to

shift, scale = smp.get_shift_scale_sk( start_frame=total_frame-100, end_frame=total_frame-10)

print ("shift: {} scaling: {:.4}".format(numpy.array(shift), scale))

if numpy.isnan(scale) or scale>3:

scale = 1

for gesture in gesturesList:

gestureID,startFrame,endFrame=gesture

## extract first 4 frames

cuboid = numpy.zeros(( IM_SZ, IM_SZ, 4), numpy.uint8)

frame_count_temp = 0

if startFrame-5 > 1:

valid_count = 0

for x in range(startFrame-5, startFrame-1):

[img, flag] = smp.get_shift_scale_depth_sk_normalize(shift, scale, x, IM_SZ, show_flag=True)

cuboid[:, :, frame_count_temp] = img

frame_count_temp +=1

if flag:# we actually detect some region

valid_count += 1

if valid_count==4: # all 4 frames are valid

Feature_all[:,:,:,cuboid_count] = cuboid

Targets[cuboid_count] = 201

cuboid_count += 1

## extract last 5 frames

cuboid = numpy.zeros(( IM_SZ, IM_SZ, 4), numpy.uint8)

frame_count_temp = 0

if endFrame+5 < smp.getNumFrames():

valid_count = 0

for x in range(endFrame+1, endFrame+5):

[img, flag] = smp.get_shift_scale_depth_sk_normalize(shift, scale, x, IM_SZ, show_flag=True)

cuboid[:, :, frame_count_temp] = img

frame_count_temp +=1

if flag:# we actually detect some region

valid_count += 1

if valid_count==4: # all 4 frames are valid

Feature_all[:,:,:,cuboid_count] = cuboid

Targets[cuboid_count] = 201

cuboid_count += 1

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

## delete the sample

del smp

print "Elapsed time %d sec" % int(time.time() - time_tic)

if(not file_count%20 and file_count>0) or file_count==649:

random_idx = numpy.random.permutation(range(cuboid_count))

save_dir = '.\ConvNet_3DCNN\storage_neutral_sk_normalize\\'

print "batch: %d" % batch_num

data_temp = Feature_all[:,:,:,random_idx[:]]

data_id = Targets[random_idx[:]]

save_path= os.path.join(save_dir,'data_batch_neutral_'+str(batch_num))

out_file = open(save_path, 'wb')

dic = {'batch_label':['batch 1 of'+ str(batch_num)], 'data':data_temp,

'data_id':data_id}

cPickle.dump(dic, out_file)

out_file.close()

# pre-allocating the memory

batch_num += 1

Feature_all = numpy.zeros(shape=(90, 90, 4, 100000), dtype=numpy.uint8 )

Targets = numpy.zeros(shape=(100000, 1), dtype=numpy.uint8)

cuboid_count = 0