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

# 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

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("./train_data/Train1\\")

# Get the list of training samples

samples=os.listdir(data)

STATE_NO = 10

batch_num = 1

# 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

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

# load prestore template

ref_depth = numpy.load('distance_median.npy')

template = cv2.imread('template.png')

template = numpy.mean(template, axis=2)

template /= template.max()

for file_count, file in enumerate(samples):

time_tic = time.time()

if (file_count<650 and file_count>-1):

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(template, ref_depth, start_frame=total_frame-100, end_frame=total_frame-10)

if numpy.isnan(scale):

scale = 1

for gesture in gesturesList:

gestureID,startFrame,endFrame=gesture

cuboid = numpy.zeros((IM_SZ, IM_SZ, endFrame-startFrame+1), numpy.uint8)

frame_count_temp = 0

for x in range(startFrame, endFrame):

depth_original = smp.getDepth(x)

mask = numpy.mean(smp.getUser(x), axis=2) > 150

if mask.sum() < 1000: # Kinect detect nothing

print "skip "+ str(x)

else:

depth_user = depth_original * mask

depth_user_normalized = depth_user * 1.0 / float(smp.data['maxDepth'])

depth_user_normalized = depth_user_normalized *255 /depth_user_normalized.max()

rows,cols = depth_user_normalized.shape

M = numpy.float32([[1,0, shift[1]],[0,1, shift[0]]])

affine_image = cv2.warpAffine(depth_user_normalized, M,(cols, rows))

resize_image = imresize(affine_image, scale)

resize_image_median = cv2.medianBlur(resize_image,5)

rows, cols = resize_image_median.shape

image_crop = resize_image_median[rows/2-160:rows/2+160, cols/2-160:cols/2+160]

cuboid[:, :, frame_count_temp] = imresize(image_crop, (IM_SZ,IM_SZ))

frame_count_temp +=1

if True: # show the segmented images here

cv2.imshow('image',image_crop)

cv2.waitKey(10)

fr_no = frame_count_temp -1

for frame in range(fr_no-3):

Feature_all[:,:,:,cuboid_count] = cuboid[:,:, frame:frame+4]

state_no = numpy.floor(frame*(STATE_NO*1.0/(fr_no-3)))

Targets[cuboid_count] = state_no+STATE_NO*(gestureID-1)

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 = '.\storage\\'

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_'+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, protocol=cPickle.HIGHEST_PROTOCOL)

out_file.close()

# pre-allocating the memory

batch_num += 1

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

Targets = numpy.zeros(shape=(20000, 1), dtype=numpy.uint8)

cuboid_count = 0