# coding=gbk

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

# 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, zipfile

from numpy import log

from shutil import copyfile

import matplotlib.pyplot as plt

import time

import cPickle

import numpy

import scipy.io as sio

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

### local import

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

from ChalearnLAPEvaluation import evalGesture,exportGT_Gesture

from ChalearnLAPSample import GestureSample

from utils import Extract_feature_Realtime

from utils import Extract_feature_UNnormalized

from utils import normalize

from utils import imdisplay

from utils import createSubmisionFile

############### viterbi path import

from utils import viterbi_path, viterbi_path_log

from utils import viterbi_colab_clean

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

### theano import

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

sys.path.append(r'.\TheanoDL')

try:

import theano

import theano.tensor as T

from theano.tensor.shared_randomstreams import RandomStreams

from logistic_sgd import LogisticRegression

from mlp import HiddenLayer

from rbm import RBM

from grbm import GBRBM

from utils import zero_mean_unit_variance

from utils import normalize

from GRBM_DBN import GRBM_DBN

from sklearn import preprocessing

except ImportError:

sys.exit('Please install Theano!')

print("Congratulations, you finish all the reqruired modules!")

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

### The path you need to set!!!#####

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

# Data folder (Test data)

#data_path=os.path.join("I:\Kaggle_multimodal\Test\Test\\")

#data_path=os.path.join("D:\\ComputerScienceLanguage\\Kinect\\CODE+Paper\\LAP\\13\\trainning1");

#data_path=os.path.join("M:\\ALP14\\small\\tran\\");

##########################################################################################################测试修改

data_path=os.path.join("D:\\360\\down\\");

# Predictions folder (output)

outPred=r'.\training\test'

# Submision folder (output)

outSubmision=r'.\training\test_submission'

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

### Some initialization ############

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

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

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

'Head','Spine','HipCenter']#11个点

njoints = len(used_joints)

### load the pre-store normalization constant

f = open('SK_normalization.pkl','rb')#预先处理好的

SK_normalization = cPickle.load(f)#cPicke是 将对象打包保存为文件的类

Mean1 = SK_normalization ['Mean1']#这里的均值和 方差是 后面真实数据归一化要使用的

Std1 = SK_normalization['Std1']

#这是vitebi算法要的数据

## Load Prior and transitional Matrix 预处理好的转换矩阵

dic=sio.loadmat('Transition_matrix.mat')#scipy.io 是个科学计算模块，i模块实现了MATLAB数据的导入

Transition_matrix = dic['Transition_matrix']

Prior = dic['Prior']

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

### Start predicting here ##########

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

samples=os.listdir(data_path)#listdir展示所有路径

for file_count, file in enumerate(samples):

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

# continue;

time_tic = time.time()

if file_count > -1: # do nothing, just for bebug convenience

''' 这里 是对一个样本文件的处理，

先获取动作list，然后对一个文件中的所有动作，一起提取特征。

通过网络计算 概率可能度， （一个视频开始的前5帧，最后一帧无效）'''

print("\t Processing file " + file)

# Create the object to access the sample

smp=GestureSample(os.path.join(data_path,file))#读取数据，按特定格式， os.path.join是链接路径用的

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

# USE Ground Truth information to learn the model

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

# Get the list of actions for this frame

gesturesList=smp.getGestures()#得到帧对应的动作标签

#提取所有帧中，原始的骨架点，得到一个矩阵，Skeleton_matrix ，同时返回骨架是否归0化

Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(smp, used_joints, 1, smp.getNumFrames())

import cPickle

cPickle.dump(Skeleton_matrix,open("testSkeleton_matrix","wb"))

#print Skeleton_matrix

#print Skeleton_matrix.get_value()

Feature = Extract_feature_Realtime(Skeleton_matrix, njoints)

Feature_normalized = normalize(Feature, Mean1, Std1)

### Feed into DBN, theano requires the shared tensor representation

shared_x = theano.shared(numpy.asarray(Feature_normalized,

dtype=theano.config.floatX),

borrow=True)

numpy_rng = numpy.random.RandomState(123)

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

### model 1 第一种网络构架模式 #

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

dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,

hidden_layers_sizes=[1000, 1000, 500],

n_outs=201)

dbn.load('dbn_2014-05-23-20-07-28.npy')#预先训练好的构架

#这里就是theano的奇葩函数构架

validate_model = theano.function(inputs=[],

outputs=dbn.logLayer.p_y_given_x,#输出是逻辑回归层的输出

givens={ dbn.x: shared_x})

observ_likelihood_1 = validate_model()#调用函数得到结果

del dbn

"""

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

### model 2

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

dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,

hidden_layers_sizes=[1000, 1000, 500],

n_outs=201)

dbn.load('dbn_2014-05-24-05-53-17.npy')

validate_model = theano.function(inputs=[],

outputs=dbn.logLayer.p_y_given_x,

givens={ dbn.x: shared_x})

observ_likelihood_2 = validate_model()

del dbn

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

### model 3

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

dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,

hidden_layers_sizes=[2000, 2000, 1000],

n_outs=201, finetune_lr=0.1)

dbn.load('dbn_2014-05-25-10-11-56.npy')

# Optimization complete with best validation score of 38.194915 %,with test performance 38.113636 %

#....The score for this prediction is 0.792685963841

validate_model = theano.function(inputs=[],

outputs=dbn.logLayer.p_y_given_x,

givens={ dbn.x: shared_x})

observ_likelihood_3 = validate_model()

del dbn

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

### model 4

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

dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,

hidden_layers_sizes=[2000, 2000, 1000],

n_outs=201, finetune_lr=0.1)

dbn.load('dbn_2014-05-25-11-09-00.npy')

validate_model = theano.function(inputs=[],

outputs=dbn.logLayer.p_y_given_x,

givens={ dbn.x: shared_x})

observ_likelihood_4 = validate_model()

del dbn

"""

#sio.savemat('observ_likelihood.mat', {'observ_likelihood':observ_likelihood})

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

# viterbi path decoding

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

# log_observ_likelihood = log(observ_likelihood_1.T) + log(observ_likelihood_2.T) \

# + log(observ_likelihood_3.T) + log(observ_likelihood_4.T)

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

# viterbi path decoding

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

#这里自己改了，只有第一个网络结构的，

log_observ_likelihood = log(observ_likelihood_1.T)

#这里是一个矩阵， 行是样本，列是概率 [1884个样本, 201列] 用T转置了

'''这时，矩阵是 【201行个id， 1884个样本列】

【-1，0：5】其实就是一个视频开始的前5帧 第201个动作id 的 log（概率） =0

log=0，那么概率=1， log（小于1）=负数。

'''

#（一个视频开始的前5帧，最后5帧无效）

log_observ_likelihood[-1, 0:5] = 0 # We force first and last 5 frames are in the ergodic state

#？？？？？？？？？为何是最后最后一行， 不是全部的？？？

log_observ_likelihood[-1, -5:] = 0#这 最后5帧也无效

#下面就是vibiter算法了

print("\t Viterbi path decoding " )

# do it in log space avoid numeric underflow

[path, predecessor_state_index, global_score] =viterbi_path_log(

log(Prior), log(Transition_matrix), log_observ_likelihood )

#[path, predecessor_state_index, global_score] = viterbi_path(Prior, Transition_matrix, observ_likelihood)

# Some gestures are not within the vocabulary

[pred_label, begin_frame, end_frame, Individual_score, frame_length] = viterbi_colab_clean(

path, global_score, threshold=-100, mini_frame=19)

#begin_frame = begin_frame-1

end_frame = end_frame + 3

### plot the path and prediction

##绘制结果， 默认false不执行

if False:

im = imdisplay(global_score)

plt.imshow(im, cmap='gray')

plt.plot(range(global_score.shape[-1]), path, color='c',linewidth=2.0)

plt.xlim((0, global_score.shape[-1]))

# plot ground truth

for gesture in gesturesList:

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

gestureID,startFrame,endFrame=gesture

frames_count = numpy.array(range(startFrame, endFrame+1))

pred_label_temp = ((gestureID-1) *10 +5) * numpy.ones(len(frames_count))

plt.plot(frames_count, pred_label_temp, color='r', linewidth=5.0)

# plot clean path

for i in range(len(begin_frame)):

frames_count = numpy.array(range(begin_frame[i], end_frame[i]+1))

pred_label_temp = ((pred_label[i]-1) *10 +5) * numpy.ones(len(frames_count))

plt.plot(frames_count, pred_label_temp, color='#ffff00', linewidth=2.0)

plt.show()

else:

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

pred=[]

for i in range(len(begin_frame)):

pred.append([ pred_label[i], begin_frame[i], end_frame[i]] )

#pred_label 是动作的标签， begin_frame end_frame是开始和结束

smp.exportPredictions(pred,outPred)

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

## delete the sample

del smp

# Prepare submision file (only for validation and final evaluation data sets)

createSubmisionFile(outPred, outSubmision)

#用zip文件保存结果