Step4_Test_CNN_viterbi.py

from glob import glob
import os
import sys
import cPickle
import scipy.io as sio  
import time

from classes import GestureSample
from functions.preproc_functions import *
from functions.test_functions import *


## Load Prior and transitional Matrix
dic=sio.loadmat('Prior_Transition_matrix_5states.mat')
Transition_matrix = dic['Transition_matrix']
Prior = dic['Prior']


#data path and store path definition
pc = "wudi"
if pc=="wudi":
    data = r"I:\Kaggle_multimodal\Training" # dir of original data -- note that wudi has decompressed it!!!
    obs_likelihodd_dir = r"I:\Kaggle_multimodal\Precompute_state_matrix"
    outPred=r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\CNN_valid_pred'
elif pc=="lio":
    data = r"/media/lio/Elements/chalearn/trainingset"


os.chdir(data)
if pc=="wudi":
        samples=glob("*")  # because wudi unzipped all the files already!
elif pc=="lio":
        samples=glob("*.zip")

print len(samples), "samples found"

for file_count, file in enumerate(samples):
    condition = (file_count >= 650)   
    if condition:   #wudi only used first 650 for validation !!! Lio be careful!
        print("\t Processing file " + file)
        time_tic = time.time() 
        # Create the object to access the sample
        sample = GestureSample(os.path.join(data,file))

        #load ober
        load_path = os.path.join(obs_likelihodd_dir,file)
        observ_likelihood = cPickle.load(open(load_path,"rb"))
        #print observ_likelihood.shape
        ##########################
        # viterbi path decoding
        ########################
        log_observ_likelihood = log(observ_likelihood.T + numpy.finfo(numpy.float32).eps)
        log_observ_likelihood[-1, 0:5] = 0
        log_observ_likelihood[-1, -5:] = 0

        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_states(path, global_score, state_no = 5, threshold=-2, mini_frame=19)

        #heuristically we need to add 1 more frame here
        begin_frame += 1 
        end_frame +=5 # because we cut 4 frames as a cuboid so we need add extra 4 frames 

        #plotting
        gesturesList=sample.getGestures()
        import matplotlib.pyplot as plt
        STATE_NO = 5
        im  = imdisplay(global_score)
        plt.clf()
        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) *STATE_NO +2) * 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) *STATE_NO +2) * numpy.ones(len(frames_count))
            plt.plot(frames_count, pred_label_temp, color='#ffff00', linewidth=2.0)

        if False:
            plt.show()
        else:     
            from pylab import savefig
            save_dir=r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\SK_path'
            save_path= os.path.join(save_dir,file)
            savefig(save_path, bbox_inches='tight')



        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]] )

       
        sample.exportPredictions(pred,outPred)
     # ###############################################
        ## delete the sample
        del sample
        
        
TruthDir=r'I:\Kaggle_multimodal\Code_for_submission\Final_project\training\gt'
final_score = evalGesture(outPred,TruthDir)         
print("The score for this prediction is " + "{:.12f}".format(final_score))

#The score for this prediction is 0.751634118181