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Step4_combine_sk_cnn.py
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Step4_combine_sk_cnn.py
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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!!!
cnn_obs_likelihodd_dir = r"I:\Kaggle_multimodal\Precompute_state_matrix"
sk_obs_likelihood_dir = r"I:\Kaggle_multimodal\SK_threshold"
outPred=r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\CNN_valid_pred_combine_sk_cnn'
outPred = r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\valid_sk_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"
#TL = [-5, -10, -numpy.inf] # is 0.739770037290, 0.772290750017, 0.807449899917
#Alpha = [0.25, 0.5, 0.75] # 0.807809390788, 0.811087250153, 0.772292829366
for alpha in ([0]):
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 obser cnn
load_path = os.path.join(cnn_obs_likelihodd_dir,file)
observ_likelihood_cnn = cPickle.load(open(load_path,"rb"))
observ_likelihood_cnn = observ_likelihood_cnn[:-1,:]
#load obser sk --log!
load_path = os.path.join(sk_obs_likelihood_dir,file)
observ_likelihood_file = cPickle.load(open(load_path,"rb"))
observ_likelihood_sk = observ_likelihood_file['log_observ_likelihood']
#print observ_likelihood.shape
##########################
# viterbi path decoding
########################
#log_observ_likelihood = log(observ_likelihood_cnn.T + numpy.finfo(numpy.float32).eps) + observ_likelihood_sk
log_observ_likelihood = alpha * log(observ_likelihood_cnn.T + numpy.finfo(numpy.float32).eps) + (1-alpha) *observ_likelihood_sk
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=-numpy.inf, mini_frame=19)
#heuristically we need to add 1 more frame here
begin_frame += 1
end_frame +=4 # because we cut 4 frames as a cuboid so we need add extra 4 frames
#plotting
if False:
gesturesList=sample.getGestures()
import matplotlib.pyplot as plt
STATE_NO = 5
im = imdisplay(global_score)
fig = plt.figure()
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]))
plt.ylim([101,0])
plt.yticks([0, 50, 101])
# 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
import matplotlib.pyplot as pl
save_dir=r'C:\Users\PC-User\Documents\GitHub\chalearn2014_wudi_lio\path_combined'
save_path= os.path.join(save_dir,file)
savefig(save_path+'.eps', format='eps', bbox_inches='tight')
#plt.show()
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))
#CNN: 0.751634118181, SK: 0.782659033771, combined: 0.817439832426