def main():
""" Main script. Show how to perform all competition steps """
# Data folder (Training data)
data='./data/';
# Train folder (output)
outTrain='./training/train/'
# Test folder (output)
outTest='./training/test/'
# Predictions folder (output)
outPred='./results/step1all/';
# Ground truth folder (output)
outGT='./GT/';
# Submision folder (output)
outSubmision='./submision/'
# Divide data into train and test
# createDataSets(data,outTrain,outTest,0.3);
# Learn your model
# if os.path.exists("model.npy"):
# model=numpy.load("model.npy");
# else:
# model=learnModel(outTrain);
# numpy.save("model",model);
# # Predict over test dataset
# predict(model,outTest,outPred);
# # Create evaluation gt from labeled data
# exportGT_Gesture(outTest,outGT);
# Evaluate your predictions
score=evalGesture(outPred, outGT);
print("The score for this prediction is " + "{:.12f}".format(score));
def main():
prediction_dir = r'I:\Kaggle_multimodal\StartingKit_track3\CoDaLab_Gesure_track3\matlab\prediction_650_conv'
#prediction_dir = r'I:\Kaggle_multimodal\StartingKit_track3\CoDaLab_Gesure_track3\matlab\prediction_650'
#truth_dir = r'I:\Kaggle_multimodal\validation_labels'
truth_dir = r'I:\Kaggle_multimodal\validation'
final_score = evalGesture(prediction_dir, truth_dir)
print "final_score " + str(final_score)
def main(): prediction_dir = r'I:\Kaggle_multimodal\StartingKit_track3\CoDaLab_Gesure_track3\matlab\prediction_650_conv' #prediction_dir = r'I:\Kaggle_multimodal\StartingKit_track3\CoDaLab_Gesure_track3\matlab\prediction_650' #truth_dir = r'I:\Kaggle_multimodal\validation_labels' truth_dir = r'I:\Kaggle_multimodal\validation' final_score = evalGesture(prediction_dir,truth_dir) print "final_score "+str(final_score)
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]])
smp.exportPredictions(pred, outPred)
# ###############################################
## delete the sample
del smp
TruthDir = './training/gt/'
final_score = evalGesture(outPred, TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# Submision folder (output)
outSubmision = './training/submision/'
# Prepare submision file (only for validation and final evaluation data sets)
createSubmisionFile(outPred, outSubmision)
from ChalearnLAPEvaluation import evalGesture
predPath=r'.\training\test/'
predPath=r'.\ConvNet_3DCNN\training\Test_3DCNN_ConvNet__2014-06-25_18.21.33_250/'
#predPath=r'I:\Kaggle_multimodal\Code_for_submission\Final_project\ConvNet_3DCNN\training\pred_sk_norm'
#predPath=r'I:\Kaggle_multimodal\StartingKit_track3\Final_project\training\test_combined/'
TruthDir=r'I:\Kaggle_multimodal\ChalearnLAP2104_EvaluateTrack3\input/ref/'
#TruthDir=r'I:\Kaggle_multimodal\Valid_650_labels/'
final_score = evalGesture(predPath,TruthDir, begin_add=0, end_add=0)
print("The score for this prediction is " + "{:.12f}".format(final_score))
#The score for this prediction is 0.816150551922--combined
#The score for this prediction is 0.787309630209--skeleton
cPickle.dump(dic, out_file, protocol=cPickle.HIGHEST_PROTOCOL)
out_file.close()
pred=[]
for i in range(len(begin_frame)):
pred.append([ pred_label[i], begin_frame[i], end_frame[i]] )
smp.exportPredictions(pred,outPred)
# ###############################################
## delete the sample
del smp
TruthDir=r'I:\Kaggle_multimodal\ChalearnLAP2104_EvaluateTrack3\input\ref//'
CNN_NAME = 'ConvNet__2014-05-28_01.59.00_150'
outPred='./ConvNet_3DCNN/training/Test_3DCNN_' + CNN_NAME
final_score = evalGesture(outPred,TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# Submision folder (output)
outSubmision='./training/submision/'
# Prepare submision file (only for validation and final evaluation data sets)
createSubmisionFile(outPred, outSubmision)
#ConvNet__2014-06-25_18.21.33_250 The score for this prediction is 0.637141036068
#ConvNet__2014-06-25_18.56.59_162 The score for this prediction is 0.679523343823
#ConvNet__2014-05-28_01.59.00_150 The score for this prediction is 0.702863204283
#'I:\Kaggle_multimodal\StartingKit_track3\Final_project\ConvNet_3DCNN\tmp\ConvNet__2014-05-26_03.40.18'
# The score for this prediction is 0.702863204283
plt.plot(frames_count, pred_label_temp, color='#ffff00', linewidth=2.0)
from pylab import *
if True:
save_dir=r'.\ConvNet_3DCNN\training\Depth_path_combined'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path= os.path.join(save_dir,file)
savefig(save_path, bbox_inches='tight')
#plt.show()
else:
plt.show()
#def exportPredictions(self, prediction,predPath):
""" Export the given prediction to the correct file in the given predictions path """
if not os.path.exists(predPath):
os.makedirs(predPath)
output_filename = os.path.join(predPath, file + '_prediction.csv')
output_file = open(output_filename, 'wb')
for row in prediction:
output_file.write(repr(int(row[0])) + "," + repr(int(row[1])) + "," + repr(int(row[2])) + "\n")
output_file.close()
TruthDir='./training/gt/'
final_score = evalGesture(predPath,TruthDir)
print("The score for this prediction is " + "{:.12f}".format(final_score))
# The score for this prediction is 0.830624726889!!!!!!!!!! Combine two depth!!!!!
from ChalearnLAPEvaluation import evalGesture
predPath = r'.\training\test/'
predPath = r'.\ConvNet_3DCNN\training\Test_3DCNN_ConvNet__2014-06-25_18.21.33_250/'
#predPath=r'I:\Kaggle_multimodal\Code_for_submission\Final_project\ConvNet_3DCNN\training\pred_sk_norm'
#predPath=r'I:\Kaggle_multimodal\StartingKit_track3\Final_project\training\test_combined/'
TruthDir = r'I:\Kaggle_multimodal\ChalearnLAP2104_EvaluateTrack3\input/ref/'
#TruthDir=r'I:\Kaggle_multimodal\Valid_650_labels/'
final_score = evalGesture(predPath, TruthDir, begin_add=0, end_add=0)
print("The score for this prediction is " + "{:.12f}".format(final_score))
#The score for this prediction is 0.816150551922--combined
#The score for this prediction is 0.787309630209--skeleton
#input_dir = sys.argv[1]
#output_dir = sys.argv[2]
input_dir = './'
output_dir = './result'
submit_dir = os.path.join(input_dir, 'prediction')
truth_dir = os.path.join(input_dir, 'reference')
print(submit_dir)
print(truth_dir)
if not os.path.isdir(submit_dir):
print("%s doesn't exist" % submit_dir)
if os.path.isdir(submit_dir) and os.path.isdir(truth_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_filename = os.path.join(output_dir, 'scores.txt')
output_file = open(output_filename, 'w')
# Call evaluation for this track
score = evalGesture(submit_dir, truth_dir)
print("Score: %f" % score)
# Store the score
output_file.write("Overlap: %f" % score)
output_file.close()
