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Step1_3DCNN_normalize_by_sk.py
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Step1_3DCNN_normalize_by_sk.py
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#-------------------------------------------------------------------------------
# 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 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("I:\Kaggle_multimodal\Training\\")
# Get the list of training samples
samples=os.listdir(data)
STATE_NO = 10
batch_num = 25
# 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
###############################
### 155 complete noise, need to exclude for training example for all! Too much reflection
for file_count, file in enumerate(samples):
time_tic = time.time()
if (file_count<650 and file_count!=155 and file_count!=538 and file_count>480):
print("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)
#shift, scale = smp.get_shift_scale_user_sk( start_frame=total_frame-100, end_frame=total_frame-10)
shift, scale = smp.get_shift_scale_sk( start_frame=total_frame-100, end_frame=total_frame-10)
print ("shift: {} scaling: {:.4}".format(numpy.array(shift), scale))
if numpy.isnan(scale) or scale>3:
scale = 1
for gesture in gesturesList:
#for gesture in [gesturesList[0]]: # for examine purposes
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):
[img, flag] = smp.get_shift_scale_depth_sk_normalize(shift, scale, x, IM_SZ, show_flag=False)
cuboid[:, :, frame_count_temp] = imresize(img, (IM_SZ,IM_SZ))
frame_count_temp +=1
if True: # show the segmented images here
cv2.imshow('image',img)
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 = '.\ConvNet_3DCNN\storage_sk_normalize\\'
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