import numpy as np
import sys
import pickle
import random
from PIL import Image
import cv2
from keras.utils import np_utils
import config
from sklearn.metrics import classification_report
from keras import backend as K
import math
server = config.server()
data_output_path = config.data_output_path()
data_folder_rgb = r'{}rgb/'.format(data_output_path)
data_folder_seq = r'{}seq3/'.format(data_output_path)
def getTrainData(keys, batch_size, classes, mode, train, opt_size, seq=False):
"""
mode 1: Single Stream
mode 2: Two Stream
mode 3: Multiple Stream
"""
while 1:
for i in range(0, len(keys), batch_size):
if not seq:
if mode == 1:
X_train, Y_train = stack_single_stream(
chunk=keys[i:i + batch_size],
opt_size=opt_size,
old_epochs=0,
cross_index=cross_index)
model.load_weights('weights-old/save-imp/{}_{}e_cr{}.h5'.format(
pre_file, 45, cross_index))
data_type = [0]
else:
print "Error stream"
result_model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
out_file = r'{}database/{}-test{}-split{}.pickle'.format(
config.data_output_path(), dataset, seq_len, cross_index)
with open(out_file, 'rb') as f2:
keys = pickle.load(f2)
if index > (len(keys) - 1):
print 'Out of number data test'
sys.exit()
class_file = 'data/{}-classInd.txt'.format(dataset)
classInd = []
with open(class_file) as f0:
for line in f0:
class_name = line.rstrip()
if class_name:
classInd.append(class_name)
