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showimages_collected.py
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showimages_collected.py
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from datetime import datetime
from dataloader.framesclass import ActionsDataLoader
from models.unet_acresnet import UNetAc
from models.vision import ResNet50Model
import numpy as np
import tensorflow as tf
import os
from scipy import signal
import matplotlib.pyplot as plt
import cv2
from scipy.io import loadmat
flags = tf.app.flags
slim = tf.contrib.slim
flags.DEFINE_string('model', None, 'Model type, it can AudioCoeff')
flags.DEFINE_string('datatype', 'frames', 'music or outdoor or old')
flags.DEFINE_string('train_file', None, 'File for training data')
flags.DEFINE_string('init_checkpoint', None, 'Checkpoint file for model initialization')
flags.DEFINE_integer('batch_size', 4, 'Batch size choose')
flags.DEFINE_integer('plot', 1, 'plot')
flags.DEFINE_integer('nr_frames', 1, 'Number of frames') # 12*FLAGS.sample_length max
flags.DEFINE_integer('sample_length', 1, 'Length in seconds of a sequence sample')
flags.DEFINE_float('threshold', 0.1, 'threshold')
FLAGS = flags.FLAGS
'''Show energy for image with 2 objects'''
def main(_):
plotdecodeimages()
def plotdecodeimages():
dataset = FLAGS.train_file.split('/')[-1]
dataset = dataset.split('.')[0]
s = FLAGS.init_checkpoint.split('/')[-1]
name = (s.split('_')[1]).split('.ckpt')[0]
name = '{}_{}_{}_{}'.format(FLAGS.model, dataset, 'AcousticFrames', name)
data_dir = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [name])
random_pick = True
build_spectrogram = True
normalize = False
# Create data loaders according to the received program arguments
print('{} - Creating data loaders'.format(datetime.now()))
modalities = []
modalities.append(1)
modalities.append(2)
plot = FLAGS.plot
threshold = FLAGS.threshold
with tf.device('/cpu:0'):
train_data = ActionsDataLoader(FLAGS.train_file, 'testing', batch_size=FLAGS.batch_size, num_epochs=1, sample_length=1,
datakind=FLAGS.datatype, buffer_size=10, shuffle=False, embedding=1,
normalize=normalize, build_spectrogram=build_spectrogram, correspondence=0,
random_pick=random_pick, modalities=modalities, nr_frames=1)
# Build model
print('{} - Building model'.format(datetime.now()))
with tf.device('/gpu:0'):
model = UNetAc(input_shape=[36, 48, 12])
model_video = ResNet50Model(input_shape=[224, 298, 3], num_classes=None)
handle = tf.placeholder(tf.string, shape=())
iterator = tf.data.Iterator.from_string_handle(handle, train_data.data.output_types,
train_data.data.output_shapes)
train_iterat = train_data.data.make_initializable_iterator()
next_batch = iterator.get_next()
mfcc = tf.reshape(next_batch[1], shape=[-1, 12])
images = tf.reshape(next_batch[2], shape=[-1, 224, 298, 3])
acoustic = tf.reshape(next_batch[0], shape=[-1, 36, 48, 12])
classname = tf.reshape(next_batch[3], shape=[-1, 1])
# mfcc = mfcc - tf.reduce_min(mfcc, axis=[1], keep_dims=True)
# mfcc = mfcc / tf.reduce_max(mfcc, axis=[1], keep_dims=True)
mfccmap = tf.reshape(mfcc, (-1, 1, 12))
mfccmap = tf.tile(mfccmap, (1, 36 * 48, 1))
mfccmap = tf.reshape(mfccmap, (-1, 36, 48, 12))
model_video._build_model(images)
model._build_model(mfccmap, model_video.output)
output = model.output
var_list1 = slim.get_variables(model_video.scope + '/')
var_list2 = slim.get_variables(model.scope + '/')
var_list = var_list2 + var_list1
if os.path.exists(data_dir):
print("Features already computed!")
else:
os.makedirs(data_dir) # mkdir creates one directory, makedirs all intermediate directories
total_size = 0
batch_count = 0
num = 0
classeslist = ['Train', 'Boat', 'Drone', 'Fountain', 'Drill',
'Razor', 'Hair dryer', 'Vacuumcleaner', 'Cart', 'Traffic']
print('{} - Starting'.format(datetime.now()))
with tf.Session(
config=tf.ConfigProto(allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True))) as session:
train_handle = session.run(train_iterat.string_handle())
# Initialize student model
if FLAGS.init_checkpoint is None:
print('{} - Initializing student model'.format(datetime.now()))
model.init_model(session, FLAGS.init_checkpoint)
print('{} - Done'.format(datetime.now()))
else:
print('{} - Restoring student model'.format(datetime.now()))
saver = tf.train.Saver(var_list=var_list)
saver.restore(session, FLAGS.init_checkpoint)
print('{} - Done'.format(datetime.now()))
#variables_in_checkpoint = tf.train.list_variables('path.ckpt')
session.run(train_iterat.initializer)
while True:
try:
reconstructed, im, cl = session.run(
[output, images, classname],
feed_dict={handle: train_handle,
model.network['keep_prob']: 1.0,
model.network['is_training']: 0,
model_video.network['keep_prob']: 1.0,
model_video.network['is_training']: 0
})
total_size += reconstructed.shape[0]
if plot:
for h in range(np.shape(reconstructed)[0]):
# original
# draw rectangles around contours
m = np.zeros((3, 224, 298), dtype=np.float32)
mtot = np.sum(m, axis=0)
mtot[mtot > 1.0] = 1.0
# reconstructed
imgray = cv2.cvtColor(im[h], cv2.COLOR_BGR2GRAY)
plt.imshow(imgray, cmap=plt.cm.gray)
map2 = find_logen(reconstructed[h])
mean2 = np.mean(map2)
std2 = np.std(map2)
m2 = 1 * (map2 > mean2)
m2 = cv2.resize(m2 * 1.0, (298, 224))
m2 = 1.0*(m2>0.5)
map2 = cv2.resize(map2 * 1.0, (298, 224))
plt.imshow(map2, cmap=plt.cm.viridis, alpha=0.7)
plt.suptitle(classeslist[cl[h][0]])
plt.axis('off')
outImage_path = '{}/{}_images_{}.png'.format(data_dir, dataset, num)
plt.savefig(outImage_path)
plt.clf()
num = num + 1
print(total_size)
except tf.errors.OutOfRangeError:
break
batch_count += 1
def find_logen(mfcc):
mfcc = np.reshape(mfcc, (-1, 12))
# lo_freq = 0
# hi_freq = 6400
lifter_num = 22
filter_num = 24
mfcc_num = 12
# fft_len = 512
# filter_mat = createfilters(fft_len, filter_num, lo_freq, hi_freq, 2 * hi_freq)
dct_base = np.zeros((filter_num, mfcc_num))
for m in range(mfcc_num):
dct_base[:, m] = np.cos((m + 1) * np.pi / filter_num * (np.arange(filter_num) + 0.5))
lifter = 1 + (lifter_num / 2) * np.sin(np.pi * (1 + np.arange(mfcc_num)) / lifter_num)
mfnorm = np.sqrt(2.0 / filter_num)
# lifter
mfcc /= np.expand_dims(lifter, 0)
mfcc *= mfnorm
dct_transpose = np.transpose(dct_base)#np.linalg.pinv(dct_base)
melspec = np.dot(mfcc, dct_transpose)
# dct_logen = np.cos((1) * np.pi / filter_num * (np.arange(filter_num) + 0.5))
# logen = np.dot(melspec, dct_logen)
melspec = np.exp(melspec)
# filter_mat_pi = np.linalg.pinv(filter_mat)
# beam = np.dot(melspec, filter_mat_pi)
sumexpenergies = np.sum(melspec, -1)
sumexpenergies = 1/sumexpenergies
map = np.reshape(sumexpenergies, (36, 48))
return map
if __name__ == '__main__':
flags.mark_flags_as_required(['train_file'])
tf.app.run()