-
Notifications
You must be signed in to change notification settings - Fork 0
/
temporalnetmulticam.py
484 lines (431 loc) · 21.2 KB
/
temporalnetmulticam.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
from __future__ import print_function
from numpy.random import seed
seed(1)
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import os
from keras.models import Model, Sequential
from keras.layers import Input, Convolution2D, MaxPooling2D, Flatten, Activation, Dense, Dropout, ZeroPadding2D
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras import backend as K
K.set_image_dim_ordering('th')
from sklearn.metrics import confusion_matrix, accuracy_score
import h5py
import scipy.io as sio
import cv2
import glob
import gc
from keras.layers.advanced_activations import ELU
from keras.callbacks import EarlyStopping
from sklearn.metrics import roc_curve, auc
data_folder = '/ssd_drive/MultiCam_OF2/'
mean_file = '/ssd_drive/flow_mean.mat'
L = 10
num_features = 4096
def plot_training_info(case, metrics, save, history):
'''
Function to create plots for train and validation loss and accuracy
Input:
* case: name for the plot, an 'accuracy.png' or 'loss.png' will be concatenated after the name.
* metrics: list of metrics to store: 'loss' and/or 'accuracy'
* save: boolean to store the plots or only show them.
* history: History object returned by the Keras fit function.
'''
plt.ioff()
if 'accuracy' in metrics:
fig = plt.figure()
plt.plot(history['acc'])
plt.plot(history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
if save == True:
plt.savefig(case + 'accuracy.png')
plt.gcf().clear()
else:
plt.show()
plt.close(fig)
# summarize history for loss
if 'loss' in metrics:
fig = plt.figure()
plt.plot(history['loss'])
plt.plot(history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
#plt.ylim(1e-3, 1e-2)
plt.yscale("log")
plt.legend(['train', 'val'], loc='upper left')
if save == True:
plt.savefig(case + 'loss.png')
plt.gcf().clear()
else:
plt.show()
plt.close(fig)
def generator(list1, lits2):
'''
Auxiliar generator: returns the ith element of both given list with each call to next()
'''
for x,y in zip(list1,lits2):
yield x, y
def saveFeatures(feature_extractor, features_file, labels_file, features_key, labels_key):
'''
Function to load the optical flow stacks, do a feed-forward through the feature extractor (VGG16) and
store the output feature vectors in the file 'features_file' and the labels in 'labels_file'.
Input:
* feature_extractor: model VGG16 until the fc6 layer.
* features_file: path to the hdf5 file where the extracted features are going to be stored
* labels_file: path to the hdf5 file where the labels of the features are going to be stored
* features_key: name of the key for the hdf5 file to store the features
* labels_key: name of the key for the hdf5 file to store the labels
'''
class0 = 'Falls'
class1 = 'NotFalls'
# Load the mean file to subtract to the images
d = sio.loadmat(mean_file)
flow_mean = d['image_mean']
h5features = h5py.File(features_file,'w')
h5labels = h5py.File(labels_file,'w')
fall_videos = np.zeros((24,2), dtype=np.int)
i = 0
while i < 3:
fall_videos[i,:] = [i*7, i*7+7]
i += 1
fall_videos[i,:] = [i*7, i*7+14]
i += 1
while i < 23:
fall_videos[i,:] = [i*7, i*7+7]
i += 1
fall_videos[i,:] = [i*7, i*7]
not_fall_videos = np.zeros((24,2), dtype=np.int)
i = 0
while i < 23:
not_fall_videos[i,:] = [i*7, i*7+14]
i += 1
not_fall_videos[i,:] = [i*7, i*7+7]
stages = []
for i in range(1,25):
stages.append('chute{:02}'.format(i))
idx_falls, idx_nofalls = 0, 0
# For each stage7scenario
for stage, nb_stage in zip(stages, range(len(stages))):
h5features.create_group(stage)
h5labels.create_group(stage)
cameras = glob.glob(data_folder + stage + '/cam*')
cameras.sort()
for camera, nb_camera in zip(cameras, range(1, len(cameras)+1)):
h5features[stage].create_group('cam{}'.format(nb_camera))
h5labels[stage].create_group('cam{}'.format(nb_camera))
not_falls = glob.glob(camera + '/NotFalls/notfall*'.format(nb_camera))
not_falls.sort()
print(camera + '/NotFalls/notfall*'.format(nb_camera), len(not_falls))
for not_fall in not_falls:
label = 1
x_images = glob.glob(not_fall + '/flow_x*.jpg')
x_images.sort()
y_images = glob.glob(not_fall + '/flow_x*.jpg')
y_images.sort()
nb_stacks = int(len(x_images))-L+1
features_notfall = h5features[stage]['cam{}'.format(nb_camera)].create_dataset('notfall{:04}'.format(idx_nofalls), shape=(nb_stacks, num_features), dtype='float64')
labels_notfall = h5labels[stage]['cam{}'.format(nb_camera)].create_dataset('notfall{:04}'.format(idx_nofalls), shape=(nb_stacks, 1), dtype='float64')
idx_nofalls += 1
# NO FALL
flow = np.zeros(shape=(224,224,2*L,nb_stacks), dtype=np.float64)
gen = generator(x_images,y_images)
for i in range(len(x_images)):
flow_x_file, flow_y_file = gen.next()
img_x = cv2.imread(flow_x_file, cv2.IMREAD_GRAYSCALE)
img_y = cv2.imread(flow_y_file, cv2.IMREAD_GRAYSCALE)
for s in list(reversed(range(min(10,i+1)))):
if i-s < nb_stacks:
flow[:,:,2*s, i-s] = img_x
flow[:,:,2*s+1,i-s] = img_y
del img_x,img_y
gc.collect()
flow = flow - np.tile(flow_mean[...,np.newaxis], (1, 1, 1, flow.shape[3]))
flow = np.transpose(flow, (3, 2, 0, 1))
predictions = np.zeros((flow.shape[0], num_features), dtype=np.float64)
truth = np.zeros((flow.shape[0], 1), dtype=np.float64)
for i in range(flow.shape[0]):
prediction = feature_extractor.predict(np.expand_dims(flow[i, ...],0))
predictions[i, ...] = prediction
truth[i] = label
features_notfall[:,:] = predictions
labels_notfall[:,:] = truth
del predictions, truth, flow, features_notfall, labels_notfall, x_images, y_images, nb_stacks
gc.collect()
if stage == 'chute24':
continue
falls = glob.glob(camera + '/Falls/fall*'.format(nb_camera))
print(camera + '/Falls/fall*'.format(nb_camera), len(falls))
falls.sort()
h5features.close()
h5labels.close()
h5features = h5py.File(features_file,'a')
h5labels = h5py.File(labels_file,'a')
for fall in falls:
label = 0
x_images = glob.glob(fall + '/flow_x*.jpg')
x_images.sort()
y_images = glob.glob(fall + '/flow_y*.jpg')
y_images.sort()
nb_stacks = int(len(x_images))-L+1
features_fall = h5features[stage]['cam{}'.format(nb_camera)].create_dataset('fall{:04}'.format(idx_falls), shape=(nb_stacks, num_features), dtype='float64')
labels_fall = h5labels[stage]['cam{}'.format(nb_camera)].create_dataset('fall{:04}'.format(idx_falls), shape=(nb_stacks, 1), dtype='float64')
idx_falls += 1
flow = np.zeros(shape=(224,224,2*L,nb_stacks), dtype=np.float64)
gen = generator(x_images,y_images)
for i in range(len(x_images)):
flow_x_file, flow_y_file = gen.next()
img_x = cv2.imread(flow_x_file, cv2.IMREAD_GRAYSCALE)
img_y = cv2.imread(flow_y_file, cv2.IMREAD_GRAYSCALE)
for s in list(reversed(range(min(10,i+1)))):
if i-s < nb_stacks:
flow[:,:,2*s, i-s] = img_x
flow[:,:,2*s+1,i-s] = img_y
del img_x,img_y
gc.collect()
flow = flow - np.tile(flow_mean[...,np.newaxis], (1, 1, 1, flow.shape[3]))
flow = np.transpose(flow, (3, 2, 0, 1))
predictions = np.zeros((flow.shape[0], num_features), dtype=np.float64)
truth = np.zeros((flow.shape[0], 1), dtype=np.float64)
for i in range(flow.shape[0]):
prediction = feature_extractor.predict(np.expand_dims(flow[i, ...],0))
predictions[i, ...] = prediction
truth[i] = label
features_fall[:,:] = predictions
labels_fall[:,:] = truth
del predictions, truth, flow, features_fall, labels_fall
h5features.close()
h5labels.close()
def main(learning_rate, mini_batch_size, batch_norm, weight_0, epochs, model_file, weights_file):
# Name of the experiment
exp = 'lr{}_batchs{}_batchnorm{}_w0_{}'.format(learning_rate, mini_batch_size, batch_norm, w0)
# Path to the weights of the UCF101 pre-training for the VGG16
vgg_16_weights = 'weights.h5'
# Balance the number of positive and negative samples
save_plots = True
features_file = 'features_multicam.h5'
labels_file = 'labels_multicam.h5'
features_key = 'features'
labels_key = 'labels'
# Whether to save the features in a jdf5 file o use the available ones
save_features = True
# =============================================================================================================
# VGG-16 ARCHITECTURE
# =============================================================================================================
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(20, 224, 224)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, name='fc6', init='glorot_uniform'))
# =============================================================================================================
# WEIGHT INITIALIZATION
# =============================================================================================================
layerscaffe = ['conv1_1', 'conv1_2', 'conv2_1', 'conv2_2', 'conv3_1', 'conv3_2', 'conv3_3', 'conv4_1', 'conv4_2', 'conv4_3', 'conv5_1', 'conv5_2', 'conv5_3', 'fc6', 'fc7', 'fc8']
i = 0
h5 = h5py.File(vgg_16_weights)
layer_dict = dict([(layer.name, layer) for layer in model.layers])
# Copy the weights stored in the 'vgg_16_weights' file to the feature extractor part of the VGG16
for layer in layerscaffe[:-3]:
w2, b2 = h5['data'][layer]['0'], h5['data'][layer]['1']
w2 = np.transpose(np.asarray(w2), (0,1,2,3))
w2 = w2[:, :, ::-1, ::-1]
b2 = np.asarray(b2)
layer_dict[layer].W.set_value(w2)
layer_dict[layer].b.set_value(b2)
i += 1
# Copy the weights of the first fully-connected layer (fc6)
layer = layerscaffe[-3]
w2, b2 = h5['data'][layer]['0'], h5['data'][layer]['1']
w2 = np.transpose(np.asarray(w2), (1,0))
b2 = np.asarray(b2)
layer_dict[layer].W.set_value(w2)
layer_dict[layer].b.set_value(b2)
i += 1
# =============================================================================================================
# FEATURE EXTRACTION
# =============================================================================================================
if save_features:
saveFeatures(model, features_file, labels_file, features_key, labels_key)
# =============================================================================================================
# TRAINING
# =============================================================================================================
adam = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0005)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
e = EarlyStopping(monitor='val_loss', min_delta=0, patience=100, verbose=0, mode='auto')
do_training = True
compute_metrics = True
threshold = 0.5
if do_training:
h5features = h5py.File(features_file, 'r')
h5labels = h5py.File(labels_file, 'r')
# Load the data separated by cameras for cross-validation
stages = []
for i in range(1,25):
stages.append('chute{:02}'.format(i))
cams_x = []
cams_y = []
for stage, nb_stage in zip(stages, range(len(stages))):
for cam, nb_cam in zip(h5features[stage].keys(), range(8)):
temp_x = []
temp_y = []
for key in h5features[stage][cam].keys():
temp_x.append(np.asarray(h5features[stage][cam][key]))
temp_y.append(np.asarray(h5labels[stage][cam][key]))
temp_x = np.concatenate(temp_x,axis=0)
temp_y = np.concatenate(temp_y,axis=0)
if nb_stage == 0:
cams_x.append(temp_x)
cams_y.append(temp_y)
else:
cams_x[nb_cam] = np.concatenate([cams_x[nb_cam], temp_x], axis=0)
cams_y[nb_cam] = np.concatenate([cams_y[nb_cam], temp_y], axis=0)
# cams_x[nb_cam] contains all the optical flow stacks of the nb_cam camera
sensitivities = []
specificities = []
aucs = []
accuracies = []
# LEAVE-ONE-CAMERA-OUT CROSS-VALIDATION
for cam in range(8):
print('='*30)
print('LEAVE-ONE-OUT STEP {}/8'.format(cam+1))
print('='*30)
test_x = cams_x[cam]
test_y = cams_y[cam]
train_x = cams_x[0:cam] + cams_x[cam+1:]
train_y = cams_y[0:cam] + cams_y[cam+1:]
X = []
_y = []
# Balance the positive and negative samples
for cam_x, cam_y in zip(train_x, train_y):
all0 = np.asarray(np.where(cam_y==0)[0])
all1 = np.asarray(np.where(cam_y==1)[0])
all1 = np.random.choice(all1, len(all0), replace=False)
allin = np.concatenate((all0.flatten(),all1.flatten()))
allin.sort()
X.append(np.asarray(cam_x[allin,...]))
_y.append(np.asarray(cam_y[allin]))
X = np.asarray(np.concatenate(X,axis=0))
_y = np.asarray(np.concatenate(_y,axis=0))
X2 = np.asarray(test_x)
_y2 = np.asarray(test_y)
# ==================== CLASSIFIER ========================
extracted_features = Input(shape=(4096,), dtype='float32', name='input')
if batch_norm:
x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(extracted_features)
x = Activation('relu')(x)
else:
x = ELU(alpha=1.0)(extracted_features)
x = Dropout(0.9)(x)
x = Dense(4096, name='fc2', init='glorot_uniform')(x)
if batch_norm:
x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)
x = Activation('relu')(x)
else:
x = ELU(alpha=1.0)(x)
x = Dropout(0.8)(x)
x = Dense(1, name='predictions', init='glorot_uniform')(x)
x = Activation('sigmoid')(x)
classifier = Model(input=extracted_features, output=x, name='classifier')
classifier.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
# ==================== TRAINING ========================
# weighting of each class: only the fall class gets a different weight
class_weight = {0:weight_0, 1:1}
if mini_batch_size == 0:
history = classifier.fit(X, _y, validation_data=(X2, _y2), batch_size=X.shape[0], nb_epoch=epochs, shuffle=True, class_weight=class_weight, callbacks=[e])
else:
history = classifier.fit(X, _y, validation_data=(X2, _y2), batch_size=mini_batch_size, nb_epoch=epochs, shuffle=True, class_weight=class_weight, callbacks=[e])
plot_training_info(exp, ['accuracy', 'loss'], save_plots, history.history)
# ==================== EVALUATION ========================
if compute_metrics:
predicted = classifier.predict(X2)
print(len(predicted))
for i in range(len(predicted)):
if predicted[i] < threshold:
predicted[i] = 0
else:
predicted[i] = 1
# Array of predictions 0/1
predicted = np.asarray(predicted).astype(int)
# Compute metrics and print them
cm = confusion_matrix(_y2, predicted,labels=[0,1])
tp = cm[0][0]
fn = cm[0][1]
fp = cm[1][0]
tn = cm[1][1]
tpr = tp/float(tp+fn)
fpr = fp/float(fp+tn)
fnr = fn/float(fn+tp)
tnr = tn/float(tn+fp)
precision = tp/float(tp+fp)
recall = tp/float(tp+fn)
specificity = tn/float(tn+fp)
f1 = 2*float(precision*recall)/float(precision+recall)
accuracy = accuracy_score(_y2, predicted)
fpr, tpr, _ = roc_curve(_y2, predicted)
roc_auc = auc(fpr, tpr)
print('TP: {}, TN: {}, FP: {}, FN: {}'.format(tp,tn,fp,fn))
print('TPR: {}, TNR: {}, FPR: {}, FNR: {}'.format(tpr,tnr,fpr,fnr))
print('Sensitivity/Recall: {}'.format(recall))
print('Specificity: {}'.format(specificity))
print('Precision: {}'.format(precision))
print('F1-measure: {}'.format(f1))
print('Accuracy: {}'.format(accuracy))
print('AUC: {}'.format(roc_auc))
# Store the metrics for this epoch
sensitivities.append(tp/float(tp+fn))
specificities.append(tn/float(tn+fp))
aucs.append(roc_auc)
accuracies.append(accuracy)
print('LEAVE-ONE-OUT RESULTS ===================')
print("Sensitivity: %.2f%% (+/- %.2f%%)" % (np.mean(sensitivities), np.std(sensitivities)))
print("Specificity: %.2f%% (+/- %.2f%%)" % (np.mean(specificities), np.std(specificities)))
print("AUC: %.2f%% (+/- %.2f%%)" % (np.mean(aucs), np.std(aucs)))
print("accuracy: %.2f%% (+/- %.2f%%)" % (np.mean(accuracies), np.std(accuracies)))
if __name__ == '__main__':
if not os.path.exists('models'):
os.makedirs('models')
if not os.path.exists('weights'):
os.makedirs('weights')
model_file = 'models/exp_'
weights_file = 'weights/exp_'
batch_norm = True
learning_rate = 0.01
mini_batch_size = 1024
w0 = 1
epochs = 3000
main(learning_rate, mini_batch_size, batch_norm, w0, epochs, model_file, weights_file)