diff --git a/AEComparisons/scores/Thermal_Dummy/DSTCAE_UpSamp-r_sigma_all_videos.csv b/AEComparisons/scores/Thermal_Dummy/DSTCAE_UpSamp-r_sigma_all_videos.csv
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@@ -0,0 +1,2 @@
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+0.57,0.18
diff --git a/AEComparisons/scores/Thermal_Dummy/DSTCAE_UpSamp-r_sigma_per_video.csv b/AEComparisons/scores/Thermal_Dummy/DSTCAE_UpSamp-r_sigma_per_video.csv
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@@ -0,0 +1,5 @@
+AUROC:r_sigma(per_video),AUPR:r_sigma(per_video)
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Binary files /dev/null and b/Models/Thermal_Dummy/DSTCAE_UpSamp.h5 differ
diff --git a/logs/Thermal_Dummy/DSTCAE_C3D-training-1596807119.192993.log b/logs/Thermal_Dummy/DSTCAE_C3D-training-1596807119.192993.log
new file mode 100644
index 0000000000000000000000000000000000000000..829247d715da1e9bd250d10e536f95d246bbf337
--- /dev/null
+++ b/logs/Thermal_Dummy/DSTCAE_C3D-training-1596807119.192993.log
@@ -0,0 +1,3 @@
+epoch,loss
+0,0.017104100435972214
+1,0.016974546015262604
diff --git a/logs/Thermal_Dummy/DSTCAE_Deconv-training-1596807109.180957.log b/logs/Thermal_Dummy/DSTCAE_Deconv-training-1596807109.180957.log
new file mode 100644
index 0000000000000000000000000000000000000000..73f6e1d028c84b4a8e1142133bd92005376187a4
--- /dev/null
+++ b/logs/Thermal_Dummy/DSTCAE_Deconv-training-1596807109.180957.log
@@ -0,0 +1,3 @@
+epoch,loss
+0,0.016834937036037445
+1,0.01682722195982933
diff --git a/logs/Thermal_Dummy/DSTCAE_UpSamp-training-1596807034.75265.log b/logs/Thermal_Dummy/DSTCAE_UpSamp-training-1596807034.75265.log
new file mode 100644
index 0000000000000000000000000000000000000000..2b4ce0e2c1dc6bc584e7460b84e11522e1ae2506
--- /dev/null
+++ b/logs/Thermal_Dummy/DSTCAE_UpSamp-training-1596807034.75265.log
@@ -0,0 +1,3 @@
+epoch,loss
+0,0.017667459324002266
+1,0.017270581796765327
diff --git a/npData/Thermal_Dummy/training_data-imgdim_64x64-win_8.npy b/npData/Thermal_Dummy/training_data-imgdim_64x64-win_8.npy
new file mode 100644
index 0000000000000000000000000000000000000000..d6fa983abc6e190fe45a9b9964f76d3a0ecb9875
Binary files /dev/null and b/npData/Thermal_Dummy/training_data-imgdim_64x64-win_8.npy differ
diff --git a/stcae_test.py b/stcae_test.py
index 0b9aeebd5dafd895f52ba55cc0fa7ffe37f07b00..d443fa09d2485cdda953a8ef54b292061c7667aa 100644
--- a/stcae_test.py
+++ b/stcae_test.py
@@ -3,53 +3,59 @@ import time
if __name__ == "__main__":
- start_time = time.time()
-
- ##----Set path of learned model-----
- # Either Fall Detection or Intrusion Detection
-
- ##----Fall Detection----
- #learned_model = 'Models/Thermal_Fall/DSTCAE_UpSamp.h5'
- #learned_model = 'Models/Thermal_Fall/DSTCAE_Deconv.h5'
- learned_model = 'Models/Thermal_Fall/DSTCAE_C3D.h5'
-
- ##----Intrusion Detection----
- # models not released for privacy concerns
- #learned_model = 'Models/Thermal_Intrusion/DSTCAE_UpSamp.h5'
- #learned_model = 'Models/Thermal_Intrusion/DSTCAE_Deconv.h5'
- #learned_model = 'Models/Thermal_Intrusion/DSTCAE_C3D.h5'
-
- ##--Choose dataset (corresponding to model intrusion or fall)
- #dset = 'Thermal_Intrusion'
- dset = 'Thermal_Fall'
-
- ##--Choose evaluation measure
- #RE = 'r_sigma'
- RE = 'r_mu'
- #RE = 'r'
-
- ##--Evaluation type : per_video or all videos
- ## per-video not allowed for Intrusion detection -> because we have videos with only non-intrusion also.
- ## This helps test our model in only non-intrusion classes also. But this raises error more than 1 class is needed to
- ## calculate AUROC/AUPR
- ## Note: should be used in case of animation of a intrusion video
-
- evaluation_type = 'per_video' # not for intrusion case (except if you want an animation of video)
- #evaluation_type = 'all_videos'
-
- ## Optional: Animation per video
- do_animate = False
- use_indicative_threshold = False
-
- ##--Set frame and window size
- img_width, img_height, win_len = 64, 64, 8
-
- if learned_model == None:
- print('No model path given, please update learned_model variable in dstcae_c3d_main_test.py')
- else:
- dstcae_ae_exp = SeqExp(pre_load=learned_model, dset=dset, win_len=win_len, img_width=img_width, img_height=img_height)
- print(dstcae_ae_exp.model.summary())
- print(dstcae_ae_exp.model.optimizer.get_config())
- dstcae_ae_exp.test(eval_type=evaluation_type, RE_type=RE, animate=do_animate, indicative_threshold_animation=use_indicative_threshold)
-
- print("Total Time %.2f s or %.2f mins" % (time.time() - start_time, (time.time() - start_time) / 60))
\ No newline at end of file
+ start_time = time.time()
+
+ '''----Set path of learned model-----
+ Either Fall Detection or Intrusion Detection'''
+
+ '''----Dummy Fall Detection (for demonstration)----'''
+ learned_model = 'Models/Thermal_Dummy/DSTCAE_UpSamp.h5'
+ #learned_model = 'Models/Thermal_Dummy/DSTCAE_Deconv.h5'
+ #learned_model = 'Models/Thermal_Dummy/DSTCAE_C3D.h5'
+
+ '''----Fall Detection----'''
+ #learned_model = 'Models/Thermal_Fall/DSTCAE_UpSamp.h5'
+ #learned_model = 'Models/Thermal_Fall/DSTCAE_Deconv.h5'
+ #learned_model = 'Models/Thermal_Fall/DSTCAE_C3D.h5'
+
+ '''----Intrusion Detection---'''
+ ''' models not released for privacy concerns'''
+ #learned_model = 'Models/Thermal_Intrusion/DSTCAE_UpSamp.h5'
+ #learned_model = 'Models/Thermal_Intrusion/DSTCAE_Deconv.h5'
+ #learned_model = 'Models/Thermal_Intrusion/DSTCAE_C3D.h5'
+
+ '''--Choose dataset (corresponding to model intrusion or fall)'''
+ dset = 'Thermal_Dummy'
+ #dset = 'Thermal_Fall'
+ #dset = 'Thermal_Intrusion'
+
+
+ '''--Choose evaluation measure'''
+ RE = 'r_sigma'
+ #RE = 'r_mu'
+ #RE = 'r'
+
+ '''--Evaluation type : per_video or all videos
+ -> per-video not allowed for Intrusion detection because we have videos with only non-intrusion also.
+ This helps test our model in only non-intrusion classes also. But this raises error more than 1 class is needed to calculate AUROC/AUPR
+ -> Note: per_video should be used in case of animation of an intrusion or fall video'''
+
+ #evaluation_type = 'per_video' # not for intrusion case (except if you want an animation of video)
+ evaluation_type = 'all_videos'
+
+ ''' Optional: Animation per video '''
+ do_animate = False
+ use_indicative_threshold = False
+
+ '''Set frame and window size '''
+ img_width, img_height, win_len = 64, 64, 8
+
+ if learned_model == None:
+ print('No model path given, please update learned_model variable in dstcae_c3d_main_test.py')
+ else:
+ dstcae_exp = SeqExp(pre_load=learned_model, dset=dset, win_len=win_len, img_width=img_width, img_height=img_height)
+ print(dstcae_exp.model.summary())
+ print(dstcae_exp.model.optimizer.get_config())
+ dstcae_exp.test(eval_type=evaluation_type, RE_type=RE, animate=do_animate, indicative_threshold_animation=use_indicative_threshold)
+
+ print("Total Time %.2f s or %.2f mins" % (time.time() - start_time, (time.time() - start_time) / 60))
\ No newline at end of file
diff --git a/stcae_train.py b/stcae_train.py
index ab57dca76e14af4b336e9bce1f37d59558e07812..c3b213c9a1adec3b89ee57bf2bc64186df4e69a1 100644
--- a/stcae_train.py
+++ b/stcae_train.py
@@ -3,33 +3,34 @@ from models import *
from seq_exp import SeqExp
if __name__ == "__main__":
- '''
+ '''
These are the training setting.
- '''
- start_time = time.time()
+ '''
+ start_time = time.time()
- ##--Choose dataset (corresponding to model intrusion or fall)
- #dset = 'Thermal_Fall'
- #dset = 'Thermal_Intrusion'
+ #----Choose dataset (corresponding to model intrusion or fall)
+ dset = 'Thermal_Dummy'# Dummy fall dataset to demonstrate
+ #dset = 'Thermal_Fall'
+ #dset = 'Thermal_Intrusion'
- img_width, img_height, win_len, epochs = 64, 64, 8, 500
+ img_width, img_height, win_len, epochs = 64, 64, 8, 2
- # Model to train
- model, model_name, model_type = DSTCAE_UpSampling(img_width, img_height, win_len)
- #model, model_name, model_type = DSTCAE_Deconv(img_width, img_height, win_len)
- #model, model_name, model_type = DSTCAE_C3D(img_width, img_height, win_len)
+ #----Model to train-------
+ model, model_name, model_type = DSTCAE_UpSampling(img_width, img_height, win_len)
+ #model, model_name, model_type = DSTCAE_Deconv(img_width, img_height, win_len)
+ #model, model_name, model_type = DSTCAE_C3D(img_width, img_height, win_len)
- print('model loaded')
- print(model.summary())
- print(model.optimizer.get_config())
+ print('model architecture loaded')
+ print(model.summary())
+ print(model.optimizer.get_config())
- exp_3D = SeqExp(model = model, model_name = model_name, epochs = epochs, win_len = win_len, dset = dset,
- img_width = img_width, img_height = img_height, batch_size=32)
+ dstcae_exp = SeqExp(model=model, model_name=model_name, epochs=epochs, win_len=win_len, dset=dset,
+ img_width=img_width, img_height=img_height, batch_size=32)
- exp_3D.set_train_data()
- print(exp_3D.train_data.shape)
- print('data loaded')
+ dstcae_exp.set_train_data()
+ print(dstcae_exp.train_data.shape)
+ print('data loaded')
- exp_3D.train()
+ dstcae_exp.train()
- print("Total Time %.2f s or %.2f mins" % (time.time() - start_time, (time.time() - start_time) / 60))
\ No newline at end of file
+ print("Total Time %.2f s or %.2f mins" % (time.time() - start_time, (time.time() - start_time) / 60))