Initial Commit

stcae_test.py

+from seq_exp import *
+import time
+
+if __name__ == "__main__":
+
+        start_time = time.time()
+
+        ##--Set path of learned model!
+        # One of Fall Detection or Intrusion Detection
+
+        ##---Fall Detection----
+        #pre_load = 'Models/Thermal_Fall/DSTCAE_UpSamp.h5'
+        pre_load = 'Models/Thermal_Fall/DSTCAE_Deconv.h5'
+        #pre_load = 'Models/Thermal_Fall/DSTCAE_C3D.h5'
+
+        ##---Intrusion Detection----
+        # models not released for privacy concerns
+        #pre_load = 'Models/Thermal_Intrusion/DSTCAE_UpSamp.h5'
+        #pre_load = 'Models/Thermal_Intrusion/DSTCAE_Deconv.h5'
+        #pre_load = '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'
+        RE = 'x_mean' # for cross context mean: r\mu in paper
+        #RE = 'x_std' # for cross context std: r\sigma in paper
+
+        ##--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 pre_load == None:
+                print('No model path given, please update pre_load variable in dstcae_c3d_main_test.py')
+
+        else:
+                dstcae_ae_exp = SeqExp(pre_load = pre_load, 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))
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