def generatedata(path,feature_extractor):
x_train=[]
y_train=[]
for video_file in tqdm(glob.glob(path)):
video_name = os.path.basename(video_file).split('.')[0]
if video_name[0:6] == "Normal":
y_train.append(0)
else:
y_train.append(1)
video_clips, num_frames = get_video_clips(video_file)
rgb_features = []
for i, clip in enumerate(video_clips):
clip = np.array(clip)
if len(clip) < params.frame_count:
continue
clip = preprocess_input(clip)
rgb_feature = feature_extractor.predict(clip)[0]
rgb_features.append(rgb_feature)
rgb_features = np.array(rgb_features)
rgb_feature_bag = interpolate(rgb_features, params.features_per_bag)
x_train.append(rgb_feature_bag)
return np.array(x_train),y_train
def run_demo():
sample_video_path = './testing/*.mp4'
# read video
score_TT = 0
score_TF = 0
score_FF = 0
score_FT = 0
step = 0
real_n = 0
real_a = 0
for video_file in glob.glob(sample_video_path):
step += 1
print("\nStep : ",step)
video_name = os.path.basename(video_file).split('.')[0]
print("Video_name : ",video_name)
if video_name[0:6] == "Normal":
real_n += 1
else:
real_a += 1
video_clips, num_frames = get_video_clips(video_file)
print("Number of clips in the video : ", len(video_clips))
rgb_features = []
print("\tprocessing clip ...")
for i, clip in enumerate(video_clips):
clip = np.array(clip)
if len(clip) < params.frame_count:
continue
clip = preprocess_input(clip)
rgb_feature = feature_extractor.predict(clip)[0]
rgb_features.append(rgb_feature)
rgb_features = np.array(rgb_features)
# bag features
rgb_feature_bag = interpolate(rgb_features, params.features_per_bag)
# classify using the trained classifier model
leng,widt=rgb_feature_bag.shape
predictions = model.predict(rgb_feature_bag.reshape(1,leng,widt))
predictions = np.array(predictions).squeeze()
if predictions> 0.5:
print("*** anomaly video ***")
if video_name[0:6] == "Normal":
score_TF += 1
else:
score_FF += 1
else:
if video_name[0:6] == "Normal":
score_TT += 1
else:
score_FT += 1
print(' ** Normal video ** ')
print("Number of Video files = ",step)
print("Number of Actual-Normal-Videos = ",real_n,"Number of Predicted Normal Videos = ",score_TT + score_FT)
print("Number of Actual-Anomaly Videos = ",real_a,"Number of Predicted Anomaly Videos = ",score_TF + score_FF)
print(" ** Accuaracy of Prediction ** ")
if real_n!=0:
print(" TT = ",round(score_TT/real_n,2),"TF = ",round(score_TF/real_n,2))
if real_a!=0:
print(" FT = ",round(score_FT/real_a,2),"FF = ",round(score_FF/real_a,2))
Accuracy = (score_TT + score_FF)/(score_TT+ score_TF+ score_FT+ score_FF)
print("Accuracy = ",Accuracy)
normal_videos.sort()
print("Processing normal videos...")
for vid_name in normal_videos:
print("Processing {}".format(vid_name))
vid_path = os.path.join(cfg.normal_videos_path, vid_name)
feats_path = os.path.join(cfg.raw_normal_train_features,
vid_name[:-9] + ".npy")
clips, frames = video_util.get_video_clips(vid_path)
# Remove last clip if number of frames is not equal to 16
if frames % 16 != 0:
clips = clips[:-1]
prep_clips = [c3d.preprocess_input(np.array(clip)) for clip in clips]
prep_clips = np.vstack(prep_clips)
features = feature_extractor.predict(prep_clips)
features = sklearn.preprocessing.normalize(features, axis=1)
with open(feats_path, "wb") as f:
np.save(f, features)
abnormal_videos = os.listdir(cfg.abnormal_videos_path)
abnormal_videos.sort()
print("Processing abnormal videos...")
for vid_name in abnormal_videos:
print("Processing {}".format(vid_name))
vid_path = os.path.join(cfg.abnormal_videos_path, vid_name)
feats_path = os.path.join(cfg.raw_abnormal_train_features,
