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
feature_extractor = c3d_feature_extractor()
x_train,y_train=generatedata('./training/*.mp4',feature_extractor)
from keras import backend as K
def recall_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
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
feature_extractor = c3d_feature_extractor()
model = build_classifier_model()
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))
