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))
model.add(Dense(1, kernel_initializer='glorot_normal', kernel_regularizer=l2(0.01), activation='sigmoid'))
return model
model=classifier_model()
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy',f1_m])
model.fit(x_train,y_train,epochs=100)
model.save_weights("model.h5")
"""###Testing the model"""
model=build_classifier_model()
import os
from c3d import preprocess_input,C3D,c3d_feature_extractor
from classifier import classifier_model ,conv_dict,build_classifier_model,load_weights
import glob
import configuration as cfg
from utils.video_util import *
from utils.array_util import *
import cv2
def run_demo():
sample_video_path = './testing/*.mp4'
# read video
score_TT = 0
score_TF = 0
import numpy as np
import os
def load_test_set(videos_path, videos_list):
feats = []
for vid in videos_list:
vid_path = os.path.join(videos_path, vid)
with open(vid_path, "rb") as f:
feat = np.load(f)
feats.append(feat)
feats = np.array(feats)
return feats
classifier_model = classifier.build_classifier_model()
vid_list = os.listdir(cfg.processed_test_features)
vid_list.sort()
test_set = load_test_set(cfg.processed_test_features, vid_list)
for filename, example in zip(vid_list, test_set):
predictions_file = filename[:-4] + '.npy'
pred_path = os.path.join(cfg.preds_folder, predictions_file)
pred = classifier_model.predict_on_batch(example)
with open(pred_path, "wb") as f:
np.save(pred_path, pred, allow_pickle=True)