def generate_c3d_features(c3d, filename):
"""
Function to process the current video file and generate the c3d fetures.
Parameters
c3d :torch.nn.Module
The c3d feature extraction model.
filename :str
The filename of the videos to return the features
Returns
-------
features :numpy.array
The features of the video in the required shape.
"""
total_length = find_length(filename)
block = generate_block(filename)
start_time = time.time()
feature_arr = []
for i, curr_block in enumerate(block):
print("\t\t\t\t\t [{}/{}]".format(i + 1, total_length),
sep='\r',
end='\r')
features = c3d(curr_block['block'].to(device))
#features = (features - features.mean())/(features.max() - features.mean())
feature_arr.append(features.cpu().detach().numpy())
del features
total_time = time.time() - start_time
features = np.rollaxis(np.array(feature_arr), 1)[0]
return features
def predict_scores(c3d, anomaly_ann, filename, seg_length, base_path):
"""
Function to predict the anomaly scores of a video and plot the scores
by iterating over the segment length of the video.
Parameters
----------
c3d :torch.nn.Module
The c3d feature extraction model.
anomaly_ann :torch.nn.Module
The anomaly ann model that predicts the anomaly scores.
filename :str
Filename of the plot figure.
seg_length :int
length of the video segment to be processed as
multipliers of 16.
base_path :str
Path of the folder containing the video files.
Returns
-------
None
"""
block = generate_block(os.path.join(base_path, filename), seg_length)
score_arr = []
start_time = time.time()
for i, curr_block in enumerate(block):
features = c3d(curr_block)
features = features.norm(dim=0)
features = (features-features.mean())/(features.max()-features.mean())
scores = anomaly_ann(features)
del features
print(scores,sep='\r', end='\r')
score_arr.append(scores.item())
del scores
total_time = time.time()-start_time
plot_score(score_arr, os.path.join('results/plots', base_path.split('/')[-1]), filename.split('.')[0]+'.png')
def find_length(filename, seg_length=1):
"""
Function to find the length of the video in terms of specified segments.
"""
return sum(1 for _ in generate_block(filename, 1))
#db_access = db()
vid_base_path = args.vid_base_path
features_base_path = args.features_base_path
vid_file = args.vid_file
features_base_path = args.features_base_path
vid_path = os.path.join(vid_base_path, vid_file)
feature_file = vid_file.split('.')[0] + '.csv'
feature_path = os.path.join(features_base_path, feature_file)
assert (
os.path.exists(vid_path)), "Video, '{}' does not exist".format(vid_path)
assert (os.path.exists(feature_path)
), "Feature file, '{}' does not exist".format(feature_path)
vid = generate_block(vid_path, 1, return_frame=True)
features = generate_tensor(feature_path)
weights_path = args.weights_path
detector = anomaly_ann(weights_path, no_sigmoid=True)
cv2.namedWindow("preview")
font = cv2.FONT_HERSHEY_SIMPLEX
text_pos = (10, 30)
score_processor = score_processor(base_threshold, alert_threshold, True)
min, max = get_min_max(feature_file)
for i, block in enumerate(vid):
score = detector(features[i]).item()
score = (score - min) / (max - min)
base_threshold = 0.3
alert_threshold = 0.6
weights_dict = {'c3d' : 'weights/c3d.pickle', 'ann': 'weights/weights_L1L2.mat'}
verbose = True
parser = argparse.ArgumentParser()
parser.add_argument("--vid", type=str, default='SampleVideos/videos/RoadAccidents022_x264.mp4')
parser.add_argument('--no_sigmoid', action='store_true', help='If true, no sigmoid for ann')
args = parser.parse_args()
filename = args.vid
no_sigmoid = args.no_sigmoid
if filename == '0':
filename = 0
vid = generate_block(filename, 1, return_frame=True)
csv_index = filename.split('/')[-1].split('.')[0]+'.csv'
min, max = get_min_max(csv_index)
detector = anomaly_detector(weights_dict, no_sigmoid='True')
score_processor = score_processor(base_threshold, alert_threshold, True)
cv2.namedWindow("preview")
font = cv2.FONT_HERSHEY_SIMPLEX
text_pos = (10, 30)
for i, block in enumerate(vid):
score = detector.predict(block['block'])
print(score)
score = (score-min)/(max-min)
disp_score = score*100