def extract_feats(vidreader,
extract_path,
num_prev_frames,
num_blocks,
smoothMasks,
window=5,
overlap=2):
assert (window >
overlap), "Window size needs to be greater than overlap window"
_file = open_write_header(extract_path)
start = time.time()
frame_idx = num_prev_frames + num_blocks / 2
numFrames = len(smoothMasks)
while (frame_idx < numFrames):
#print 'Extracting Feats ... {0}%\r'.format((frame_idx*100/numFrames)),
(cnt, frames) = vidreader.read(frame_idx, window)
if cnt == window:
w_frames = []
for frame in frames:
frame = GRAY(frame) * smoothMasks[frame_idx]
w_frames.extend(frame.flatten())
w_frames = np.array(w_frames) / float(255)
w_frames.tofile(_file, ' ', '%0.3f')
_file.write(os.linesep)
frame_idx += window - overlap
_file.close()
time_taken = time.time() - start
def process(self,cur_frame,prev_frames):
N = prev_frames.__len__();
assert(self.K<=N ),("length of prev frames less than " + str(self.K))
shape = cur_frame.shape[:2];
prev_frames = [GRAY(frame).flatten() for frame in prev_frames];
cur_frame = GRAY(cur_frame)
mean = np.mean(prev_frames,axis=0)
mean_subtracted = [frame - mean for frame in prev_frames];
mean_subtracted = np.asarray(mean_subtracted)
eigv,eigt = np.linalg.eig(np.cov(mean_subtracted));
eigt = np.dot(mean_subtracted.T,eigt);
eigt = eigt / np.linalg.norm(eigt,axis=0)
idx = np.argsort(eigv)[::-1]
eigt = eigt[:,idx]; eigv = eigv[idx]
score = np.dot(cur_frame.flatten()-mean,eigt[:,:self.K])
recon = np.dot(eigt[:,:self.K],score)+mean
recon = np.uint8(recon.reshape(shape))
diff = self.__frame_differencing__(recon,cur_frame)
return diff;
def extract_feats(vidreader,extract_path,num_prev_frames,num_blocks,smoothMasks,window=5,overlap=2):
assert(window>overlap),"Window size needs to be greater than overlap window"
_file = open_write_header(extract_path);
start = time.time();
frame_idx = num_prev_frames + num_blocks/2
numFrames= len(smoothMasks);
while(frame_idx < numFrames):
#print 'Extracting Feats ... {0}%\r'.format((frame_idx*100/numFrames)),
(cnt,frames) = vidreader.read(frame_idx,window);
if cnt==window:
w_frames = [];
for frame in frames:
frame = GRAY(frame) * smoothMasks[frame_idx];
w_frames.extend(frame.flatten());
w_frames = np.array(w_frames)/float(255);
w_frames.tofile(_file,' ','%0.3f');
_file.write(os.linesep)
frame_idx += window-overlap;
_file.close();
time_taken = time.time()-start;
def process(self, cur_frame, prev_frames):
N = prev_frames.__len__()
assert (self.K <= N), ("length of prev frames less than " +
str(self.K))
shape = cur_frame.shape[:2]
prev_frames = [GRAY(frame).flatten() for frame in prev_frames]
cur_frame = GRAY(cur_frame)
mean = np.mean(prev_frames, axis=0)
mean_subtracted = [frame - mean for frame in prev_frames]
mean_subtracted = np.asarray(mean_subtracted)
eigv, eigt = np.linalg.eig(np.cov(mean_subtracted))
eigt = np.dot(mean_subtracted.T, eigt)
eigt = eigt / np.linalg.norm(eigt, axis=0)
idx = np.argsort(eigv)[::-1]
eigt = eigt[:, idx]
eigv = eigv[idx]
score = np.dot(cur_frame.flatten() - mean, eigt[:, :self.K])
recon = np.dot(eigt[:, :self.K], score) + mean
recon = np.uint8(recon.reshape(shape))
diff = self.__frame_differencing__(recon, cur_frame)
return diff
