def predict(model,data,output):
""" Access the sample information to predict the pose. """
# Get the list of training samples
samples=os.listdir(data);
# Access to each sample
for file in samples:
# Create the object to access the sample
smp=ActionSample(os.path.join(data,file));
# Create a random set of actions for this sample
numFrame=0;
pred=[];
while numFrame<smp.getNumFrames():
# Generate an initial displacement
start=numFrame+random.randint(1,100);
# Generate the action duration
end=min(start+random.randint(10,100),smp.getNumFrames());
# Generate the action ID
actionID=random.randint(1,11);
# Check if the number of frames are correct
if start<end-1 and end<smp.getNumFrames():
# Store the prediction
pred.append([actionID,start,end])
# Move ahead
numFrame=end+1;
# Store the prediction
smp.exportPredictions(pred,output);
# Remove the sample object
del smp;
def predict(model,data,output):
""" Access the sample information to predict the pose. """
actionID = 0 #initialize
# Get the list of training samples
samples=os.listdir(data);
print samples
# Access to each sample
for file in samples:
# Create the object to access the sample
smp=ActionSample(os.path.join(data,file));
print file
# Create a random set of actions for this sample
numFrame=0;
pred=[];
seqn = os.path.splitext(file)[0];
while numFrame<smp.getNumFrames():
# Generate an initial displacement
#start=numFrame+random.randint(1,100);
start = numFrame
# Generate the action duration
#end=min(start+random.randint(10,100),smp.getNumFrames());
end = min(numFrame+30,smp.getNumFrames())
actionFileName = "test_%s_frame%d-%d.avi" % (seqn,start,end)
actionFileFullName = "%s%s%s%s%s" % (trainingVideos,os.path.sep,seqn, os.path.sep, actionFileName)
w=int(smp.rgb.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
h=int(smp.rgb.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
fps=int(smp.rgb.get(cv2.cv.CV_CAP_PROP_FPS))
if os.path.exists(actionFileFullName) and os.path.getsize(actionFileFullName) > 0:
print actionFileFullName, ' exists'
else:
out = cv2.VideoWriter(actionFileFullName,cv2.cv.CV_FOURCC('X','V','I','D'),fps,(w,h))
for n in range(start,end):
image=smp.getRGB(n+1);
#print type(image)
#print n
#img=cv2.cv.fromarray(image)
#cv2.imshow('my',image)
#cv2.waitKey(10)
#print type(img)
out.write(image)
out.release()
featureFileName = "densetr_%s_frame%d-%d.txt" % (seqn,start,end)
featureFileFullName = "%s%s%s%s%s" % (denseFeatures,os.path.sep,seqn, os.path.sep, featureFileName)
#if not os.path.exists(featureFileFullName):
# continue
if os.path.exists(featureFileFullName) and os.path.getsize(featureFileFullName) > 0:
print featureFileFullName, ' exists'
else:
fout = open(featureFileFullName,"w")
print featureFileFullName
cmd = []
cmd.append(denseTrajectoryExe)
seqn = os.path.splitext(file)[0];
cmd.append(actionFileFullName)
print cmd
proc = Popen(cmd, stdout=PIPE, stderr=PIPE)
#proc[actionID].stdout.flush()
#proc[actionID].stderr.flush()
stdout, stderr = proc.communicate()
fout.write(stdout)
fout.close()
hst = numpy.zeros(bowTraj.vocszHOG)
if not os.path.exists(featureFileFullName):
print featureFileFullName, ' not found'
continue
htot = 0
fin = open(featureFileFullName,"r")
for line in fin:
D.read(line)
#descHOG.append(D.HOG)
#X = bowTraj.calcFeatures(scipy.vstack(tuple(D.HOG)))
#actionID = model.predict(X)
idx = bowTraj.bowHOG.kmeans.predict(D.HOG)
hst[idx] = hst[idx] + 1
htot = htot + 1
if htot > 0:
hst = (1.0 / float(htot)) * hst
fin.close()
print 'Retrieved model:'
print model
actionID = model.predict(hst)[0]
print hst
print 'Predicted: ', actionID
# Generate the action ID
#actionID=random.randint(1,11);
# Check if the number of frames are correct
if start<end-1 and end<smp.getNumFrames():
# Store the prediction
pred.append([actionID,start,end])
# Move ahead
#numFrame=end+1;
numFrame=start+15;
# Store the prediction
smp.exportPredictions(pred,output);
# Remove the sample object
del smp;
