def showLabeledImage(data, ind_j, dir_, rgb=0):
cv.NamedWindow("a")
if rgb:
file_ = data['data']['filename'][ind_j]
img = icuReader.getRGBImage(dir_+file_[:file_.find(".depth")]+".rgb")
# imgD=img[:,::-1, :]
imgD=img
else:
img = icuReader.getDepthImage(dir_+data['data']['filename'][ind_j])
imgD = icuReader.constrain(img, 500, 5000)
imgD = np.dstack([imgD, imgD, imgD])
# com_xyz = np.array(data['data']['com'])
com_xyz = np.array(data['data']['moving_com'])
com = list(world2depth(com_xyz).T) #COM in image coords
# Add previous location markers
r = 5
for jj in range(ind_j):
s = (slice(com[jj][0]-r, com[jj][0]+r),slice(com[jj][1]-r, com[jj][1]+r))
imgD[s[0], s[1], 0] = 100
imgD[s[0], s[1], 1:3] = 0
# Add current location marker
r = 10
s = (slice(com[ind_j][0]-r, com[ind_j][0]+r),slice(com[ind_j][1]-r, com[ind_j][1]+r))
colorInd = 2#randint(0, 2)
if colorInd==0: # Vary the colors every new person
r=255; g=0; b=0
elif colorInd==1:
r=0; g=255; b=0
elif colorInd==2:
r=0; g=0; b=255
imgD[s[0], s[1], 0] = r
imgD[s[0], s[1], 1] = g
imgD[s[0], s[1], 2] = b
# Add touch boxes
touchEnabled = 1
if touchEnabled:
rad = 13
cv2.putText(imgD, "Touches", (5, 30), cv2.FONT_HERSHEY_SIMPLEX, .75, (255,255,255), thickness=1)
for i in range(2):
cv2.rectangle(imgD, (60-rad, 40*i+60-rad), (60+rad, 40*i+60+rad), [255, 255, 255])
if 'touches' in data['data'].keys():
rad = 10
times = [x[1] for x in data['data']['touches']]
if any(np.equal(times,ind_j)):
for i in data['data']['touches'][np.argwhere(np.equal(times,ind_j))[0]][0]:
cv2.circle(imgD, (60, 40*i+60), rad, [0, 255, 0], thickness=4)
# Print time on screen
time_ = data['data']['time'][ind_j]
hours = int(time_ / 3600)
minutes = int(time_ / 60) - hours*60
seconds = int(time_) - 60*minutes - 3600*hours
text = str(hours) + "hr " + str(minutes) + "min " + str(seconds) + "s"
cv2.putText(imgD, text, (10, 470), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), thickness=2)
# Print duration to screen
text = "Dur: " + str(data['elapsed'])
cv2.putText(imgD, text, (450, 470), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), thickness=2)
#Show image
cv2.imshow("a", imgD)
cv2.waitKey(1)
# Get counts for each label
counts = [[], []]
counts = np.histogram(truthLabels, 9, [.5,9.5])
# Get indices for each label
labelInds = []
for i in xrange(len(labelNames)):
labelInds.append([y for x,y in zip(p, range(0,len(p))) if 'label' in x.keys() and int(x['label'])==labelNames[i]])
# Show label images
for lab in xrange(len(labelNames)):
figure(lab)
for i in xrange(len(inds[lab])):
subplot(2,int(len(inds[lab])/2),i)
img = icuReader.getDepthImage(dir_+p[inds[lab][i]]['data']['filename'][0])
imshow(img)
axis('off')
title(labelNames[lab])
### Time ###
# Get time for each label
labeledTimes = []
for i in xrange(len(labelNames)):
labeledTimes.append(np.sum([p[x]['elapsed'] for x in inds[i]]))
# Time histogram
times = [x['elapsed'] for x in p if x['elapsed'] > 10]
times_hist = np.histogram(times, bins=20, range=[10,250])
plot(times_hist[0])
title('Histogram of action durations (Median: 37 sec)')
