session_id)
mask = cv2.imread(mask_path, 0)
vp_path = '../data/gt/2016-ITS-BrnoCompSpeed/results/{}/system_dubska_bmvc14.json'.format(
session_id)
with open(vp_path, 'r') as f_buff:
"""
vp has 2 keys
- cars, list of cars detected, its frame and posX and posY
- camera_calibration, the calibration parameter result (pp, vp1, and vp2)
"""
vp = json.load(f_buff)
vp1 = vp['camera_calibration']['vp1']
vp2 = vp['camera_calibration']['vp2']
fi = FrameIterator('../data/sample/{}'.format(session_id))
bg = BackgroundModel(fi)
print("Learning")
bg.learn(tot_frame_init=1)
print("Done")
ctr_frame = 0
# what I want is the function to find most left tangen and right tangen of
# given blob list and a point
while True:
img = next(fi)
frame = img
fg = bg.apply(img)
session_id)
mask = cv2.imread(mask_path, 0)
vp_path = '../data/gt/2016-ITS-BrnoCompSpeed/results/{}/system_dubska_bmvc14.json'.format(
session_id)
with open(vp_path, 'r') as f_buff:
"""
vp has 2 keys
- cars, list of cars detected, its frame and posX and posY
- camera_calibration, the calibration parameter result (pp, vp1, and vp2)
"""
vp = json.load(f_buff)
vp1 = vp['camera_calibration']['vp1']
vp2 = vp['camera_calibration']['vp2']
fi = FrameIterator('../data/sync/{}'.format(session_id))
ctr_frame = 1
while True:
img = next(fi)
div_line = Line.from_two_points(vp2, (10, img.shape[0] / 2))
frame = div_line.draw(img)
loc = (20, img.shape[0] - 20)
cv2.putText(frame, 'Frame - {}'.format(ctr_frame), loc,
cv2.FONT_HERSHEY_PLAIN, 3, (0, 128, 128), 4)
cv2.imshow('default', frame)
if (cv2.waitKey(1) & 0xFF == ord('q')):
def get_session (ses_id) :
fi = {}
for view in (("left", "right", "center")) :
fi[view] = FrameIterator (os.path.join (path_this, 'data/sync_25fps/session{}_{}'.format (ses_id, view)))
return fi
with open (vp_path, 'r') as f_buff :
"""
vp has 2 keys
- cars, list of cars detected, its frame and posX and posY
- camera_calibration, the calibration parameter result (pp, vp1, and vp2)
"""
vp = json.load (f_buff)
vps[_id][view] = {
'vp1' : vp['camera_calibration']['vp1'],
'vp2' : vp['camera_calibration']['vp2']
}
# generate frame iterator
fi = {_id : {}}
for view in session[_id] :
fi[_id][view] = FrameIterator ('../data/sync_25fps/session{}_{}'.format (_id, view))
# define background model
bms = {_id : {}}
for view in session[_id] :
bms[_id][view] = BackgroundModel (fi[_id][view])
print ("Learning for session {}-{}".format (_id, view))
bms[_id][view].learn (tot_frame_init=2)
print ("Done")
# initialing prev blobs
prev_fg = {_id : {}}
for view in session[_id] :
prev_fg[_id][view] = [None, None]
for i in range (2) :
img = next (fi[_id][view])
"""
vp has 2 keys
- cars, list of cars detected, its frame and posX and posY
- camera_calibration, the calibration parameter result (pp, vp1, and vp2)
"""
vp = json.load (f_buff)
vps[_id][view] = {
'vp1' : vp['camera_calibration']['vp1'],
'vp2' : vp['camera_calibration']['vp2']
}
bms = {}
for _id in session:
bms[_id] = {}
for view in session[_id] :
fi = FrameIterator ('../data/sample/session{}_{}'.format (_id, view))
bms[_id][view] = BackgroundModel (fi)
print ("Learning for session {}-{}".format (_id, view))
bms[_id][view].learn (tot_frame_init=1)
print ("Done")
_id = 0
ctr = 0
while True :
view_frame = None
# load image from each view
for view in session[_id] :
fpath = '../data/sample/session0_{}/{:04d}.jpg'.format (
view,
session[_id][view] + ctr