def ucf101_stats(save_path, directory=".", filename="ucf101_stats.pkl"):
"""Generate statistaic for UCF-101 dataset.
Paramters
---------
save_path : string
UCF-101 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf101_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
ucf101_stats = {}
ucf101_list = [
"ApplyEyeMakeup", "ApplyLipstick", "Archery", "BabyCrawling",
"BalanceBeam", "BandMarching", "BaseballPitch", "Basketball",
"BasketballDunk", "BenchPress", "Biking", "Billiards", "BlowDryHair",
"BlowingCandles", "BodyWeightSquats", "Bowling", "BoxingPunchingBag",
"BoxingSpeedBag", "BreastStroke", "BrushingTeeth", "CleanAndJerk",
"CliffDiving", "CricketBowling", "CricketShot", "CuttingInKitchen",
"Diving", "Drumming", "Fencing", "FieldHockeyPenalty",
"FloorGymnastics", "FrisbeeCatch", "FrontCrawl", "GolfSwing",
"Haircut", "HammerThrow", "Hammering", "HandstandPushups",
"HandstandWalking", "HeadMassage", "HighJump", "HorseRace",
"HorseRiding", "HulaHoop", "IceDancing", "JavelinThrow",
"JugglingBalls", "JumpRope", "JumpingJack", "Kayaking", "Knitting",
"LongJump", "Lunges", "MilitaryParade", "Mixing", "MoppingFloor",
"Nunchucks", "ParallelBars", "PizzaTossing", "PlayingCello",
"PlayingDaf", "PlayingDhol", "PlayingFlute", "PlayingGuitar",
"PlayingPiano", "PlayingSitar", "PlayingTabla", "PlayingViolin",
"PoleVault", "PommelHorse", "PullUps", "Punch", "PushUps", "Rafting",
"RockClimbingIndoor", "RopeClimbing", "Rowing", "SalsaSpin",
"ShavingBeard", "Shotput", "SkateBoarding", "Skiing", "Skijet",
"SkyDiving", "SoccerJuggling", "SoccerPenalty", "StillRings",
"SumoWrestling", "Surfing", "Swing", "TableTennisShot", "TaiChi",
"TennisSwing", "ThrowDiscus", "TrampolineJumping", "Typing",
"UnevenBars", "VolleyballSpiking", "WalkingWithDog", "WallPushups",
"WritingOnBoard", "YoYo"
]
ucf101_stats["ucf101_list"] = ucf101_list
for list_name in ucf101_list:
fns_temp = glob.glob(save_path + list_name + "/" + "*.avi")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
ucf101_stats[list_name] = file_names
f = file(tools.check_path(directory) + filename, mode="wb")
pickle.dump(ucf101_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def ucf50_stats(save_path, directory=".", filename="ucf50_stats.pkl"):
"""Generate statistaic for UCF-50 dataset.
Paramters
---------
save_path : string
UCF-50 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf50_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
ucf50_stats = {}
ucf50_list = ["BaseballPitch", "Basketball", "BenchPress", "Biking",
"Billiards", "BreastStroke", "CleanAndJerk", "Diving",
"Drumming", "Fencing", "GolfSwing", "HighJump", "HorseRace",
"HorseRiding", "HulaHoop", "JavelinThrow", "JugglingBalls",
"JumpRope", "JumpingJack", "Kayaking", "Lunges",
"MilitaryParade", "Mixing", "Nunchucks", "PizzaTossing",
"PlayingGuitar", "PlayingPiano", "PlayingTabla",
"PlayingViolin", "PoleVault", "PommelHorse", "PullUps",
"Punch", "PushUps", "RockClimbingIndoor", "RopeClimbing",
"Rowing", "SalsaSpin", "SkateBoarding", "Skiing", "Skijet",
"SoccerJuggling", "Swing", "TaiChi", "TennisSwing",
"ThrowDiscus", "TrampolineJumping", "VolleyballSpiking",
"WalkingWithDog", "YoYo"]
ucf50_stats["ucf50_list"] = ucf50_list
for list_name in ucf50_list:
fns_temp = glob.glob(save_path+list_name+"/"+"*.avi")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
ucf50_stats[list_name] = file_names
f = file(tools.check_path(directory)+filename, mode="wb")
pickle.dump(ucf50_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def ucf50_stats(save_path, directory=".", filename="ucf50_stats.pkl"):
"""Generate statistaic for UCF-50 dataset.
Paramters
---------
save_path : string
UCF-50 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf50_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
ucf50_stats = {}
ucf50_list = [
"BaseballPitch", "Basketball", "BenchPress", "Biking", "Billiards",
"BreastStroke", "CleanAndJerk", "Diving", "Drumming", "Fencing",
"GolfSwing", "HighJump", "HorseRace", "HorseRiding", "HulaHoop",
"JavelinThrow", "JugglingBalls", "JumpRope", "JumpingJack", "Kayaking",
"Lunges", "MilitaryParade", "Mixing", "Nunchucks", "PizzaTossing",
"PlayingGuitar", "PlayingPiano", "PlayingTabla", "PlayingViolin",
"PoleVault", "PommelHorse", "PullUps", "Punch", "PushUps",
"RockClimbingIndoor", "RopeClimbing", "Rowing", "SalsaSpin",
"SkateBoarding", "Skiing", "Skijet", "SoccerJuggling", "Swing",
"TaiChi", "TennisSwing", "ThrowDiscus", "TrampolineJumping",
"VolleyballSpiking", "WalkingWithDog", "YoYo"
]
ucf50_stats["ucf50_list"] = ucf50_list
for list_name in ucf50_list:
fns_temp = glob.glob(save_path + list_name + "/" + "*.avi")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
ucf50_stats[list_name] = file_names
f = file(tools.check_path(directory) + filename, mode="wb")
pickle.dump(ucf50_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def tracking_stats(save_path, directory=".", filename="tracking_stats.pkl"):
"""Generate statistics for Tracking Dataset.
You can obtain the dataset from here:
http://cmp.felk.cvut.cz/~vojirtom/dataset/
Parameters
----------
save_path : string
Tracking dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "tracking_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
tracking_stats = {}
tracking_primary_list = [
"Babenko", "BoBot", "Cehovin", "Ellis_ijcv2011", "Godec", "Kalal",
"Kwon", "Kwon_VTD", "Other", "PROST", "Ross", "Thang", "Wang"
]
tracking_stats["primary_list"] = tracking_primary_list
secondary_list = {}
for primary_class in tracking_primary_list:
secondary_class = next(os.walk(save_path + primary_class))[1]
secondary_list[primary_class] = secondary_class
for category in secondary_class:
file_path = save_path + primary_class + "/" + category + "/" + "*.*"
fns_temp = glob.glob(file_path)
file_names = []
for fn in fns_temp:
if fn[-4:] != ".txt" and fn[-3:] != ".db":
file_names.append(os.path.basename(fn))
tracking_stats[category] = file_names
tracking_stats["secondary_list"] = secondary_list
f = file(directory + "/" + filename, mode="wb")
pickle.dump(tracking_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def tracking_stats(save_path, directory=".", filename="tracking_stats.pkl"):
"""Generate statistics for Tracking Dataset.
You can obtain the dataset from here:
http://cmp.felk.cvut.cz/~vojirtom/dataset/
Parameters
----------
save_path : string
Tracking dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "tracking_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
tracking_stats = {}
tracking_primary_list = ["Babenko", "BoBot", "Cehovin", "Ellis_ijcv2011",
"Godec", "Kalal", "Kwon", "Kwon_VTD", "Other",
"PROST", "Ross", "Thang", "Wang"]
tracking_stats["primary_list"] = tracking_primary_list
secondary_list = {}
for primary_class in tracking_primary_list:
secondary_class = next(os.walk(save_path+primary_class))[1]
secondary_list[primary_class] = secondary_class
for category in secondary_class:
file_path = save_path+primary_class+"/"+category+"/"+"*.*"
fns_temp = glob.glob(file_path)
file_names = []
for fn in fns_temp:
if fn[-4:] != ".txt" and fn[-3:] != ".db":
file_names.append(os.path.basename(fn))
tracking_stats[category] = file_names
tracking_stats["secondary_list"] = secondary_list
f = file(directory+"/"+filename, mode="wb")
pickle.dump(tracking_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def caltech256_stats(save_path,
directory=".",
filename="caltech256_stats.pkl"):
"""Generate statistic for Caltech-256 dataset.
Paramters
---------
save_path : string
Caltech-256 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf50_stats.pkl"
Returns
-------
A file named with respective filename
"""
if not os.path.isdir(save_path):
raise ValueError("Either path is not existed or is a relative path")
caltech256_stats = {}
caltech256_list = next(os.walk(save_path))[1]
caltech256_stats["caltech256_list"] = caltech256_list
for list_name in caltech256_list:
fns_temp = glob.glob(
os.path.join(save_path, list_name) + "/" + "*.jpg")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
caltech256_stats[list_name] = file_names
f = file(tools.check_path(directory) + filename, mode="wb")
pickle.dump(caltech256_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def caltech256_stats(save_path, directory=".",
filename="caltech256_stats.pkl"):
"""Generate statistic for Caltech-256 dataset.
Paramters
---------
save_path : string
Caltech-256 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf50_stats.pkl"
Returns
-------
A file named with respective filename
"""
if not os.path.isdir(save_path):
raise ValueError("Either path is not existed or is a relative path")
caltech256_stats = {}
caltech256_list = next(os.walk(save_path))[1]
caltech256_stats["caltech256_list"] = caltech256_list
for list_name in caltech256_list:
fns_temp = glob.glob(os.path.join(save_path, list_name)+"/"+"*.jpg")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
caltech256_stats[list_name] = file_names
f = file(tools.check_path(directory)+filename, mode="wb")
pickle.dump(caltech256_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def ucf101_stats(save_path, directory=".", filename="ucf101_stats.pkl"):
"""Generate statistaic for UCF-101 dataset.
Paramters
---------
save_path : string
UCF-101 dataset save path
directory : string
directory you want to save the stats, in default,
it's in current folder
filename : string
filename you want to save the stats, in default,
it's named "ucf101_stats.pkl"
Returns
-------
A file named with respective filename
"""
save_path = tools.check_path(save_path)
ucf101_stats = {}
ucf101_list = ["ApplyEyeMakeup", "ApplyLipstick", "Archery",
"BabyCrawling", "BalanceBeam", "BandMarching",
"BaseballPitch", "Basketball", "BasketballDunk",
"BenchPress", "Biking", "Billiards", "BlowDryHair",
"BlowingCandles", "BodyWeightSquats", "Bowling",
"BoxingPunchingBag", "BoxingSpeedBag", "BreastStroke",
"BrushingTeeth", "CleanAndJerk", "CliffDiving",
"CricketBowling", "CricketShot", "CuttingInKitchen",
"Diving", "Drumming", "Fencing", "FieldHockeyPenalty",
"FloorGymnastics", "FrisbeeCatch", "FrontCrawl",
"GolfSwing", "Haircut", "HammerThrow", "Hammering",
"HandstandPushups", "HandstandWalking", "HeadMassage",
"HighJump", "HorseRace", "HorseRiding", "HulaHoop",
"IceDancing", "JavelinThrow", "JugglingBalls", "JumpRope",
"JumpingJack", "Kayaking", "Knitting", "LongJump", "Lunges",
"MilitaryParade", "Mixing", "MoppingFloor", "Nunchucks",
"ParallelBars", "PizzaTossing", "PlayingCello",
"PlayingDaf", "PlayingDhol", "PlayingFlute",
"PlayingGuitar", "PlayingPiano", "PlayingSitar",
"PlayingTabla", "PlayingViolin", "PoleVault", "PommelHorse",
"PullUps", "Punch", "PushUps", "Rafting",
"RockClimbingIndoor", "RopeClimbing", "Rowing", "SalsaSpin",
"ShavingBeard", "Shotput", "SkateBoarding", "Skiing",
"Skijet", "SkyDiving", "SoccerJuggling", "SoccerPenalty",
"StillRings", "SumoWrestling", "Surfing", "Swing",
"TableTennisShot", "TaiChi", "TennisSwing", "ThrowDiscus",
"TrampolineJumping", "Typing", "UnevenBars",
"VolleyballSpiking", "WalkingWithDog", "WallPushups",
"WritingOnBoard", "YoYo"]
ucf101_stats["ucf101_list"] = ucf101_list
for list_name in ucf101_list:
fns_temp = glob.glob(save_path+list_name+"/"+"*.avi")
file_names = []
for fn in fns_temp:
file_names.append(os.path.basename(fn))
ucf101_stats[list_name] = file_names
f = file(tools.check_path(directory)+filename, mode="wb")
pickle.dump(ucf101_stats, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def dvs_ucf101_exp(ucf101_dir,
ucf101_stats_path,
recording_save_path,
viewer_id,
screen_height,
screen_width,
work_win_scale,
bg_color,
fps):
"""Setup an experiment for UCF-101 dataset.
Parameters
----------
ucf101_dir : string
absolute path of UCF-101 dataset
e.g. /home/user/UCF-101
ucf101_stats_path : string
path to vot dataset stats
recording_save_path : string
path to logged recording data
viewer_id : int
the ID of jAER viewer, for Linux is 1, Mac OS X is 2
screen_height : int
height of the screen in pixel
screen_width : int
width of the screen in pixel
work_win_scale : float
the scaling factor that calculates working window size
bg_color : list
background color definition
fps : int
frame per second while displaying the video,
will round to closest number
"""
ucf101_dir = tools.check_path(ucf101_dir) # fix path if it's not valid
# Load UCF-101 stats
f = file(ucf101_stats_path, mode="r")
ucf101_stats = pickle.load(f)
f.close()
ucf101_list = ucf101_stats["ucf101_list"]
# Set read video function based on platform
if _platform == "darwin":
read_video = helpers.read_video_macosx
elif _platform == "linux2":
read_video = helpers.read_video_macosx
# Create full background
background = np.ones((screen_height, screen_width, 3))*bg_color
# Setup OpenCV display window
window_title = "DVS-UCF101-EXP"
cv2.namedWindow(window_title, cv2.WND_PROP_FULLSCREEN)
# Experiment setup calibration
# Not without tuning images
swin_h, swin_w = helpers.calibration(win_h=screen_height,
win_w=screen_width,
scale=work_win_scale,
window_title=window_title,
bg_color=bg_color)
# Main routine
s = tools.init_dvs()
tools.reset_dvs_time(s)
for class_name in ucf101_list:
if not os.path.exists(str(recording_save_path+class_name+"/")):
os.mkdir(str(recording_save_path+class_name+"/"))
for video_name in ucf101_stats[class_name]:
video_path = str(ucf101_dir+class_name+"/"+video_name)
frames, num_frames = read_video(video_path)
new_frames = gui.rescale_image_sequence(frames, swin_h, swin_w,
bg_color)
new_frames = gui.create_border_sequence(new_frames, screen_height,
screen_width, bg_color)
cv2.imshow(window_title, new_frames[0])
print "[MESSAGE] Adapting video sequence %s" % str(video_name)
cv2.waitKey(delay=2000)
tools.start_log_dvs(s, recording_save_path,
str(class_name+"/"+video_name[:-4]),
viewer_id)
for i in xrange(num_frames):
cv2.imshow(window_title, new_frames[i])
key = cv2.waitKey(delay=int(1000/fps)) & 0xFF
if key == 27:
cv2.destroyAllWindows()
quit()
cv2.imshow(window_title, new_frames[-1])
tools.stop_log_dvs(s, viewer_id)
print "[MESSAGE] Releasing video sequence %s" % str(video_name)
cv2.waitKey(delay=2000)
cv2.imshow(window_title, background)
cv2.waitKey(delay=1000)
tools.reset_dvs_time(s)
print "[MESSAGE] Video sequence %s is logged." % str(video_name)
# Destory both scoket and opencv window
tools.destroy_dvs(s)
cv2.destroyAllWindows()
def dvs_ucf50_exp(ucf50_dir,
ucf50_stats_path,
recording_save_path,
viewer_id,
screen_height,
screen_width,
work_win_scale,
bg_color,
fps):
"""Setup an experiment for UCF-50 dataset.
Parameters
----------
ucf50_dir : string
absolute path of UCF-50 dataset
e.g. /home/user/UCF50
ucf50_stats_path : string
path to vot dataset stats
recording_save_path : string
path to logged recording data
viewer_id : int
the ID of jAER viewer, for Linux is 1, Mac OS X is 2
screen_height : int
height of the screen in pixel
screen_width : int
width of the screen in pixel
work_win_scale : float
the scaling factor that calculates working window size
bg_color : list
background color definition
fps : int
frame per second while displaying the video,
will round to closest number
"""
ucf50_dir = tools.check_path(ucf50_dir) # fix path if it's not valid
# Load UCF-50 stats
f = file(ucf50_stats_path, mode="r")
ucf50_stats = pickle.load(f)
f.close()
ucf50_list = ucf50_stats["ucf50_list"]
# Set read video function based on platform
if _platform == "darwin":
read_video = helpers.read_video_macosx
elif _platform == "linux2":
read_video = helpers.read_video_macosx
# Create full background
background = np.ones((screen_height, screen_width, 3))*bg_color
# Setup OpenCV display window
window_title = "DVS-UCF50-EXP"
cv2.namedWindow(window_title, cv2.WND_PROP_FULLSCREEN)
# Experiment setup calibration
# Not without tuning images
swin_h, swin_w = helpers.calibration(win_h=screen_height,
win_w=screen_width,
scale=work_win_scale,
window_title=window_title,
bg_color=bg_color)
# Main routine
s = tools.init_dvs()
tools.reset_dvs_time(s)
for class_name in ucf50_list:
if not os.path.exists(str(recording_save_path+class_name+"/")):
os.mkdir(str(recording_save_path+class_name+"/"))
for video_name in ucf50_stats[class_name]:
video_path = str(ucf50_dir+class_name+"/"+video_name)
frames, num_frames = read_video(video_path)
new_frames = gui.rescale_image_sequence(frames, swin_h, swin_w,
bg_color)
new_frames = gui.create_border_sequence(new_frames, screen_height,
screen_width, bg_color)
cv2.imshow(window_title, new_frames[0])
print "[MESSAGE] Adapting video sequence %s" % str(video_name)
cv2.waitKey(delay=2000)
tools.start_log_dvs(s, recording_save_path,
str(class_name+"/"+video_name[:-4]),
viewer_id)
for i in xrange(num_frames):
cv2.imshow(window_title, new_frames[i])
key = cv2.waitKey(delay=int(1000/fps)) & 0xFF
if key == 27:
cv2.destroyAllWindows()
quit()
cv2.imshow(window_title, new_frames[-1])
tools.stop_log_dvs(s, viewer_id)
print "[MESSAGE] Releasing video sequence %s" % str(video_name)
cv2.waitKey(delay=2000)
cv2.imshow(window_title, background)
cv2.waitKey(delay=1000)
tools.reset_dvs_time(s)
print "[MESSAGE] Video sequence %s is logged." % str(video_name)
# Destory both scoket and opencv window
tools.destroy_dvs(s)
cv2.destroyAllWindows()
def dvs_vot_exp(tracking_dir, tracking_stats_path, recording_save_path,
viewer_id, screen_height, screen_width, work_win_scale,
bg_color, fps):
"""Setup an experiment for VOT dataset.
Parameters
----------
tracking_dir : string
absolute path of Tracking dataset
e.g. /home/user/vot2015
tracking_stats_path : string
path to tracking dataset stats
recording_save_path : string
path to logged recording data
viewer_id : int
the ID of jAER viewer, for Linux is 1, Mac OS X is 2
screen_height : int
height of the screen in pixel
screen_width : int
width of the screen in pixel
work_win_scale : float
the scaling factor that calculates working window size
bg_color : list
background color definition
fps : int
frame per second while displaying the video,
will round to closest number
"""
tracking_dir = tools.check_path(tracking_dir) # fix path if it's not valid
recording_save_path = tools.check_path(str(recording_save_path))
if not os.path.exists(str(recording_save_path)):
os.mkdir(str(recording_save_path))
# Load VOT stats
f = file(tracking_stats_path, mode="r")
tracking_stats = pickle.load(f)
f.close()
# primary list
pl = tracking_stats["primary_list"]
# secondary list
sl = tracking_stats["secondary_list"]
# Create full background
background = np.ones((screen_height, screen_width, 3)) * bg_color
# Setup OpenCV display window
window_title = "DVS-TRACKING-EXP"
cv2.namedWindow(window_title, cv2.WND_PROP_FULLSCREEN)
# Experiment setup calibration
# Not without tuning images
swin_h, swin_w = helpers.calibration(win_h=screen_height,
win_w=screen_width,
scale=work_win_scale,
window_title=window_title,
bg_color=bg_color)
# Init a general UDP socket
s = tools.init_dvs()
tools.reset_dvs_time(s)
for pcg in pl:
# remove sequence Kalal until I got more memory
if pcg != "Kalal":
for scg in sl[pcg]:
print "[MESSAGE] Display video sequence " + scg
seq_base_path = tracking_dir + pcg + "/" + scg + "/"
frames = []
for fn in tracking_stats[scg]:
frames.append(cv2.imread(seq_base_path + fn))
frames = gui.rescale_image_sequence(frames, swin_h, swin_w,
bg_color)
frames = gui.create_border_sequence(frames, screen_height,
screen_width, bg_color)
cv2.imshow(window_title, frames[0])
print "[MESSAGE] Adapting video sequence " + scg
cv2.waitKey(delay=2000)
tools.start_log_dvs(s, recording_save_path, scg, viewer_id)
for frame in frames:
cv2.imshow(window_title, frame)
key = cv2.waitKey(delay=int(1000 / fps)) & 0xFF
if key == 27:
cv2.destroyAllWindows()
quit()
cv2.imshow(window_title, frames[-1])
tools.stop_log_dvs(s, viewer_id)
print "[MESSAGE] Releasing video sequence " + scg
cv2.waitKey(delay=2000)
cv2.imshow(window_title, background)
cv2.waitKey(delay=1000)
tools.reset_dvs_time(s)
print "[MESSAGE] Video sequence " + scg + " is logged."
# Destory both scoket and opencv window
tools.destroy_dvs(s)
cv2.destroyAllWindows()
def dvs_vot_exp(vot_dir,
vot_stats_path,
recording_save_path,
viewer_id,
screen_height,
screen_width,
work_win_scale,
bg_color,
fps):
"""Setup an experiment for VOT dataset.
Parameters
----------
vot_dir : string
absolute path of VOT dataset
e.g. /home/user/vot2015
vot_stats_path : string
path to vot dataset stats
recording_save_path : string
path to logged recording data
viewer_id : int
the ID of jAER viewer, for Linux is 1, Mac OS X is 2
screen_height : int
height of the screen in pixel
screen_width : int
width of the screen in pixel
work_win_scale : float
the scaling factor that calculates working window size
bg_color : list
background color definition
fps : int
frame per second while displaying the video,
will round to closest number
"""
vot_dir = tools.check_path(vot_dir) # fix path if it's not valid
recording_save_path = tools.check_path(str(recording_save_path))
if not os.path.exists(str(recording_save_path)):
os.mkdir(str(recording_save_path))
# Load VOT stats
f = file(vot_stats_path, mode="r")
vot_stats = pickle.load(f)
f.close()
vot_list = vot_stats['vot_list']
num_frames = vot_stats['num_frames']
# Load groundtruth and image lists
print "[MESSAGE] Loading ground truth and image lists."
gts = []
for i in xrange(len(num_frames)):
temp_gt = np.loadtxt(vot_dir+vot_list[i]+"/groundtruth.txt",
dtype=float, delimiter=",")
temp_gt = np.reshape(temp_gt, (temp_gt.shape[0], 4, 2))
gts.append(temp_gt)
lists = []
for i in xrange(len(num_frames)):
temp_list = tools.create_vot_image_list(vot_dir+vot_list[i]+"/",
num_frames[i])
lists.append(temp_list)
print "[MESSAGE] Ground truths and image lists are loaded."
# Create full background
background = np.ones((screen_height, screen_width, 3))*bg_color
# Setup OpenCV display window
window_title = "DVS-VOT-EXP"
cv2.namedWindow(window_title, cv2.WND_PROP_FULLSCREEN)
# Experiment setup calibration
# Not without tuning images
swin_h, swin_w = helpers.calibration(win_h=screen_height,
win_w=screen_width,
scale=work_win_scale,
window_title=window_title,
bg_color=bg_color)
# Init a general UDP socket
s = tools.init_dvs()
tools.reset_dvs_time(s)
for k in xrange(len(num_frames)):
print "[MESSAGE] Display video sequence %i" % (k+1)
frames = []
for i in xrange(num_frames[k]):
frames.append(cv2.imread(lists[k][i]))
new_frames = gui.rescale_image_sequence(frames, swin_h, swin_w,
bg_color)
# new_frames = gui.draw_poly_box_sequence(new_frames, gts[k])
new_frames = gui.create_border_sequence(new_frames,
screen_height, screen_width,
bg_color)
cv2.imshow(window_title, new_frames[0])
print "[MESSAGE] Adapting video sequence %i" % (k+1)
cv2.waitKey(delay=2000)
tools.start_log_dvs(s, recording_save_path, vot_list[k], viewer_id)
for i in xrange(num_frames[k]):
cv2.imshow(window_title, new_frames[i])
key = cv2.waitKey(delay=int(1000/fps)) & 0xFF
if key == 27:
cv2.destroyAllWindows()
quit()
cv2.imshow(window_title, new_frames[-1])
tools.stop_log_dvs(s, viewer_id)
print "[MESSAGE] Releasing video sequence %i" % (k+1)
cv2.waitKey(delay=2000)
cv2.imshow(window_title, background)
cv2.waitKey(delay=1000)
tools.reset_dvs_time(s)
print "[MESSAGE] Video sequence %i is logged." % (k+1)
# Destory both scoket and opencv window
tools.destroy_dvs(s)
cv2.destroyAllWindows()
def dvs_vot_exp(vot_dir, vot_stats_path, recording_save_path, viewer_id,
screen_height, screen_width, work_win_scale, bg_color, fps):
"""Setup an experiment for VOT dataset.
Parameters
----------
vot_dir : string
absolute path of VOT dataset
e.g. /home/user/vot2015
vot_stats_path : string
path to vot dataset stats
recording_save_path : string
path to logged recording data
viewer_id : int
the ID of jAER viewer, for Linux is 1, Mac OS X is 2
screen_height : int
height of the screen in pixel
screen_width : int
width of the screen in pixel
work_win_scale : float
the scaling factor that calculates working window size
bg_color : list
background color definition
fps : int
frame per second while displaying the video,
will round to closest number
"""
vot_dir = tools.check_path(vot_dir) # fix path if it's not valid
recording_save_path = tools.check_path(str(recording_save_path))
if not os.path.exists(str(recording_save_path)):
os.mkdir(str(recording_save_path))
# Load VOT stats
f = file(vot_stats_path, mode="r")
vot_stats = pickle.load(f)
f.close()
vot_list = vot_stats['vot_list']
num_frames = vot_stats['num_frames']
# Load groundtruth and image lists
print "[MESSAGE] Loading ground truth and image lists."
gts = []
for i in xrange(len(num_frames)):
temp_gt = np.loadtxt(vot_dir + vot_list[i] + "/groundtruth.txt",
dtype=float,
delimiter=",")
temp_gt = np.reshape(temp_gt, (temp_gt.shape[0], 4, 2))
gts.append(temp_gt)
lists = []
for i in xrange(len(num_frames)):
temp_list = tools.create_vot_image_list(vot_dir + vot_list[i] + "/",
num_frames[i])
lists.append(temp_list)
print "[MESSAGE] Ground truths and image lists are loaded."
# Create full background
background = np.ones((screen_height, screen_width, 3)) * bg_color
# Setup OpenCV display window
window_title = "DVS-VOT-EXP"
cv2.namedWindow(window_title, cv2.WND_PROP_FULLSCREEN)
# Experiment setup calibration
# Not without tuning images
swin_h, swin_w = helpers.calibration(win_h=screen_height,
win_w=screen_width,
scale=work_win_scale,
window_title=window_title,
bg_color=bg_color)
# Init a general UDP socket
s = tools.init_dvs()
tools.reset_dvs_time(s)
for k in xrange(len(num_frames)):
print "[MESSAGE] Display video sequence %i" % (k + 1)
frames = []
for i in xrange(num_frames[k]):
frames.append(cv2.imread(lists[k][i]))
new_frames = gui.rescale_image_sequence(frames, swin_h, swin_w,
bg_color)
# new_frames = gui.draw_poly_box_sequence(new_frames, gts[k])
new_frames = gui.create_border_sequence(new_frames, screen_height,
screen_width, bg_color)
cv2.imshow(window_title, new_frames[0])
print "[MESSAGE] Adapting video sequence %i" % (k + 1)
cv2.waitKey(delay=2000)
tools.start_log_dvs(s, recording_save_path, vot_list[k], viewer_id)
for i in xrange(num_frames[k]):
cv2.imshow(window_title, new_frames[i])
key = cv2.waitKey(delay=int(1000 / fps)) & 0xFF
if key == 27:
cv2.destroyAllWindows()
quit()
cv2.imshow(window_title, new_frames[-1])
tools.stop_log_dvs(s, viewer_id)
print "[MESSAGE] Releasing video sequence %i" % (k + 1)
cv2.waitKey(delay=2000)
cv2.imshow(window_title, background)
cv2.waitKey(delay=1000)
tools.reset_dvs_time(s)
print "[MESSAGE] Video sequence %i is logged." % (k + 1)
# Destory both scoket and opencv window
tools.destroy_dvs(s)
cv2.destroyAllWindows()