class Dataset:
TYPE_POSITIVE = 0
TYPE_NEGATIVE = 1
TYPE_ACCURACY = 2
TYPE_HEATMAP = 3
LEFT_HAND = 0
RIGHT_HAND = 1
NO_HAND = 2
DISTANCE_550 = 0
DISTANCE_750 = 4
DISTANCE_1000 = 1
DISTANCE_1250 = 5
DISTANCE_1500 = 2
DISTANCE_1750 = 6
DISTANCE_2000 = 3
# Constructor of the Dataset class
#
# @param camera_height Value of the camera height while gathering informations
# @param hand Identifier of the hand (Ø|1|2)
# @param skeleton Skeletal joints of the detected subject
# @param depth_map Depth map of the captured scene
# @param image RGB image of the captured scene
# @param type Identifier of the type of data (0|1|2|3)
# @param distance Identifier of the type of distance chosen (0|1|2|3|4|5|6) or the actual distance between the fingertip and the target
# @param target Coordinates of the target
# @return None
def __init__(self,
camera_height=None,
hand=None,
skeleton=None,
depth_map=None,
image=None,
type=None,
distance=None,
target=None):
self.settings = Settings()
self.utils = Utils()
# Initialise each attributes with respective parameters; otherwise with a default value
if camera_height is None:
camera_height = 1500
self.camera_height = camera_height
if hand is None:
hand = self.settings.LEFT_HAND
self.hand = hand
if skeleton is None:
skeleton = {
"head": [],
"shoulder": {
"left": [],
"right": [],
"center": []
},
"elbow": {
"left": [],
"right": []
},
"hand": {
"left": [],
"right": []
}
}
self.skeleton = skeleton
if depth_map is None:
depth_map = []
self.depth_map = np.array(depth_map)
if image is None:
image = ""
self.image = image
if type is None:
type = Dataset.TYPE_POSITIVE
self.type = type
if distance is None:
distance = Dataset.DISTANCE_550
self.distance = distance
if target is None:
target = []
self.target = target
# Returns a JSON encoded string of the dataset object
#
# @param None
# @return string JSON encoded string of the dataset object
def to_JSON(self):
# Convert the depth map to a serializable state
self.depth_map = self.depth_map.tolist()
# Encode the RGB image in a base64 string
self.image = self.utils.getBase64(self.image)
# Get rid of extra attributes to clean the output
obj = deepcopy(self)
del obj.settings
del obj.utils
return json.dumps(obj,
default=lambda o: o.__dict__,
separators=(',', ':'))
# Save the dataset informations as a file
#
# @param None
# @return None
def save(self):
print "Saving dataset informations..."
# Save the dataset to the right folder
if self.type == Dataset.TYPE_POSITIVE:
filename = self.settings.getPositiveFolder()
elif self.type == Dataset.TYPE_NEGATIVE:
filename = self.settings.getNegativeFolder()
elif self.type == Dataset.TYPE_ACCURACY:
filename = self.settings.getAccuracyFolder()
else:
raise ValueError("Invalid type of dataset to save", self.type)
# Retrieve the number of files saved so far
# Be careful that due to the sample file, the counter does not need to be incremented. Otherwise, the files would replace each others
filename += str(
self.utils.getFileNumberInFolder(filename)).zfill(3) + ".json"
self.utils.dumpJsonToFile(self.to_JSON(), filename)
# Toggle the type identifier of the dataset
#
# @param value Identifier of the new type of the dataset
# @return None
def toggleType(self, value):
self.type = value
print "type toggled to {0}".format(value)
# Toggle the distance identifier of the dataset
#
# @param value Identifier of the new distance of the dataset
# @return None
def toggleDistance(self, value):
self.distance = value
print "distance toggled to {0}".format(value)
# Update the distance of the dataset
#
# @param value Distance value
# @return None
def setDistance(self, value):
self.distance = value
print "distance changed to {0}".format(value)
# Toggle the hand identifier of the dataset
#
# @param value Identifier of the new hand of the dataset
# @return None
def toggleHand(self, value):
self.hand = value
print "hand toggled"
# Returns the actual distance
#
# @param None
# @return numeric Actual distance value (translated if identifier)
def getWishedDistance(self):
if self.distance == Dataset.DISTANCE_550:
return 550
elif self.distance == Dataset.DISTANCE_750:
return 750
elif self.distance == Dataset.DISTANCE_1000:
return 1000
elif self.distance == Dataset.DISTANCE_1250:
return 1250
elif self.distance == Dataset.DISTANCE_1500:
return 1500
elif self.distance == Dataset.DISTANCE_1750:
return 1750
elif self.distance == Dataset.DISTANCE_2000:
return 2000
else:
return self.distance
class Dataset:
TYPE_POSITIVE = 0
TYPE_NEGATIVE = 1
TYPE_ACCURACY = 2
TYPE_HEATMAP = 3
LEFT_HAND = 0
RIGHT_HAND = 1
NO_HAND = 2
DISTANCE_550 = 0
DISTANCE_750 = 4
DISTANCE_1000 = 1
DISTANCE_1250 = 5
DISTANCE_1500 = 2
DISTANCE_1750 = 6
DISTANCE_2000 = 3
# Constructor of the Dataset class
#
# @param camera_height Value of the camera height while gathering informations
# @param hand Identifier of the hand (Ø|1|2)
# @param skeleton Skeletal joints of the detected subject
# @param depth_map Depth map of the captured scene
# @param image RGB image of the captured scene
# @param type Identifier of the type of data (0|1|2|3)
# @param distance Identifier of the type of distance chosen (0|1|2|3|4|5|6) or the actual distance between the fingertip and the target
# @param target Coordinates of the target
# @return None
def __init__(self, camera_height=None, hand=None, skeleton=None, depth_map=None, image=None, type=None, distance=None, target=None):
self.settings = Settings()
self.utils = Utils()
# Initialise each attributes with respective parameters; otherwise with a default value
if camera_height is None:
camera_height = 1500
self.camera_height = camera_height
if hand is None:
hand = self.settings.LEFT_HAND
self.hand = hand
if skeleton is None:
skeleton = {
"head": [],
"shoulder": {
"left": [],
"right": [],
"center": []
},
"elbow": {
"left": [],
"right": []
},
"hand": {
"left": [],
"right": []
}
}
self.skeleton = skeleton
if depth_map is None:
depth_map = []
self.depth_map = np.array(depth_map)
if image is None:
image = ""
self.image = image
if type is None:
type = Dataset.TYPE_POSITIVE
self.type = type
if distance is None:
distance = Dataset.DISTANCE_550
self.distance = distance
if target is None:
target = []
self.target = target
# Returns a JSON encoded string of the dataset object
#
# @param None
# @return string JSON encoded string of the dataset object
def to_JSON(self):
# Convert the depth map to a serializable state
self.depth_map = self.depth_map.tolist()
# Encode the RGB image in a base64 string
self.image = self.utils.getBase64(self.image)
# Get rid of extra attributes to clean the output
obj = deepcopy(self)
del obj.settings
del obj.utils
return json.dumps(obj, default=lambda o: o.__dict__, separators=(',', ':'))
# Save the dataset informations as a file
#
# @param None
# @return None
def save(self):
print "Saving dataset informations..."
# Save the dataset to the right folder
if self.type == Dataset.TYPE_POSITIVE:
filename = self.settings.getPositiveFolder()
elif self.type == Dataset.TYPE_NEGATIVE:
filename = self.settings.getNegativeFolder()
elif self.type == Dataset.TYPE_ACCURACY:
filename = self.settings.getAccuracyFolder()
else:
raise ValueError("Invalid type of dataset to save", self.type)
# Retrieve the number of files saved so far
# Be careful that due to the sample file, the counter does not need to be incremented. Otherwise, the files would replace each others
filename += str(self.utils.getFileNumberInFolder(filename)).zfill(3)+".json"
self.utils.dumpJsonToFile(self.to_JSON(), filename)
# Toggle the type identifier of the dataset
#
# @param value Identifier of the new type of the dataset
# @return None
def toggleType(self, value):
self.type = value
print "type toggled to {0}".format(value)
# Toggle the distance identifier of the dataset
#
# @param value Identifier of the new distance of the dataset
# @return None
def toggleDistance(self, value):
self.distance = value
print "distance toggled to {0}".format(value)
# Update the distance of the dataset
#
# @param value Distance value
# @return None
def setDistance(self, value):
self.distance = value
print "distance changed to {0}".format(value)
# Toggle the hand identifier of the dataset
#
# @param value Identifier of the new hand of the dataset
# @return None
def toggleHand(self, value):
self.hand = value
print "hand toggled"
# Returns the actual distance
#
# @param None
# @return numeric Actual distance value (translated if identifier)
def getWishedDistance(self):
if self.distance == Dataset.DISTANCE_550:
return 550
elif self.distance == Dataset.DISTANCE_750:
return 750
elif self.distance == Dataset.DISTANCE_1000:
return 1000
elif self.distance == Dataset.DISTANCE_1250:
return 1250
elif self.distance == Dataset.DISTANCE_1500:
return 1500
elif self.distance == Dataset.DISTANCE_1750:
return 1750
elif self.distance == Dataset.DISTANCE_2000:
return 2000
else:
return self.distance
