def __init__(self):
#load algorithm constants
#how round a circle needs to be. Perfect circle = 1
self.eccentricity = VN_config.get_float('algorithm', 'eccentricity', 0.6)
#Minimum ratio while comparing contour area to ellipse area
self.area_ratio = VN_config.get_float('algorithm','area_ratio', 0.8)
#Minimum ratio between outer and inner circle area in a ring
self.ring_ratio = VN_config.get_float('algorithm','ring_ratio', 0.1)
#The smallest span of min max pixels that get enhanced(Largest range is 255, Smaller numbers make image suspitable to noise)
self.min_range = VN_config.get_integer('algorithm', 'min_range', 10)
#reduce the grayscale resoltion(steps) by this multipler( 1 is full res, 2 is half res, 4 is quarter res )
self.res_reduction = VN_config.get_integer('algorithm', 'res_reduction', 32)
def __init__(self):
#load algorithm constants
#how round a circle needs to be. Perfect circle = 1
self.eccentricity = VN_config.get_float('algorithm', 'eccentricity',
0.6)
#Minimum ratio while comparing contour area to ellipse area
self.area_ratio = VN_config.get_float('algorithm', 'area_ratio', 0.8)
#Minimum ratio between outer and inner circle area in a ring
self.ring_ratio = VN_config.get_float('algorithm', 'ring_ratio', 0.1)
#The smallest span of min max pixels that get enhanced(Largest range is 255, Smaller numbers make image suspitable to noise)
self.min_range = VN_config.get_integer('algorithm', 'min_range', 10)
#reduce the grayscale resoltion(steps) by this multipler( 1 is full res, 2 is half res, 4 is quarter res )
self.res_reduction = VN_config.get_integer('algorithm',
'res_reduction', 32)
def __init__(self):
self.targetLocation = PositionVector()
self.vehicleLocation = PositionVector()
self.backgroundColor = (74,88,109)
#define camera
self.camera_width = VN_config.get_integer('camera', 'width', 640)
self.camera_height = VN_config.get_integer('camera', 'height', 640)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov',72.42 )
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov', 72.42)
self.camera_fov = math.sqrt(self.camera_vfov**2 + self.camera_hfov**2)
self.camera_frameRate = 30
def __init__(self):
self.targetLocation = PositionVector()
self.vehicleLocation = PositionVector()
self.backgroundColor = (74, 88, 109)
#define camera
self.camera_width = VN_config.get_integer('camera', 'width', 640)
self.camera_height = VN_config.get_integer('camera', 'height', 640)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 72.42)
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov',
72.42)
self.camera_fov = math.sqrt(self.camera_vfov**2 + self.camera_hfov**2)
self.camera_frameRate = 30
def __init__(self):
# get which camera we will use
self.camera_index = VN_config.get_integer('camera','index',0)
# get image resolution
self.img_width = VN_config.get_integer('camera','width',640)
self.img_height = VN_config.get_integer('camera','height',480)
# get image center
self.img_center_x = self.img_width / 2
self.img_center_y = self.img_height / 2
# define field of view
self.cam_hfov = VN_config.get_float('camera','horizontal-fov',70.42)
self.cam_vfov = VN_config.get_float('camera','vertical-fov',43.3)
#get camera distortion matrix and intrinsics. Defaults: logitech c920
mtx = np.array([[ 614.01269552,0,315.00073982],
[0,614.43556296,237.14926858],
[0,0,1.0]])
dist = np.array([0.12269303, -0.26618881,0.00129035, 0.00081791,0.17005303])
self.matrix = VN_config.get_array('camera','matrix', mtx)
self.distortion = VN_config.get_array('camera', 'distortion', dist)
self.newcameramtx, self.roi=cv2.getOptimalNewCameraMatrix(self.matrix,self.distortion,(self.img_width,self.img_height),1,(self.img_width,self.img_height))
#create a camera object
self.camera = None
#number of cores available for use
desiredCores = VN_config.get_integer('processing', 'desired_cores', 4)
self.cores_available = min(desiredCores, multiprocessing.cpu_count())
#does the user want to capture images in the background
self.background_capture = VN_config.get_boolean('processing','background_capture', True)
# background image processing variables
self.proc = None # background process object
self.parent_conn = None # parent end of communicatoin pipe
self.img_counter = 0 # num images requested so far
self.is_backgroundCap = False #state variable for background capture
def __init__(self):
# add variable initialisation here
self.api = None
self.vehicle = None
self.last_mode_call = 0
self.last_mode_state = 'STABILIZE'
self.mode_update_rate = VN_config.get_float('vehicle control', 'mode_update_rate', 0.75)
self.last_home_call = 0
self.last_home = None
self.home_update_rate = VN_config.get_float('vehicle control', 'home_update_rate', 10)
self.last_set_velocity = 0
self.vel_update_rate = VN_config.get_float('vehicle control', 'vel_update_rate', 0.1)
self.last_report_landing_target = 0
self.landing_update_rate = VN_config.get_float('vehicle control', 'landing_update_rate', 0.02)
def __init__(self):
#create sub driectory for logs and videos
self.location = VN_config.get_string('logging', 'location',
os.environ['HOME'] + '/visnav/')
path = self.location + 'logs'
if not os.path.exists(path):
os.makedirs(path)
path = self.location + 'vids'
if not os.path.exists(path):
os.makedirs(path)
#set default startegy name
self.strat_name = 'Smart_Camera'
# get image resolution
self.img_width = VN_config.get_integer('camera', 'width', 640)
self.img_height = VN_config.get_integer('camera', 'height', 480)
#####TEXT LOGGING######
#levels = 'debug' , 'general' , 'aircraft' , 'algorithm' , ' performance'
#multiple message levels can be selected by concatination strings i.e. 'debug, aircraft'
#what type of messages we print to the terminal
self.print_level = VN_config.get_string('logging', 'print_level',
'debug, general')
#what type of messages we log to a file
self.log_level = VN_config.get_string('logging', 'log_level',
'aircraft, algorithm, general')
#####VIDEO RECORDING######
#levels = 'frame' , 'gui'
#multiple message levels can be selected at once by concatination strings i.e. 'frame, gui'
#what type of images we display on the screen
self.display_level = VN_config.get_string('logging', 'display_level',
'raw, gui')
#what type of images we record
self.record_level = VN_config.get_string('logging', 'record_level',
'raw')
#Write a video or individual images
self.record_type = VN_config.get_string('logging', 'record_type',
'image')
#Note about useful logging practices:
#Inorder to replay a flight through the program log_level must include 'aircraft' and 'algorithm'
#record level must include 'frame'
#all other logging levels are for user diagnostics
#a list of video writers and their tag
self.video_writers = []
#text logger
self.logger = None
def __init__(self):
#The number of core to be used while processing image data
#This number may be less than the actaul number of cores on the CPU depending on the users specifications
#cores = min(desiredCores, multiprocessing.cpu_count()) //dont allow more cores than the CPU has available and don;t run more than the user wants
desired_cores = VN_config.get_integer('processing', 'desired_cores', 4)
available_cores = min(desired_cores, multiprocessing.cpu_count())
#check if a core is already in use for background image capture
cores_image_capture = int(
VN_config.get_boolean('processing', 'background_capture', True))
self.cores_processing = max(available_cores - cores_image_capture, 1)
#The time(in millis) is takes to capture an Image
#Frame rate = 1000/captureTime
#****On some cameras frame rate is dependent on camera exposure level
#****i.e. dark = slower fps and light = fast frame rate
#****Capture time is dependent on available CPU
#This time will be dynamically calculated using an rolling average
self.captureTime = 0
self.captureTimeSet = np.zeros(4)
#The time(in millis) it takes to process an image
#****Process time is dependent on available CPU and the image
#This time will be dynamically calculated using an rolling average
self.processTime = 0
self.processTimeSet = np.zeros(4)
#How often a image is dispatched to be processed(in millis)
#Determined by splitting up processTime among available number of cores
#*****This will not be smaller than captureTime because we won't allow it to process frames more often than capturing frames; it will cause sync issues with Pipe()
#*****If the CPU is slow and has limited cores, we may process everyother frame or every nth frame!!!
#runTime = max(processTime/processing_cores,captureTime)
self.runTime = 0
#set up a pipe to pass info between background processes and dispatcher
self.parent_conn, self.child_conn = multiprocessing.Pipe()
#last time we started to process an image
self.lastDispatch = 0
#last time an image process completed
self.lastRetreival = 0
def __init__(self):
#The number of core to be used while processing image data
#This number may be less than the actaul number of cores on the CPU depending on the users specifications
#cores = min(desiredCores, multiprocessing.cpu_count()) //dont allow more cores than the CPU has available and don;t run more than the user wants
desired_cores = VN_config.get_integer('processing', 'desired_cores', 4)
available_cores = min(desired_cores, multiprocessing.cpu_count())
#check if a core is already in use for background image capture
cores_image_capture = int(VN_config.get_boolean('processing','background_capture', True))
self.cores_processing = max(available_cores - cores_image_capture,1)
#The time(in millis) is takes to capture an Image
#Frame rate = 1000/captureTime
#****On some cameras frame rate is dependent on camera exposure level
#****i.e. dark = slower fps and light = fast frame rate
#****Capture time is dependent on available CPU
#This time will be dynamically calculated using an rolling average
self.captureTime = 0
self.captureTimeSet = np.zeros(4)
#The time(in millis) it takes to process an image
#****Process time is dependent on available CPU and the image
#This time will be dynamically calculated using an rolling average
self.processTime = 0
self.processTimeSet = np.zeros(4)
#How often a image is dispatched to be processed(in millis)
#Determined by splitting up processTime among available number of cores
#*****This will not be smaller than captureTime because we won't allow it to process frames more often than capturing frames; it will cause sync issues with Pipe()
#*****If the CPU is slow and has limited cores, we may process everyother frame or every nth frame!!!
#runTime = max(processTime/processing_cores,captureTime)
self.runTime = 0
#set up a pipe to pass info between background processes and dispatcher
self.parent_conn, self.child_conn = multiprocessing.Pipe()
#last time we started to process an image
self.lastDispatch = 0
#last time an image process completed
self.lastRetreival = 0
def __init__(self):
# get which camera we will use
self.camera_index = VN_config.get_integer('camera', 'index', 0)
# get image resolution
self.img_width = VN_config.get_integer('camera', 'width', 640)
self.img_height = VN_config.get_integer('camera', 'height', 480)
# get image center
self.img_center_x = self.img_width / 2
self.img_center_y = self.img_height / 2
# define field of view
self.cam_hfov = VN_config.get_float('camera', 'horizontal-fov', 70.42)
self.cam_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3)
#get camera distortion matrix and intrinsics. Defaults: logitech c920
mtx = np.array([[614.01269552, 0, 315.00073982],
[0, 614.43556296, 237.14926858], [0, 0, 1.0]])
dist = np.array(
[0.12269303, -0.26618881, 0.00129035, 0.00081791, 0.17005303])
self.matrix = VN_config.get_array('camera', 'matrix', mtx)
self.distortion = VN_config.get_array('camera', 'distortion', dist)
self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(
self.matrix, self.distortion, (self.img_width, self.img_height), 1,
(self.img_width, self.img_height))
#create a camera object
self.camera = None
#number of cores available for use
desiredCores = VN_config.get_integer('processing', 'desired_cores', 4)
self.cores_available = min(desiredCores, multiprocessing.cpu_count())
#does the user want to capture images in the background
self.background_capture = VN_config.get_boolean(
'processing', 'background_capture', True)
# background image processing variables
self.proc = None # background process object
self.parent_conn = None # parent end of communicatoin pipe
self.img_counter = 0 # num images requested so far
self.is_backgroundCap = False #state variable for background capture
def __init__(self):
# add variable initialisation here
self.api = None
self.vehicle = None
self.last_mode_call = 0
self.last_mode_state = 'STABILIZE'
self.mode_update_rate = VN_config.get_float('vehicle control',
'mode_update_rate', 0.75)
self.last_home_call = 0
self.last_home = None
self.home_update_rate = VN_config.get_float('vehicle control',
'home_update_rate', 10)
self.last_set_velocity = 0
self.vel_update_rate = VN_config.get_float('vehicle control',
'vel_update_rate', 0.1)
self.last_report_landing_target = 0
self.landing_update_rate = VN_config.get_float('vehicle control',
'landing_update_rate',
0.02)
def __init__(self):
# create sub driectory for logs and videos
self.location = VN_config.get_string("logging", "location", os.environ["HOME"] + "/visnav/")
path = self.location + "logs"
if not os.path.exists(path):
os.makedirs(path)
path = self.location + "vids"
if not os.path.exists(path):
os.makedirs(path)
# set default startegy name
self.strat_name = "Smart_Camera"
# get image resolution
self.img_width = VN_config.get_integer("camera", "width", 640)
self.img_height = VN_config.get_integer("camera", "height", 480)
#####TEXT LOGGING######
# levels = 'debug' , 'general' , 'aircraft' , 'algorithm' , ' performance'
# multiple message levels can be selected by concatination strings i.e. 'debug, aircraft'
# what type of messages we print to the terminal
self.print_level = VN_config.get_string("logging", "print_level", "debug, general")
# what type of messages we log to a file
self.log_level = VN_config.get_string("logging", "log_level", "aircraft, algorithm, general")
#####VIDEO RECORDING######
# levels = 'frame' , 'gui'
# multiple message levels can be selected at once by concatination strings i.e. 'frame, gui'
# what type of images we display on the screen
self.display_level = VN_config.get_string("logging", "display_level", "raw, gui")
# what type of images we record
self.record_level = VN_config.get_string("logging", "record_level", "raw")
# Write a video or individual images
self.record_type = VN_config.get_string("logging", "record_type", "image")
# Note about useful logging practices:
# Inorder to replay a flight through the program log_level must include 'aircraft' and 'algorithm'
# record level must include 'frame'
# all other logging levels are for user diagnostics
# a list of video writers and their tag
self.video_writers = []
# text logger
self.logger = None
def __init__(self):
#load config file
VN_config.get_file('Smart_Camera')
#get camera specs
self.camera_index = VN_config.get_integer('camera','camera_index',0)
self.camera_width = VN_config.get_integer('camera', 'camera_width', 640)
self.camera_height = VN_config.get_integer('camera', 'camera_height', 480)
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov', 72.42)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3)
#use simulator
self.simulator = VN_config.get_boolean('simulator','use_simulator',True)
#Run the program no matter what mode or location; Useful for debug purposes
self.always_run = VN_config.get_boolean('general', 'always_run', True)
#how many frames have been captured
self.frame_count = 0
#debugging:
self.kill_camera = False
def __init__(self):
#create sub driectory for logs and videos
self.location = VN_config.get_string('logging','location','../../visnav/')
path = self.location + 'logs'
if not os.path.exists(path):
os.makedirs(path)
path = self.location + 'vids'
if not os.path.exists(path):
os.makedirs(path)
#set default startegy name
self.strat_name = 'Smart_Camera'
# get image resolution
self.img_width = VN_config.get_integer('camera','width',640)
self.img_height = VN_config.get_integer('camera','height',480)
#####TEXT LOGGING######
#levels = 'debug' , 'general' , 'aircraft' , 'algorithm' , ' performance'
#multiple message levels can be selected by concatination strings i.e. 'debug, aircraft'
#what type of messages we print to the terminal
self.print_level = VN_config.get_string('logging','print_level','debug, general')
#what type of messages we log to a file
self.log_level = VN_config.get_string('logging','log_level','aircraft , algorithm, general')
#####VIDEO RECORDING######
#levels = 'frame' , 'gui'
#multiple message levels can be selected at once by concatination strings i.e. 'frame, gui'
#what type of images we display on the screen
self.display_level = VN_config.get_string('logging', 'display_level', 'raw, gui')
#what type of images we record
self.record_level = VN_config.get_string('logging', 'record_level', 'raw')
#Note about useful logging practices:
#Inorder to replay a flight through the program log_level must include 'aircraft' and 'algorithm'
#record level must include 'frame'
#all other logging levels are for user diagnostics
#a list of video writers and their tag
self.video_writers = []
#text logger
self.logger = None
def __init__(self):
self.targetLocation = PositionVector()
self.vehicleLocation = PositionVector()
self.backgroundColor = (74,88,109)
#load target
filename = VN_config.get_string('simulator', 'target_location', '../../visnav/target.jpg')
target_size = VN_config.get_float('algorithm', 'outer_ring', 1.0)
self.load_target(filename,target_size)
#define camera
self.camera_width = VN_config.get_integer('camera', 'width', 640)
self.camera_height = VN_config.get_integer('camera', 'height', 640)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov',72.42 )
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov', 72.42)
self.camera_fov = math.sqrt(self.camera_vfov**2 + self.camera_hfov**2)
self.camera_frameRate = 30
def __init__(self):
self.targetLocation = PositionVector()
self.vehicleLocation = PositionVector()
self.backgroundColor = (74, 88, 109)
#load target
filename = VN_config.get_string('simulator', 'target_location',
'../../visnav/target.jpg')
target_size = VN_config.get_float('algorithm', 'outer_ring', 1.0)
self.load_target(filename, target_size)
#define camera
self.camera_width = VN_config.get_integer('camera', 'width', 640)
self.camera_height = VN_config.get_integer('camera', 'height', 640)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 72.42)
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov',
72.42)
self.camera_fov = math.sqrt(self.camera_vfov**2 + self.camera_hfov**2)
self.camera_frameRate = 30
def __init__(self):
#load algorithm constants
#how round a circle needs to be. Perfect circle = 1
self.eccentricity = VN_config.get_float('algorithm', 'eccentricity',
0.6)
#acceptable distance(pixels) between cocentric circle centers
self.distance_threshold = VN_config.get_integer(
'algorithm', 'distance_threshold', 15)
# number of circles needed for a valid target(times 2); 2 circles are often overlayed
self.min_circles = VN_config.get_integer('algorithm', 'min_circls', 5)
#pixels: used to identify repeat circles(stacked circles). Problem caused by findContours()
self.radius_tolerance = VN_config.get_integer('algorithm',
'radius_tolerance', 2)
#Tolerance used in comparing actaul ratios and preceived ratios
self.ratio_tolerance = VN_config.get_float('algorithm',
'ratio_tolerance', 0.015)
#target specific data
#target_code is the unique ratio between rings
target_code_def = np.array([0.8, 0.91, 0.76, 0.84, 0.7, 0.66, 0.49])
self.target_code = VN_config.get_array('algorithm', 'target_code',
target_code_def)
#the outer_ring is a scaling factor for targets of various sizes; radius of outer ring in meters
self.outer_ring = VN_config.get_float('algorithm', 'outer_ring',
0.08255)
#define field of view
self.cam_hfov = VN_config.get_float('camera', 'horizontal-fov', 70.42)
self.cam_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3)\
#define camera size
self.cam_width = VN_config.get_integer('camera', 'width', 640)
self.cam_height = VN_config.get_integer('camera', 'height', 480)
def __init__(self):
# load config file
VN_config.get_file("Smart_Camera")
# get camera specs
self.camera_index = VN_config.get_integer("camera", "camera_index", 0)
self.camera_width = VN_config.get_integer("camera", "camera_width", 640)
self.camera_height = VN_config.get_integer("camera", "camera_height", 480)
self.camera_hfov = VN_config.get_float("camera", "horizontal-fov", 72.42)
self.camera_vfov = VN_config.get_float("camera", "vertical-fov", 43.3)
# simulator
self.simulator = VN_config.get_boolean("simulator", "use_simulator", True)
self.target_file = VN_config.get_string(
"simulator", "target_location", os.environ["HOME"] + "/visnav/target.jpg"
)
self.target_size = VN_config.get_float("algorithm", "outer_ring", 1.0)
# Run the program no matter what mode or location; Useful for debug purposes
self.always_run = VN_config.get_boolean("general", "always_run", True)
# how many frames have been captured
self.frames_captured = 0
# debugging:
self.kill_camera = False
def __init__(self):
#load algorithm constants
#how round a circle needs to be. Perfect circle = 1
self.eccentricity = VN_config.get_float('algorithm', 'eccentricity', 0.6)
#acceptable distance(pixels) between cocentric circle centers
self.distance_threshold = VN_config.get_integer('algorithm','distance_threshold', 15)
# number of circles needed for a valid target(times 2); 2 circles are often overlayed
self.min_circles = VN_config.get_integer('algorithm','min_circls',5)
#pixels: used to identify repeat circles(stacked circles). Problem caused by findContours()
self.radius_tolerance = VN_config.get_integer('algorithm', 'radius_tolerance', 2)
#Tolerance used in comparing actaul ratios and preceived ratios
self.ratio_tolerance = VN_config.get_float('algorithm', 'ratio_tolerance', 0.015)
#target specific data
#target_code is the unique ratio between rings
target_code_def = np.array([0.8,0.91,0.76,0.84,0.7,0.66,0.49])
self.target_code = VN_config.get_array('algorithm', 'target_code',target_code_def)
#the outer_ring is a scaling factor for targets of various sizes; radius of outer ring in meters
self.outer_ring = VN_config.get_float('algorithm', 'outer_ring', 0.08255)
#define field of view
self.cam_hfov = VN_config.get_float('camera', 'horizontal-fov', 70.42)
self.cam_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3)
#define camera size
self.cam_width = VN_config.get_integer('camera', 'width', 640)
self.cam_height = VN_config.get_integer('camera', 'height', 480)
def __init__(self):
#load config file
VN_config.get_file('Smart_Camera')
#get camera specs
self.camera_index = VN_config.get_integer('camera', 'camera_index', 0)
self.camera_width = VN_config.get_integer('camera', 'camera_width',
640)
self.camera_height = VN_config.get_integer('camera', 'camera_height',
480)
self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov',
72.42)
self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3)
#simulator
self.simulator = VN_config.get_boolean('simulator', 'use_simulator',
True)
self.target_file = VN_config.get_string(
'simulator', 'target_location',
os.environ['HOME'] + '/visnav/target.jpg')
self.target_size = VN_config.get_float('algorithm', 'outer_ring', 1.0)
#Run the program no matter what mode or location; Useful for debug purposes
self.always_run = VN_config.get_boolean('general', 'always_run', True)
#how many frames have been captured
self.frames_captured = 0
#debugging:
self.kill_camera = False
#!/usr/bin/python
#SYSTEM IMPORTS
import numpy as np
#COMMOM IMPORTS
from Common.VN_config import VN_config
#####ALGORITHM#######
#how round a circle needs to be. Perfect circle = 1
VN_config.set_float('algorithm', 'eccentricity', 0.6)
#acceptable distance(pixels) between cocentric circle centers
VN_config.set_integer('algorithm', 'distance_threshold', 15)
# number of circles needed for a valid target(times 2); 2 circles are often overlayed
VN_config.set_integer('algorithm', 'min_circles', 5)
#pixels: used to identify repeat circles(stacked circles). Problem caused by findContours()
VN_config.set_integer('algorithm', 'radius_tolerance', 2)
#Tolerance used in comparing actaul ratios and preceived ratios
VN_config.set_float('algorithm', 'ratio_tolerance', 0.015)
#target specific data
#target_code is the unique ratio between rings
target_code_def = np.array([0.8, 0.91, 0.76, 0.84, 0.7, 0.66, 0.49])
VN_config.set_array('algorithm', 'target_code', target_code_def)
#the outer_ring is a scaling factor for targets of various sizes; radius of outer ring in meters
VN_config.set_float('algorithm', 'outer_ring', 0.08255)
####PROCESSING#######
VN_config.set_integer('processing', 'desired_cores', 4)
#check if a core is already in use for background image capture
#!/usr/bin/python
#SYSTEM IMPORTS
import numpy as np
#COMMOM IMPORTS
from Common.VN_config import VN_config
#####ALGORITHM#######
#how round a circle needs to be. Perfect circle = 1
VN_config.set_float('algorithm', 'eccentricity', 0.6)
#acceptable distance(pixels) between cocentric circle centers
VN_config.set_integer('algorithm','distance_threshold', 15)
# number of circles needed for a valid target(times 2); 2 circles are often overlayed
VN_config.set_integer('algorithm','min_circles',5)
#pixels: used to identify repeat circles(stacked circles). Problem caused by findContours()
VN_config.set_integer('algorithm', 'radius_tolerance', 2)
#Tolerance used in comparing actaul ratios and preceived ratios
VN_config.set_float('algorithm', 'ratio_tolerance', 0.015)
#target specific data
#target_code is the unique ratio between rings
target_code_def = np.array([0.8,0.91,0.76,0.84,0.7,0.66,0.49])
VN_config.set_array('algorithm', 'target_code',target_code_def)
#the outer_ring is a scaling factor for targets of various sizes; radius of outer ring in meters
VN_config.set_float('algorithm', 'outer_ring', 0.08255)
