[input]
path=data/Image0.tif
scale=2
num_images=1

# Define matrix_size to determines the number of surrounding pixels to # be
# compared with, ideally make this number uneven # Being twice the size of the
# actual ring thickness tends to be good 
matrix_size=26

# Whether or not to remove high intensity pixels
remove_high_intensity_pixels=false

[output]
dump_ring_to_image=false

[ring]
start=10
end=130
step=2

# Giving thickness of rpiv.ing NOTE if rings raddi apiv.nd thickness are too
# big, you will get errors from the gpu saying CL_piv.INVALID_WORK_GROUP_SIZE
thickness=6

# Set the maximum number of rings that should detected per ring pattern, if the
# number of found rings is greater than max_count, then the output is ignored
max_count=100

# Should be the same dimension as input image
width=1080
height=1280

[filter]
# When images maximum is less than min ignore output. Min is the likelyness
# value for a pixel to be the center of a ring and ranges from 0 to 1
min=0.125

# Consider only pixels that are above 0.8 * max when searching for clusters of
# pixels. Where max, is the highest intensity in the image
threshold=0.8

[dup]
# Set minumum distance between rings, and how close two different radius are
# considerend to be same ring
threshold=8.0

[remove]
# Up to what radii difference is a ring considered an inner or outer ring of two
# intersecting rings
threshold=4.0

[radii]
# Vary up from -radii_range to randii_range the radius to find the polynomial
# that corresponds to the rings contrast
range=6

# Set the minimum contrast a ring should have to be considered a ring
threshold=0.01

# By how much may the ring center be displaced to find it's actual center and
# it's actual radius
displacement=1