def noticeability_for_regions(grid, database):
area_of_region = grid.area_of_region()
regions = grid.calculate_regions(database)
n = noticeability.Noticeability(grid, database, regions)
n_regions = []
for region in regions:
n_regions.append(n.calculate(region[0], region[1]))
return n_regions
def draw_heatmap(database, grid):
"""Heatmap for noticeability.
See: http://matplotlib.org/examples/pylab_examples/hexbin_demo.html
"""
n = noticeability_for_regions(grid, database)
# Replace all NOT_APPLICABLE with zero.
# TODO: There's GOT to be a cleaner way!
for i,x in enumerate(n):
if n[i][0] == globals.NOT_APPLICABLE:
n[i][0] = 0
if n[i][1] == globals.NOT_APPLICABLE:
n[i][1] = 0
# Create a two-dimensional array to hold values for non-deceptive agent's
# noticeability. Rearrange for a {(0,0),(0,1),(0,2)...} layout instead of
# the region (top-to-bottom, left-to-right).
#
# TODO: This is upside down.
data = []
n_obj = noticeability.Noticeability(grid, database,
grid.calculate_regions(database))
for y in range(0, grid.num_divisions):
row = []
for x in range(0, grid.num_divisions):
top_left = point.Point(grid.bounding_box[0].x + (x * grid.divide.x),
grid.bounding_box[0].y - (y * grid.divide.y))
bottom_right = point.Point(grid.bounding_box[0].x + ((x + 1) * grid.divide.x),
grid.bounding_box[0].y - ((y + 1) * grid.divide.y))
n = n_obj.calculate(top_left, bottom_right)
if n[0] == globals.NOT_APPLICABLE:
n[0] = 0
if n[1] == globals.NOT_APPLICABLE:
n[1] = 0
row.append(n[1])
data.append(row)
# Swap color map for either BuGn or OrRd.
fig = pyplot.figure()
ax1 = fig.add_subplot(111)
cmap = cm.get_cmap('OrRd', 10)
ax1.imshow(data, interpolation='nearest', cmap=cmap)
pyplot.show()
if __name__ == '__main__':
if len(sys.argv) != 2:
print 'Requires path to data file'
sys.exit()
# Only process regions in this list. Leave empty to process all regions.
restrict_to_regions = []
# Read in database of agents from Kinect.
database = setup_database(sys.argv[1])
# Set up bounding box around agent positions.
grid = grid.Grid(database)
# Prepare all regions within bounding box.
regions = grid.calculate_regions(database)
n = noticeability.Noticeability(grid, database, regions)
s = suspiciousness.Suspiciousness(database, regions)
print 'n(region) = [non-deceptive, deceptive]'
print
if len(restrict_to_regions) > 0:
for region_num in restrict_to_regions:
print 'Region', str(region_num),
print n.calculate(regions[region_num][0], regions[region_num][1])
else:
for i, region in enumerate(regions):
print 'Region', str(i),
print n.calculate(region[0], region[1])
