00001
00002
00003
00004 from __future__ import division
00005
00006 from enhanced_grid import *
00007 from image import *
00008
00009
00010 def demo_stitch():
00011 i = 0
00012 j = 0
00013
00014 large_grid = Grid2D((128*14, 9*128))
00015
00016 for k in range(126):
00017 fname = 'perlin_noise/perlin_lin_channels_' + str(k) + '.png'
00018 grid = rgb_image_to_grid(fname)
00019
00020 print k
00021
00022 for small_i in range(128):
00023 for small_j in range(128):
00024 large_grid[128 * i + small_i, j*128 + small_j] = grid[small_i, small_j]
00025
00026 i += 1
00027
00028 if i >= 14:
00029 i = 0
00030 j += 1
00031
00032 grid_to_rgb_image(large_grid, 'large_im.png')
00033
00034
00035 def demo_edge():
00036 grid = rgb_image_to_grid('bar.png')
00037 new_grid = edge(grid, 0)
00038 grid_to_rgb_image(new_grid, 'edge2_%2d.png' % 0 )
00039
00040 def demo_normalize():
00041 grid = rgb_image_to_grid('perlin_lin_channels_104.png')
00042 new_grid = normalize(grid)
00043 grid_to_rgb_image(new_grid, 'normalize.png')
00044 def demo_entropy():
00045 grid = rgb_image_to_grid('bar.png')
00046
00047 for k in range(10, 200, 10):
00048 new_grid = normalize(entropy2(grid, k))
00049 grid_to_rgb_image(new_grid, 'entropy2_%3d.png' % k)
00050 def demo():
00051
00052
00053
00054 demo_entropy()
00055
00056 demo()