Chris,
RED, GRN, BLU = 0, 1, 2
line_pix = ((rmin <= data[:,:,RED] <= rmax) &
(gmin <= data[:,:,GRN] <= gmax) &
(bmin <= data[:,:,BLU] <= bmax))
I think this part doesn"t work because the data falls out of numpy
array-type when you slice it like this. It is coerced to a
normal list [Python in a Nutshell, page 306, para 3, says this
is something being "fixed" in numarray] so these compares fail
with:
TypeError: list indices must be integers
Here is a test harness if you have any time to fool with it.
<code>
import wx
import numpy as N
YOU_HAVE_AN_IMAGE = 0
if YOU_HAVE_AN_IMAGE:
path = "8bitchart01_small.tiff" # put its name here
image = wx.Image(path, wx.BITMAP_TYPE_ANY)
w, h = image.GetWidth(), image.GetHeight()
# Rappin page 362, Smart page 285
data = N.fromstring(image.GetData(), N.uint8)
data.reshape(w, h, 3)
else:
# for testing:
#>>> data = numpy.random.randint(0,255,(5,5,3))
#>>> print data <snip>
data = [
[[ 64, 237, 108], # x y
[185, 240, 68], # [0][1]
[ 17, 116, 55], # [0][2]
[107, 11, 185], # [0][3]
[154, 30, 52]], # [0][4]
[[251, 73, 150], # x y
[101, 91, 251], # [1][1]
[ 47, 225, 97], # [1][2]
[ 61, 23, 79], # [1][3]
[161, 22, 165]], # [1][4]
[[ 4, 162, 188], # [2][0]
[ 25, 131, 211], # [2][1]
[169, 83, 214], # [2][2]
[162, 112, 177],
[254, 139, 95]],
[[ 2, 88, 216], # [3][0]
[230, 63, 192],
[113, 151, 142],
[ 71, 78, 250],
[104, 65, 127]],
[[ 6, 191, 220], # [4][0]
[ 38, 141, 4],
[245, 41, 23],
[165, 24, 5],
[ 23, 127, 183]]]
# red, grn, blu = data[0][0]
# print "r=%r, g=%r, b=%r" % (red, grn, blu) # prints "r=64, g=237, b=108"
# this is a threshold filter
rmin, rmax, gmin, gmax, bmin, bmax = 0,178, 0,178, 100,255
def test1(data):
very_blue_pixels = []
w = 5 # data dependent
x = y = 0
# searches rows first (x is turning faster, so...)
for yslice in data:
for xyslice in yslice:
if 0: print "x=%r, y=%r, type(xyslice)= %r" % (x, y, type(xyslice))
if 0: print "data[x=%i][y=%i]=%r" % (x, y, data[x][y])
red, grn, blu = xyslice
if (rmin <= red <= rmax) and \
(gmin <= grn <= gmax) and \
(bmin <= blu <= bmax):
if 0: print "a very blue pixel at x=%i, y=%i, rgb=%r" % (x, y,
xyslice)
very_blue_pixels.append((x,y))
y += 1
if y==w: y=0
x += 1
print very_blue_pixels
# returns [(0, 3), (1, 1), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (3, 0),
(3, 2), (3, 3), (3, 4), (4, 4)]
def test2(data, THRESH):
# for my min/max dataset I can optimize like this
# at the cost of getting less output
# [(3, 0), (1, 1), (0, 2), (1, 2), (2, 2), (0, 3), (3, 3), (4, 4)]
very_blue_pixels = []
w = 5 # data dependent
x = y = 0
for xslice in data:
for xyslice in xslice:
red, grn, blu = xyslice
if (red <= THRESH) and (grn <= THRESH) and (THRESH <= blu):
very_blue_pixels.append((x, y, xyslice))
if 1: print "I see blue data[x=%i][y=%i] = %r" % (x, y, xyslice)
y += 1
if y==w: y=0
x += 1
print very_blue_pixels
#test1(data)
#test2(data, 178)
</code>
# ====
# CB wrote:
# > but, you can get rid of the loops:
# > if (rmin <= red <= rmax) and \
# > (gmin <= grn <= gmax) and \
# > (bmin <= blu <= bmax):
# > this could all be done in one fell swoop:
RED, GRN, BLU = 0, 1, 2
rmin, rmax, gmin, gmax, bmin, bmax = 0,178, 0,178, 100,255
line_pix = ((rmin <= data[:,:,RED] <= rmax) &
(gmin <= data[:,:,GRN] <= gmax) &
(bmin <= data[:,:,BLU] <= bmax))
# data(line_pix) = 0 # black returns:
# SyntaxError: can't assign to function call
# data(~line_pix) = 255 # white -CB
# > or maybe:
data = N.where(data, line_pix, 0, 255)
_______________________________________________
FloatCanvas mailing list
[email protected]
http://mail.mithis.com/cgi-bin/mailman/listinfo/floatcanvas
