codebase / stimlib

Commit 254e3bc8fcb7eba1708594772763cdc7d5bfc095

Authored by David Mayerich
1 parent 59f31ea4

added a full classification function for envi files and updated color selection

Showing 1 changed file with 19 additions and 6 deletions   Show diff stats
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python/classify.py
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... ... @@ -17,6 +17,20 @@ import random
17 17 import progressbar
18 18 import matplotlib.pyplot as plt
19 19  
  20 +#generate N qualitative colors and return the value for color c
  21 +def qualcolor(c, N):
  22 + dN = numpy.ceil(numpy.sqrt(N)).astype(numpy.int32)
  23 + h = c/N
  24 +
  25 + sp = c/N * 2 * numpy.pi * dN + numpy.pi/2
  26 + s = numpy.sin(sp) * 0.25 + 0.75
  27 +
  28 + vp = c/N * 2 * numpy.pi * dN
  29 + v = numpy.sin(vp) * 0.25 + 0.75
  30 +
  31 + rgb = numpy.array(colorsys.hsv_to_rgb(h, s, v))
  32 + return rgb * 255
  33 +
20 34 #generate a 2D color class map using a stack of binary class images
21 35 #input: C is a C x Y x X binary image
22 36 #output: an RGB color image with a unique color for each class
... ... @@ -33,8 +47,7 @@ def class2color(C):
33 47  
34 48 #for each class
35 49 for c in range(0, nc):
36   - hsv = (c * 1.0 / nc, 1, 1)
37   - color = numpy.asarray(colorsys.hsv_to_rgb(hsv[0], hsv[1], hsv[2])) * 255
  50 + color = qualcolor(c, nc)
38 51 RGB[C[c, ...], :] = color
39 52  
40 53 return RGB
... ... @@ -148,22 +161,22 @@ def random_mask(M, n):
148 161  
149 162 def envi_batch_predict(E, C, batch=10000):
150 163  
151   - Fv = E.loadbatch(batch).transpose()
  164 + Fv = E.loadbatch(batch)
152 165 i = 0
153 166 Tv = []
154 167 plt.ion()
155 168 bar = progressbar.ProgressBar(max_value=numpy.count_nonzero(E.mask))
156 169 while not Fv == []:
157 170 if i == 0:
158   - Tv = C.predict(Fv)
  171 + Tv = C.predict(Fv.transpose())
159 172 else:
160   - Tv = numpy.concatenate((Tv, C.predict(Fv).transpose()), 0)
  173 + Tv = numpy.concatenate((Tv, C.predict(Fv.transpose()).transpose()), 0)
161 174 tempmask = E.batchmask()
162 175 Lv = spectral.unsift2(Tv, tempmask)
163 176 Cv = label2class(Lv.squeeze(), background=0)
164 177 RGB = class2color(Cv)
165 178 plt.imshow(RGB)
166 179 plt.pause(0.05)
167   - Fv = E.loadbatch(batch).transpose()
  180 + Fv = E.loadbatch(batch)
168 181 i = i + 1
169 182 bar.update(len(Tv))
170 183 \ No newline at end of file
... ...