import som4
import weights
import image
ow = 640 / 8
oh = 480 / 8
d = weights.data("weights")
img = image.image(ow, oh)
num_classes = img.get_all_images("./forest")
num_classes = img.get_all_images("./beach")
s = som4.som(ow * oh * 3, num_classes, 0.01)
s.init()
img.save_all()
print num_classes
for i in range(0, num_classes):
dataset = img.get_data_set(i)
indx = s.learn(dataset, str(i))
print "-------------------- ", i
for j in range(0, 400):
print s.train(indx)
w, m = s.get_weights()
d.save(ow, oh, num_classes, w, m)
import weights
import image
ow = 640 / 8
oh = 480 / 8
d = weights.data("weights")
ow, oh, inp, w, m = d.load()
print "w = ", ow, " h = ", oh, " classes = ", inp
img1 = image.image(ow, oh)
img2 = image.image(ow, oh)
s = som4.som(ow * oh * 3, inp, 0.01)
s.init()
print len(s.get_weights()[0]), " ", len(w)
s.put_weights(w, m)
nb = img1.get_all_images("./test/b")
nf = img2.get_all_images("./test/f")
print "beach test\n"
for i in range(0, nb):
dataset = img1.get_data_set(i)
name = img1.get_name(i)
ct = int(s.find(dataset))
