#!/usr/bin/env python
from nltk.corpus import wordnet as wn
from imagenet import ImageNet
CLASSES = '../../Classes/classes.txt'
OUTFILE = 'definitions.txt'
inet = ImageNet(CLASSES)
with open(OUTFILE, 'w') as f:
for i in range(0, 1000):
w = inet.name(i)
w_ = w.replace(' ', '_')
ss = wn.synsets(w_)
print w
if not ss:
print "ERR: %d %s not found" % (i, w)
for s in ss:
f.write(w_ + " " + s.definition() + "\n")
#!/usr/bin/env python
from nltk.corpus import wordnet as wn
from imagenet import ImageNet
CLASSES = '../../Classes/classes.txt'
OUTFILE = 'definitions.txt'
inet = ImageNet(CLASSES)
with open(OUTFILE, 'w') as f:
for i in range(0, 1000):
w = inet.name(i)
w_ = w.replace(' ', '_')
ss = wn.synsets(w_)
print w
if not ss:
print "ERR: %d %s not found" % (i, w)
for s in ss:
f.write(w_ + " " + s.definition() + "\n")
outfile = os.path.join(OUTDIR, "twitter1.txt")
start_targets = random.sample(range(0, 1000), NSTART)
conffile = os.path.join(OUTDIR, 'conf0.json')
neuralgia.write_config(start_targets, conffile)
subprocess.call(["./neuralgae_draw.sh", OUTDIR, 'image0', conffile, SIZE, SCALE, BLEND])
#lastimage = "./Neuralgia/Chapter5/image199.jpg"
lastimage = os.path.join(OUTDIR, 'image0.jpg')
imagen = ImageNet("./Classes/classes.txt")
classes = ', '.join([imagen.name(c) for c in start_targets])
with open(outfile, 'w') as f:
f.write("%s: %s\n" % (lastimage, classes))
for i in range(0,20):
jsonfile = os.path.join(OUTDIR, ("conf%d.json" % i))
subprocess.call(["./classify.py", str(NTWEEN), str(NSAMPLE), lastimage, jsonfile])
subprocess.call(["./neuralgae_draw.sh", OUTDIR, "image%d" % i, jsonfile, SIZE, SCALE, BLEND])
lastimage = os.path.join(OUTDIR, ("image%d.jpg" % i))
t = neuralgia.read_config(jsonfile)
if t:
print t
net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(args.image))
print "About to predict..."
out = net.forward()
#print out.keys()
#p = out['prob'].argmax()
#print "Image {}".format(p)
targets = []
nclasses = int(args.n) + 1
top_k = net.blobs['prob'].data[0].flatten().argsort()
for i in top_k[-1:-nclasses:-1]:
print classes.name(i)
targets.append(i)
s = int(args.sample)
n = int(args.n)
if s < n:
print "Sampling %d of %d" % (s, n)
targets = random.sample(targets, s)
print "Sample: " + ', '.join([classes.name(i) for i in targets])
neuralgia.write_config(targets, args.output)
net.blobs['data'].data[...] = transformer.preprocess(
'data', caffe.io.load_image(args.image))
print "About to predict..."
out = net.forward()
#print out.keys()
#p = out['prob'].argmax()
#print "Image {}".format(p)
targets = []
nclasses = int(args.n) + 1
top_k = net.blobs['prob'].data[0].flatten().argsort()
for i in top_k[-1:-nclasses:-1]:
print classes.name(i)
targets.append(i)
s = int(args.sample)
n = int(args.n)
if s < n:
print "Sampling %d of %d" % (s, n)
targets = random.sample(targets, s)
print "Sample: " + ', '.join([classes.name(i) for i in targets])
neuralgia.write_config(targets, args.output)
outfile = os.path.join(OUTDIR, "neuralgia6.txt")
start_targets = random.sample(range(0, 1000), NSTART)
#conffile = os.path.join(OUTDIR, 'conf0.json')
#neuralgia.write_config(start_targets, conffile)
#subprocess.call(["./neuralgia.sh", OUTDIR, 'image0', conffile, SIZE, SCALE, BLEND])
lastimage = "./Neuralgia/Chapter5/image199.jpg"
imagen = ImageNet("./Classes/classes.txt")
classes = ', '.join([imagen.name(c) for c in start_targets])
with open(outfile, 'w') as f:
f.write("%s: %s\n" % (lastimage, classes))
for i in range(0,1):
jsonfile = "./Neuralgia/conf%d.json" % i
subprocess.call(["./classify.py", str(NTWEEN), str(NSAMPLE), lastimage, jsonfile])
subprocess.call(["./neuralgia.sh", OUTDIR, "image%d" % i, jsonfile, SIZE, SCALE, BLEND])
lastimage = "./Neuralgia/image%d.jpg" % i
t = neuralgia.read_config(jsonfile)
if t:
print t
