space = np.random.randn(4)*0.18+1
print space
img_all = []
label_all= []
for s in space:
require = copy(RE)
require[0] = [ int(round(n*s)) for n in require[0] ]
require[1] = [ int(round(n*s)) for n in require[1] ]
center_scale = np.random.choice(space, 1)
center = (int(round(128*center_scale)), int(round(128*center_scale)))
print center, require
m = Maker(require)
a = np.random.randint(360)
m.generate(5, a, center=center)
img = [ i[None,None,None,:,:] for i in m.imgs ]
label = [ l[None,None,None,:,:] for l in m.labels]
img = np.concatenate(img, axis=0)
label = np.concatenate(label, axis=0)
img_all.append(img)
label_all.append(label)
img_all = np.concatenate(img_all, axis=1)
img_all = np.transpose(img_all, (1,0,2,3,4))
label_all = np.concatenate(label_all,axis=1)
label_all = np.transpose(label_all, (1,0,2,3,4))
print img_all.shape
assert img_all.shape[-2:] == (256,256)
})
elif ds_name == 'three_fruits':
paths = easydict.EasyDict({
'datasetFile':
os.path.join(dirs_suffix, 'datasets',
'three_fruits'),
'textDir':
'text_c10',
'maxEpochs':
'1000'
})
maker = Maker(datasetFile=paths.datasetFile,
textDir=paths.textDir,
checking_folder=checking_folder,
lang=lang,
client_txt=desc,
pre_trained_gen=os.path.join(
dirs_suffix, 'checkpoints',
ds_name + '_cls_test',
'gen_' + paths.maxEpochs + '.pth'),
pre_trained_disc=os.path.join(
dirs_suffix, 'checkpoints',
ds_name + '_cls_test',
'disc_' + paths.maxEpochs + '.pth'),
ID=ID)
maker.generate()
os.remove(filepath)
time.sleep(0.5)
print('Listening folder ' + rootdir + '...')
space = np.random.randn(4) * 0.18 + 1
print space
img_all = []
label_all = []
for s in space:
require = copy(RE)
require[0] = [int(round(n * s)) for n in require[0]]
require[1] = [int(round(n * s)) for n in require[1]]
center_scale = np.random.choice(space, 1)
center = (int(round(128 * center_scale)), int(round(128 * center_scale)))
print center, require
m = Maker(require)
a = np.random.randint(360)
m.generate(5, a, center=center)
img = [i[None, None, None, :, :] for i in m.imgs]
label = [l[None, None, None, :, :] for l in m.labels]
img = np.concatenate(img, axis=0)
label = np.concatenate(label, axis=0)
img_all.append(img)
label_all.append(label)
img_all = np.concatenate(img_all, axis=1)
img_all = np.transpose(img_all, (1, 0, 2, 3, 4))
label_all = np.concatenate(label_all, axis=1)
label_all = np.transpose(label_all, (1, 0, 2, 3, 4))
print img_all.shape
assert img_all.shape[-2:] == (256, 256)
