def load_dataset():
from scipy.io import loadmat
from dlearn.data.dataset import Dataset
matdata = loadmat(os.path.join('..', '..', 'data', 'human_sar', 'CUHK_SAR.mat'))
m, n = matdata['images'].shape
S = [choose_seg(matdata['segmentations'][i, 0], 'Upper')
for i in xrange(m)]
S = np.asarray(S)
matdata = loadmat('XY_CUHK_SAR.mat')
X = matdata['X'].T.astype('float32')
X = X / 100.0
X = X - X.mean(axis=0)
dataset = Dataset(X, S)
dataset.split(0.7, 0.2)
return dataset
def create_dataset(rawdata):
from dlearn.data.dataset import Dataset
from dlearn.utils import imgproc
def imgprep(img):
img = imgproc.resize(img, [160, 80], keep_ratio='height')
img = imgproc.subtract_luminance(img)
img = np.rollaxis(img, 2)
return (img / 100.0).astype(np.float32)
def choose_attr_uni(attr, title):
ind = conf.attr_uni_titles.index(title)
vals = conf.attr_uni[ind]
ind = [conf.attr_names.index(v) for v in vals]
return np.where(attr[ind] == 1)[0][0]
def choose_attr_mul(attr, title):
ind = conf.attr_mul_titles.index(title)
vals = conf.attr_mul[ind]
ind = [conf.attr_names.index(v) for v in vals]
return attr[ind].astype(np.float32)
m = len(rawdata)
X = [0] * m
A = [0] * m
i = 0
for (img, attr) in rawdata:
X[i] = imgprep(img)
A[i] = choose_attr_mul(attr, 'Upper Body Colors')
i += 1
X = np.asarray(X)
A = np.asarray(A)
X = X - X.mean(axis=0)
dataset = Dataset(X, A)
dataset.split(0.7, 0.2)
return dataset
def create_dataset(rawdata):
from dlearn.data.dataset import Dataset
from dlearn.utils import imgproc
def imgprep(img):
if img.shape != (160, 80):
img = imgproc.resize(img, [160, 80], keep_ratio='height')
img = imgproc.subtract_luminance(img)
img = np.rollaxis(img, 2)
return (img / 100.0).astype(np.float32)
def choose_attr_uni(attr, title):
ind = conf.attr_uni_titles.index(title)
vals = conf.attr_uni[ind]
ind = [conf.attr_names.index(v) for v in vals]
return np.where(attr[ind] == 1)[0][0]
def choose_attr_mul(attr, title):
ind = conf.attr_mul_titles.index(title)
vals = conf.attr_mul[ind]
ind = [conf.attr_names.index(v) for v in vals]
return attr[ind].astype(np.float32)
def choose_seg(seg, title):
val = conf.seg_pix[title]
img = (seg == val).astype(np.float32)
img = imgproc.resize(img, [37, 17])
return img.astype(np.float32)
m = len(rawdata)
X = [0] * (2 * m)
A = [0] * (2 * m)
S = [0] * (2 * m)
i = 0
for (img, attr, seg) in rawdata:
X[i] = imgprep(img)
A[i] = choose_attr_mul(attr, 'Upper Body Colors')
S[i] = choose_seg(seg, 'Upper')
# S[i] = (seg >= 0.5).astype(np.float32)
# S[i] = imgproc.resize(S[i], [37, 17]).astype(np.float32)
# Mirror
X[i + 1] = X[i][:, :, ::-1].copy()
A[i + 1] = A[i].copy()
S[i + 1] = S[i][:, ::-1].copy()
i += 2
X = np.asarray(X)
A = np.asarray(A)
S = np.asarray(S)
X = X - X.mean(axis=0)
def parse_list(arglist):
d = {'X': X, 'A': A, 'S': S}
l = map(lambda x: d[x], arglist)
return l[0] if len(l) == 1 else l
dataset = Dataset(parse_list(args.input), parse_list(args.target))
dataset.split(0.7, 0.2)
return dataset
