def read_seg(seg_dir):
seg = Image.open(seg_dir)
h, w = seg.size
if (len(args.scale_size) == 1):
if (h > w):
tw = args.scale_size[0]
th = (tw * h) / w
th = int((th // 64) * 64)
else:
th = args.scale_size[0]
tw = (th * w) / h
tw = int((tw // 64) * 64)
else:
tw = args.scale_size[1]
th = args.scale_size[0]
seg = np.asarray(seg).reshape((w, h, 1))
seg = np.squeeze(seg)
seg = scipy.misc.imresize(seg, (tw // 8, th // 8), "nearest", mode="F")
t = []
t.extend([transforms.ToTensor()])
trans = transforms.Compose(t)
pair = [seg, seg]
transformed = list(trans(*pair))
seg = transformed[0]
return to_one_hot(seg)
def read_seg(seg_dir, scale_size):
seg = Image.open(seg_dir)
# seg_im = np.array(seg.getpalette()).reshape((-1,3))
# seg_cv = cv2.imread(seg_dir)
h,w = seg.size
if(len(scale_size) == 1):
if(h > w):
tw = scale_size[0]
th = (tw * h) / w
th = int((th // 64) * 64)
else:
th = scale_size[0]
tw = (th * w) / h
tw = int((tw // 64) * 64)
else:
tw = scale_size[1]
th = scale_size[0]
seg = np.asarray(seg).reshape((w,h,1))
seg_ori = np.squeeze(seg)
small_seg = scipy.misc.imresize(seg_ori, (tw//8, th//8),"nearest",mode="F")
large_seg = scipy.misc.imresize(seg_ori, (tw,th),"nearest",mode="F")
t = []
t.extend([transforms.ToTensor()])
trans = transforms.Compose(t)
pair = [large_seg, small_seg]
transformed = list(trans(*pair))
large_seg = transformed[0]
small_seg = transformed[1]
return to_one_hot(large_seg), to_one_hot(small_seg), seg_ori
def create_transforms():
normalize = transforms.Normalize(mean=(128, 128, 128), std=(128, 128, 128))
t = []
t.extend([transforms.ToTensor(), normalize])
return transforms.Compose(t)