def show(self, idx):
img_land = imread(self._landpath % self.ids[idx])
img_seg = imread(self._segpath % self.ids[idx])
img_edge = vision.shift_edge(img_seg)
f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(12, 4))
f.suptitle('Sample-{} in NewZealand Dataset'.format(idx))
ax1.imshow(img_land)
ax1.set_title('Land Sample')
ax2.imshow(img_seg, 'gray')
ax2.set_title('Segmap Sample')
ax3.imshow(img_edge, 'gray')
ax3.set_title('Edge Sample')
plt.show()
def __getitem__(self, idx):
src_file = self.srcpath % self.datalist[idx]
tar_file = self.tarpath % self.datalist[idx]
src = imread(src_file)
tar = imread(tar_file)
assert len(tar.shape) == 2, "Mask should be 2D."
tar = vision.shift_edge(tar, self.tar_ch)
# src => uint8 to float tensor
src = (src / 255).transpose((2, 0, 1))
src = torch.from_numpy(src).float()
# tar => float to float tensor
tar = tar.transpose((2, 0, 1))
tar = torch.from_numpy(tar).float()
sample = {
"src": src,
"tar": tar,
}
return sample
def __getitem__(self, idx):
img_id = self.ids[idx]
img_land = imread(self._landpath % img_id)
img_land = (img_land / 255).astype('float32')
img_seg = imread(self._segpath % img_id)
img_edge = vision.shift_edge(img_seg)
# img_seg = rgb2gray(img_seg)
# img_edge = np.expand_dims(vision.canny_edge(img_seg), dim=-1)
# img_edge = (img_edge / 255).astype("float32")
img_seg = (np.expand_dims(img_seg, -1) / 255).astype('float32')
img_edge = (np.expand_dims(img_edge, -1) / 255).astype('float32')
img_land = img_land.transpose((2, 0, 1))
img_seg = img_seg.transpose((2, 0, 1))
img_edge = img_edge.transpose((2, 0, 1))
return img_land, img_seg, img_edge