def __tile(self, img_path, mask_path, number_of_tiles=12):
img = MultiImage(img_path)[-1]
mask = MultiImage(mask_path)[-1]
shape = img.shape
pad0, pad1 = (self.__patch_size -
shape[0] % self.__patch_size) % self.__patch_size, (
self.__patch_size -
shape[1] % self.__patch_size) % self.__patch_size
img = np.pad(img, [[pad0 // 2, pad0 - pad0 // 2],
[pad1 // 2, pad1 - pad1 // 2], [0, 0]],
constant_values=255)
mask = np.pad(mask, [[pad0 // 2, pad0 - pad0 // 2],
[pad1 // 2, pad1 - pad1 // 2], [0, 0]],
constant_values=0)
img = img.reshape(img.shape[0] // self.__patch_size, self.__patch_size,
img.shape[1] // self.__patch_size, self.__patch_size,
3)
img = img.transpose(0, 2, 1, 3, 4).reshape(-1, self.__patch_size,
self.__patch_size, 3)
mask = mask.reshape(mask.shape[0] // self.__patch_size,
self.__patch_size,
mask.shape[1] // self.__patch_size,
self.__patch_size, 3)
mask = mask.transpose(0, 2, 1, 3, 4).reshape(-1, self.__patch_size,
self.__patch_size, 3)
if len(img) < number_of_tiles:
mask = np.pad(
mask,
[[0, number_of_tiles - len(img)], [0, 0], [0, 0], [0, 0]],
constant_values=0)
img = np.pad(
img, [[0, number_of_tiles - len(img)], [0, 0], [0, 0], [0, 0]],
constant_values=255)
idxs = np.argsort(img.reshape(img.shape[0],
-1).sum(-1))[:number_of_tiles]
img = img[idxs]
mask = mask[idxs]
return img, mask
def __getitem__(self, item):
name = self.images[item]
path = os.path.join(self.data_dir, name)
label = self.labels[item]
label = np.array([(1 if i < label else 0) for i in range(5)],
dtype=np.float32)
img = MultiImage(path)[self.tiff_scale]
img = cv.cvtColor(img, cv.COLOR_RGB2BGR)
img = tile(img, self.sz, self.N, self.transforms, self.random)
img = torch.from_numpy(img.transpose(2, 0, 1))
return img, label
def __getitem__(self, item):
name = self.images[item]
if not os.path.splitext(name)[1]:
name += ".tiff"
path = os.path.join(self.data_dir, name)
img = MultiImage(path)[self.tiff_scale]
img = cv.cvtColor(img, cv.COLOR_RGB2BGR)
img, _ = get_minimal_image(img)
img = tile(img, self.tile_size, self.num_tiles)
if self.transforms is not None:
img = self.transforms(image=img)["image"]
img = torch.from_numpy(img.transpose(2, 0, 1))
return img
