def transform_img_aff(self, img_org, mask):
"""
img_org is the original image from batch['original']
mask is the cam mask from cam_dict
"""
img = img_org.astype('uint8')
mask = mask.astype('uint8')
#
if self.rescale:
img, mask = imutils.random_scale((img, mask), scale_range=self.rescale, order=(3, 0))
if self.img_normal:
img = self.img_normal(img)
if self.hor_flip:
img, mask = imutils.random_lr_flip((img, mask))
if self.crop_method == "random":
img, mask = imutils.random_crop((img, mask), self.crop_size, (0, 255))
else:
img = imutils.top_left_crop(img, self.crop_size, 0)
mask = imutils.top_left_crop(label, self.crop_size, 255)
img = imutils.HWC_to_CHW(img)
reduced_mask = imutils.pil_rescale(mask, 0.25, 0)
return {'img':img, 'mask':mask, 'reduced_mask': reduced_mask}
def transform_img(self, img_org):
img = img_org
if self.resize_long:
img = imutils.random_resize_long(img, self.resize_long[0], self.resize_long[1])
if self.rescale:
img = imutils.random_scale(img, scale_range=self.rescale, order=3)
if self.img_normal:
img = self.img_normal(img)
if self.hor_flip:
img = imutils.random_lr_flip(img)
if self.crop_size:
if self.crop_method == "random":
img = imutils.random_crop(img, self.crop_size, 0)
else:
img = imutils.top_left_crop(img, self.crop_size, 0)
if self.to_torch:
img = imutils.HWC_to_CHW(img)
return img
def __getitem__(self, idx):
name = self.img_name_list[idx]
name_str = decode_int_filename(name)
img = imageio.imread(get_img_path(name_str, self.voc12_root))
# label = imageio.imread(os.path.join(self.label_dir, name_str + '.png'))
img = np.asarray(img)
if self.rescale:
img = imutils.random_scale(img[None, ...], scale_range=self.rescale, order=3)
if self.img_normal:
img = self.img_normal(img)
if self.hor_flip:
img = imutils.random_lr_flip(img)
if self.crop_method == "random":
img = imutils.random_crop(img, self.crop_size, (0, 255))
else:
img = imutils.top_left_crop(img, self.crop_size, 0)
# label = imutils.top_left_crop(label, self.crop_size, 255)
img = imutils.HWC_to_CHW(img)
return {'name': name, 'img': img}
def __getitem__(self, idx):
name = self.img_name_list[idx]
name_str = decode_int_filename(name)
img = np.asarray(imageio.imread(get_img_path(name_str, self.voc12_root)))
original = copy.deepcopy(img)
if self.resize_long:
img = imutils.random_resize_long(img, self.resize_long[0], self.resize_long[1])
if self.rescale:
img = imutils.random_scale(img, scale_range=self.rescale, order=3)
if self.img_normal:
img = self.img_normal(img)
if self.hor_flip:
img = imutils.random_lr_flip(img)
if self.crop_size:
if self.crop_method == "random":
img = imutils.random_crop(img, self.crop_size, 0)
else:
img = imutils.top_left_crop(img, self.crop_size, 0)
if self.to_torch:
img = imutils.HWC_to_CHW(img)
return {'name': name_str, 'img': img, 'original':original}
def get_multi_scale(self, img_org):
img = img_org
ms_img_list = []
for s in self.scales:
if s == 1:
s_img = img
else:
s_img = imutils.pil_rescale(img, s, order=3)
s_img = self.img_normal(s_img)
s_img = imutils.HWC_to_CHW(s_img)
ms_img_list.append(np.stack([s_img, np.flip(s_img, -1)], axis=0))
if len(self.scales) == 1:
ms_img_list = ms_img_list[0]
out = torch.from_numpy(ms_img_list)
return out
def __getitem__(self, idx):
name = self.img_name_list[idx]
name_str = decode_int_filename(name)
img = imageio.imread(get_img_path(name_str, self.voc12_root))
ms_img_list = []
for s in self.scales:
if s == 1:
s_img = img
else:
s_img = imutils.pil_rescale(img, s, order=3)
s_img = self.img_normal(s_img)
s_img = imutils.HWC_to_CHW(s_img)
ms_img_list.append(np.stack([s_img, np.flip(s_img, -1)], axis=0))
if len(self.scales) == 1:
ms_img_list = ms_img_list[0]
out = {"name": name_str, "img": ms_img_list, "size": (img.shape[0], img.shape[1]),
"label": torch.from_numpy(self.label_list[idx])}
return out