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
def __getitem__(self, idx):
# name = self.img_name_list[idx]
name = self.sample_list_ct[idx][:-4]
# name_str = decode_int_filename(name)
# img = imageio.imread(get_img_path(name_str, self.voc12_root))
img = np.load(self.data_dir + 'ct/' + self.sample_list_ct[idx])
label = np.load(self.data_dir + 'seg/' + self.sample_list_seg[idx])
img = ndimage.zoom(img, (0.5, 0.5), order=3)
label = ndimage.zoom(label, (0.5, 0.5), order=0)
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,
"img": ms_img_list,
"size": (img.shape[0], img.shape[1]),
"label": torch.from_numpy(np.array([label.max()]))
}
return out
def __getitem__(self, idx):
name = self.img_name_list[idx]
name_str = name # decode_int_filename(name)
#img = imageio.imread(get_img_path(name_str, self.voc12_root))
print(get_img_path(name_str, self.voc12_root).strip('['))
try:
img = imageio.imread(get_img_path(name_str, self.voc12_root))
except:
print("this didn't work")
#import bpython
#bpython.embed(locals())
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
def __getitem__(self, index):
datafiles = self.imgs[index]
name = datafiles['name']
x_path = datafiles['img']
img_y = datafiles['label']
img = imageio.imread(x_path)
if img.shape != (128, 128, 3):
img = np.expand_dims(img, axis=2)
img = np.concatenate((img, img, img), axis=-1)
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)
#print(s_img.shape)
s_img = imutils.HWC_to_CHW(s_img)
ms_img_list.append(
np.stack([s_img, np.flip(s_img, -1)],
axis=0)) # np.flip(img, -1)左右翻转, 将原图与翻转后的图像拼在一起
if len(self.scales) == 1:
ms_img_list = ms_img_list[0]
out = {
"name": name,
"img": ms_img_list,
"size": (img.shape[0], img.shape[1]),
"label": img_y
}
return out
def __getitem__(self, idx):
name_str = self.img_name_list[idx]
#name_str = decode_int_filename(name)
#if self.image_folder.split('/')[1] == 'kaggle':
paths_imgs = glob.glob('{}/{}*.png'.format(self.image_folder,
name_str))
img = combine_imgs(paths_imgs)
#else:
# img = np.asarray(imageio.imread(get_img_path_2(name_str,
# self.voc12_root,self.image_folder)))
#img = imageio.imread(get_img_path_2(name_str, self.voc12_root,self.image_folder))
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
def mywork(model, args):
img_dir = "/home/pfc/code/object_detect/irn/voc12/data/VOC12/infer/JPEGImages"
img_normal = voc12.dataloader.TorchvisionNormalize()
with torch.no_grad():
for img_name in os.listdir(img_dir):
curimg_path = os.path.join(img_dir, img_name)
img = imageio.imread(curimg_path)
size = (img.shape[0], img.shape[1])
ms_img_list = []
for s in args.cam_scales:
if s == 1:
s_img = img
else:
s_img = imutils.pil_rescale(img, s, order=3)
s_img = 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(args.cam_scales) == 1:
ms_img_list = ms_img_list[0]
strided_size = imutils.get_strided_size(size, 4)
strided_up_size = imutils.get_strided_up_size(size, 16)
# img_variable = Variable(img.unsqueeze(0))
outputs = [model(torch.Tensor(img)) for img in ms_img_list]
strided_cam = torch.sum(
torch.stack([
F.interpolate(torch.unsqueeze(o, 0),
strided_size,
mode='bilinear',
align_corners=False)[0] for o in outputs
]), 0)
highres_cam = [
F.interpolate(torch.unsqueeze(o, 1),
strided_up_size,
mode='bilinear',
align_corners=False) for o in outputs
]
highres_cam = torch.sum(torch.stack(highres_cam, 0),
0)[:, 0, :size[0], :size[1]]
valid_cat = torch.zeros(1).type(torch.uint8)
strided_cam = strided_cam[valid_cat]
strided_cam /= F.adaptive_max_pool2d(strided_cam, (1, 1)) + 1e-5
highres_cam = highres_cam[valid_cat]
highres_cam /= F.adaptive_max_pool2d(highres_cam, (1, 1)) + 1e-5
# save cams
np.save(
os.path.join(args.cam_out_dir, img_name + '.npy'), {
"keys": valid_cat,
"cam": strided_cam.cpu(),
"high_res": highres_cam.cpu().numpy()
})
def __getitem__(self, idx):
out = super().__getitem__(idx)
reduced_label = imutils.pil_rescale(out['label'], 0.25, 0)
out['aff_bg_pos_label'], out['aff_fg_pos_label'], out['aff_neg_label'] = self.extract_aff_lab_func(reduced_label)
return out
def __getitem__(self, idx):
name, _ = self.images_df.iloc[idx]
ds = pydicom.dcmread(os.path.join(self.images_dir, f'{name}.dcm'))
img = np.tile(ds.pixel_array[..., None], 3)
mask = cv2.imread(os.path.join(self.masks_path, name + '.png'), 0)
img, mask = self.transform(img, mask)
out = {
'img': img.permute(2, 0, 1),
'label': mask.squeeze(-1),
'name': name
}
reduced_label = imutils.pil_rescale(out['label'].numpy(), 0.25, 0)
out['aff_bg_pos_label'], out['aff_fg_pos_label'], out[
'aff_neg_label'] = self.extract_aff_lab_func(reduced_label)
return out
