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_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, label = imutils.random_scale((img, label),
scale_range=self.rescale,
order=(3, 0))
if self.img_normal:
img = self.img_normal(img)
if self.hor_flip:
img, label = imutils.random_lr_flip((img, label))
if self.crop_method == "random":
img, label = imutils.random_crop((img, label), 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, 'label': label}
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, 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),
pilmode='RGB'))
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}
def __getitem__(self, idx):
name = self.img_name_list[idx]
name_str = name
img = np.asarray(imageio.imread(name_str))
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.copy()}
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:
s_img = skimage.transform.rescale(img,
scale=s,
order=3,
multichannel=True,
preserve_range=True,
anti_aliasing=False)
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))
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 = np.asarray(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)
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, 'img': img}
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 __getitem__(self, idx):
sample = super().__getitem__(idx)
ms_img_list = []
img = sample['img']
for s in self.scales:
height, width = img.shape[:2]
target_height, target_width = (int(np.round(height * s)),
int(np.round(width * s)))
transform = []
if s != 1:
transform.append(
alb.Resize(target_height,
target_width,
interpolation=cv2.INTER_CUBIC))
transform.append(
alb.Normalize((0.5, ) * self.num_channels,
(0.5, ) * self.num_channels, 2**16 - 1))
transform = alb.Compose(transform)
s_img = transform(image=img)['image']
s_img = imutils.HWC_to_CHW(s_img)
# ResNet50 CAM compute cam as sum of original + flipped image flipped back
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": sample['name'],
"img": ms_img_list,
"size": (img.shape[0], img.shape[1]),
"label": sample['label']
}
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):
name_str = self.img_name_list[idx]
#ame_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 = np.asarray(imageio.imread(get_img_path_2(name_str, self.voc12_root,self.image_folder)))
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}
def __getitem__(self, idx):
name = self.img_name_list[idx]
#得到磁盘存储img文件名
name_str = decode_int_filename(name)
bg_img = np.asarray(
imageio.imread(get_img_path(name_str, self.voc12_root)))
label_one_hot = self.label_list[idx]
label_index = np.arange(len(label_one_hot))
label_index = label_index[label_one_hot == 0]
fg_label = np.random.choice(label_index)
fg_name = np.random.choice(self.seg_fg_dir[fg_label])
fg_pack = np.load(os.path.join('result/sem_seg_fg', str(fg_label),
fg_name),
allow_pickle=True).item()
fg_img = fg_pack['img']
fg_seg = fg_pack['seg']
aug_img = imutils.data_aug_rotation(bg_img, fg_img, fg_seg)
aug_label_one_hot = np.zeros(len(label_one_hot))
aug_label_one_hot[label_one_hot == 1] = 1
aug_label_one_hot[fg_label] = 1
img = bg_img
if self.resize_long:
img = imutils.random_resize_long(img, self.resize_long[0],
self.resize_long[1])
aug_img = imutils.random_resize_long(aug_img, self.resize_long[0],
self.resize_long[1])
if self.rescale:
img = imutils.random_scale(img, scale_range=self.rescale, order=3)
aug_img = imutils.random_scale(aug_img,
scale_range=self.rescale,
order=3)
if self.img_normal:
img = self.img_normal(img)
aug_img = self.img_normal(aug_img)
if self.hor_flip:
img = imutils.random_lr_flip(img)
aug_img = imutils.random_lr_flip(aug_img)
if self.crop_size:
if self.crop_method == "random":
img = imutils.random_crop(img, self.crop_size, 0)
aug_img = imutils.random_crop(aug_img, self.crop_size, 0)
else:
img = imutils.top_left_crop(img, self.crop_size, 0)
aug_img = imutils.top_left_crop(aug_img, self.crop_size, 0)
if self.to_torch:
img = imutils.HWC_to_CHW(img)
aug_img = imutils.HWC_to_CHW(aug_img)
return {
'name': name_str,
'img': img,
'label': label_one_hot,
'aug_img': aug_img,
'aug_label': aug_label_one_hot
}
