def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
if self.file_client is None:
self.file_client = FileClient(self.io_backend, **self.kwargs)
filepath = str(results[f'{self.key}_path'])
img_bytes = self.file_client.get(filepath)
img = imfrombytes(img_bytes,
flag=self.flag,
channel_order=self.channel_order) # HWC
if len(img.shape) == 2:
img = np.expand_dims(img, axis=2)
results[self.key] = img
results[f'{self.key}_path'] = filepath
results[f'{self.key}_ori_shape'] = img.shape
if self.save_original_img:
results[f'ori_{self.key}'] = img.copy()
return results
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
if self.file_client is None:
self.file_client = FileClient(self.io_backend, **self.kwargs)
filepaths = results[f'{self.key}_path']
if not isinstance(filepaths, list):
raise TypeError(
f'filepath should be list, but got {type(filepaths)}')
filepaths = [str(v) for v in filepaths]
imgs = []
shapes = []
if self.save_original_img:
ori_imgs = []
def get_cache_key(path):
l = path.split("/")
return os.path.join(l[-2], l[-1])
global cache_dict
for filepath in filepaths:
if self.use_mem:
key = get_cache_key(filepath)
if cache_dict.__contains__(key):
img_bytes = cache_dict.get(key)
else:
img_bytes = self.file_client.get(filepath)
cache_dict[key] = img_bytes
else:
img_bytes = self.file_client.get(filepath)
img = imfrombytes(img_bytes, flag=self.flag) # HWC, BGR
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
imgs.append(img)
shapes.append(img.shape)
if self.save_original_img:
ori_imgs.append(img.copy())
results[self.key] = imgs
results[f'{self.key}_path'] = filepaths
results[f'{self.key}_ori_shape'] = shapes
if self.save_original_img:
results[f'ori_{self.key}'] = ori_imgs
return results
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
if self.file_client is None:
self.file_client = FileClient(self.io_backend, **self.kwargs)
filepaths = results[f'{self.key}_path']
if not isinstance(filepaths, list):
raise TypeError(
f'filepath should be list, but got {type(filepaths)}')
filepaths = [str(v) for v in filepaths]
imgs = []
shapes = []
if self.save_original_img:
ori_imgs = []
for filepath in filepaths:
if self.make_bin:
bin_path = get_bin_path(filepath)
if os.path.isfile(bin_path):
img = read_bin(bin_path)
else:
raise NotImplementedError(
"please make sure all pkl file exist first")
# img_bytes = self.file_client.get(filepath)
# img = imfrombytes(img_bytes, flag=self.flag, channel_order=self.channel_order) # HWC, BGR
# make_ndarray_bin(img, bin_path)
else:
img_bytes = self.file_client.get(filepath)
img = imfrombytes(img_bytes,
flag=self.flag,
channel_order=self.channel_order) # HWC, BGR
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
imgs.append(img)
shapes.append(img.shape)
if self.save_original_img:
ori_imgs.append(img.copy())
results[self.key] = imgs
results[f'{self.key}_path'] = filepaths
results[f'{self.key}_ori_shape'] = shapes
if self.save_original_img:
results[f'ori_{self.key}'] = ori_imgs
return results