You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
81 lines
3.1 KiB
81 lines
3.1 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
|
# |
|
# Licensed under the Apache License, Version 2.0 (the "License"); |
|
# you may not use this file except in compliance with the License. |
|
# You may obtain a copy of the License at |
|
# |
|
# http://www.apache.org/licenses/LICENSE-2.0 |
|
# |
|
# Unless required by applicable law or agreed to in writing, software |
|
# distributed under the License is distributed on an "AS IS" BASIS, |
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
# See the License for the specific language governing permissions and |
|
# limitations under the License. |
|
|
|
import copy |
|
import os.path as osp |
|
|
|
from .operators import * |
|
from .batch_operators import BatchRandomResize, BatchRandomResizeByShort, _BatchPad |
|
from paddlers import transforms as T |
|
|
|
|
|
def decode_image(im_path, |
|
to_rgb=True, |
|
to_uint8=True, |
|
decode_bgr=True, |
|
decode_sar=True, |
|
read_geo_info=False): |
|
""" |
|
Decode an image. |
|
|
|
Args: |
|
im_path (str): Path of the image to decode. |
|
to_rgb (bool, optional): If True, convert input image(s) from BGR format to |
|
RGB format. Defaults to True. |
|
to_uint8 (bool, optional): If True, quantize and convert decoded image(s) to |
|
uint8 type. Defaults to True. |
|
decode_bgr (bool, optional): If True, automatically interpret a non-geo |
|
image (e.g. jpeg images) as a BGR image. Defaults to True. |
|
decode_sar (bool, optional): If True, automatically interpret a two-channel |
|
geo image (e.g. geotiff images) as a SAR image, set this argument to |
|
True. Defaults to True. |
|
read_geo_info (bool, optional): If True, read geographical information from |
|
the image. Deafults to False. |
|
|
|
Returns: |
|
np.ndarray|tuple: If `read_geo_info` is False, return the decoded image. |
|
Otherwise, return a tuple that contains the decoded image and a dictionary |
|
of geographical information (e.g. geographical transform and geographical |
|
projection). |
|
""" |
|
|
|
# Do a presence check. osp.exists() assumes `im_path` is a path-like object. |
|
if not osp.exists(im_path): |
|
raise ValueError(f"{im_path} does not exist!") |
|
decoder = T.DecodeImg( |
|
to_rgb=to_rgb, |
|
to_uint8=to_uint8, |
|
decode_bgr=decode_bgr, |
|
decode_sar=decode_sar, |
|
read_geo_info=read_geo_info) |
|
# Deepcopy to avoid inplace modification |
|
sample = {'image': copy.deepcopy(im_path)} |
|
sample = decoder(sample) |
|
if read_geo_info: |
|
return sample['image'], sample['geo_info_dict'] |
|
else: |
|
return sample['image'] |
|
|
|
|
|
def build_transforms(transforms_info): |
|
transforms = list() |
|
for op_info in transforms_info: |
|
op_name = list(op_info.keys())[0] |
|
op_attr = op_info[op_name] |
|
if not hasattr(T, op_name): |
|
raise ValueError( |
|
"There is no transform operator named '{}'.".format(op_name)) |
|
transforms.append(getattr(T, op_name)(**op_attr)) |
|
eval_transforms = T.Compose(transforms) |
|
return eval_transforms
|
|
|