chulutao
3 years ago
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PaddleSeg commit fec42fd869b6f796c74cd510671595e3512bc8e9 |
PaddleSeg commit fec42fd869b6f796c74cd510671595e3512bc8e9 |
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# 开发规范 |
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请注意，paddlers/models/ppxxx系列除了修改import路径和支持多通道模型外，不要增删改任何代码。 |
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新增的模型需放在paddlers/models/下的seg、det、cls、cd目录下。 |
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# 遥感数据集 |
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遥感影像的格式多种多样，不同传感器产生的数据格式也可能不同。PaddleRS至少兼容以下6种格式图片读取： |
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- `tif` |
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- `png`, `jpeg`, `bmp` |
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- `img` |
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- `npy` |
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标注图要求必须为单通道的png格式图像，像素值即为对应的类别，像素标注类别需要从0开始递增。例如0，1，2，3表示有4种类别，255用于指定不参与训练和评估的像素，标注类别最多为256类。 |
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## L8 SPARCS数据集 |
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[L8 SPARCS公开数据集](https://www.usgs.gov/land-resources/nli/landsat/spatial-procedures-automated-removal-cloud-and-shadow-sparcs-validation)进行云雪分割，该数据集包含80张卫星影像，涵盖10个波段。原始标注图片包含7个类别，分别是`cloud`, `cloud shadow`, `shadow over water`, `snow/ice`, `water`, `land`和`flooded`。由于`flooded`和`shadow over water`2个类别占比仅为`1.8%`和`0.24%`，我们将其进行合并，`flooded`归为`land`，`shadow over water`归为`shadow`，合并后标注包含5个类别。 |
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数值、类别、颜色对应表： |
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|Pixel value|Class|Color| |
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|---|---|---| |
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|0|cloud|white| |
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|1|shadow|black| |
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|2|snow/ice|cyan| |
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|3|water|blue| |
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|4|land|grey| |
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<p align="center"> |
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<img src="./images/dataset.png" align="middle" |
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</p> |
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<p align='center'> |
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L8 SPARCS数据集示例 |
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</p> |
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执行以下命令下载并解压经过类别合并后的数据集： |
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```shell script |
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mkdir dataset && cd dataset |
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wget https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip |
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unzip remote_sensing_seg.zip |
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cd .. |
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``` |
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其中`data`目录存放遥感影像，`data_vis`目录存放彩色合成预览图，`mask`目录存放标注图。 |
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@ -1,157 +0,0 @@ |
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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import numpy as np |
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import os.path as osp |
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import cv2 |
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import copy |
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import random |
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import imghdr |
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from PIL import Image |
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try: |
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from collections.abc import Sequence |
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except Exception: |
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from collections import Sequence |
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# from paddlers.transforms.operators import Transform |
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class Transform(object): |
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""" |
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Parent class of all data augmentation operations |
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""" |
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def __init__(self): |
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pass |
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def apply_im(self, image): |
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pass |
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def apply_mask(self, mask): |
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pass |
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def apply_bbox(self, bbox): |
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pass |
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def apply_segm(self, segms): |
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pass |
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def apply(self, sample): |
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sample['image'] = self.apply_im(sample['image']) |
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if 'mask' in sample: |
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sample['mask'] = self.apply_mask(sample['mask']) |
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if 'gt_bbox' in sample: |
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sample['gt_bbox'] = self.apply_bbox(sample['gt_bbox']) |
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return sample |
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def __call__(self, sample): |
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if isinstance(sample, Sequence): |
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sample = [self.apply(s) for s in sample] |
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else: |
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sample = self.apply(sample) |
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return sample |
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class ImgDecode(Transform): |
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""" |
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Decode image(s) in input. |
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Args: |
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to_rgb (bool, optional): If True, convert input images from BGR format to RGB format. Defaults to True. |
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""" |
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def __init__(self, to_rgb=True): |
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super(ImgDecode, self).__init__() |
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self.to_rgb = to_rgb |
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def read_img(self, img_path, input_channel=3): |
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img_format = imghdr.what(img_path) |
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name, ext = osp.splitext(img_path) |
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if img_format == 'tiff' or ext == '.img': |
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try: |
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import gdal |
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except: |
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try: |
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from osgeo import gdal |
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except: |
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raise Exception( |
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"Failed to import gdal! You can try use conda to install gdal" |
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) |
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six.reraise(*sys.exc_info()) |
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dataset = gdal.Open(img_path) |
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if dataset == None: |
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raise Exception('Can not open', img_path) |
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im_data = dataset.ReadAsArray() |
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return im_data.transpose((1, 2, 0)) |
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elif img_format in ['jpeg', 'bmp', 'png', 'jpg']: |
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if input_channel == 3: |
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return cv2.imread(img_path, cv2.IMREAD_ANYDEPTH | |
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cv2.IMREAD_ANYCOLOR | cv2.IMREAD_COLOR) |
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else: |
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return cv2.imread(img_path, cv2.IMREAD_ANYDEPTH | |
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cv2.IMREAD_ANYCOLOR) |
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elif ext == '.npy': |
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return np.load(img_path) |
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else: |
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raise Exception('Image format {} is not supported!'.format(ext)) |
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def apply_im(self, im_path): |
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if isinstance(im_path, str): |
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try: |
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image = self.read_img(im_path) |
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except: |
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raise ValueError('Cannot read the image file {}!'.format( |
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im_path)) |
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else: |
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image = im_path |
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if self.to_rgb: |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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return image |
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def apply_mask(self, mask): |
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try: |
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mask = np.asarray(Image.open(mask)) |
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except: |
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raise ValueError("Cannot read the mask file {}!".format(mask)) |
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if len(mask.shape) != 2: |
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raise Exception( |
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"Mask should be a 1-channel image, but recevied is a {}-channel image.". |
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format(mask.shape[2])) |
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return mask |
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def apply(self, sample): |
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""" |
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Args: |
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sample (dict): Input sample, containing 'image' at least. |
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Returns: |
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dict: Decoded sample. |
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""" |
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sample['image'] = self.apply_im(sample['image']) |
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if 'mask' in sample: |
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sample['mask'] = self.apply_mask(sample['mask']) |
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im_height, im_width, _ = sample['image'].shape |
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se_height, se_width = sample['mask'].shape |
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if im_height != se_height or im_width != se_width: |
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raise Exception( |
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"The height or width of the im is not same as the mask") |
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sample['im_shape'] = np.array( |
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sample['image'].shape[:2], dtype=np.float32) |
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sample['scale_factor'] = np.array([1., 1.], dtype=np.float32) |
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return sample |
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