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copper 2023-05-23 11:49:52 +08:00
parent 804a3064f6
commit dab53f5dfa
18 changed files with 740 additions and 488 deletions
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239
env.yaml Normal file
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@ -0,0 +1,239 @@
name: cveo_ss
channels:
- conda-forge
- defaults
dependencies:
- appdirs=1.4.4=pyh9f0ad1d_0
- astroid=2.8.6=py37h03978a9_1
- blosc=1.21.1=h74325e0_3
- boost-cpp=1.78.0=h9f4b32c_1
- brotli=1.0.9=h8ffe710_7
- brotli-bin=1.0.9=h8ffe710_7
- brotlipy=0.7.0=py37hcc03f2d_1004
- bzip2=1.0.8=h8ffe710_4
- c-blosc2=2.3.1=hdf67494_0
- ca-certificates=2022.6.15.1=h5b45459_0
- cairo=1.16.0=hb19e0ff_1008
- certifi=2022.6.15.1=pyhd8ed1ab_0
- cffi=1.15.1=py37hd8e9650_0
- cfitsio=3.470=h0af3d06_7
- charls=2.2.0=h39d44d4_0
- charset-normalizer=2.1.1=pyhd8ed1ab_0
- cloudpickle=2.2.0=pyhd8ed1ab_0
- colorama=0.4.5=pyhd8ed1ab_0
- coverage=6.4.4=py37hcc03f2d_0
- cryptography=3.4.7=py37h20c650d_0
- curl=7.83.1=h789b8ee_0
- cycler=0.11.0=pyhd8ed1ab_0
- cytoolz=0.12.0=py37hcc03f2d_0
- dask-core=2021.9.1=pyhd8ed1ab_0
- decorator=5.1.0=pyhd8ed1ab_0
- draco=1.5.3=h5362a0b_0
- exiv2=0.27.5=h02b4549_0
- expat=2.4.8=h39d44d4_0
- fontconfig=2.14.0=hce3cb01_0
- fonttools=4.37.1=py37hcc03f2d_0
- freeglut=3.2.2=h0e60522_1
- freetype=2.12.1=h546665d_0
- freexl=1.0.6=ha8e266a_0
- fsspec=2022.8.2=pyhd8ed1ab_0
- future=0.18.2=py37h03978a9_3
- gdal=3.3.1=py37hb11e9a8_2
- geos=3.9.1=h39d44d4_2
- geotiff=1.6.0=ha8a8a2d_6
- gettext=0.19.8.1=ha2e2712_1008
- giflib=5.2.1=h8d14728_2
- gsl=2.7=hdfb1a43_0
- hdf4=4.2.15=h0e5069d_4
- hdf5=1.10.6=nompi_h5268f04_1114
- httplib2=0.20.4=pyhd8ed1ab_0
- icu=68.2=h0e60522_0
- idna=3.3=pyhd8ed1ab_0
- imagecodecs=2021.8.26=py37h91eda04_1
- imageio=2.21.3=pyhfa7a67d_0
- intel-openmp=2022.1.0=h57928b3_3787
- isort=5.10.1=pyhd8ed1ab_0
- jasper=2.0.33=h77af90b_0
- jbig=2.1=h8d14728_2003
- jinja2=3.1.2=pyhd8ed1ab_1
- jpeg=9e=h8ffe710_2
- jsoncpp=1.9.4=h2d74725_3
- jxrlib=1.1=h8ffe710_2
- kealib=1.4.14=h96bfa42_2
- kiwisolver=1.4.4=py37h8c56517_0
- krb5=1.19.3=h1176d77_0
- laszip=3.4.3=h6538335_1
- laz-perf=3.0.0=h2d74725_0
- lazy-object-proxy=1.7.1=py37hcc03f2d_1
- lcms2=2.12=h2a16943_0
- lerc=3.0=h0e60522_0
- libaec=1.0.6=h39d44d4_0
- libblas=3.9.0=16_win64_mkl
- libbrotlicommon=1.0.9=h8ffe710_7
- libbrotlidec=1.0.9=h8ffe710_7
- libbrotlienc=1.0.9=h8ffe710_7
- libcblas=3.9.0=16_win64_mkl
- libclang=11.1.0=default_h5c34c98_1
- libcurl=7.83.1=h789b8ee_0
- libdeflate=1.8=h8ffe710_0
- libffi=3.4.2=h8ffe710_5
- libgdal=3.3.1=h7e75cf7_2
- libglib=2.72.1=h3be07f2_0
- libiconv=1.16=he774522_0
- libkml=1.3.0=hf2ab4e4_1015
- liblapack=3.9.0=16_win64_mkl
- liblapacke=3.9.0=16_win64_mkl
- libnetcdf=4.8.1=nompi_hf689e7d_100
- libopencv=4.5.3=py37h6700db3_1
- libpng=1.6.37=h1d00b33_4
- libpq=13.5=hfcc5ef8_1
- libprotobuf=3.16.0=h7755175_0
- librttopo=1.1.0=hb340de5_6
- libspatialindex=1.9.3=h39d44d4_4
- libspatialite=5.0.1=h762a7f4_6
- libsqlite=3.39.3=hcfcfb64_0
- libssh2=1.10.0=h680486a_3
- libtiff=4.3.0=hd413186_2
- libwebp=1.2.4=h8ffe710_0
- libwebp-base=1.2.4=h8ffe710_0
- libxcb=1.13=hcd874cb_1004
- libxml2=2.9.14=hf5bbc77_4
- libxslt=1.1.35=h34f844d_0
- libzip=1.9.2=hfed4ece_1
- libzlib=1.2.12=hcfcfb64_3
- libzopfli=1.0.3=h0e60522_0
- locket=1.0.0=pyhd8ed1ab_0
- lz4-c=1.9.3=h8ffe710_1
- m2w64-gcc-libgfortran=5.3.0=6
- m2w64-gcc-libs=5.3.0=7
- m2w64-gcc-libs-core=5.3.0=7
- m2w64-gmp=6.1.0=2
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
- markupsafe=2.1.1=py37hcc03f2d_1
- matplotlib-base=3.5.3=py37hbaab90a_2
- mccabe=0.6.1=py_1
- mkl=2022.1.0=h6a75c08_874
- mock=4.0.3=py37h03978a9_3
- msys2-conda-epoch=20160418=1
- munkres=1.1.4=pyh9f0ad1d_0
- networkx=2.7.1=pyhd8ed1ab_0
- nitro=2.7.dev6=h39d44d4_5
- nose2=0.9.2=py_0
- numpy=1.21.2=py37h940b05c_0
- opencv=4.5.3=py37h03978a9_1
- openjpeg=2.4.0=hb211442_1
- openssl=1.1.1q=h8ffe710_0
- owslib=0.27.2=pyhd8ed1ab_1
- packaging=21.3=pyhd8ed1ab_0
- pandas=1.3.5=py37h9386db6_0
- partd=1.3.0=pyhd8ed1ab_0
- pcre=8.45=h0e60522_0
- pdal=2.3.0=hde8ebe7_6
- pip=21.2.4=pyhd8ed1ab_0
- pixman=0.40.0=h8ffe710_0
- pkg-config=0.29.2=h2bf4dc2_1008
- pkgconfig=1.5.5=py37h03978a9_2
- platformdirs=2.5.2=pyhd8ed1ab_1
- plotly=5.10.0=pyhd8ed1ab_0
- pooch=1.6.0=pyhd8ed1ab_0
- poppler=21.03.0=h9ff6ed8_0
- poppler-data=0.4.11=hd8ed1ab_0
- postgresql=13.5=h1c22c4f_1
- proj=8.0.1=h1cfcee9_0
- psycopg2=2.9.2=py37hd8e9650_0
- pthread-stubs=0.4=hcd874cb_1001
- py-opencv=4.5.3=py37h4038f58_1
- pycparser=2.20=pyh9f0ad1d_2
- pygments=2.10.0=pyhd8ed1ab_0
- pylint=2.11.1=pyhd8ed1ab_0
- pyopenssl=21.0.0=pyhd8ed1ab_0
- pyparsing=3.0.9=pyhd8ed1ab_0
- pyproj=3.2.1=py37h9f67652_0
- pyqt=5.12.3=py37h03978a9_8
- pyqt-impl=5.12.3=py37hf2a7229_8
- pyqt5-sip=4.19.18=py37hf2a7229_8
- pyqtads=3.8.2=py37hf2a7229_0
- pyqtchart=5.12=py37hf2a7229_8
- pyqtwebengine=5.12.1=py37hf2a7229_8
- pyqtwebkit=5.212=py37h9e7b984_2
- pysocks=1.7.1=py37h03978a9_5
- python=3.7.10=h7840368_101_cpython
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python_abi=3.7=2_cp37m
- pytz=2021.1=pyhd8ed1ab_0
- pywavelets=1.1.1=py37hec80d1f_3
- pywin32=303=py37hcc03f2d_0
- pyyaml=5.4.1=py37hcc03f2d_1
- qca=2.2.1=hd7ce7fb_3
- qgis=3.18.3=py37h3dc7164_2
- qjson=0.9.0=hd7ce7fb_1006
- qscintilla2=2.11.2=py37h9e7b984_6
- qt=5.12.9=h5909a2a_4
- qtkeychain=0.12.0=h552f0f6_0
- qtlocation=5.12.9=ha925a31_0
- qtwebkit=5.212=hb258684_1
- qwt=6.1.6=h552f0f6_0
- qwtpolar=1.1.1=hd7ce7fb_7
- requests=2.28.1=pyhd8ed1ab_1
- scikit-image=0.18.3=py37h9386db6_0
- scipy=1.7.1=py37hb6553fb_0
- setuptools=59.8.0=py37h03978a9_1
- six=1.16.0=pyh6c4a22f_0
- snappy=1.1.9=h82413e6_1
- sqlite=3.39.3=hcfcfb64_0
- tbb=2021.5.0=h91493d7_2
- tenacity=8.0.1=pyhd8ed1ab_0
- tifffile=2021.11.2=pyhd8ed1ab_0
- tiledb=2.3.4=h78dabda_0
- tk=8.6.12=h8ffe710_0
- toml=0.10.2=pyhd8ed1ab_0
- tomli=2.0.1=pyhd8ed1ab_0
- toolz=0.12.0=pyhd8ed1ab_0
- typed-ast=1.5.4=py37hcc03f2d_0
- typing-extensions=4.3.0=hd8ed1ab_0
- typing_extensions=4.3.0=pyha770c72_0
- ucrt=10.0.20348.0=h57928b3_0
- unicodedata2=14.0.0=py37hcc03f2d_1
- urllib3=1.26.11=pyhd8ed1ab_0
- vc=14.2=hb210afc_7
- vs2015_runtime=14.29.30139=h890b9b1_7
- wheel=0.37.1=pyhd8ed1ab_0
- win_inet_pton=1.1.0=py37h03978a9_4
- wrapt=1.13.3=py37hcc03f2d_1
- xerces-c=3.2.3=h0e60522_5
- xorg-libxau=1.0.9=hcd874cb_0
- xorg-libxdmcp=1.1.3=hcd874cb_0
- xz=5.2.6=h8d14728_0
- yaml=0.2.5=he774522_0
- zfp=0.5.5=h0e60522_8
- zlib=1.2.12=hcfcfb64_3
- zlib-ng=2.0.6=h8ffe710_0
- zstd=1.5.2=h7755175_4
- pip:
- attrs==21.4.0
- autopep8==2.0.0
- conda-pack==0.6.0
- cython==0.29.24
- efficientnet-pytorch==0.7.1
- filelock==3.12.0
- huggingface-hub==0.14.1
- importlib-metadata==4.8.1
- joblib==1.1.0
- munch==2.5.0
- nuitka==0.8.3
- opencv-python==4.5.3.56
- ordered-set==4.1.0
- pathlib==1.0.1
- pillow==6.2.2
- pretrainedmodels==0.7.4
- pycodestyle==2.9.1
- pycryptodome==3.14.1
- rios==0.0.0.0.dev20200902
- scikit-learn==1.0.2
- sklearn==0.0
- threadpoolctl==3.1.0
- timm==0.6.13
- torch==1.13.1
- torchvision==0.14.1
- tqdm==4.65.0
- zipp==3.8.1

2
lic/license.lic
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@ -1 +1 @@
vd4FiYncytyziGH9GNCAA8hGGr1/79Xmphtc5+PHPJDpxvqj1hP7+985QMojYO4M5Qn/aqEAvFgeDN3CA8x1YAK8SdCgSXSBJpRBK8wqPQjBY1ak96QfdPCrTLunr+xuPxK3Gxe772adTTsee2+ot7WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP
IaqFuRlbPMtYTReB0p+cxn8sffVeOjbq+d46I2texZIVIeLbwCxJ7w3mqlramQy3p0totEfoSkjIrQV1GjtrOOWsuYRqv5ZZ5A+/PdTd7ZU8WlMAl7sknJGJFWvciG1VL9n9XtJUG+CJg4oLYkdwR5WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP

8
plugins/ai_method/basic_cd.py
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@ -8,7 +8,7 @@ from rscder.utils.project import Project
from rscder.utils.geomath import geo2imageRC, imageRC2geo
import math
from .packages import get_model
import numpy as np
class BasicAICD(AlgFrontend):
@staticmethod
@ -62,9 +62,9 @@ class BasicAICD(AlgFrontend):
for i in range(xblocks +1):
block_xy1 = (start1x + i * cell_size[0], start1y+j * cell_size[1])
block_xy2 = (start2x + i * cell_size[0], start2y+j * cell_size[1])
block_xy = (i * cell_size[0], j * cell_size[1])
block_xy1 = [start1x + i * cell_size[0], start1y+j * cell_size[1]]
block_xy2 = [start2x + i * cell_size[0], start2y+j * cell_size[1]]
block_xy = [i * cell_size[0], j * cell_size[1]]
if block_xy1[1] > end1y or block_xy2[1] > end2y:
break

8
plugins/ai_method/packages/__init__.py
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@ -13,10 +13,10 @@ class Front:
self.model = model.to(self.device)
def __call__(self, inp1, inp2):
inp1 = torch.from_numpy(inp1).to(self.device)
inp1 = torch.transpose(inp1, 0, 2).transpose(1, 2).unsqueeze(0)
inp2 = torch.from_numpy(inp2).to(self.device)
inp2 = torch.transpose(inp2, 0, 2).transpose(1, 2).unsqueeze(0)
inp1 = torch.from_numpy(inp1).float().to(self.device) / 255.0
inp1 = inp1.unsqueeze(0)
inp2 = torch.from_numpy(inp2).float().to(self.device) / 255.0
inp2 = inp2.unsqueeze(0)
out = self.model(inp1, inp2)
out = out.sigmoid()

4
plugins/ai_method/packages/models/DVCA/decoder.py
View File

@ -48,8 +48,8 @@ class DVCADecoder(Decoder):
in_channels = in_channels * 2
self.aspp = nn.Sequential(
ASPP(in_channels, out_channels, atrous_rates),
nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
# ASPP(in_channels, out_channels, atrous_rates),
nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
)

8
plugins/ai_method/packages/models/__init__.py
View File

@ -3,11 +3,11 @@ from .DVCA import DVCA
from .DPFCN import DPFCN
from . import encoders
from . import utils
from . import losses
from . import datasets
# from . import utils
# from . import losses
# from . import datasets
from .__version__ import __version__
# from .__version__ import __version__
from typing import Optional
import torch

92
plugins/ai_method/packages/models/encoders/__init__.py
View File

@ -4,50 +4,50 @@ import torch
import torch.utils.model_zoo as model_zoo
from ._preprocessing import preprocess_input
from .densenet import densenet_encoders
from .dpn import dpn_encoders
from .efficientnet import efficient_net_encoders
from .inceptionresnetv2 import inceptionresnetv2_encoders
from .inceptionv4 import inceptionv4_encoders
from .mobilenet import mobilenet_encoders
# from .densenet import densenet_encoders
# from .dpn import dpn_encoders
# from .efficientnet import efficient_net_encoders
# from .inceptionresnetv2 import inceptionresnetv2_encoders
# from .inceptionv4 import inceptionv4_encoders
# from .mobilenet import mobilenet_encoders
from .resnet import resnet_encoders
from .senet import senet_encoders
from .timm_efficientnet import timm_efficientnet_encoders
from .timm_gernet import timm_gernet_encoders
from .timm_mobilenetv3 import timm_mobilenetv3_encoders
from .timm_regnet import timm_regnet_encoders
from .timm_res2net import timm_res2net_encoders
from .timm_resnest import timm_resnest_encoders
from .timm_sknet import timm_sknet_encoders
from .timm_universal import TimmUniversalEncoder
from .vgg import vgg_encoders
from .xception import xception_encoders
from .swin_transformer import swin_transformer_encoders
from .mit_encoder import mit_encoders
# from .senet import senet_encoders
# from .timm_efficientnet import timm_efficientnet_encoders
# from .timm_gernet import timm_gernet_encoders
# from .timm_mobilenetv3 import timm_mobilenetv3_encoders
# from .timm_regnet import timm_regnet_encoders
# from .timm_res2net import timm_res2net_encoders
# from .timm_resnest import timm_resnest_encoders
# from .timm_sknet import timm_sknet_encoders
# from .timm_universal import TimmUniversalEncoder
# from .vgg import vgg_encoders
# from .xception import xception_encoders
# from .swin_transformer import swin_transformer_encoders
# from .mit_encoder import mit_encoders
# from .hrnet import hrnet_encoders
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
encoders = {}
encoders.update(resnet_encoders)
encoders.update(dpn_encoders)
encoders.update(vgg_encoders)
encoders.update(senet_encoders)
encoders.update(densenet_encoders)
encoders.update(inceptionresnetv2_encoders)
encoders.update(inceptionv4_encoders)
encoders.update(efficient_net_encoders)
encoders.update(mobilenet_encoders)
encoders.update(xception_encoders)
encoders.update(timm_efficientnet_encoders)
encoders.update(timm_resnest_encoders)
encoders.update(timm_res2net_encoders)
encoders.update(timm_regnet_encoders)
encoders.update(timm_sknet_encoders)
encoders.update(timm_mobilenetv3_encoders)
encoders.update(timm_gernet_encoders)
encoders.update(swin_transformer_encoders)
encoders.update(mit_encoders)
# encoders.update(dpn_encoders)
# encoders.update(vgg_encoders)
# encoders.update(senet_encoders)
# encoders.update(densenet_encoders)
# encoders.update(inceptionresnetv2_encoders)
# encoders.update(inceptionv4_encoders)
# encoders.update(efficient_net_encoders)
# encoders.update(mobilenet_encoders)
# encoders.update(xception_encoders)
# encoders.update(timm_efficientnet_encoders)
# encoders.update(timm_resnest_encoders)
# encoders.update(timm_res2net_encoders)
# encoders.update(timm_regnet_encoders)
# encoders.update(timm_sknet_encoders)
# encoders.update(timm_mobilenetv3_encoders)
# encoders.update(timm_gernet_encoders)
# encoders.update(swin_transformer_encoders)
# encoders.update(mit_encoders)
# encoders.update(hrnet_encoders)
@ -55,15 +55,15 @@ def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **
if name.startswith("tu-"):
name = name[3:]
encoder = TimmUniversalEncoder(
name=name,
in_channels=in_channels,
depth=depth,
output_stride=output_stride,
pretrained=weights is not None,
**kwargs
)
return encoder
# encoder = TimmUniversalEncoder(
# name=name,
# in_channels=in_channels,
# depth=depth,
# output_stride=output_stride,
# pretrained=weights is not None,
# **kwargs
# )
# return encoder
try:
Encoder = encoders[name]["encoder"]

0
plugins/ai_method/utils.py
View File

27
plugins/export_to/main.py
View File

@ -8,6 +8,7 @@ from PyQt5.QtGui import QIcon
from PyQt5.QtCore import Qt
from osgeo import gdal, gdal_array
import numpy as np
import threading
class ExportDialog(QDialog):
TXT='*.txt'
@ -122,12 +123,12 @@ class ExportPlugin(BasicPlugin):
def export_rgb_action(self):
dialog = ExportDialog(self.mainwindow, ExportDialog.TIF)
btn = QPushButton('选择样式文件')
style_path = ''
self.style_path = ''
def select_style():
select_file = QFileDialog.getOpenFileName(self, '选择样式文件', '*.*')
select_file = QFileDialog.getOpenFileName(self.mainwindow, '选择样式文件', '*.*')
if select_file[0]:
# style_path.setText(select_file[0])
style_path = select_file[0]
self.style_path = select_file[0]
btn.setText(select_file[0])
btn.clicked.connect(select_style)
dialog.layout().addWidget(btn)
@ -137,21 +138,25 @@ class ExportPlugin(BasicPlugin):
[1, 255, 0, 0]
]
if style_path is '':
if self.style_path is '':
style = default_style
else:
style = np.loadtxt(style_path, comments='#', delimiter=',')
style = np.loadtxt(self.style_path, comments='#', delimiter=',')
style = style.tolist()
if dialog.exec_():
result = dialog.result_layer.path
result = dialog.result_layer.result_path['cmi']
out = dialog.out_path
self.render(style, result, out)
t = threading.Thread(target=self.render, args=(style, result, out)) # 创建线程并执行RENDER函数
# self.render(style, result, out)
t.start()
def render(self, style:list, path, out):
self.send_message.emit('正在导出' )
data = gdal_array.LoadFile(path)
if len(data.shape) == 3:
data = data[..., 0]
data = data[0]
data = data / 255.0 # scale to 0-1 range for gdal_array.SaveArray()
def get_color(v):
first_color = []
@ -166,7 +171,7 @@ class ExportPlugin(BasicPlugin):
second_value = s[0]
second_color = s[1:]
break
if second_value == -1:
if second_value == 1:
return np.array(style[-1][1:])
first_dis = (v - first_value) / (second_value - first_value)
@ -174,7 +179,7 @@ class ExportPlugin(BasicPlugin):
first_color = np.array(first_color)
second_color = np.array(second_color)
color = first_color* first_dis + second_color * second_dis
return color
return np.floor(color)
get_color = np.frompyfunc(get_color, nin=1, nout=1)
@ -196,3 +201,5 @@ class ExportPlugin(BasicPlugin):
out_ds.GetRasterBand(i+1).WriteArray(rgbs[..., i])
del out_ds
self.send_message.emit('导出成功，结果保存至:'+out ) # 发送消息到控制台中显示。 创建完成消息后，关闭窗口。)

11
plugins/filter_collection/lms_filter.py
View File

@ -24,6 +24,7 @@ def adaptiveMedianDeNoise(count, original):
for i in range(c, rows - c):
for j in range(c, cols - c):
startWindow = 3
k = int(startWindow / 2)
median = np.median(original[i - k:i + k + 1, j - k:j + k + 1])
mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
@ -42,7 +43,7 @@ def adaptiveMedianDeNoise(count, original):
mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
if mi < median < ma or startWindow > count:
if mi < median < ma or startWindow >= count:
break
if mi < median < ma or startWindow > count:
@ -68,8 +69,8 @@ class AdaptiveFilter(AlgFrontend):
def get_widget(parent=None):
widget = QtWidgets.QWidget(parent)
x_size_input = QtWidgets.QLineEdit(widget)
x_size_input.setText('3')
x_size_input.setValidator(QtGui.QIntValidator())
x_size_input.setText('9')
x_size_input.setValidator(QtGui.QIntValidator(6, 21))
x_size_input.setObjectName('xinput')
# y_size_input = QtWidgets.QLineEdit(widget)
# y_size_input.setValidator(QtGui.QIntValidator())
@ -96,13 +97,13 @@ class AdaptiveFilter(AlgFrontend):
@staticmethod
def get_params(widget:QtWidgets.QWidget=None):
if widget is None:
return dict(x_size=3)
return dict(x_size=9)
x_input = widget.findChild(QtWidgets.QLineEdit, 'xinput')
# y_input = widget.findChild(QtWidgets.QLineEdit, 'yinput')
if x_input is None:
return dict(x_size=3)
return dict(x_size=9)
x_size = int(x_input.text())
# y_size = int(y_input.text())

6
plugins/filter_collection/lmsnp_filter.py
View File

@ -42,10 +42,10 @@ def adaptiveMedianDeNoise(count, original):
mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
if mi < median < ma or startWindow > count:
if mi < median < ma or startWindow >= count:
break
if mi < median < ma or startWindow > count:
if mi < median < ma or startWindow >= count:
if mi < original[i, j] < ma:
newI[i, j] = original[i, j]
else:
@ -98,7 +98,7 @@ class AdaptiveNPFilter(AlgFrontend):
return dict()
@staticmethod
def run_alg(pth, x_size, *args, **kargs):
def run_alg(pth, *args, **kargs):
# x_size = int(x_size)
# y_size = int(y_size)
# pth = layer.path

13
plugins/filter_collection/main.py
View File

@ -114,12 +114,17 @@ class MainPlugin(BasicPlugin):
pth = layer.path
if pth is None:
return
out_path = alg.run_alg(pth, **p, send_message=self.send_message)
self.send_message.emit('滤波完成')
self.send_message.emit('结果在：'+out_path)
self.send_message.emit('滤波开始')
try:
out_path = alg.run_alg(pth, **p, send_message=self.send_message)
self.send_message.emit('滤波完成')
self.send_message.emit('结果在：'+out_path)
self.alg_ok.emit(layer, out_path)
self.alg_ok.emit(layer, out_path)
except Exception as e:
self.send_message.emit('滤波出现异常：'+str(e))
def run(self, key):
if key not in FILTER:

2
plugins/follow/main.py
View File

@ -157,6 +157,6 @@ class FollowPlugin(BasicPlugin):
self.current_widget = FOLLOW[self.combox.currentData()].get_widget(ActionManager().follow_box)
self.layout.addWidget(self.current_widget)
self.layout.addLayout(self.btn_box)
self.layout.addWidget(self.btn_box)

2
plugins/unsupervised_method/scripts/AHT.py
View File

@ -12419,6 +12419,6 @@ Size = Size2i
def AHT(file1, file2, outfile):
return _AHT.AHT(file1, file2, outfile)
return _AHT.LHBA(file1, file2, outfile)

BIN
plugins/unsupervised_method/scripts/_AHT.pyd
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BIN
plugins/unsupervised_method/scripts/_AHT_NO.pyd Normal file
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3
plugins/unsupervised_method/scripts/__init__.py
View File

@ -2,7 +2,6 @@ from rscder.utils.icons import IconInstance
from .SH import SH
from .LHBA import LHBA
from .OCD import OCD
from .AHT import AHT
from .ACD import ACD
import numpy as np
from datetime import datetime
@ -686,7 +685,7 @@ class AHTAlg(AlgFrontend):
send_message.emit('图像一提取完成')
# 运算
send_message.emit('开始LHBA计算.....')
send_message.emit('开始AHT计算.....')
time.sleep(0.1)
out_normal_tif = os.path.join(Project().cmi_path, '{}_{}_cmi.tif'.format(
layer_parent.name, int(np.random.rand() * 100000)))

1
test.lic Normal file
View File

@ -0,0 +1 @@
IaqFuRlbPMtYTReB0p+cxn8sffVeOjbq+d46I2texZIVIeLbwCxJ7w3mqlramQy3p0totEfoSkjIrQV1GjtrOOWsuYRqv5ZZ5A+/PdTd7ZU8WlMAl7sknJGJFWvciG1VL9n9XtJUG+CJg4oLYkdwR5WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP