update

env.yaml

@@ -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

lic/license.lic

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

plugins/ai_method/basic_cd.py

@@ -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_xy1 = [start1x + i * cell_size[0], start1y+j * cell_size[1]]
-                block_xy2 = (start2x + i * cell_size[0], start2y+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_xy = [i * cell_size[0], j * cell_size[1]]
             
                 if block_xy1[1] > end1y or block_xy2[1] > end2y:
                     break

plugins/ai_method/packages/__init__.py

@@ -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.from_numpy(inp1).float().to(self.device) / 255.0
-        inp1 = torch.transpose(inp1, 0, 2).transpose(1, 2).unsqueeze(0)
+        inp1 = inp1.unsqueeze(0)
-        inp2 = torch.from_numpy(inp2).to(self.device)
+        inp2 = torch.from_numpy(inp2).float().to(self.device) / 255.0
-        inp2 = torch.transpose(inp2, 0, 2).transpose(1, 2).unsqueeze(0)
+        inp2 = inp2.unsqueeze(0)
 
         out = self.model(inp1, inp2)
         out = out.sigmoid()

plugins/ai_method/packages/models/DVCA/decoder.py

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

plugins/ai_method/packages/models/__init__.py

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

plugins/ai_method/packages/models/encoders/__init__.py

@@ -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 .densenet import densenet_encoders
-from .dpn import dpn_encoders
+# from .dpn import dpn_encoders
-from .efficientnet import efficient_net_encoders
+# from .efficientnet import efficient_net_encoders
-from .inceptionresnetv2 import inceptionresnetv2_encoders
+# from .inceptionresnetv2 import inceptionresnetv2_encoders
-from .inceptionv4 import inceptionv4_encoders
+# from .inceptionv4 import inceptionv4_encoders
-from .mobilenet import mobilenet_encoders
+# from .mobilenet import mobilenet_encoders
 from .resnet import resnet_encoders
-from .senet import senet_encoders
+# from .senet import senet_encoders
-from .timm_efficientnet import timm_efficientnet_encoders
+# from .timm_efficientnet import timm_efficientnet_encoders
-from .timm_gernet import timm_gernet_encoders
+# from .timm_gernet import timm_gernet_encoders
-from .timm_mobilenetv3 import timm_mobilenetv3_encoders
+# from .timm_mobilenetv3 import timm_mobilenetv3_encoders
-from .timm_regnet import timm_regnet_encoders
+# from .timm_regnet import timm_regnet_encoders
-from .timm_res2net import timm_res2net_encoders
+# from .timm_res2net import timm_res2net_encoders
-from .timm_resnest import timm_resnest_encoders
+# from .timm_resnest import timm_resnest_encoders
-from .timm_sknet import timm_sknet_encoders
+# from .timm_sknet import timm_sknet_encoders
-from .timm_universal import TimmUniversalEncoder
+# from .timm_universal import TimmUniversalEncoder
-from .vgg import vgg_encoders
+# from .vgg import vgg_encoders
-from .xception import xception_encoders
+# from .xception import xception_encoders
-from .swin_transformer import swin_transformer_encoders
+# from .swin_transformer import swin_transformer_encoders
-from .mit_encoder import mit_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(dpn_encoders)
-encoders.update(vgg_encoders)
+# encoders.update(vgg_encoders)
-encoders.update(senet_encoders)
+# encoders.update(senet_encoders)
-encoders.update(densenet_encoders)
+# encoders.update(densenet_encoders)
-encoders.update(inceptionresnetv2_encoders)
+# encoders.update(inceptionresnetv2_encoders)
-encoders.update(inceptionv4_encoders)
+# encoders.update(inceptionv4_encoders)
-encoders.update(efficient_net_encoders)
+# encoders.update(efficient_net_encoders)
-encoders.update(mobilenet_encoders)
+# encoders.update(mobilenet_encoders)
-encoders.update(xception_encoders)
+# encoders.update(xception_encoders)
-encoders.update(timm_efficientnet_encoders)
+# encoders.update(timm_efficientnet_encoders)
-encoders.update(timm_resnest_encoders)
+# encoders.update(timm_resnest_encoders)
-encoders.update(timm_res2net_encoders)
+# encoders.update(timm_res2net_encoders)
-encoders.update(timm_regnet_encoders)
+# encoders.update(timm_regnet_encoders)
-encoders.update(timm_sknet_encoders)
+# encoders.update(timm_sknet_encoders)
-encoders.update(timm_mobilenetv3_encoders)
+# encoders.update(timm_mobilenetv3_encoders)
-encoders.update(timm_gernet_encoders)
+# encoders.update(timm_gernet_encoders)
-encoders.update(swin_transformer_encoders)
+# encoders.update(swin_transformer_encoders)
-encoders.update(mit_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(
+        # encoder = TimmUniversalEncoder(
-            name=name,
+        #     name=name,
-            in_channels=in_channels,
+        #     in_channels=in_channels,
-            depth=depth,
+        #     depth=depth,
-            output_stride=output_stride,
+        #     output_stride=output_stride,
-            pretrained=weights is not None,
+        #     pretrained=weights is not None,
-            **kwargs
+        #     **kwargs
-        )
+        # )
-        return encoder
+        # return encoder
 
     try:
         Encoder = encoders[name]["encoder"]

plugins/export_to/main.py

@@ -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。)

plugins/filter_collection/lms_filter.py

@@ -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.setText('9')
-        x_size_input.setValidator(QtGui.QIntValidator())
+        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())

plugins/filter_collection/lmsnp_filter.py

@@ -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

plugins/filter_collection/main.py

@@ -114,12 +114,17 @@ class MainPlugin(BasicPlugin):
         pth = layer.path
         if pth is None:
             return
+        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)   
+        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:

plugins/follow/main.py

@@ -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)
 
     

plugins/unsupervised_method/scripts/AHT.py

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

plugins/unsupervised_method/scripts/__init__.py

@@ -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)))

test.lic

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