diff --git a/env.yaml b/env.yaml new file mode 100644 index 0000000..3fca7fd --- /dev/null +++ b/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 \ No newline at end of file diff --git a/lic/license.lic b/lic/license.lic index fcdb940..f89c6df 100644 --- a/lic/license.lic +++ b/lic/license.lic @@ -1 +1 @@ -vd4FiYncytyziGH9GNCAA8hGGr1/79Xmphtc5+PHPJDpxvqj1hP7+985QMojYO4M5Qn/aqEAvFgeDN3CA8x1YAK8SdCgSXSBJpRBK8wqPQjBY1ak96QfdPCrTLunr+xuPxK3Gxe772adTTsee2+ot7WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP \ No newline at end of file +IaqFuRlbPMtYTReB0p+cxn8sffVeOjbq+d46I2texZIVIeLbwCxJ7w3mqlramQy3p0totEfoSkjIrQV1GjtrOOWsuYRqv5ZZ5A+/PdTd7ZU8WlMAl7sknJGJFWvciG1VL9n9XtJUG+CJg4oLYkdwR5WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP \ No newline at end of file diff --git a/plugins/ai_method/basic_cd.py b/plugins/ai_method/basic_cd.py index 4c69963..d772c85 100644 --- a/plugins/ai_method/basic_cd.py +++ b/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_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 diff --git a/plugins/ai_method/packages/__init__.py b/plugins/ai_method/packages/__init__.py index b4939be..ab15fe8 100644 --- a/plugins/ai_method/packages/__init__.py +++ b/plugins/ai_method/packages/__init__.py @@ -1,39 +1,39 @@ -from .models import create_model -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np - - -class Front: - - def __init__(self, model) -> None: - self.model = model - self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') - 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) - - out = self.model(inp1, inp2) - out = out.sigmoid() - out = out.cpu().detach().numpy()[0,0] - return out - -def get_model(name): - - try: - model = create_model(name, encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7 - encoder_weights="imagenet", # use `imagenet` pre-trained weights for encoder initialization - in_channels=3, # model input channels (1 for gray-scale images, 3 for RGB, etc.) - classes=1, # model output channels (number of classes in your datasets) - siam_encoder=True, # whether to use a siamese encoder - fusion_form='concat', # the form of fusing features from two branches. e.g. concat, sum, diff, or abs_diff.) - ) - except: - return None - +from .models import create_model +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class Front: + + def __init__(self, model) -> None: + self.model = model + self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + self.model = model.to(self.device) + + def __call__(self, inp1, inp2): + 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() + out = out.cpu().detach().numpy()[0,0] + return out + +def get_model(name): + + try: + model = create_model(name, encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7 + encoder_weights="imagenet", # use `imagenet` pre-trained weights for encoder initialization + in_channels=3, # model input channels (1 for gray-scale images, 3 for RGB, etc.) + classes=1, # model output channels (number of classes in your datasets) + siam_encoder=True, # whether to use a siamese encoder + fusion_form='concat', # the form of fusing features from two branches. e.g. concat, sum, diff, or abs_diff.) + ) + except: + return None + return Front(model) \ No newline at end of file diff --git a/plugins/ai_method/packages/models/DVCA/decoder.py b/plugins/ai_method/packages/models/DVCA/decoder.py index 72fb393..3bade23 100644 --- a/plugins/ai_method/packages/models/DVCA/decoder.py +++ b/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), - 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(), ) diff --git a/plugins/ai_method/packages/models/__init__.py b/plugins/ai_method/packages/models/__init__.py index 5c3a1fa..fbe2097 100644 --- a/plugins/ai_method/packages/models/__init__.py +++ b/plugins/ai_method/packages/models/__init__.py @@ -1,42 +1,42 @@ -from .RCNN import RCNN -from .DVCA import DVCA -from .DPFCN import DPFCN - -from . import encoders -from . import utils -from . import losses -from . import datasets - -from .__version__ import __version__ - -from typing import Optional -import torch - - -def create_model( - arch: str, - encoder_name: str = "resnet34", - encoder_weights: Optional[str] = "imagenet", - in_channels: int = 3, - classes: int = 1, - **kwargs, -) -> torch.nn.Module: - """Models wrapper. Allows to create any model just with parametes - - """ - - archs = [DVCA, DPFCN, RCNN] - archs_dict = {a.__name__.lower(): a for a in archs} - try: - model_class = archs_dict[arch.lower()] - except KeyError: - raise KeyError("Wrong architecture type `{}`. Available options are: {}".format( - arch, list(archs_dict.keys()), - )) - return model_class( - encoder_name=encoder_name, - encoder_weights=encoder_weights, - in_channels=in_channels, - classes=classes, - **kwargs, - ) +from .RCNN import RCNN +from .DVCA import DVCA +from .DPFCN import DPFCN + +from . import encoders +# from . import utils +# from . import losses +# from . import datasets + +# from .__version__ import __version__ + +from typing import Optional +import torch + + +def create_model( + arch: str, + encoder_name: str = "resnet34", + encoder_weights: Optional[str] = "imagenet", + in_channels: int = 3, + classes: int = 1, + **kwargs, +) -> torch.nn.Module: + """Models wrapper. Allows to create any model just with parametes + + """ + + archs = [DVCA, DPFCN, RCNN] + archs_dict = {a.__name__.lower(): a for a in archs} + try: + model_class = archs_dict[arch.lower()] + except KeyError: + raise KeyError("Wrong architecture type `{}`. Available options are: {}".format( + arch, list(archs_dict.keys()), + )) + return model_class( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + in_channels=in_channels, + classes=classes, + **kwargs, + ) diff --git a/plugins/ai_method/packages/models/encoders/__init__.py b/plugins/ai_method/packages/models/encoders/__init__.py index 231d1d2..9b32658 100644 --- a/plugins/ai_method/packages/models/encoders/__init__.py +++ b/plugins/ai_method/packages/models/encoders/__init__.py @@ -1,113 +1,113 @@ -import functools - -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 .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 .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(hrnet_encoders) - - -def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs): - - 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 - - try: - Encoder = encoders[name]["encoder"] - except KeyError: - raise KeyError("Wrong encoder name `{}`, supported encoders: {}".format(name, list(encoders.keys()))) - - params = encoders[name]["params"] - params.update(depth=depth) - encoder = Encoder(**params) - - if weights is not None: - try: - settings = encoders[name]["pretrained_settings"][weights] - except KeyError: - raise KeyError("Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format( - weights, name, list(encoders[name]["pretrained_settings"].keys()), - )) - encoder.load_state_dict(model_zoo.load_url(settings["url"], map_location=torch.device(DEVICE))) - - encoder.set_in_channels(in_channels, pretrained=weights is not None) - if output_stride != 32: - encoder.make_dilated(output_stride) - - return encoder - - -def get_encoder_names(): - return list(encoders.keys()) - - -def get_preprocessing_params(encoder_name, pretrained="imagenet"): - settings = encoders[encoder_name]["pretrained_settings"] - - if pretrained not in settings.keys(): - raise ValueError("Available pretrained options {}".format(settings.keys())) - - formatted_settings = {} - formatted_settings["input_space"] = settings[pretrained].get("input_space") - formatted_settings["input_range"] = settings[pretrained].get("input_range") - formatted_settings["mean"] = settings[pretrained].get("mean") - formatted_settings["std"] = settings[pretrained].get("std") - return formatted_settings - - -def get_preprocessing_fn(encoder_name, pretrained="imagenet"): - params = get_preprocessing_params(encoder_name, pretrained=pretrained) - return functools.partial(preprocess_input, **params) +import functools + +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 .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 .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(hrnet_encoders) + + +def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs): + + 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 + + try: + Encoder = encoders[name]["encoder"] + except KeyError: + raise KeyError("Wrong encoder name `{}`, supported encoders: {}".format(name, list(encoders.keys()))) + + params = encoders[name]["params"] + params.update(depth=depth) + encoder = Encoder(**params) + + if weights is not None: + try: + settings = encoders[name]["pretrained_settings"][weights] + except KeyError: + raise KeyError("Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format( + weights, name, list(encoders[name]["pretrained_settings"].keys()), + )) + encoder.load_state_dict(model_zoo.load_url(settings["url"], map_location=torch.device(DEVICE))) + + encoder.set_in_channels(in_channels, pretrained=weights is not None) + if output_stride != 32: + encoder.make_dilated(output_stride) + + return encoder + + +def get_encoder_names(): + return list(encoders.keys()) + + +def get_preprocessing_params(encoder_name, pretrained="imagenet"): + settings = encoders[encoder_name]["pretrained_settings"] + + if pretrained not in settings.keys(): + raise ValueError("Available pretrained options {}".format(settings.keys())) + + formatted_settings = {} + formatted_settings["input_space"] = settings[pretrained].get("input_space") + formatted_settings["input_range"] = settings[pretrained].get("input_range") + formatted_settings["mean"] = settings[pretrained].get("mean") + formatted_settings["std"] = settings[pretrained].get("std") + return formatted_settings + + +def get_preprocessing_fn(encoder_name, pretrained="imagenet"): + params = get_preprocessing_params(encoder_name, pretrained=pretrained) + return functools.partial(preprocess_input, **params) diff --git a/plugins/ai_method/utils.py b/plugins/ai_method/utils.py deleted file mode 100644 index e69de29..0000000 diff --git a/plugins/export_to/main.py b/plugins/export_to/main.py index bb6868d..86a65d3 100644 --- a/plugins/export_to/main.py +++ b/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) @@ -137,22 +138,26 @@ 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 = [] second_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) @@ -195,4 +200,6 @@ class ExportPlugin(BasicPlugin): for i in range(3): out_ds.GetRasterBand(i+1).WriteArray(rgbs[..., i]) - del out_ds \ No newline at end of file + 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。) \ No newline at end of file diff --git a/plugins/filter_collection/lms_filter.py b/plugins/filter_collection/lms_filter.py index 81def22..704ed77 100644 --- a/plugins/filter_collection/lms_filter.py +++ b/plugins/filter_collection/lms_filter.py @@ -1,140 +1,141 @@ -from misc import AlgFrontend -from misc.utils import format_now -from osgeo import gdal, gdal_array -from skimage.filters import rank -from skimage.morphology import rectangle -from filter_collection import FILTER -from PyQt5.QtWidgets import QDialog, QAction -from PyQt5 import QtCore, QtGui, QtWidgets -from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer -from rscder.utils.icons import IconInstance -import os -from datetime import datetime -import numpy as np -import torch -import torch.nn.functional as F -def adaptiveMedianDeNoise(count, original): - # 初始窗口大小 - startWindow = 3 - # 卷积范围 - c = count // 2 - rows, cols = original.shape - newI = np.zeros(original.shape) - # median = - - for i in range(c, rows - c): - for j in range(c, cols - c): - 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]) - ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) - if mi < median < ma: - if mi < original[i, j] < ma: - newI[i, j] = original[i, j] - else: - newI[i, j] = median - - else: - while True: - startWindow = startWindow + 2 - 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]) - ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) - - if mi < median < ma or startWindow > count: - break - - if mi < median < ma or startWindow > count: - if mi < original[i, j] < ma: - newI[i, j] = original[i, j] - else: - newI[i, j] = median - - return newI - -@FILTER.register -class AdaptiveFilter(AlgFrontend): - - @staticmethod - def get_name(): - return '自适应滤波' - - @staticmethod - def get_icon(): - return IconInstance().ARITHMETIC2 - - @staticmethod - 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.setObjectName('xinput') - # y_size_input = QtWidgets.QLineEdit(widget) - # y_size_input.setValidator(QtGui.QIntValidator()) - # y_size_input.setObjectName('yinput') - # y_size_input.setText('3') - - size_label = QtWidgets.QLabel(widget) - size_label.setText('窗口大小:') - - # time_label = QtWidgets.QLabel(widget) - # time_label.setText('X') - - hlayout1 = QtWidgets.QHBoxLayout() - - hlayout1.addWidget(size_label) - hlayout1.addWidget(x_size_input) - # hlayout1.addWidget(time_label) - # hlayout1.addWidget(y_size_input) - - widget.setLayout(hlayout1) - - return widget - - @staticmethod - def get_params(widget:QtWidgets.QWidget=None): - if widget is None: - return dict(x_size=3) - - x_input = widget.findChild(QtWidgets.QLineEdit, 'xinput') - # y_input = widget.findChild(QtWidgets.QLineEdit, 'yinput') - - if x_input is None: - return dict(x_size=3) - - x_size = int(x_input.text()) - # y_size = int(y_input.text()) - - return dict(x_size=x_size) - - @staticmethod - def run_alg(pth, x_size, *args, **kargs): - x_size = int(x_size) - # y_size = int(y_size) - # pth = layer.path - if pth is None: - return - - ds = gdal.Open(pth) - band_count = ds.RasterCount - name = os.path.splitext(os.path.basename(pth))[0] - out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveFilter.get_name(), format_now())) - out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType) - out_ds.SetProjection(ds.GetProjection()) - out_ds.SetGeoTransform(ds.GetGeoTransform()) - - for i in range(band_count): - band = ds.GetRasterBand(i+1) - data = band.ReadAsArray() - - data = adaptiveMedianDeNoise(x_size, data) - - out_band = out_ds.GetRasterBand(i+1) - out_band.WriteArray(data) - - out_ds.FlushCache() - del out_ds - del ds - return out_path +from misc import AlgFrontend +from misc.utils import format_now +from osgeo import gdal, gdal_array +from skimage.filters import rank +from skimage.morphology import rectangle +from filter_collection import FILTER +from PyQt5.QtWidgets import QDialog, QAction +from PyQt5 import QtCore, QtGui, QtWidgets +from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer +from rscder.utils.icons import IconInstance +import os +from datetime import datetime +import numpy as np +import torch +import torch.nn.functional as F +def adaptiveMedianDeNoise(count, original): + # 初始窗口大小 + startWindow = 3 + # 卷积范围 + c = count // 2 + rows, cols = original.shape + newI = np.zeros(original.shape) + # median = + + 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]) + ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) + if mi < median < ma: + if mi < original[i, j] < ma: + newI[i, j] = original[i, j] + else: + newI[i, j] = median + + else: + while True: + startWindow = startWindow + 2 + 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]) + ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) + + if mi < median < ma or startWindow >= count: + break + + if mi < median < ma or startWindow > count: + if mi < original[i, j] < ma: + newI[i, j] = original[i, j] + else: + newI[i, j] = median + + return newI + +@FILTER.register +class AdaptiveFilter(AlgFrontend): + + @staticmethod + def get_name(): + return '自适应滤波' + + @staticmethod + def get_icon(): + return IconInstance().ARITHMETIC2 + + @staticmethod + def get_widget(parent=None): + widget = QtWidgets.QWidget(parent) + x_size_input = QtWidgets.QLineEdit(widget) + 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()) + # y_size_input.setObjectName('yinput') + # y_size_input.setText('3') + + size_label = QtWidgets.QLabel(widget) + size_label.setText('窗口大小:') + + # time_label = QtWidgets.QLabel(widget) + # time_label.setText('X') + + hlayout1 = QtWidgets.QHBoxLayout() + + hlayout1.addWidget(size_label) + hlayout1.addWidget(x_size_input) + # hlayout1.addWidget(time_label) + # hlayout1.addWidget(y_size_input) + + widget.setLayout(hlayout1) + + return widget + + @staticmethod + def get_params(widget:QtWidgets.QWidget=None): + if widget is None: + 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=9) + + x_size = int(x_input.text()) + # y_size = int(y_input.text()) + + return dict(x_size=x_size) + + @staticmethod + def run_alg(pth, x_size, *args, **kargs): + x_size = int(x_size) + # y_size = int(y_size) + # pth = layer.path + if pth is None: + return + + ds = gdal.Open(pth) + band_count = ds.RasterCount + name = os.path.splitext(os.path.basename(pth))[0] + out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveFilter.get_name(), format_now())) + out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType) + out_ds.SetProjection(ds.GetProjection()) + out_ds.SetGeoTransform(ds.GetGeoTransform()) + + for i in range(band_count): + band = ds.GetRasterBand(i+1) + data = band.ReadAsArray() + + data = adaptiveMedianDeNoise(x_size, data) + + out_band = out_ds.GetRasterBand(i+1) + out_band.WriteArray(data) + + out_ds.FlushCache() + del out_ds + del ds + return out_path diff --git a/plugins/filter_collection/lmsnp_filter.py b/plugins/filter_collection/lmsnp_filter.py index 0afabd2..8d773d8 100644 --- a/plugins/filter_collection/lmsnp_filter.py +++ b/plugins/filter_collection/lmsnp_filter.py @@ -1,128 +1,128 @@ -from misc import AlgFrontend -from misc.utils import format_now -from osgeo import gdal, gdal_array -from skimage.filters import rank -from skimage.morphology import rectangle -from filter_collection import FILTER -from PyQt5.QtWidgets import QDialog, QAction -from PyQt5 import QtCore, QtGui, QtWidgets -from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer -from rscder.utils.icons import IconInstance -import os -from datetime import datetime -import numpy as np -import torch -import torch.nn.functional as F -def adaptiveMedianDeNoise(count, original): - # 初始窗口大小 - startWindow = 3 - # 卷积范围 - c = count // 2 - rows, cols = original.shape - newI = np.zeros(original.shape) - # median = - - for i in range(c, rows - c): - for j in range(c, cols - c): - 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]) - ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) - if mi < median < ma: - if mi < original[i, j] < ma: - newI[i, j] = original[i, j] - else: - newI[i, j] = median - - else: - while True: - startWindow = startWindow + 2 - 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]) - ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) - - if mi < median < ma or startWindow > count: - break - - if mi < median < ma or startWindow > count: - if mi < original[i, j] < ma: - newI[i, j] = original[i, j] - else: - newI[i, j] = median - - return newI - -@FILTER.register -class AdaptiveNPFilter(AlgFrontend): - - @staticmethod - def get_name(): - return '自动滤波（无参自适应滤波）' - - @staticmethod - def get_icon(): - return IconInstance().ARITHMETIC2 - - @staticmethod - 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.setObjectName('xinput') - # # y_size_input = QtWidgets.QLineEdit(widget) - # # y_size_input.setValidator(QtGui.QIntValidator()) - # # y_size_input.setObjectName('yinput') - # # y_size_input.setText('3') - - # size_label = QtWidgets.QLabel(widget) - # size_label.setText('窗口大小:') - - # # time_label = QtWidgets.QLabel(widget) - # # time_label.setText('X') - - # hlayout1 = QtWidgets.QHBoxLayout() - - # hlayout1.addWidget(size_label) - # hlayout1.addWidget(x_size_input) - # # hlayout1.addWidget(time_label) - # # hlayout1.addWidget(y_size_input) - - # widget.setLayout(hlayout1) - - return None - - @staticmethod - def get_params(widget:QtWidgets.QWidget=None): - return dict() - - @staticmethod - def run_alg(pth, x_size, *args, **kargs): - # x_size = int(x_size) - # y_size = int(y_size) - # pth = layer.path - if pth is None: - return - - ds = gdal.Open(pth) - band_count = ds.RasterCount - name = os.path.splitext(os.path.basename(pth))[0] - out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveNPFilter.get_name(), format_now())) - out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType) - out_ds.SetProjection(ds.GetProjection()) - out_ds.SetGeoTransform(ds.GetGeoTransform()) - - for i in range(band_count): - band = ds.GetRasterBand(i+1) - data = band.ReadAsArray() - - data = adaptiveMedianDeNoise(5, data) - - out_band = out_ds.GetRasterBand(i+1) - out_band.WriteArray(data) - - out_ds.FlushCache() - del out_ds - del ds - return out_path +from misc import AlgFrontend +from misc.utils import format_now +from osgeo import gdal, gdal_array +from skimage.filters import rank +from skimage.morphology import rectangle +from filter_collection import FILTER +from PyQt5.QtWidgets import QDialog, QAction +from PyQt5 import QtCore, QtGui, QtWidgets +from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer +from rscder.utils.icons import IconInstance +import os +from datetime import datetime +import numpy as np +import torch +import torch.nn.functional as F +def adaptiveMedianDeNoise(count, original): + # 初始窗口大小 + startWindow = 3 + # 卷积范围 + c = count // 2 + rows, cols = original.shape + newI = np.zeros(original.shape) + # median = + + for i in range(c, rows - c): + for j in range(c, cols - c): + 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]) + ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) + if mi < median < ma: + if mi < original[i, j] < ma: + newI[i, j] = original[i, j] + else: + newI[i, j] = median + + else: + while True: + startWindow = startWindow + 2 + 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]) + ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1]) + + if mi < median < ma or startWindow >= count: + break + + if mi < median < ma or startWindow >= count: + if mi < original[i, j] < ma: + newI[i, j] = original[i, j] + else: + newI[i, j] = median + + return newI + +@FILTER.register +class AdaptiveNPFilter(AlgFrontend): + + @staticmethod + def get_name(): + return '自动滤波（无参自适应滤波）' + + @staticmethod + def get_icon(): + return IconInstance().ARITHMETIC2 + + @staticmethod + 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.setObjectName('xinput') + # # y_size_input = QtWidgets.QLineEdit(widget) + # # y_size_input.setValidator(QtGui.QIntValidator()) + # # y_size_input.setObjectName('yinput') + # # y_size_input.setText('3') + + # size_label = QtWidgets.QLabel(widget) + # size_label.setText('窗口大小:') + + # # time_label = QtWidgets.QLabel(widget) + # # time_label.setText('X') + + # hlayout1 = QtWidgets.QHBoxLayout() + + # hlayout1.addWidget(size_label) + # hlayout1.addWidget(x_size_input) + # # hlayout1.addWidget(time_label) + # # hlayout1.addWidget(y_size_input) + + # widget.setLayout(hlayout1) + + return None + + @staticmethod + def get_params(widget:QtWidgets.QWidget=None): + return dict() + + @staticmethod + def run_alg(pth, *args, **kargs): + # x_size = int(x_size) + # y_size = int(y_size) + # pth = layer.path + if pth is None: + return + + ds = gdal.Open(pth) + band_count = ds.RasterCount + name = os.path.splitext(os.path.basename(pth))[0] + out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveNPFilter.get_name(), format_now())) + out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType) + out_ds.SetProjection(ds.GetProjection()) + out_ds.SetGeoTransform(ds.GetGeoTransform()) + + for i in range(band_count): + band = ds.GetRasterBand(i+1) + data = band.ReadAsArray() + + data = adaptiveMedianDeNoise(5, data) + + out_band = out_ds.GetRasterBand(i+1) + out_band.WriteArray(data) + + out_ds.FlushCache() + del out_ds + del ds + return out_path diff --git a/plugins/filter_collection/main.py b/plugins/filter_collection/main.py index 80357c8..c1b8e8e 100644 --- a/plugins/filter_collection/main.py +++ b/plugins/filter_collection/main.py @@ -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: diff --git a/plugins/follow/main.py b/plugins/follow/main.py index 1a64d25..44c35f3 100644 --- a/plugins/follow/main.py +++ b/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) \ No newline at end of file diff --git a/plugins/unsupervised_method/scripts/AHT.py b/plugins/unsupervised_method/scripts/AHT.py index bb3a1c9..dcc8428 100644 --- a/plugins/unsupervised_method/scripts/AHT.py +++ b/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) diff --git a/plugins/unsupervised_method/scripts/_AHT.pyd b/plugins/unsupervised_method/scripts/_AHT.pyd index 5c78396..3502e2d 100644 Binary files a/plugins/unsupervised_method/scripts/_AHT.pyd and b/plugins/unsupervised_method/scripts/_AHT.pyd differ diff --git a/plugins/unsupervised_method/scripts/_AHT_NO.pyd b/plugins/unsupervised_method/scripts/_AHT_NO.pyd new file mode 100644 index 0000000..5c78396 Binary files /dev/null and b/plugins/unsupervised_method/scripts/_AHT_NO.pyd differ diff --git a/plugins/unsupervised_method/scripts/__init__.py b/plugins/unsupervised_method/scripts/__init__.py index b4ffddb..5eaf072 100644 --- a/plugins/unsupervised_method/scripts/__init__.py +++ b/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))) diff --git a/test.lic b/test.lic new file mode 100644 index 0000000..f89c6df --- /dev/null +++ b/test.lic @@ -0,0 +1 @@ +IaqFuRlbPMtYTReB0p+cxn8sffVeOjbq+d46I2texZIVIeLbwCxJ7w3mqlramQy3p0totEfoSkjIrQV1GjtrOOWsuYRqv5ZZ5A+/PdTd7ZU8WlMAl7sknJGJFWvciG1VL9n9XtJUG+CJg4oLYkdwR5WePYUsC4y4NcS5+rlP1if87xtYqVeSwx3c64cOmAGP \ No newline at end of file