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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 from rscder.utils.geomath import geo2imageRC, imageRC2geo
import math import math
from .packages import get_model from .packages import get_model
import numpy as np
class BasicAICD(AlgFrontend): class BasicAICD(AlgFrontend):
@staticmethod @staticmethod
@ -62,9 +62,9 @@ class BasicAICD(AlgFrontend):
for i in range(xblocks +1): 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: if block_xy1[1] > end1y or block_xy2[1] > end2y:
break break

76
plugins/ai_method/packages/__init__.py
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@ -1,39 +1,39 @@
from .models import create_model from .models import create_model
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import numpy as np import numpy as np
class Front: class Front:
def __init__(self, model) -> None: def __init__(self, model) -> None:
self.model = model self.model = model
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.model = model.to(self.device) self.model = model.to(self.device)
def __call__(self, inp1, inp2): 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 = self.model(inp1, inp2)
out = out.sigmoid() out = out.sigmoid()
out = out.cpu().detach().numpy()[0,0] out = out.cpu().detach().numpy()[0,0]
return out return out
def get_model(name): def get_model(name):
try: try:
model = create_model(name, encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7 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 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.) 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) classes=1, # model output channels (number of classes in your datasets)
siam_encoder=True, # whether to use a siamese encoder 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.) fusion_form='concat', # the form of fusing features from two branches. e.g. concat, sum, diff, or abs_diff.)
) )
except: except:
return None return None
return Front(model) return Front(model)

4
plugins/ai_method/packages/models/DVCA/decoder.py
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@ -48,8 +48,8 @@ class DVCADecoder(Decoder):
in_channels = in_channels * 2 in_channels = in_channels * 2
self.aspp = nn.Sequential( 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.BatchNorm2d(out_channels),
nn.ReLU(), nn.ReLU(),
) )

84
plugins/ai_method/packages/models/__init__.py
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@ -1,42 +1,42 @@
from .RCNN import RCNN from .RCNN import RCNN
from .DVCA import DVCA from .DVCA import DVCA
from .DPFCN import DPFCN from .DPFCN import DPFCN
from . import encoders 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 from typing import Optional
import torch import torch
def create_model( def create_model(
arch: str, arch: str,
encoder_name: str = "resnet34", encoder_name: str = "resnet34",
encoder_weights: Optional[str] = "imagenet", encoder_weights: Optional[str] = "imagenet",
in_channels: int = 3, in_channels: int = 3,
classes: int = 1, classes: int = 1,
**kwargs, **kwargs,
) -> torch.nn.Module: ) -> torch.nn.Module:
"""Models wrapper. Allows to create any model just with parametes """Models wrapper. Allows to create any model just with parametes
""" """
archs = [DVCA, DPFCN, RCNN] archs = [DVCA, DPFCN, RCNN]
archs_dict = {a.__name__.lower(): a for a in archs} archs_dict = {a.__name__.lower(): a for a in archs}
try: try:
model_class = archs_dict[arch.lower()] model_class = archs_dict[arch.lower()]
except KeyError: except KeyError:
raise KeyError("Wrong architecture type `{}`. Available options are: {}".format( raise KeyError("Wrong architecture type `{}`. Available options are: {}".format(
arch, list(archs_dict.keys()), arch, list(archs_dict.keys()),
)) ))
return model_class( return model_class(
encoder_name=encoder_name, encoder_name=encoder_name,
encoder_weights=encoder_weights, encoder_weights=encoder_weights,
in_channels=in_channels, in_channels=in_channels,
classes=classes, classes=classes,
**kwargs, **kwargs,
) )

226
plugins/ai_method/packages/models/encoders/__init__.py
View File

@ -1,113 +1,113 @@
import functools import functools
import torch import torch
import torch.utils.model_zoo as model_zoo import torch.utils.model_zoo as model_zoo
from ._preprocessing import preprocess_input 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 .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 # from .hrnet import hrnet_encoders
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
encoders = {} encoders = {}
encoders.update(resnet_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) # encoders.update(hrnet_encoders)
def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs): def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs):
if name.startswith("tu-"): if name.startswith("tu-"):
name = name[3:] 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: try:
Encoder = encoders[name]["encoder"] Encoder = encoders[name]["encoder"]
except KeyError: except KeyError:
raise KeyError("Wrong encoder name `{}`, supported encoders: {}".format(name, list(encoders.keys()))) raise KeyError("Wrong encoder name `{}`, supported encoders: {}".format(name, list(encoders.keys())))
params = encoders[name]["params"] params = encoders[name]["params"]
params.update(depth=depth) params.update(depth=depth)
encoder = Encoder(**params) encoder = Encoder(**params)
if weights is not None: if weights is not None:
try: try:
settings = encoders[name]["pretrained_settings"][weights] settings = encoders[name]["pretrained_settings"][weights]
except KeyError: except KeyError:
raise KeyError("Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format( raise KeyError("Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format(
weights, name, list(encoders[name]["pretrained_settings"].keys()), weights, name, list(encoders[name]["pretrained_settings"].keys()),
)) ))
encoder.load_state_dict(model_zoo.load_url(settings["url"], map_location=torch.device(DEVICE))) 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) encoder.set_in_channels(in_channels, pretrained=weights is not None)
if output_stride != 32: if output_stride != 32:
encoder.make_dilated(output_stride) encoder.make_dilated(output_stride)
return encoder return encoder
def get_encoder_names(): def get_encoder_names():
return list(encoders.keys()) return list(encoders.keys())
def get_preprocessing_params(encoder_name, pretrained="imagenet"): def get_preprocessing_params(encoder_name, pretrained="imagenet"):
settings = encoders[encoder_name]["pretrained_settings"] settings = encoders[encoder_name]["pretrained_settings"]
if pretrained not in settings.keys(): if pretrained not in settings.keys():
raise ValueError("Available pretrained options {}".format(settings.keys())) raise ValueError("Available pretrained options {}".format(settings.keys()))
formatted_settings = {} formatted_settings = {}
formatted_settings["input_space"] = settings[pretrained].get("input_space") formatted_settings["input_space"] = settings[pretrained].get("input_space")
formatted_settings["input_range"] = settings[pretrained].get("input_range") formatted_settings["input_range"] = settings[pretrained].get("input_range")
formatted_settings["mean"] = settings[pretrained].get("mean") formatted_settings["mean"] = settings[pretrained].get("mean")
formatted_settings["std"] = settings[pretrained].get("std") formatted_settings["std"] = settings[pretrained].get("std")
return formatted_settings return formatted_settings
def get_preprocessing_fn(encoder_name, pretrained="imagenet"): def get_preprocessing_fn(encoder_name, pretrained="imagenet"):
params = get_preprocessing_params(encoder_name, pretrained=pretrained) params = get_preprocessing_params(encoder_name, pretrained=pretrained)
return functools.partial(preprocess_input, **params) return functools.partial(preprocess_input, **params)

0
plugins/ai_method/utils.py
View File

31
plugins/export_to/main.py
View File

@ -8,6 +8,7 @@ from PyQt5.QtGui import QIcon
from PyQt5.QtCore import Qt from PyQt5.QtCore import Qt
from osgeo import gdal, gdal_array from osgeo import gdal, gdal_array
import numpy as np import numpy as np
import threading
class ExportDialog(QDialog): class ExportDialog(QDialog):
TXT='*.txt' TXT='*.txt'
@ -122,12 +123,12 @@ class ExportPlugin(BasicPlugin):
def export_rgb_action(self): def export_rgb_action(self):
dialog = ExportDialog(self.mainwindow, ExportDialog.TIF) dialog = ExportDialog(self.mainwindow, ExportDialog.TIF)
btn = QPushButton('选择样式文件') btn = QPushButton('选择样式文件')
style_path = '' self.style_path = ''
def select_style(): 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]: if select_file[0]:
# style_path.setText(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.setText(select_file[0])
btn.clicked.connect(select_style) btn.clicked.connect(select_style)
dialog.layout().addWidget(btn) dialog.layout().addWidget(btn)
@ -137,22 +138,26 @@ class ExportPlugin(BasicPlugin):
[1, 255, 0, 0] [1, 255, 0, 0]
] ]
if style_path is '': if self.style_path is '':
style = default_style style = default_style
else: else:
style = np.loadtxt(style_path, comments='#', delimiter=',') style = np.loadtxt(self.style_path, comments='#', delimiter=',')
style = style.tolist() style = style.tolist()
if dialog.exec_(): if dialog.exec_():
result = dialog.result_layer.path result = dialog.result_layer.result_path['cmi']
out = dialog.out_path 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): def render(self, style:list, path, out):
self.send_message.emit('正在导出' )
data = gdal_array.LoadFile(path) data = gdal_array.LoadFile(path)
if len(data.shape) == 3: 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): def get_color(v):
first_color = [] first_color = []
second_color = [] second_color = []
@ -166,7 +171,7 @@ class ExportPlugin(BasicPlugin):
second_value = s[0] second_value = s[0]
second_color = s[1:] second_color = s[1:]
break break
if second_value == -1: if second_value == 1:
return np.array(style[-1][1:]) return np.array(style[-1][1:])
first_dis = (v - first_value) / (second_value - first_value) first_dis = (v - first_value) / (second_value - first_value)
@ -174,7 +179,7 @@ class ExportPlugin(BasicPlugin):
first_color = np.array(first_color) first_color = np.array(first_color)
second_color = np.array(second_color) second_color = np.array(second_color)
color = first_color* first_dis + second_color * second_dis 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) get_color = np.frompyfunc(get_color, nin=1, nout=1)
@ -195,4 +200,6 @@ class ExportPlugin(BasicPlugin):
for i in range(3): for i in range(3):
out_ds.GetRasterBand(i+1).WriteArray(rgbs[..., i]) out_ds.GetRasterBand(i+1).WriteArray(rgbs[..., i])
del out_ds 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。)

281
plugins/filter_collection/lms_filter.py
View File

@ -1,140 +1,141 @@
from misc import AlgFrontend from misc import AlgFrontend
from misc.utils import format_now from misc.utils import format_now
from osgeo import gdal, gdal_array from osgeo import gdal, gdal_array
from skimage.filters import rank from skimage.filters import rank
from skimage.morphology import rectangle from skimage.morphology import rectangle
from filter_collection import FILTER from filter_collection import FILTER
from PyQt5.QtWidgets import QDialog, QAction from PyQt5.QtWidgets import QDialog, QAction
from PyQt5 import QtCore, QtGui, QtWidgets from PyQt5 import QtCore, QtGui, QtWidgets
from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer
from rscder.utils.icons import IconInstance from rscder.utils.icons import IconInstance
import os import os
from datetime import datetime from datetime import datetime
import numpy as np import numpy as np
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
def adaptiveMedianDeNoise(count, original): def adaptiveMedianDeNoise(count, original):
# 初始窗口大小 # 初始窗口大小
startWindow = 3 startWindow = 3
# 卷积范围 # 卷积范围
c = count // 2 c = count // 2
rows, cols = original.shape rows, cols = original.shape
newI = np.zeros(original.shape) newI = np.zeros(original.shape)
# median = # median =
for i in range(c, rows - c): for i in range(c, rows - c):
for j in range(c, cols - c): for j in range(c, cols - c):
k = int(startWindow / 2) startWindow = 3
median = np.median(original[i - k:i + k + 1, j - k:j + k + 1]) k = int(startWindow / 2)
mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1]) median = np.median(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]) mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1])
if mi < median < ma: ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
if mi < original[i, j] < ma: if mi < median < ma:
newI[i, j] = original[i, j] if mi < original[i, j] < ma:
else: newI[i, j] = original[i, j]
newI[i, j] = median else:
newI[i, j] = median
else:
while True: else:
startWindow = startWindow + 2 while True:
k = int(startWindow / 2) startWindow = startWindow + 2
median = np.median(original[i - k:i + k + 1, j - k:j + k + 1]) k = int(startWindow / 2)
mi = np.min(original[i - k:i + k + 1, j - k:j + k + 1]) median = np.median(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]) 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:
break
if mi < median < ma or startWindow > count:
if mi < original[i, j] < ma: if mi < median < ma or startWindow > count:
newI[i, j] = original[i, j] if mi < original[i, j] < ma:
else: newI[i, j] = original[i, j]
newI[i, j] = median else:
newI[i, j] = median
return newI
return newI
@FILTER.register
class AdaptiveFilter(AlgFrontend): @FILTER.register
class AdaptiveFilter(AlgFrontend):
@staticmethod
def get_name(): @staticmethod
return '自适应滤波' def get_name():
return '自适应滤波'
@staticmethod
def get_icon(): @staticmethod
return IconInstance().ARITHMETIC2 def get_icon():
return IconInstance().ARITHMETIC2
@staticmethod
def get_widget(parent=None): @staticmethod
widget = QtWidgets.QWidget(parent) def get_widget(parent=None):
x_size_input = QtWidgets.QLineEdit(widget) widget = QtWidgets.QWidget(parent)
x_size_input.setText('3') x_size_input = QtWidgets.QLineEdit(widget)
x_size_input.setValidator(QtGui.QIntValidator()) x_size_input.setText('9')
x_size_input.setObjectName('xinput') x_size_input.setValidator(QtGui.QIntValidator(6, 21))
# y_size_input = QtWidgets.QLineEdit(widget) x_size_input.setObjectName('xinput')
# y_size_input.setValidator(QtGui.QIntValidator()) # y_size_input = QtWidgets.QLineEdit(widget)
# y_size_input.setObjectName('yinput') # y_size_input.setValidator(QtGui.QIntValidator())
# y_size_input.setText('3') # y_size_input.setObjectName('yinput')
# y_size_input.setText('3')
size_label = QtWidgets.QLabel(widget)
size_label.setText('窗口大小:') size_label = QtWidgets.QLabel(widget)
size_label.setText('窗口大小:')
# time_label = QtWidgets.QLabel(widget)
# time_label.setText('X') # time_label = QtWidgets.QLabel(widget)
# time_label.setText('X')
hlayout1 = QtWidgets.QHBoxLayout()
hlayout1 = QtWidgets.QHBoxLayout()
hlayout1.addWidget(size_label)
hlayout1.addWidget(x_size_input) hlayout1.addWidget(size_label)
# hlayout1.addWidget(time_label) hlayout1.addWidget(x_size_input)
# hlayout1.addWidget(y_size_input) # hlayout1.addWidget(time_label)
# hlayout1.addWidget(y_size_input)
widget.setLayout(hlayout1)
widget.setLayout(hlayout1)
return widget
return widget
@staticmethod
def get_params(widget:QtWidgets.QWidget=None): @staticmethod
if widget is None: def get_params(widget:QtWidgets.QWidget=None):
return dict(x_size=3) if widget is None:
return dict(x_size=9)
x_input = widget.findChild(QtWidgets.QLineEdit, 'xinput')
# y_input = widget.findChild(QtWidgets.QLineEdit, 'yinput') x_input = widget.findChild(QtWidgets.QLineEdit, 'xinput')
# y_input = widget.findChild(QtWidgets.QLineEdit, 'yinput')
if x_input is None:
return dict(x_size=3) if x_input is None:
return dict(x_size=9)
x_size = int(x_input.text())
# y_size = int(y_input.text()) x_size = int(x_input.text())
# y_size = int(y_input.text())
return dict(x_size=x_size)
return dict(x_size=x_size)
@staticmethod
def run_alg(pth, x_size, *args, **kargs): @staticmethod
x_size = int(x_size) def run_alg(pth, x_size, *args, **kargs):
# y_size = int(y_size) x_size = int(x_size)
# pth = layer.path # y_size = int(y_size)
if pth is None: # pth = layer.path
return if pth is None:
return
ds = gdal.Open(pth)
band_count = ds.RasterCount ds = gdal.Open(pth)
name = os.path.splitext(os.path.basename(pth))[0] band_count = ds.RasterCount
out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveFilter.get_name(), format_now())) name = os.path.splitext(os.path.basename(pth))[0]
out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType) out_path = os.path.join(Project().other_path, '{}_{}_{}.tif'.format(name, AdaptiveFilter.get_name(), format_now()))
out_ds.SetProjection(ds.GetProjection()) out_ds = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType)
out_ds.SetGeoTransform(ds.GetGeoTransform()) out_ds.SetProjection(ds.GetProjection())
out_ds.SetGeoTransform(ds.GetGeoTransform())
for i in range(band_count):
band = ds.GetRasterBand(i+1) for i in range(band_count):
data = band.ReadAsArray() band = ds.GetRasterBand(i+1)
data = band.ReadAsArray()
data = adaptiveMedianDeNoise(x_size, data)
data = adaptiveMedianDeNoise(x_size, data)
out_band = out_ds.GetRasterBand(i+1)
out_band.WriteArray(data) out_band = out_ds.GetRasterBand(i+1)
out_band.WriteArray(data)
out_ds.FlushCache()
del out_ds out_ds.FlushCache()
del ds del out_ds
return out_path del ds
return out_path

256
plugins/filter_collection/lmsnp_filter.py
View File

@ -1,128 +1,128 @@
from misc import AlgFrontend from misc import AlgFrontend
from misc.utils import format_now from misc.utils import format_now
from osgeo import gdal, gdal_array from osgeo import gdal, gdal_array
from skimage.filters import rank from skimage.filters import rank
from skimage.morphology import rectangle from skimage.morphology import rectangle
from filter_collection import FILTER from filter_collection import FILTER
from PyQt5.QtWidgets import QDialog, QAction from PyQt5.QtWidgets import QDialog, QAction
from PyQt5 import QtCore, QtGui, QtWidgets from PyQt5 import QtCore, QtGui, QtWidgets
from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer from rscder.utils.project import PairLayer, Project, RasterLayer, ResultPointLayer
from rscder.utils.icons import IconInstance from rscder.utils.icons import IconInstance
import os import os
from datetime import datetime from datetime import datetime
import numpy as np import numpy as np
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
def adaptiveMedianDeNoise(count, original): def adaptiveMedianDeNoise(count, original):
# 初始窗口大小 # 初始窗口大小
startWindow = 3 startWindow = 3
# 卷积范围 # 卷积范围
c = count // 2 c = count // 2
rows, cols = original.shape rows, cols = original.shape
newI = np.zeros(original.shape) newI = np.zeros(original.shape)
# median = # median =
for i in range(c, rows - c): for i in range(c, rows - c):
for j in range(c, cols - c): for j in range(c, cols - c):
k = int(startWindow / 2) k = int(startWindow / 2)
median = np.median(original[i - k:i + k + 1, j - k:j + k + 1]) 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]) 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]) ma = np.max(original[i - k:i + k + 1, j - k:j + k + 1])
if mi < median < ma: if mi < median < ma:
if mi < original[i, j] < ma: if mi < original[i, j] < ma:
newI[i, j] = original[i, j] newI[i, j] = original[i, j]
else: else:
newI[i, j] = median newI[i, j] = median
else: else:
while True: while True:
startWindow = startWindow + 2 startWindow = startWindow + 2
k = int(startWindow / 2) k = int(startWindow / 2)
median = np.median(original[i - k:i + k + 1, j - k:j + k + 1]) 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]) 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]) 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 break
if mi < median < ma or startWindow > count: if mi < median < ma or startWindow >= count:
if mi < original[i, j] < ma: if mi < original[i, j] < ma:
newI[i, j] = original[i, j] newI[i, j] = original[i, j]
else: else:
newI[i, j] = median newI[i, j] = median
return newI return newI
@FILTER.register @FILTER.register
class AdaptiveNPFilter(AlgFrontend): class AdaptiveNPFilter(AlgFrontend):
@staticmethod @staticmethod
def get_name(): def get_name():
return '自动滤波（无参自适应滤波）' return '自动滤波（无参自适应滤波）'
@staticmethod @staticmethod
def get_icon(): def get_icon():
return IconInstance().ARITHMETIC2 return IconInstance().ARITHMETIC2
@staticmethod @staticmethod
def get_widget(parent=None): def get_widget(parent=None):
# widget = QtWidgets.QWidget(parent) # widget = QtWidgets.QWidget(parent)
# x_size_input = QtWidgets.QLineEdit(widget) # x_size_input = QtWidgets.QLineEdit(widget)
# x_size_input.setText('3') # x_size_input.setText('3')
# x_size_input.setValidator(QtGui.QIntValidator()) # x_size_input.setValidator(QtGui.QIntValidator())
# x_size_input.setObjectName('xinput') # x_size_input.setObjectName('xinput')
# # y_size_input = QtWidgets.QLineEdit(widget) # # y_size_input = QtWidgets.QLineEdit(widget)
# # y_size_input.setValidator(QtGui.QIntValidator()) # # y_size_input.setValidator(QtGui.QIntValidator())
# # y_size_input.setObjectName('yinput') # # y_size_input.setObjectName('yinput')
# # y_size_input.setText('3') # # y_size_input.setText('3')
# size_label = QtWidgets.QLabel(widget) # size_label = QtWidgets.QLabel(widget)
# size_label.setText('窗口大小:') # size_label.setText('窗口大小:')
# # time_label = QtWidgets.QLabel(widget) # # time_label = QtWidgets.QLabel(widget)
# # time_label.setText('X') # # time_label.setText('X')
# hlayout1 = QtWidgets.QHBoxLayout() # hlayout1 = QtWidgets.QHBoxLayout()
# hlayout1.addWidget(size_label) # hlayout1.addWidget(size_label)
# hlayout1.addWidget(x_size_input) # hlayout1.addWidget(x_size_input)
# # hlayout1.addWidget(time_label) # # hlayout1.addWidget(time_label)
# # hlayout1.addWidget(y_size_input) # # hlayout1.addWidget(y_size_input)
# widget.setLayout(hlayout1) # widget.setLayout(hlayout1)
return None return None
@staticmethod @staticmethod
def get_params(widget:QtWidgets.QWidget=None): def get_params(widget:QtWidgets.QWidget=None):
return dict() return dict()
@staticmethod @staticmethod
def run_alg(pth, x_size, *args, **kargs): def run_alg(pth, *args, **kargs):
# x_size = int(x_size) # x_size = int(x_size)
# y_size = int(y_size) # y_size = int(y_size)
# pth = layer.path # pth = layer.path
if pth is None: if pth is None:
return return
ds = gdal.Open(pth) ds = gdal.Open(pth)
band_count = ds.RasterCount band_count = ds.RasterCount
name = os.path.splitext(os.path.basename(pth))[0] 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_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 = gdal.GetDriverByName('GTiff').Create(out_path, ds.RasterXSize, ds.RasterYSize, band_count, ds.GetRasterBand(1).DataType)
out_ds.SetProjection(ds.GetProjection()) out_ds.SetProjection(ds.GetProjection())
out_ds.SetGeoTransform(ds.GetGeoTransform()) out_ds.SetGeoTransform(ds.GetGeoTransform())
for i in range(band_count): for i in range(band_count):
band = ds.GetRasterBand(i+1) band = ds.GetRasterBand(i+1)
data = band.ReadAsArray() data = band.ReadAsArray()
data = adaptiveMedianDeNoise(5, data) data = adaptiveMedianDeNoise(5, data)
out_band = out_ds.GetRasterBand(i+1) out_band = out_ds.GetRasterBand(i+1)
out_band.WriteArray(data) out_band.WriteArray(data)
out_ds.FlushCache() out_ds.FlushCache()
del out_ds del out_ds
del ds del ds
return out_path return out_path

13
plugins/filter_collection/main.py
View File

@ -114,12 +114,17 @@ class MainPlugin(BasicPlugin):
pth = layer.path pth = layer.path
if pth is None: if pth is None:
return return
out_path = alg.run_alg(pth, **p, send_message=self.send_message) self.send_message.emit('滤波开始')
self.send_message.emit('滤波完成') try:
self.send_message.emit('结果在：'+out_path) 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): def run(self, key):
if key not in FILTER: 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.current_widget = FOLLOW[self.combox.currentData()].get_widget(ActionManager().follow_box)
self.layout.addWidget(self.current_widget) 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): def AHT(file1, file2, outfile):
return _AHT.AHT(file1, file2, outfile) return _AHT.LHBA(file1, file2, outfile)

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

1
test.lic Normal file
View File

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