mirror of https://github.com/kritiksoman/GIMP-ML
clean up
DESKTOP-F04AGRR\Kritik Soman
3 years ago
parent
8730796215
commit
23586ce863
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# Default ignored files
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/shelf/
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/workspace.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="Python 3.8 (gimpenv3)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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<orderEntry type="module" module-name="gimp" />
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<orderEntry type="module" module-name="GIMP-ML" />
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</component>
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<component name="PyDocumentationSettings">
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<option name="format" value="PLAIN" />
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<option name="myDocStringFormat" value="Plain" />
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</component>
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</module>
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@ -1,114 +0,0 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="CsvFileAttributes">
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<option name="attributeMap">
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<map>
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<entry key="/gimpml/plugins/monodepth/monodepth.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/gimpml/plugins/semseg/semseg.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/gimpml/tools/complete_install.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/gimpml/tools/inpainting.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/gimpml/tools/monodepth.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="/install.bat">
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<value>
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<Attribute>
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<option name="separator" value=";" />
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</Attribute>
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</value>
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</entry>
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<entry key="/testscases/test.py">
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<value>
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<Attribute>
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<option name="separator" value=":" />
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</Attribute>
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</value>
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</entry>
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<entry key="C:\Users\Kritik Soman\AppData\Roaming\JetBrains\PyCharmCE2020.2\scratches\backtranslate.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="C:\Users\Kritik Soman\AppData\Roaming\JetBrains\PyCharmCE2020.2\scratches\scratch.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="D:\PycharmProjects\gimp\plug-ins\python\foggify.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="D:\PycharmProjects\gimp\plug-ins\python\histogram-export.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="\gimpml\tools\complete_install.py">
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<value>
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<Attribute>
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<option name="separator" value=":" />
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</Attribute>
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</value>
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</entry>
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<entry key="\gimpml\tools\model_info.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="\tmp.py">
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<value>
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<Attribute>
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<option name="separator" value=":" />
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</Attribute>
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</value>
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</entry>
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<entry key="\tmponedrive.py">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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</map>
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</option>
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</component>
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</project>
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@ -1,6 +0,0 @@
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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@ -1,4 +0,0 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (gimpenv3)" project-jdk-type="Python SDK" />
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</project>
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@ -1,10 +0,0 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/../GIMP-ML/.idea/GIMP-ML.iml" filepath="$PROJECT_DIR$/../GIMP-ML/.idea/GIMP-ML.iml" />
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<module fileurl="file://$PROJECT_DIR$/.idea/GIMP3-ML-pip.iml" filepath="$PROJECT_DIR$/.idea/GIMP3-ML-pip.iml" />
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<module fileurl="file://$PROJECT_DIR$/../gimp/.idea/gimp.iml" filepath="$PROJECT_DIR$/../gimp/.idea/gimp.iml" />
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</modules>
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</component>
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</project>
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@ -1,8 +0,0 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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<mapping directory="$PROJECT_DIR$/gimpml/tools/DPT" vcs="Git" />
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<mapping directory="$PROJECT_DIR$/../gimp" vcs="Git" />
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</component>
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</project>
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Binary file not shown.
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@ -1,142 +0,0 @@
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Metadata-Version: 2.1
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Name: gimpml
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Version: 0.0.6
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Summary: A.I. for GIMP
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Home-page: https://github.com/kritiksoman/GIMP-ML
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Author: Kritik Soman
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Author-email: kritiksoman@ieee.org
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License: UNKNOWN
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Keywords: sample,setuptools,development
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Platform: UNKNOWN
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Classifier: Development Status :: 3 - Alpha
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Classifier: Intended Audience :: Developers
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Classifier: Topic :: Software Development :: Build Tools
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Classifier: Programming Language :: Python :: 3.8
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Requires-Python: >=2.7
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Description-Content-Type: text/markdown
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This branch is under development. Dedicated for GIMP 3 and Python 3. :star: :star: :star: :star: are welcome. <br>
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<img src="https://github.com/kritiksoman/tmp/blob/master/cover.png" width="1280" height="180"> <br>
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# Objectives
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[1] Model Ensembling. <br>
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[2] Deep learning inference package for different computer vision tasks. <br>
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[3] Bridge gap between CV research work and real world data. <br>
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[4] Add AI to routine image editing workflows. <br>
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# Contribution
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[<img src="http://img.youtube.com/vi/vFFNp0xhEiU/0.jpg" width="800" height="600">](http://www.youtube.com/watch?v=vFFNp0xhEiU)<br> <br>
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Welcome people interested in contribution !!
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Join us on Slack --> [<img src="https://woocommerce.com/wp-content/uploads/2015/02/Slack_RGB.png" width="130" height="50">](https://join.slack.com/t/gimp-mlworkspace/shared_invite/zt-rbaxvztx-GRvj941idw3sQ0trS686YA)<br>
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Contribution guidelines available --> [Link](https://github.com/kritiksoman/GIMP-ML/blob/GIMP3-ML/CONTRIBUTION.md).<br>
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# Screenshot of Menu
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# Installation Steps
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[1] Install [GIMP](https://www.gimp.org/downloads/devel/) 2.99.6 (Only windows and linux) <br>
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[2] Clone this repository: git clone https://github.com/kritiksoman/GIMP-ML.git <br>
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[3] Change branch : <br>
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```git checkout GIMP3-ML``` <br>
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[3] On linux, run for GPU/CPU: <br>
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```bash GIMP-ML/install.bat```<br>
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On windows, run for CPU: <br>
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```GIMP-ML\install.bat```<br>
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On windows, run for GPU: <br>
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```GIMP-ML\install.bat gpu```<br>
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[4] Follow steps that are printed in terminal or cmd. <br>
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FYI: weights link --> [Link](https://drive.google.com/drive/folders/10IiBO4fuMiGQ-spBStnObbk9R-pGp6u8?usp=sharing)
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| Windows | Linux |
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| ------------- |:-------------:|
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|[<img src="http://img.youtube.com/vi/Rc88_qHSEjc/0.jpg" width="400" height="300">](http://www.youtube.com/watch?v=Rc88_qHSEjc)| [<img src="http://img.youtube.com/vi/MUdUzxYDwaU/0.jpg" width="400" height="300">](http://www.youtube.com/watch?v=MUdUzxYDwaU) |
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# Use as a Python Package
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```Python
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import cv2
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import gimpml
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image = cv2.imread('sampleinput/img.png')
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alpha = cv2.imread('sampleinput/alpha.png')
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out = gimpml.kmeans(image)
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cv2.imwrite('output/tmp-kmeans.jpg', out)
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out = gimpml.deblur(image)
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cv2.imwrite('output/tmp-deblur.jpg', out)
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out = gimpml.deepcolor(image)
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cv2.imwrite('output/tmp-deepcolor.jpg', out)
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out = gimpml.dehaze(image)
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cv2.imwrite('output/tmp-dehaze.jpg', out)
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out = gimpml.denoise(image)
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cv2.imwrite('output/tmp-denoise.jpg', out)
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out = gimpml.matting(image, alpha)
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cv2.imwrite('output/tmp-matting.png', out) # save as png
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out = gimpml.enlighten(image)
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cv2.imwrite('output/tmp-enlighten.jpg', out)
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face = cv2.imread('sampleinput/face.png')
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out = gimpml.parseface(face[:, :, ::-1])
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cv2.imwrite('output/tmp-parseface.png', out[:, :, ::-1])
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mask1 = cv2.imread('sampleinput/mask1.png')
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mask2 = cv2.imread('sampleinput/mask2.png')
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out = gimpml.interpolateframe(mask1, mask2, 'output/interpolateframes')
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face = cv2.imread('sampleinput/face.png')
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out = gimpml.depth(face[:, :, ::-1])
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cv2.imwrite('output/tmp-depth.png', out[:, :, ::-1])
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image = cv2.imread('sampleinput/face.png')
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out = gimpml.semseg(image[:, :, ::-1])
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cv2.imwrite('output/tmp-semseg.png', out[:, :, ::-1])
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image = cv2.imread('sampleinput/face.png')
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out = gimpml.super(image[:, :, ::-1])
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cv2.imwrite('output/tmp-super.png', out[:, :, ::-1])
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image = cv2.imread('sampleinput/inpaint.png')
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mask = cv2.imread('sampleinput/inpaint-mask.png')
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out = gimpml.inpaint(image[:, :, ::-1], mask[:, :, 0])
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cv2.imwrite('output/tmp-inpaint.png', out[:, :, ::-1])
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```
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# Model Zoo
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| Name | License | Dataset |
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| ------------- |:-------------:| :-------------:|
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| [deblur](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#de-blur) | [BSD 3-clause](https://github.com/VITA-Group/DeblurGANv2/blob/master/LICENSE) | GoPro |
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| [faceparse](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#face-parsing) | [MIT](https://github.com/zllrunning/face-parsing.PyTorch/blob/master/LICENSE) | CelebAMask-HQ |
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| [coloring](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#deep-image-coloring) | [MIT](https://github.com/junyanz/interactive-deep-colorization/blob/master/LICENSE) | ImageNet |
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| [monodepth](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#monodepth) | [MIT](https://github.com/intel-isl/DPT/blob/main/LICENSE) | [Multiple](https://arxiv.org/pdf/1907.01341v3.pdf) |
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| [super-resolution](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#image-super-resolution) | [MIT](https://github.com/twtygqyy/pytorch-SRResNet/blob/master/LICENSE) | ImageNet |
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| [matting](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#deep-image-matting) | [Non-commercial purposes](https://github.com/poppinace/indexnet_matting/blob/master/Adobe%20Deep%20Image%20Mattng%20Dataset%20License%20Agreement.pdf) | Adobe Deep Image Matting |
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| [semantic-segmentation](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#semantic-segmentation) | [MIT](https://github.com/intel-isl/DPT/blob/main/LICENSE) | ADE20K |
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| [kmeans](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#k-means-clustering) | [BSD](https://github.com/scipy/scipy/blob/master/LICENSE.txt) | - |
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| [dehazing](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#de-haze) | [MIT](https://github.com/MayankSingal/PyTorch-Image-Dehazing/blob/master/LICENSE) | [Custom](https://sites.google.com/site/boyilics/website-builder/project-page) |
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| [denoising](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#de-noise) | [GPL3](https://github.com/SaoYan/DnCNN-PyTorch/blob/master/LICENSE) | BSD68 |
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| [enlighten](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#enlightening) | [BSD](https://github.com/VITA-Group/EnlightenGAN/blob/master/License) | [Custom](https://arxiv.org/pdf/1906.06972.pdf) |
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| [interpolate-frames](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#interpolate-frames) | [MIT](https://github.com/hzwer/arXiv2020-RIFE/blob/main/LICENSE) | [HD](https://arxiv.org/pdf/2011.06294.pdf) |
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| [inpainting](https://github.com/kritiksoman/GIMP-ML/wiki/User-Manual#in-painting) | [CC BY-NC 4.0](https://github.com/knazeri/edge-connect/blob/master/LICENSE.md) | [CelebA, CelebHQ, Places2, Paris StreetView](https://openaccess.thecvf.com/content_ICCVW_2019/papers/AIM/Nazeri_EdgeConnect_Structure_Guided_Image_Inpainting_using_Edge_Prediction_ICCVW_2019_paper.pdf) |
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# Citation
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Please cite using the following bibtex entry:
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```
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@article{soman2020GIMPML,
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title={GIMP-ML: Python Plugins for using Computer Vision Models in GIMP},
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author={Soman, Kritik},
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journal={arXiv preprint arXiv:2004.13060},
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year={2020}
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}
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```
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@ -1,198 +0,0 @@
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MANIFEST.in
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README.md
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setup.py
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gimpml/__init__.py
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gimpml.egg-info/PKG-INFO
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gimpml.egg-info/SOURCES.txt
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gimpml.egg-info/dependency_links.txt
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gimpml.egg-info/requires.txt
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gimpml.egg-info/top_level.txt
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gimpml/plugins/__init__.py
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gimpml/plugins/coloring/__init__.py
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gimpml/plugins/coloring/coloring.py
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gimpml/plugins/colorpalette/__init__.py
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gimpml/plugins/colorpalette/color_palette.png
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gimpml/plugins/colorpalette/colorpalette.py
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gimpml/plugins/deblur/__init__.py
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gimpml/plugins/deblur/deblur.py
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gimpml/plugins/dehaze/__init__.py
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gimpml/plugins/dehaze/dehaze.py
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gimpml/plugins/denoise/__init__.py
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gimpml/plugins/denoise/denoise.py
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gimpml/plugins/enlighten/__init__.py
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gimpml/plugins/enlighten/enlighten.py
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gimpml/plugins/faceparse/__init__.py
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gimpml/plugins/faceparse/faceparse.py
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gimpml/plugins/images/__init__.py
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gimpml/plugins/images/error_icon.png
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gimpml/plugins/images/plugin_logo.png
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gimpml/plugins/inpainting/__init__.py
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gimpml/plugins/inpainting/inpainting.py
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gimpml/plugins/interpolation/__init__.py
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gimpml/plugins/interpolation/interpolation.py
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gimpml/plugins/kmeans/__init__.py
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gimpml/plugins/kmeans/kmeans.py
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gimpml/plugins/matting/__init__.py
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gimpml/plugins/matting/matting.py
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gimpml/plugins/monodepth/__init__.py
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gimpml/plugins/monodepth/monodepth.py
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gimpml/plugins/semseg/__init__.py
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gimpml/plugins/semseg/semseg.py
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gimpml/plugins/superresolution/__init__.py
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gimpml/plugins/superresolution/superresolution.py
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gimpml/tools/__init__.py
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gimpml/tools/coloring.py
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gimpml/tools/complete_install.py
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gimpml/tools/deblur.py
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gimpml/tools/dehaze.py
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gimpml/tools/denoise.py
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gimpml/tools/enlighten.py
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gimpml/tools/faceparse.py
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gimpml/tools/inpainting.py
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gimpml/tools/interpolation.py
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gimpml/tools/kmeans.py
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gimpml/tools/matting.py
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gimpml/tools/model_info.csv
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gimpml/tools/monodepth.py
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gimpml/tools/semseg.py
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gimpml/tools/superresolution.py
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gimpml/tools/DPT/__init__.py
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gimpml/tools/DPT/monodepth_run.py
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gimpml/tools/DPT/semseg_run.py
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gimpml/tools/DPT/dpt/__init__.py
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gimpml/tools/DPT/dpt/base_model.py
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gimpml/tools/DPT/dpt/blocks.py
|
||||
gimpml/tools/DPT/dpt/midas_net.py
|
||||
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|
||||
gimpml/tools/DPT/dpt/transforms.py
|
||||
gimpml/tools/DPT/dpt/vit.py
|
||||
gimpml/tools/DPT/dpt_util/__init__.py
|
||||
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|
||||
gimpml/tools/DPT/dpt_util/misc.py
|
||||
gimpml/tools/DPT/dpt_util/pallete.py
|
||||
gimpml/tools/DeblurGANv2/__init__.py
|
||||
gimpml/tools/DeblurGANv2/__int__.py
|
||||
gimpml/tools/DeblurGANv2/adversarial_trainer.py
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
gimpml/tools/DeblurGANv2/models/fpn_inception_simple.py
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
gimpml/tools/DeblurGANv2/models/senet.py
|
||||
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|
||||
gimpml/tools/DeblurGANv2/util/__init__.py
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
gimpml/tools/pytorch-deep-image-matting/demo.py
|
||||
gimpml/tools/pytorch-deep-image-matting/deploy.py
|
||||
gimpml/tools/pytorch-deep-image-matting/tools/__init__.py
|
||||
gimpml/tools/pytorch-deep-image-matting/tools/chg_model.py
|
||||
gimpml/tools/pytorch-deep-image-matting/tools/composite.py
|
||||
gimpml/tools/pytorch-deep-image-matting/tools/loss_draw.py
|
||||
@ -1 +0,0 @@
|
||||
|
||||
@ -1,13 +0,0 @@
|
||||
numpy
|
||||
scipy
|
||||
gdown
|
||||
typing
|
||||
requests
|
||||
opencv-python<=4.3
|
||||
pretrainedmodels
|
||||
scikit-image
|
||||
timm==0.4.5
|
||||
|
||||
[:python_version <= "2.7"]
|
||||
future
|
||||
enum
|
||||
@ -1 +0,0 @@
|
||||
gimpml
|
||||
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@ -1,30 +1,28 @@
|
||||
model,file_id,fileSize,mFName,md5sum
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||||
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|
||||
edgeconnect/psv,122BaS0KobaZqsU5mZcIR4pTsoQz__1k8,10.8,InpaintingModel_dis.pth,bf9bb863592605237e620b8d73db225e
|
||||
edgeconnect/psv,11wPatO4UAIuPc_9Se99QvlKwGRiw62PY,42,InpaintingModel_gen.pth,afc0ce9b90413298972a2ef1fc65a3c7
|
||||
|
||||
|
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@ -1,214 +0,0 @@
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import torch
|
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import cv2
|
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import os
|
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import random
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import numpy as np
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from torchvision import transforms
|
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import logging
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|
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|
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def gen_trimap(alpha):
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k_size = random.choice(range(2, 5))
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iterations = np.random.randint(5, 15)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k_size, k_size))
|
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dilated = cv2.dilate(alpha, kernel, iterations=iterations)
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eroded = cv2.erode(alpha, kernel, iterations=iterations)
|
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trimap = np.zeros(alpha.shape)
|
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trimap.fill(128)
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# trimap[alpha >= 255] = 255
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trimap[eroded >= 255] = 255
|
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trimap[dilated <= 0] = 0
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|
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"""
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alpha_unknown = alpha[trimap == 128]
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num_all = alpha_unknown.size
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num_0 = (alpha_unknown == 0).sum()
|
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num_1 = (alpha_unknown == 255).sum()
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print("Debug: 0 : {}/{} {:.3f}".format(num_0, num_all, float(num_0)/num_all))
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print("Debug: 255: {}/{} {:.3f}".format(num_1, num_all, float(num_1)/num_all))
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"""
|
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|
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return trimap
|
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def compute_gradient(img):
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x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
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y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
|
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grad = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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grad = cv2.cvtColor(grad, cv2.COLOR_BGR2GRAY)
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return grad
|
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|
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|
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class MatTransform(object):
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||||
def __init__(self, flip=False):
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self.flip = flip
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def __call__(self, img, alpha, fg, bg, crop_h, crop_w):
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h, w = alpha.shape
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# trimap is dilated maybe choose some bg region(0)
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# random crop in the unknown region center
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target = np.where((alpha > 0) & (alpha < 255))
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delta_h = center_h = crop_h / 2
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delta_w = center_w = crop_w / 2
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if len(target[0]) > 0:
|
||||
rand_ind = np.random.randint(len(target[0]))
|
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center_h = min(max(target[0][rand_ind], delta_h), h - delta_h)
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center_w = min(max(target[1][rand_ind], delta_w), w - delta_w)
|
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|
||||
# choose unknown point as center not as left-top
|
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start_h = int(center_h - delta_h)
|
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start_w = int(center_w - delta_w)
|
||||
end_h = int(center_h + delta_h)
|
||||
end_w = int(center_w + delta_w)
|
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|
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# print("Debug: center({},{}) start({},{}) end({},{}) alpha:{} alpha-len:{} unknown-len:{}".format(center_h, center_w, start_h, start_w, end_h, end_w, alpha[int(center_h), int(center_w)], alpha.size, len(target[0])))
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|
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img = img[start_h:end_h, start_w:end_w]
|
||||
fg = fg[start_h:end_h, start_w:end_w]
|
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bg = bg[start_h:end_h, start_w:end_w]
|
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alpha = alpha[start_h:end_h, start_w:end_w]
|
||||
|
||||
# random flip
|
||||
if self.flip and random.random() < 0.5:
|
||||
img = cv2.flip(img, 1)
|
||||
alpha = cv2.flip(alpha, 1)
|
||||
fg = cv2.flip(fg, 1)
|
||||
bg = cv2.flip(bg, 1)
|
||||
|
||||
return img, alpha, fg, bg
|
||||
|
||||
|
||||
def get_files(mydir):
|
||||
res = []
|
||||
for root, dirs, files in os.walk(mydir, followlinks=True):
|
||||
for f in files:
|
||||
if (
|
||||
f.endswith(".jpg")
|
||||
or f.endswith(".png")
|
||||
or f.endswith(".jpeg")
|
||||
or f.endswith(".JPG")
|
||||
):
|
||||
res.append(os.path.join(root, f))
|
||||
return res
|
||||
|
||||
|
||||
# Dataset not composite online
|
||||
class MatDatasetOffline(torch.utils.data.Dataset):
|
||||
def __init__(self, args, transform=None, normalize=None):
|
||||
self.samples = []
|
||||
self.transform = transform
|
||||
self.normalize = normalize
|
||||
self.args = args
|
||||
self.size_h = args.size_h
|
||||
self.size_w = args.size_w
|
||||
self.crop_h = args.crop_h
|
||||
self.crop_w = args.crop_w
|
||||
self.logger = logging.getLogger("DeepImageMatting")
|
||||
assert len(self.crop_h) == len(self.crop_w)
|
||||
|
||||
fg_paths = get_files(self.args.fgDir)
|
||||
|
||||
self.cnt = len(fg_paths)
|
||||
|
||||
for fg_path in fg_paths:
|
||||
alpha_path = fg_path.replace(self.args.fgDir, self.args.alphaDir)
|
||||
img_path = fg_path.replace(self.args.fgDir, self.args.imgDir)
|
||||
bg_path = fg_path.replace(self.args.fgDir, self.args.bgDir)
|
||||
assert os.path.exists(alpha_path)
|
||||
assert os.path.exists(fg_path)
|
||||
assert os.path.exists(bg_path)
|
||||
assert os.path.exists(img_path)
|
||||
self.samples.append((alpha_path, fg_path, bg_path, img_path))
|
||||
self.logger.info("MatDatasetOffline Samples: {}".format(self.cnt))
|
||||
assert self.cnt > 0
|
||||
|
||||
def __getitem__(self, index):
|
||||
alpha_path, fg_path, bg_path, img_path = self.samples[index]
|
||||
|
||||
img_info = [fg_path, alpha_path, bg_path, img_path]
|
||||
|
||||
# read fg, alpha
|
||||
fg = cv2.imread(fg_path)[:, :, :3]
|
||||
bg = cv2.imread(bg_path)[:, :, :3]
|
||||
img = cv2.imread(img_path)[:, :, :3]
|
||||
alpha = cv2.imread(alpha_path)[:, :, 0]
|
||||
|
||||
assert bg.shape == fg.shape and bg.shape == img.shape
|
||||
img_info.append(fg.shape)
|
||||
(
|
||||
bh,
|
||||
bw,
|
||||
bc,
|
||||
) = fg.shape
|
||||
|
||||
rand_ind = random.randint(0, len(self.crop_h) - 1)
|
||||
cur_crop_h = self.crop_h[rand_ind]
|
||||
cur_crop_w = self.crop_w[rand_ind]
|
||||
|
||||
# if ratio!=1: make the img (h==croph and w>=cropw)or(w==cropw and h>=croph)
|
||||
wratio = float(cur_crop_w) / bw
|
||||
hratio = float(cur_crop_h) / bh
|
||||
ratio = wratio if wratio > hratio else hratio
|
||||
if ratio > 1:
|
||||
nbw = int(bw * ratio + 1.0)
|
||||
nbh = int(bh * ratio + 1.0)
|
||||
fg = cv2.resize(fg, (nbw, nbh), interpolation=cv2.INTER_LINEAR)
|
||||
bg = cv2.resize(bg, (nbw, nbh), interpolation=cv2.INTER_LINEAR)
|
||||
img = cv2.resize(img, (nbw, nbh), interpolation=cv2.INTER_LINEAR)
|
||||
alpha = cv2.resize(alpha, (nbw, nbh), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
# random crop(crop_h, crop_w) and flip
|
||||
if self.transform:
|
||||
img, alpha, fg, bg = self.transform(
|
||||
img, alpha, fg, bg, cur_crop_h, cur_crop_w
|
||||
)
|
||||
|
||||
# resize to (size_h, size_w)
|
||||
if self.size_h != img.shape[0] or self.size_w != img.shape[1]:
|
||||
# resize
|
||||
img = cv2.resize(
|
||||
img, (self.size_w, self.size_h), interpolation=cv2.INTER_LINEAR
|
||||
)
|
||||
fg = cv2.resize(
|
||||
fg, (self.size_w, self.size_h), interpolation=cv2.INTER_LINEAR
|
||||
)
|
||||
bg = cv2.resize(
|
||||
bg, (self.size_w, self.size_h), interpolation=cv2.INTER_LINEAR
|
||||
)
|
||||
alpha = cv2.resize(
|
||||
alpha, (self.size_w, self.size_h), interpolation=cv2.INTER_LINEAR
|
||||
)
|
||||
|
||||
trimap = gen_trimap(alpha)
|
||||
grad = compute_gradient(img)
|
||||
|
||||
if self.normalize:
|
||||
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
# first, 0-255 to 0-1
|
||||
# second, x-mean/std and HWC to CHW
|
||||
img_norm = self.normalize(img_rgb)
|
||||
else:
|
||||
img_norm = None
|
||||
|
||||
# img_id = img_info[0].split('/')[-1]
|
||||
# cv2.imwrite("result/debug/{}_img.png".format(img_id), img)
|
||||
# cv2.imwrite("result/debug/{}_alpha.png".format(img_id), alpha)
|
||||
# cv2.imwrite("result/debug/{}_fg.png".format(img_id), fg)
|
||||
# cv2.imwrite("result/debug/{}_bg.png".format(img_id), bg)
|
||||
# cv2.imwrite("result/debug/{}_trimap.png".format(img_id), trimap)
|
||||
# cv2.imwrite("result/debug/{}_grad.png".format(img_id), grad)
|
||||
alpha = torch.from_numpy(alpha.astype(np.float32)[np.newaxis, :, :])
|
||||
trimap = torch.from_numpy(trimap.astype(np.float32)[np.newaxis, :, :])
|
||||
grad = torch.from_numpy(grad.astype(np.float32)[np.newaxis, :, :])
|
||||
img = torch.from_numpy(img.astype(np.float32)).permute(2, 0, 1)
|
||||
fg = torch.from_numpy(fg.astype(np.float32)).permute(2, 0, 1)
|
||||
bg = torch.from_numpy(bg.astype(np.float32)).permute(2, 0, 1)
|
||||
|
||||
return img, alpha, fg, bg, trimap, grad, img_norm, img_info
|
||||
|
||||
def __len__(self):
|
||||
return len(self.samples)
|
||||
Loading…
Reference in New Issue