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* colors expression by KMean algorithm * object detection by imageai * object detection by cvlib * add encapsulation of object detection * remove encapsulation of objdetect v0 * objects expression to dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added imageai to requirements * add objects to dictionary * update for AnalysisMethod baseline * add objects dection support explore_analysis display * extend python version of misinf to allow imageai * account for older python * use global functionality for dict to csv convert * update for docker build * docker will build now but ipywidgets still not working * test code * include test data folder in repo * add some sample images * load cvs labels to dict * add test data * retrigger checks * add map to human coding * get orders from dict, missing dep * add module to test accuracy * retrigger checks * retrigger checks * now removing imageai * removed imageai * move labelmanager to analyse * multiple faces in mydict * fix pre-commit issues * map mydict * hide imageai * objects default using cvlib, isolate and disable imageai * correct python version * refactor faces tests * refactor objects tests * sonarcloud issues * refactor utils tests * address code smells * update readme * update notebook without imageai Co-authored-by: Ma Xianghe <825074348@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: iulusoy <inga.ulusoy@uni-heidelberg.de>
31 строка
1.9 KiB
Markdown
31 строка
1.9 KiB
Markdown
# Misinformation campaign analysis
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Extract data from from social media images and texts in disinformation campaigns.
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**_This project is currently under development!_**
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Use the pre-processed social media posts (image files) and process to collect information:
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1. Text extraction from the images
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1. Improving the preparation of the text for the data analysis (e.g., text cleaning)
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1. Performing person and face recognition in images, facial expressions recognition, as well as the extraction of any other available individual characteristics (e.g., gender, clothes)
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1. Extraction of other non-human objects in the image
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1. 5-Color analysis of the images
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This development will serve the fight to combat misinformation, by providing more comprehensive data about its content and techniques.
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The ultimate goal of this project is to develop a computer-assisted toolset to investigate the content of disinformation campaigns worldwide.
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# Installation
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The `misinformation` package can be installed using pip: Navigate into your package folder `misinformation/` and execute
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```
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pip install .
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```
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This will install the package and its dependencies locally.
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# Usage
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There are sample notebooks in the `misinformation/notebooks` folder for you to explore the package usage:
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1. Facial analysis: Use the notebook `facial_expressions.ipynb` to identify if there are faces on the image, if they are wearing masks, and if they are not wearing masks also the race, gender and dominant emotion.
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1. Object analysis: Use the notebook `ojects_expression.ipynb` to identify certain objects in the image. Currently, the following objects are being identified: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.
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There are further notebooks that are currently of exploratory nature (`colors_expression` to identify certain colors on the image, `get-text-from-image` to extract text that is contained in an image.) |