74 lines
2.3 KiB
Markdown
74 lines
2.3 KiB
Markdown
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# This repository is deprecated for at TF-2.0 rewrite visit:
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# https://github.com/oarriaga/paz
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------------------------------------------------
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# Face classification and detection.
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Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.
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* IMDB gender classification test accuracy: 96%.
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* fer2013 emotion classification test accuracy: 66%.
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For more information please consult the [publication](https://github.com/oarriaga/face_classification/blob/master/report.pdf)
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# Emotion/gender examples:
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Guided back-prop
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Real-time demo:
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<div align='center'>
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<img src='images/color_demo.gif' width='400px'>
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</div>
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[B-IT-BOTS](https://mas-group.inf.h-brs.de/?page_id=622) robotics team :)
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## Instructions
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### Run real-time emotion demo:
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> python3 video_emotion_color_demo.py
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### Run real-time guided back-prop demo:
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> python3 image_gradcam_demo.py
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### Make inference on single images:
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> python3 image_emotion_gender_demo.py <image_path>
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e.g.
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> python3 image_emotion_gender_demo.py ../images/test_image.jpg
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### Running with Docker
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With a few steps one can get its own face classification and detection running. Follow the commands below:
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* ```docker pull ekholabs/face-classifier```
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* ```docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier```
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* ```curl -v -F image=@[path_to_image] http://localhost:8084/classifyImage > image.png```
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### To train previous/new models for emotion classification:
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* Download the fer2013.tar.gz file from [here](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)
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* Move the downloaded file to the datasets directory inside this repository.
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* Untar the file:
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> tar -xzf fer2013.tar
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* Run the train_emotion_classification.py file
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> python3 train_emotion_classifier.py
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### To train previous/new models for gender classification:
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* Download the imdb_crop.tar file from [here](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) (It's the 7GB button with the tittle Download faces only).
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* Move the downloaded file to the datasets directory inside this repository.
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* Untar the file:
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> tar -xfv imdb_crop.tar
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* Run the train_gender_classification.py file
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> python3 train_gender_classifier.py
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