face_classification | time face detection and emotion/gender classification | Computer Vision library

 by   oarriaga Python Version: v1.0 License: MIT

kandi X-RAY | face_classification Summary

kandi X-RAY | face_classification Summary

face_classification is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, Keras, OpenCV applications. face_classification has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However face_classification build file is not available. You can download it from GitHub.

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

            kandi-support Support

              face_classification has a medium active ecosystem.
              It has 5337 star(s) with 1594 fork(s). There are 225 watchers for this library.
              It had no major release in the last 6 months.
              There are 42 open issues and 79 have been closed. On average issues are closed in 92 days. There are 11 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of face_classification is v1.0

            kandi-Quality Quality

              face_classification has 0 bugs and 0 code smells.

            kandi-Security Security

              face_classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              face_classification code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              face_classification is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              face_classification releases are not available. You will need to build from source code and install.
              face_classification has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed face_classification and discovered the below as its top functions. This is intended to give you an instant insight into face_classification implemented functionality, and help decide if they suit your requirements.
            • Loop through the training image
            • Performs random crop
            • Transform image
            • Convert input to RGB
            • Calculate the gradient of a preprocessed image
            • Calculate the center of the image using gradient_function
            • Deprocessing image
            • Returns ground truth data
            • Convert the class to arg
            • Display an image
            • Pretty print an image
            • Registers the gradient of the gradients
            • Process image
            • Small example of miniXception
            • Apply contrast to the image
            • Saturation of an image
            • Preprocess an input
            • Split data into training data
            • Calculate the CAM of a preprocessed image
            • Splits the IMDB data
            • Convert class to arg format
            • Get the labels for a given dataset
            • Pretty print data
            • Compile the gradients of a model
            • Make a mosaic image
            • Modify backprop
            • Simple CNN
            Get all kandi verified functions for this library.

            face_classification Key Features

            No Key Features are available at this moment for face_classification.

            face_classification Examples and Code Snippets

            Real Time Emotion Recognition (mini-Xception),Demo,FER2013 Dataset
            Pythondot img1Lines of Code : 13dot img1no licencesLicense : No License
            copy iconCopy
            # add '--mode' option to determine the dataset to visualize
            $ python3 visualization.py
            $ python3 visualization.py --tensorboard
            $ tensorboard --logdir checkpoint/tensorboard
            $ python3 test.py
            $ python3 train.py
            $ python3 train.py --evaluate

            Community Discussions

            Trending Discussions on face_classification


            Theano error deep learning python
            Asked 2018-Aug-02 at 15:54

            This is fairly standard openCV code where a loop will detect faces with haar cascade classifier and then there is a deep learning model that will detect the emotion in the face. The model was created from the 2013 kaggle dataset, and I downloaded this model from this github account if someone wants to try out the code. fer2013_mini_XCEPTION.119-0.65.hdf5 Just place a models folder in your directory and rename it to model.h5


            The code works just fine with Tensorflow but when I run the program KERAS_BACKEND=theano python haarMOD.py I get an error that is maybe due to BLAS library not linking properly?? Would anyone have any ideas on how to get theano functioning? Ultimately I am trying to get a similar variation of this code to work on a Flask server which only works with Theano.



            Answered 2018-Aug-02 at 15:54

            I got the code to work on a Windows machine by editing the .json file on my C drive C:\Users\user\.keras to reference "theano" instead of "tenserflow"

            Source https://stackoverflow.com/questions/51655266

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install face_classification

            You can download it from GitHub.
            You can use face_classification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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