face_classification | time face detection and emotion/gender classification | Computer Vision library
kandi X-RAY | face_classification Summary
kandi X-RAY | face_classification Summary
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
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Top functions reviewed by kandi - BETA
- 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
face_classification Key Features
face_classification Examples and Code Snippets
# 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
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Community Discussions
Trending Discussions on face_classification
QUESTION
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
https://github.com/oarriaga/face_classification/tree/master/trained_models
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.
ANSWER
Answered 2018-Aug-02 at 15:54I 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"
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install face_classification
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.
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