This repository was forked from @arunponnusamy 's https://github.com/arunponnusamy/gender-detection-keras. Significant changes such as a command line tool for gender-based file management have been added.
This tool can be really powerful for data cleaning. If you have a mixed dataset and just need pictures of one of the genders, you can easily get rid of the unnecesary images. It can also delete pictures with more than one person, or all associated metadata files (such as .txt or .json).
- numpy
- opencv-python
- tensorflow
- keras
- requests
- progressbar
- cvlib
Install the required packages by executing the following command.
$ pip install -r requirements.txt
Note: Python 2.x is not supported Using Python virtual environment is highly recommended.
optional arguments: -h, --help show this help message and exit -i IMAGE, --image IMAGE path to input image -d DIR, --dir DIR path to input directory -m, --nomen Delete men pictures -w, --nowomen Delete women pictures -o, --oneperson Delete pictures with more than one person -t, --nometadata Delete all metadata files --hide Hide output
$ python gender_recognition.py -i <input_image>
Activating option -d will loop through the specified directory, running the script for each image.
$ python gender_recognition.py -d <input_directory>
$ python gender_recognition_webcam.py
When you run the script for the first time, it will download the pre-trained model from this link and place it under pre-trained directory in the current path.
(If python command invokes default Python 2.7, use python3 instead)
You can download the dataset I gathered from Google Images from this link and train the network from scratch on your own if you are interested. You can add more images and play with the hyper parameters to experiment different ideas.
- scikit-learn
- matplotlib
Install them by typing pip install scikit-learn matplotlib
Start the training by running the command
$ python train.py -d <path-to-dataset>
(i.e) $ python train.py -d ~/Downloads/gender_dataset_face/
Depending on the hardware configuration of your system, the execution time will vary. On CPU, training will be slow. After the training, the model file will be saved in the current path as gender_detection.model.
If you have an Nvidia GPU, then you can install tensorflow-gpu package. It will make things run a lot faster.
If you are facing any difficulty, feel free to create a new issue .