This is a tensorflow implementation of MTCNN for both training and testing.
- Ubuntu 14.04 or 16.04 or Mac 10.*
- tensorflow 1.3 && python3.6: https://github.com/tensorflow/tensorflow
- opencv 3.0 for python3.6
pip install opencv-python - numpy 1.13
pip install numpy
notice: You should be at ROOT_DIR/prepare_data/ if you want to run the following command.
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Step1. Download Wider Face Training part only from Official Website and unzip to replace
WIDER_train -
Step2. Run
python gen_shuffle_data.py 12to generate 12net training data. Besides,python gen_tfdata_12net.pyprovide you an example to build tfrecords file. Remember changing and adding necessary params. -
Step3. Run
python tf_gen_12net_hard_example.pyto generate hard sample. Runpython gen_shuffle_data.py 24to generate random cropped training data. Then runpython gen_tfdata_24net.pyto combine these output and generate tfrecords file. -
Step4. Similar to last step. Run
python gen_24net_hard_example.pyto generate hard sample. Runpython gen_shuffle_data.py 48to generate random cropped training data. Then runpython gen_tfdata_48net.pyto combine these output and generate tfrecords file.
notice: You should be at ROOT_DIR/ if you want to run the following command.
if you have finished step 2 above, you can run python src/mtcnn_pnet_test.py to do Pnet training.
Similarly, after step 3 or step 4, you can run python src/mtcnn_rnet_test.py or python src/mtcnn_onet_test.py to train Rnet and Onet respectively.
notice: You should be at ROOT_DIR/ if you want to run the following command.
You can run python test_img.py YOUR_IMAGE_PATH --model_dir ./save_model/all_in_one/ to test mtcnn with the provided model.
You can also provide your own training model directory to do the test. If there are multiple models in the directory, the program will automatically choose the model with the maximum iterations.
[1] MTCNN paper link: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
[2] MTCNN official code: MTCNN with Caffe