Python-TensorFlow实现的人脸性别年龄识别

# Age and gender estimation based on Convolutional Neural Network and TensorFlow --- **UPDATE: There are some compatibility issues under python3 reported by others, recommend using python2 or you should adjust some codes manually** This is a TensorFlow implement of face age and gender estimation which first using dlib to detect and align faces in the picture and then using a deep CNN to estimate age and gender.As you can see below,this project can estimate more than one face in a picture at one time.   ## Dependencies This project has following dependencies and tested under CentOS7 with Python2.7.14 - tensorflow==1.4 - dlib==19.7.99 - cv2 - matplotlib==2.1.0 - imutils==0.4.3 - numpy==1.13.3 - pandas==0.20.3 ## Usage ### Make tfrecords In order to train your own models,you should first download [imdb](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/imdb_crop.tar) or [wiki](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/wiki_crop.tar) dataset,and then extract it under **data** path,after that,images path should look like > /path/to/project/data/imdb_crop/00/somepictures /path/to/project/data/imdb_crop/01/somepictures .... /path/to/project/data/imdb_crop/99/somepictures Then you can run ```bash python convert_to_records_multiCPU.py --imdb --nworks 8 ``` to convert images to tfrecords.**--imdb** means using imdb dataset,**--nworks 8** means using 8 cpu cores to convert the dataset parallelly.Because we will first detect and align faces in the pictures,which is a time consuming step,so we recommend to use as many cores as possible.Intel E5-2667 v4 and with 32 cores need approximately 50 minutes. ### Train model Once you have converted images to tfrecords,you should have the following path: > /path/to/project/data/train/train-000.tfrecords ...... /path/to/project/data/test/test-000.tfrecords ...... At present,our deep CNN uses FaceNet architecture,which based on inception-resnet-v1 to extract features.To speed up training,we use the pretrained model's weight from [this project](https://github.com/davidsandberg/facenet) and have converted the weight to adapt our model,you can download this converted pretrained facenet weight checkpoint from [here](https://mega.nz/#!4G4yxbAL!D9QG48yzCeFegCFhZfpCgOyLYbfDdU6lt2k2kK9n23g) or [here](https://pan.baidu.com/s/1dFewgqH).Extract it to path **models**. > /path/to/project/models/checkpoint /path/to/project/models/model.ckpt-0.data-00000-of-00001 /path/to/project/models/model.ckpt-0.index /path/to/project/models/model.ckpt-0.meta **NOTE:** This step is optional,you can also train your model from scratch. To start training,run ```bash python train.py --lr 1e-3 --weight_decay 1e-5 --epoch 6 --batch_size 128 --keep_prob 0.8 --cuda ``` **NOTE:** Using the flag **--cuda** will train the model with GPU. Using tensorboard to visualize learning ``` tensorboard --logdir=./train_log ```  ### Test model You can test all your trained models on testset through ``` python test.py --images "./data/test" --model_path "./models" --batch_size 128 --choose_best --cuda ``` Flag **--cuda** means using GPU when testing.**--choose_best** means testing all trained models and return the best one.If you just want to test the latest saved model,without this flag. ``` python test.py --images "./data/test" --model_path "./models" --batch_size 128 --cuda ``` ### One picture demo If you just want to test the model on your own picture,run ``` python eval.py --I "./demo/demo.jpg" --M "./models/" --font_scale 1 --thickness 1 ``` Flag **--I** tells where your picture is.If the text label too small or too large on the picture,you can use a different **--font_scale 1** and **--thickness 1** to adjust the text size and thickness. We also provide a pretrained model,you can download from [here](https://mega.nz/#!BfglkI7A!YBvFyxgKhvUnnNRu9FL-ACjdo18SmOZ-YSz9QghQRzE) or [here](https://pan.baidu.com/s/1bpllJg7) and extract it to **models** path. ### Picture from web cam  First download the pretrained model from [here](https://mega.nz/#!kaZkWDjb!xQvWi9B--FgyIPtIYfjzLDoJeh2PUBEZPotmzO9N6_M) or [here](https://pan.baidu.com/s/1kVd3TNx) and extract to **models** path. In order to get pictures from web cam, you may need to uninstall your cv2 and [install it from source](https://www.scivision.co/anaconda-python-opencv3/) if have problems when running the below command: ```bash python demo.py ``` ## TODO - [x] First version of project - [x] Code review and add some commits - [x] Add a readme doc - [x] Add a demo using pictures from web cam ## References and Acknowledgments This project is a part of my class project of Machine Learning course at Zhejiang University,following papers and codes are referred: 1. Rothe R, Timofte R, Van Gool L. Dex: Deep expectation of apparent age from a single image[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops. 2015: 10-15. 2. Rothe R, Timofte R, Van Gool L. Deep expectation of real and apparent age from a single image without facial landmarks[J]. International Journal of Computer Vision, 2016: 1-14. 3. [IMDB-WIKI – 500k+ face images with age and gender labels](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) 4. [yu4u/age-gender-estimation](https://github.com/yu4u/age-gender-estimation) 5. [davidsandberg/facenet](https://github.com/davidsandberg/facenet) 6. [Face Alignment with OpenCV and Python](https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/)