Person-Segmentation-Keras

Person segmentation with Keras (SegNet, Unet, etc.)

Dataset

Person segmentation

HumanParsing-Dataset is adopted in this repo.

Origin HumanParsing-Dataset contains 16+1 object classes. But in this repo, i just segment person which is a binary classification task.

I generate new label images by my self, you can download new label imgages at https://pan.baidu.com/s/1Y6bKUznsVc7xNWb9tqWaHA passwd: p8ks

Of course you can generate label images by yourself using convert_labels.py , gen_train_test_list.py.

I use 12706 images of HumanParsing-Dataset as training set, the remaining images as test set.

During training, i resize images with unchanged aspect ratio using padding, for details you can see this script.

Human parsing

Origin HumanParsing-Dataset contains 16+1 object classes.

background     0
hat            1
hair           2 
sunglass       3
upper-clothes  4
skirt          5
pants          6
dress          7
belt           8
left-shoe      9
right-shoe     10
face           11
left-leg       12
right-leg      13
left-arm       14
right-arm      15
bag            16
scarf          17

I have simplified the parsing task. Now it contains 4 + 1 classes.

background     0 (background, bag)
head           1 (hat, hair, sunglass, face, scarf)
upper body     2 (upper-clothes)
both hands     3 (left-arm, right-arm)
lower body     4 (skirt, pants, dress, belt, left-shoe, right-shoe, left-leg, right-leg)

New label images can be downloaded from this link https://pan.baidu.com/s/1jhqpOn8oBmiJiwzohhkfww passwd: gamc

Usage

All models are defined in 'models' directory.

An example for training Unet.

python train_segmentation.py --model='unet'

For visiualsizing the predictions, you can use the follow command

python predict.py --model='unet'

Results

Unet

Person segmentation

mIU: 0.8918

Origin images, ground truth images and predictions.

Val accuracy during training.

Val loss during training.

Human parsing

Origin images, ground truth images and predictions.

SegNet

Todo

Reference

image-segmentation-keras

SegNet-Tutorial