Human Segmentation

Description

Deep human segmentation and visual transformation of your photos. Currently supported visual transforms:

• Bokeh effect
• Black and white background
• Layer of mask above person

Examples

Bokeh effect

Black and white background

Layered mask effect

Installation

• Install requirements: pip3 install -r requirements.txt
• Download resnet-50 model weights (about 150mb) from google drive
• Put weights in weights/ folder

Training

Model was trained with supervisely person segmentation dataset. In utils/ folder you can find scripts, that convert supervisely format annotations into binary masks.

You can train any model from qubvel repo. I used Unet architecture with resnet-50 backbone as main model and more lightweight efficientnet-b1.

Notebooks with training code can be found in notebooks/ folder.

Usage

Results available via command line interface. There are some keys:

• --model_path - path to model weights
• --device - device to run inference on: gpu or cpu
• --trans_type - type of visual transformation effect: bokeh, bnw, layered.
• --result_path - path to save resulting transformed image.
• --blur_power - only for bokeh transformation option, strength of gaussian blur. Int value from 1 to 3, 1 - the weakest, 3 - the strongest.

Example of console script:

python3 inference.py 'test_photo/medium.jpg' --model_path 'weights/resnet50_089.pb' --device 'gpu' --trans_type 'layered' --result_path 'test1.jpg' --blur_power 2

Citiation

Thanks qubvel for pytorch image segmentation library.