tutorials_of_enhancement_model_server.md

Tutorials Of Enhancement Model Server

Start A Enhancement Server

It's very quick to start a enhancement server. Main code are showed below

Enhancement Server Code Snappit

The executable binary file was built in $PROJECT_ROOT/_bin/attentive_gan_derain_server.out Simply run

cd $PROJECT_ROOT/_bin
./attentive_gan_derain_server.out ../conf/server/enhancement/attentive_gan_derain/attentive_gan_server_cfg.ini

When server successfully start on http:://localhost:8091 you're supposed to see worker_nums workers were called up and occupied your GPU resources. By default 4 model workers will be created you may enlarge it if you have enough GPU memory.

Python Client Example

Local python client test is similiar with mobilenetv2 classification server you may read toturials_of_classfication_model_server.md for details.

To use test python client you may run

cd $PROJECT_ROOT/scripts
export PYTHONPATH=$PWD:$PYTHONPATH
python server/test_server.py --server attentive_gan --mode single

Unique Tips For Enhancement Model Python Client

Most of the enhancement's model output is a image corresponding to the origin image. The enhancement server's response is a json obj

resp = {
    'req_id': '',
    'code': 1,
    'msg': 'success',
    'data': {
        'enhance_result': base64_image_content
    }
}

enhance_result contains the model's output encoded with base64. If you want to save the model's output info local file you may do

with open(src_image_path, 'rb') as f:
    image_data = f.read()
    base64_data = base64.b64encode(image_data)

    post_data = {
        'img_data': base64_data.decode(),
        'req_id': 'demo',
    }
    resp = requests.post(url=url, data=json.dumps(post_data))
    output = json.loads(resp.text)['data']['enhance_result']
    out_f = open('result.jpg', 'wb')
    out_f.write(base64.b64decode(output))
    out_f.close()

Enhancement Model's Visualization Result

AttentiveGan Derain Model

attentive_gan_derain model was designed for derain task. You may refer to repo https://github.com/MaybeShewill-CV/attentive-gan-derainnet for details about training details.

Server's Input Image

Server's Output Image

EnlightenGan Model

enlighten_gan_derain model was designed for low light image enhancement task. You may refer to repo https://github.com/VITA-Group/EnlightenGAN for details about training details.

Server's Input Image

Server's Output Image