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stylepro_artistic

Module Name stylepro_artistic
Category image generation
Network StyleProNet
Dataset MS-COCO + WikiArt
Fine-tuning supported or not No
Module Size 28MB
Latest update date 2021-02-26
Data indicators -

I.Basic Information

  • Application Effect Display

    • Sample results：


  • Module Introduction

    • StyleProNet is a model for style transfer, which is light-weight and responds quickly. This module is based on StyleProNet, trained on WikiArt(MS-COCO) and WikiArt(style) datasets, and can be used for style transfer. For more information, please refer to StyleProNet.

II.Installation

III.Module API Prediction

  • 1、Command line Prediction

    • $ hub run stylepro_artistic --input_path "/PATH/TO/IMAGE"
    • If you want to call the Hub module through the command line, please refer to: PaddleHub Command Line Instruction

  • 2、Prediction Code Example

    • import paddlehub as hub
import cv2

stylepro_artistic = hub.Module(name="stylepro_artistic")
result = stylepro_artistic.style_transfer(
images=[{
    'content': cv2.imread('/PATH/TO/CONTENT_IMAGE'),
    'styles': [cv2.imread('/PATH/TO/STYLE_IMAGE')]
}])

# or
# result = stylepro_artistic.style_transfer(
#     paths=[{
#         'content': '/PATH/TO/CONTENT_IMAGE',
#         'styles': ['/PATH/TO/STYLE_IMAGE']
#     }])

  • 3、API

    • def style_transfer(images=None,
                   paths=None,
                   alpha=1,
                   use_gpu=False,
                   visualization=False,
                   output_dir='transfer_result')

      • Style transfer API.

      • Parameters

        • images (list[dict]): each element is a dict，includes:
          • content (numpy.ndarray): input image array，shape is [H, W, C]，BGR format；
          • styles (list[numpy.ndarray]) : list of style image arrays，shape is [H, W, C]，BGR format；
          • weights (list[float], optioal) : weight for each style, if not set, each style has the same weight;
        • paths (list[dict]): each element is a dict，includes:
          • content (str): path for input image；
          • styles (list[str]) : paths for style images；
          • weights (list[float], optioal) : weight for each style, if not set, each style has the same weight;
        • alpha (float) : alpha value，[0, 1] ，default is 1
        • use_gpu (bool): use GPU or not; set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU
        • visualization (bool): Whether to save the results as picture files;
        • output_dir (str): save path of images;

        NOTE: choose one parameter to provide data from paths and images

      • Return

        • res (list[dict]): results
          • path (str): path for input image
          • data (numpy.ndarray): output image
    • def save_inference_model(dirname,
                         model_filename=None,
                         params_filename=None,
                         combined=True)

      • Save model to specific path

      • Parameters

        • dirname: output dir for saving model
        • model_filename: filename for saving model
        • params_filename: filename for saving parameters
        • combined: whether save parameters into one file

IV.Server Deployment

  • PaddleHub Serving can deploy an online service of style transfer.

  • Step 1: Start PaddleHub Serving

    • Run the startup command：

    • $ hub serving start -m stylepro_artistic

    • The servitization API is now deployed and the default port number is 8866.

    • NOTE: If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.

  • Step 2: Send a predictive request

    • With a configured server, use the following lines of code to send the prediction request and obtain the result

    • import requests
import json
import cv2
import base64
import numpy as np


def cv2_to_base64(image):
  data = cv2.imencode('.jpg', image)[1]
  return base64.b64encode(data.tostring()).decode('utf8')

def base64_to_cv2(b64str):
  data = base64.b64decode(b64str.encode('utf8'))
  data = np.fromstring(data, np.uint8)
  data = cv2.imdecode(data, cv2.IMREAD_COLOR)
  return data

# Send an HTTP request
data = {'images':[
{
    'content':cv2_to_base64(cv2.imread('/PATH/TO/CONTENT_IMAGE')),
    'styles':[cv2_to_base64(cv2.imread('/PATH/TO/STYLE_IMAGE'))]
}
]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/stylepro_artistic"
r = requests.post(url=url, headers=headers, data=json.dumps(data))

# print prediction results
print(base64_to_cv2(r.json()["results"][0]['data']))

V.Release Note

  • 1.0.0

    First release

  • 1.0.3

    Remove fluid api

    • $ hub install stylepro_artistic==1.0.3