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realsr

Module Name reasr
Category Image editing
Network LP-KPN
Dataset RealSR dataset
Fine-tuning supported or not No
Module Size 64MB
Latest update date 2021-02-26
Data indicators PSNR29.05

I. Basic Information

  • Application Effect Display

    • Sample results:

  • Module Introduction

    • Realsr is a super resolution model for image and video based on "Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Mode". This model provides super resolution result with scale factor x4.

    • For more information, please refer to: realsr

II. Installation

  • 1、Environmental Dependence

    • paddlepaddle >= 2.0.0

    • paddlehub >= 2.0.0

    • NOTE: This Module relies on ffmpeg, Please install ffmpeg before using this Module.

      $ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge

  • 2、Installation

III. Module API Prediction

  • 1、Prediction Code Example

    • import paddlehub as hub

model = hub.Module(name='realsr')
model.predict('/PATH/TO/IMAGE')

# model.predict('/PATH/TO/VIDEO')

  • 2、API

    • def predict(self, input):

      • Prediction API.

      • Parameter

        • input (str): image path.

      • Return

        • If input is image path, the output is：

          • pred_img(np.ndarray): image data, ndarray.shape is in the format [H, W, C], BGR.
          • out_path(str): save path of images.

        • If input is video path, the output is ：

          • frame_pattern_combined(str): save path of frames from output video.
          • vid_out_path(str): save path of output video.
    • def run_image(self, img):

      • Prediction API for images.

      • Parameter

        • img (str｜np.ndarray): Image data, str or ndarray. ndarray.shape is in the format [H, W, C], BGR.

      • Return

        • pred_img(np.ndarray): Prediction result, ndarray.shape is in the format [H, W, C], BGR.
    • def run_video(self, video):

      • Prediction API for video.

        • Parameter

          • video(str): Video path.

        • Return

          • frame_pattern_combined(str): Save path of frames from output video.
          • vid_out_path(str): Save path of output video.

IV. Server Deployment

  • PaddleHub Serving can deploy an online service of image super resolution.

  • Step 1: Start PaddleHub Serving

    • Run the startup command:

      • $ hub serving start -m realsr

    • 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 base64

import cv2
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


org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':cv2_to_base64(org_im)}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/realsr"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
img = base64_to_cv2(r.json()["results"])
cv2.imwrite('/PATH/TO/SAVE/IMAGE', img)

V. Release Note

  • 1.0.0

    First release

  • 1.0.1

    Support paddlehub2.0

  • 1.1.0

    Update code format

    $ hub install realsr == 1.1.0