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falsr_c

Module Name falsr_c
Category Image editing
Network falsr_c
Dataset DIV2k
Fine-tuning supported or not No
Module Size 4.4MB
Data indicators PSNR37.66
Latest update date 2021-02-26

I. Basic Information

  • Application Effect Display

    • Sample results:

  • Module Introduction

    • Falsr_c is a lightweight super-resolution model based on "Accurate and Lightweight Super-Resolution with Neural Architecture Search". The model uses a multi-objective approach to deal with the over-segmentation problem, and uses an elastic search strategy based on a hybrid controller to improve the performance of the model. This model provides super resolution result with scale factor x2.

    • For more information, please refer to:falsr_c

II. Installation

III. Module API Prediction

  • 1、Command line Prediction

  • 2、Prediction Code Example

    import cv2
import paddlehub as hub

sr_model = hub.Module(name='falsr_c')
im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
res = sr_model.reconstruct(images=[im], visualization=True)
print(res[0]['data'])

  • 3、API

    • def reconstruct(images=None,
                paths=None,
                use_gpu=False,
                visualization=False,
                output_dir="falsr_c_output")

      • Prediction API.

      • Parameter

        • images (list[numpy.ndarray]): Image data，ndarray.shape is in the format [H, W, C]，BGR.
        • paths (list[str]): Image path.
        • 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 recognition results as picture files.
        • output_dir (str): Save path of images, "dcscn_output" by default.

      • Return

        • res (list[dict]): The list of model results, where each element is dict and each field is:
          • save_path (str, optional): Save path of the result, save_path is '' if no image is saved.
          • data (numpy.ndarray): Result of super resolution.
    • def save_inference_model(dirname)

      • Save the model to the specified path.

      • Parameters

        • dirname: Model save path.

IV. Server Deployment

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

  • Step 1: Start PaddleHub Serving

    • Run the startup command:

      • $ hub serving start -m falsr_c

    • 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/falsr_c"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
sr = base64_to_cv2(r.json()["results"][0]['data'])
cv2.imwrite('falsr_c_X2.png', sr)
print("save image as falsr_c_X2.png")

  • Gradio APP support

    Starting with PaddleHub 2.3.1, the Gradio APP for falsr_c is supported to be accessed in the browser using the link http://127.0.0.1:8866/gradio/falsr_c.

V. Release Note

  • 1.0.0

    First release

  • 1.1.0

    Remove Fluid API

  • 1.2.0

    Add Gradio APP support.

    $ hub install falsr_c == 1.2.0