README_en.md

MiDaS_Large

Module Name	MiDaS_Large
Category	depth estimation
Network	-
Dataset	3D Movies, WSVD, ReDWeb, MegaDepth
Fine-tuning supported or not	No
Module Size	399MB
Latest update date	2021-02-26
Data indicators	-

I.Basic Information

• Application Effect Display

  • Sample results：

    
    

• Module Introduction

  • MiDas_Large module is used for monocular depth estimation.

II.Installation

• 1、Environmental Dependence

  • paddlepaddle >= 2.0.0

  • paddlehub >= 2.0.0 | How to install PaddleHub

• 2、Installation

  • $ hub install MiDaS_Large

  • In case of any problems during installation, please refer to: Windows_Quickstart | Linux_Quickstart | Mac_Quickstart

III.Module API Prediction

• 1、Prediction Code Example

  • import paddlehub as hub
    import cv2
    
    model = hub.Module(name="MiDaS_Large")
    result = model.depth_estimation(images=[cv2.imread('/PATH/TO/IMAGE')])
    # or
    # result = model.depth_estimation(paths=['/PATH/TO/IMAGE'])

• 2、API

  • def depth_estimation(images=None,
                    paths=None,
                    batch_size=1,
                    output_dir='output',
                    visualization=False):

    • depth estimation API.

    • Parameters

      • images (list[numpy.ndarray]): image data, ndarray.shape is in the format [H, W, C], BGR;
      • paths (list[str]): image path;
      • batch_size (int): the size of batch;
      • output_dir (str): save path of images;
      • visualization (bool): Whether to save the results as picture files;

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

    • Return

      • res (list[numpy.ndarray]): depth data，ndarray.shape is [H, W]

IV.Release Note

• 1.0.0

  First release

  • $ hub install MiDaS_Large==1.0.0