Pseudo Stereo Picture Generation Demo

This program generates a set of pseudo stereo pictures from an input image.using the Intel(r) Distribution of OpenVINO(tm) toolkit.
The demo program uses a DL model, midasnet to generate a depth map from a picture , shift the pixcels horizontally according to the depth of the pixels, then compensate the lacking pixcels caused by the shifted pixels with an image inpainting DL model gmcnn.
The demo program generates a side-by-side stereo image to a file sbs3d.jpg. You can watch the pseudo 3D (like) image with a 3D viewer.

このデモプログラムはIntel(r) Distribution of OpenVINO(tm) toolkitを使用し、１枚の絵から疑似ステレオ画像を作成するものです。
プログラムは入力画像からmidasnet DLモデルを使用して深度マップを作成し、ピクセル深度に従って各ピクセルを横方向にシフトし、ピクセルシフトによりできた欠損部分をgmcnn画像修復DLモデルで補うことで、疑似ステレオ画像（っぽい）画像を生成しています。
プログラムは'sbs3d.jpg'というファイルにサイドバイサイド3Dイメージを書き出します。3Dビュワーなどを使用することでステレオ(っぽく見えなくもない）画像を見ることができます。

Generated Pseudo Stereo Images

Depth Image Generated From the Input Picture (This demo won't display this image)

Side-by-side Stereo Image Output (sbs3d.jpg)

Required DL Models to Run This Demo

The demo expects the following models in the Intermediate Representation (IR) format:

• midasnet
• gmcnn-places2-tf

You can download this model from OpenVINO Open Model Zoo. In the models.lst is the list of appropriate models for this demo that can be obtained via Model downloader. Please see more information about Model downloader here.

How to Run

0. Prerequisites

• OpenVINO 2020.2
  • If you haven't installed it, go to the OpenVINO web page and follow the Get Started guide to do it.

1. Install dependencies

The demo depends on:

• opencv-python
• numpy

To install all the required Python modules you can use:

(Linux) pip3 install -r requirements.txt
(Win10) pip install -r requirements.txt

2. Download DL models from OMZ

Use Model Downloader to download the required models and convert the downloaded model into OpenVINO IR models with Model Converter.
The source model of the midasnet is in PyTorch format. Please make sure you have installed prerequisites for pytorch_to_onnx.py converter tool.

(Linux) 
  # Install prerequisites for pytorch_to_onnx.py. This is required only for the 1st time
  python3 -m pip install -r $INTEL_OPENVINO_DIR/deployment_tools/tools/model_downloader/requirements-pytorch.in
  python3 $INTEL_OPENVINO_DIR/deployment_tools/tools/model_downloader/downloader.py --list models.lst
  python3 $INTEL_OPENVINO_DIR/deployment_tools/tools/model_downloader/converter.py --list models.lst
       
(Win10) 
  # Install prerequisites for pytorch_to_onnx.py. This is required only for the 1st time
  python3 -m pip install -r "%INTEL_OPENVINO_DIR%\deployment_tools\tools\model_downloader\requirements-pytorch.in"
  python "%INTEL_OPENVINO_DIR%\deployment_tools\tools\model_downloader\downloader.py" --list models.lst
  python "%INTEL_OPENVINO_DIR%\deployment_tools\tools\model_downloader\converter.py" --list models.lst

3. Run the demo app

(Linux) python3 pseudo-stereo-picture.py <input_image_file>
(Win10) python pseudo-stereo-picture.py <input_image_file>

Demo Output

The application draws the results on the screen.

Tested Environment

• Windows 10 x64 1909 and Ubuntu 18.04 LTS
• Intel(r) Distribution of OpenVINO(tm) toolkit 2020.2
• Python 3.6.5 x64

See Also

• Using Open Model Zoo demos
• Model Optimizer
• Model Downloader