MonoDepth Python Demo

This topic demonstrates how to run the MonoDepth demo application, which produces a disparity map for a given input image. To this end, the code uses the network described in Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer.

Below is the midasnet model inference result for <openvino_dir>/deployment_tools/demo/car_1.bmp sample image


How It Works

On startup, the demo application reads command-line parameters and loads a network and an image to the Inference Engine plugin. When inference is done, the application outputs the disparity map in PFM and PNG format (color-coded).


By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with the --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Preparing to Run

For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in <omz_dir>/demos/monodepth_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (*.xml + *.bin).

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/ --list models.lst

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/ --list models.lst

Supported Models

  • fcrn-dp-nyu-depth-v2-tf

  • midasnet


Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the application with the -h option yields the following usage message:

usage: [-h] -m MODEL -i INPUT [-l CPU_EXTENSION] [-d DEVICE]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Required. Path to an .xml file with a trained model
  -i INPUT, --input INPUT
                        Required. Path to a input image file
  -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                        Optional. Required for CPU custom layers. Absolute
                        MKLDNN (CPU)-targeted custom layers. Absolute path to
                        a shared library with the kernels implementations
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU,
                        GPU, HDDL or MYRIAD is acceptable. The demo will
                        look for a suitable plugin for device specified.
                        Default value is CPU

Running the application with the empty list of options yields the usage message given above and an error message.

Demo Output

The application outputs are the floating disparity map (PFM) as well as a color-coded version (PNG).