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/downloader.py --list models.lst
An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --list models.lst
Running the application with the
-h option yields the following usage message:
usage: monodepth_demo.py [-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.
The application outputs are the floating disparity map (PFM) as well as a color-coded version (PNG).