Image Deblurring Python* Demo¶
This topic demonstrates how to run the Image Deblurring demo application, which does inference using deblurring networks.
How It Works¶
On startup the demo application reads command line parameters and loads a network. The demo runs inference and shows results for each image captured from an input. Depending on number of inference requests processing simultaneously (-nireq parameter) the pipeline might minimize the time required to process each single image (for nireq 1) or maximize utilization of the device and overall processing performance.
For each image demo performs the following steps:
Do preprocessing consisting of normalization and padding to input shape of model.
Inference of model (user is able to set the inference options to influence the execution process).
Do postprocessing for output of model.
Display the resulting image together with source image.
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/deblurring_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: deblurring_demo.py [-h] -m MODEL -i INPUT [-d DEVICE] [-nireq NUM_INFER_REQUESTS] [-nstreams NUM_STREAMS] [-nthreads NUM_THREADS] [--loop] [-o OUTPUT] [-limit OUTPUT_LIMIT] [--no_show] [-u UTILIZATION_MONITORS] Options: -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. An input to process. The input must be a single image, a folder of images or anything that cv2.VideoCapture can process. -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. Inference options: -nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS Optional. Number of infer requests -nstreams NUM_STREAMS, --num_streams NUM_STREAMS Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode (for HETERO and MULTI device cases use format <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>). -nthreads NUM_THREADS, --num_threads NUM_THREADS Optional. Number of threads to use for inference on CPU (including HETERO cases). Input/output options: --loop Optional. Enable reading the input in a loop. -o OUTPUT, --output OUTPUT Optional. Name of the output file(s) to save. -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT Optional. Number of frames to store in output. If 0 is set, all frames are stored. --no_show Optional. Don't show output. -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS Optional. List of monitors to show initially.
When a single image is applied as an input, the demo processes and renders it quickly, then exits. To continuously visualize processed results on the screen, apply the
loop option, which enforces looping over processing a single image. Running the application with the empty list of options yields the usage message given above and an error message.
You can use the following command to do inference on CPU on images captured by a camera using a pre-trained deblurgan-v2 network:
python3 deblurring_demo.py -i 0 -d CPU -m <path_to_model>/deblurgan-v2.xml
> NOTE : If you provide a single image as an input, the demo processes and renders it quickly, then exits. To continuously visualize inference results on the screen, apply the
loop option, which enforces processing a single image in a loop.
You can save processed results to a Motion JPEG AVI file or separate JPEG or PNG files using the
To save processed results in an AVI file, specify the name of the output file with
aviextension, for example:
To save processed results as images, specify the template name of the output image file with
pngextension, for example:
-o output_%03d.jpg. The actual file names are constructed from the template at runtime by replacing regular expression
%03dwith the frame number, resulting in the following:
output_001.jpg, and so on. To avoid disk space overrun in case of continuous input stream, like camera, you can limit the amount of data stored in the output file(s) with the
limitoption. The default value is 1000. To change it, you can apply the
-limit Noption, where
Nis the number of frames to store.
> NOTE : Windows* systems may not have the Motion JPEG codec installed by default. If this is the case, you can download OpenCV FFMPEG back end using the PowerShell script provided with the OpenVINO install package and located at
<INSTALL_DIR>/opencv/ffmpeg-download.ps1. The script should be run with administrative privileges if OpenVINO is installed in a system protected folder (this is a typical case). Alternatively, you can save results as images.
The demo uses OpenCV to display the resulting images together with source images.