This demo demonstrates an example of using neural networks to colorize a grayscale image or video.
How It Works¶
On startup, the application reads command-line parameters and loads a model to OpenVINO™ Runtime plugin for execution. Once the program receives an image, it performs the following steps:
Converts the frame into the LAB color space.
Uses the L-channel to predict A and B channels.
Restores the image by converting it into the BGR color space.
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/colorization_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 IR format (*.xml + *.bin).
An example of using the Model Downloader:
omz_downloader --list models.lst
An example of using the Model Converter:
omz_converter --list models.lst
Running the Demo¶
Running the application with the
-h option yields the following usage message:
usage: colorization_demo.py [-h] -m MODEL [-d DEVICE] -i INPUT [--loop] [-o OUTPUT] [-limit OUTPUT_LIMIT] [--no_show] [-v] [-u UTILIZATION_MONITORS] Options: -h, --help Help with the script. -m MODEL, --model MODEL Required. Path to .xml file with pre-trained model. -d DEVICE, --device DEVICE Optional. Specify target device for infer: CPU, GPU, HDDL or MYRIAD. Default: CPU -i INPUT, --input INPUT Required. An input to process. The input must be a single image, a folder of images, video file or camera id. --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.
Running the application with an empty list of options yields the short version of the usage message and an error message.
To run the demo, please provide paths to the model in the IR format, and to an input video or image(s):
python colorization_demo.py \ -i <path_to_image>/<image_name>.jpg \ -m <path_to_model>/colorization-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 colorized frame. The demo reports
FPS : average rate of video frame processing (frames per second).
Latency : average time required to process one frame (from reading the frame to displaying the results). You can use both of these metrics to measure application-level performance.