Image Translation Demo¶
This demo application demonstrates an example of using neural networks to synthesize a photo-realistic image based on an exemplar image.
How It Works¶
On startup the demo application reads command line parameters and loads a model to OpenVINO™ Runtime plugin. To get the result, the demo performs the following steps:
Reading input data (semantic segmentation mask of image for translation, exemplar image and mask of exemplar image).
Preprocessing for input image and masks.
Network inference (segmentation network (optional) + translation network).
Save results to folder.
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/image_translation_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
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: image_translation_demo.py [-h] -m_trn TRANSLATION_MODEL [-m_seg SEGMENTATION_MODEL] [-ii INPUT_IMAGES] [-is INPUT_SEMANTICS] -ri REFERENCE_IMAGES [-rs REFERENCE_SEMANTICS] -o OUTPUT_DIR [-d DEVICE] Options: -h, --help Show this help message and exit. -m_trn TRANSLATION_MODEL, --translation_model TRANSLATION_MODEL Required. Path to an .xml file with a trained translation model -m_seg SEGMENTATION_MODEL, --segmentation_model SEGMENTATION_MODEL Optional. Path to an .xml file with a trained semantic segmentation model -ii INPUT_IMAGES, --input_images INPUT_IMAGES Optional. Path to a folder with input images or path to a input image -is INPUT_SEMANTICS, --input_semantics INPUT_SEMANTICS Optional. Path to a folder with semantic images or path to a semantic image -ri REFERENCE_IMAGES, --reference_images REFERENCE_IMAGES Required. Path to a folder with reference images or path to a reference image -rs REFERENCE_SEMANTICS, --reference_semantics REFERENCE_SEMANTICS Optional. Path to a folder with reference semantics or path to a reference semantic -o OUTPUT_DIR, --output_dir OUTPUT_DIR Required. Path to a folder where output files will be saved -d DEVICE, --device DEVICE Optional. Specify the target device to infer on; CPU, GPU, HDDL or MYRIAD is acceptable. Default value is CPU
Running the application with the empty list of options yields the usage message given above and an error message.
There are two ways to use this demo:
Run with segmentation model in addition to translation model. You should use only models trained on ADE20k dataset. Example: hrnet-v2-c1-segmentation. In this case only input and reference images are required without any masks. Segmentation masks will be generated via segmentation model.
You can use the following command to run demo on CPU using cocosnet and hrnet-v2-c1-segmentation models:
python3 image_translation_demo.py \ -d CPU \ -m_trn <path_to_translation_model>/cocosnet.xml \ -m_seg <path_to_segmentation_model>/hrnet-v2-c1-segmentation.xml \ -ii <path_to_input_image>/input_image.jpg \ -ri <path_to_exemplar_image>/reference_image.jpg \ -o <output_dir>
Run with only translation model. You can use the following command to run demo on CPU using cocosnet as translation model:
python3 image_translation_demo.py \ -d CPU \ -m_trn <path_to_translation_model>/cocosnet.xml \ -is <path_to_semantic_mask_of_image>/input_mask.png \ -ri <path_to_exemplar_image>/reference_image.jpg \ -rs <path_to_exemplar_semantic>/reference_mask.png \ -o <output_dir>
For segmentation masks you should use mask (with shape: [height x width]) that specifies class for each pixel. Number of classes is 151 (from ADE20k), where ‘0’ - background class.
The results of the demo processing are saved to a folder that is specified by the parameter