Image Inpainting Python Demo

This demo showcases Image Inpainting with GMCNN. The task is to estimate suitable pixel information to fill holes in images.

How It Works

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

usage: [-h] -m MODEL [-i INPUT] [-d DEVICE]
[-ml MAX_LENGTH] [-mv MAX_VERTEX] [--no_show]
-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
path to image.
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
look for a suitable plugin for device specified.
Default value is CPU
-p PARTS, --parts PARTS
Optional. Number of parts to draw mask.
-mbw MAX_BRUSH_WIDTH, --max_brush_width MAX_BRUSH_WIDTH
Optional. Max width of brush to draw mask.
-ml MAX_LENGTH, --max_length MAX_LENGTH
Optional. Max strokes length to draw mask.
-mv MAX_VERTEX, --max_vertex MAX_VERTEX
Optional. Max number of vertex to draw mask.
--no_show Optional. Don't show output

To run the demo, you can use public or pretrained models. You can download the pretrained models with the OpenVINO™ Model Downloader.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

Demo Output

The demo uses OpenCV to display the resulting image and image with mask applied and reports performance in the format of summary inference FPS.

See Also