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

This demo can work in 2 modes:

  • GUI mode: areas for inpainting can be marked interactively using mouse painting
  • Auto mode (use -ac or -ar option for it): image will be processed automatically using randomly applied mask (-ar option) or using specific color-based mask (-ac option)

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]
[-o OUTPUT] [-ac C C C] [-ar]
-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. Ignored in GUI
-mbw MAX_BRUSH_WIDTH, --max_brush_width MAX_BRUSH_WIDTH
Optional. Max width of brush to draw mask. Ignored in
GUI mode
-ml MAX_LENGTH, --max_length MAX_LENGTH
Optional. Max strokes length to draw mask. Ignored in
GUI mode
-mv MAX_VERTEX, --max_vertex MAX_VERTEX
Optional. Max number of vertex to draw mask. Ignored
in GUI mode
--no_show Optional. Don't show output. Cannot be used in GUI
-o OUTPUT, --output OUTPUT
Optional. Save output to the file with provided
filename. Ignored in GUI mode
-ac C C C, --auto_mask_color C C C
Optional. Use automatic (non-interactive) mode with
color mask.Provide color to be treated as mask (3 RGB
components in range of 0...255). Cannot be used
together with -ar.
-ar, --auto_mask_random
Optional. Use automatic (non-interactive) mode with
random mask for inpainting (with parameters set by -p,
-mbw, -mk and -mv). Cannot be used together with -ac.

To run the demo, you can use public or pretrained models. You can download the pretrained models with the OpenVINO™ Model Downloader. The list of models supported by the demo is in the models.lst file in the demo's directory.

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.

GUI Mode operation

In GUI mode user can draw mask using mouse (holding left mouse button). The brush size is adjustable using slider on the top of the screen. After the mask painting is done, inpainting processing can be started by pressing Space or Enter key.

Also, these hot keys are available:

  • Backspace or C to clear current mask
  • Space or Enter to inpaint
  • R to reset all changes
  • Tab to show original image
  • Esc or Q to quit

Demo Output

In auto mode this demo uses OpenCV to display the resulting image and image with mask applied and reports performance in the format of summary inference FPS. Processed image can be also written to file.

In GUI mode this demo provides interactive means to apply mask and see the result of processing instantly (see hotkeys above).

See Also