infer_swinir_super_resolution

Run SwinIR super resolution. This plugin can enlarge an image by a factor 4 each side.

More than a simple linear interpolation, this plugin can add details while upscaling.

Original image

Output image

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Initialize the workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_swinir_super_resolution", auto_connect=True)
   
# Run on your image
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_swinir_super_resolution/main/icons/cat.jpeg")

# Inspect your results
display(algo.get_input(0).get_image())
display(algo.get_output(0).get_image())

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

• If you haven't started using Ikomia Studio yet, download and install it from this page.

• For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

• use_gan (bool) - Default True: If True, algorithm will use GAN method to upscale image, else will use PSNR method.
• large_model (bool) - Default False: If True, algorithm will use the large model, else will use medium model.
• cuda (bool) - Default True: Run with cuda or cpu.
• tile (int) - Default 256: Size of tile. Instead of passing whole image to the deep learning model, which consumes a lot of memory, model is fed with square tiles of fixed size one by one.
• overlap_ratio (float) - Default 0.1: Overlap between tiles in percentage. Overlapping tiles then blending the results lead to a smoother image. Set it to 0 to have no overlap like in the original repo. 1,0 is max overlap.
• scale (int) - Default 4: Scale factor. Must be 2 or 4. scale 2 is not available for large models.

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_swinir_super_resolution", auto_connect=True)

algo.set_parameters({
    "use_gan": "True",
    "large_model": "False",
    "cuda": "True",
    "tile": "256",
    "overlap_ratio": "0.1",
    "scale": "4"
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_swinir_super_resolution/main/icons/cat.jpeg")

# Inspect your results
display(algo.get_input(0).get_image())
display(algo.get_output(0).get_image())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_swinir_super_resolution", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_swinir_super_resolution/main/icons/cat.jpeg")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()