infer_ddcolor_colorization

Original picture made by Adam Littman Davis on Unsplash

🚀 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

# Init your workflow
wf = Workflow("Colorization workflow")

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

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_ddcolor_colorization/main/images/original.jpg")

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

• cuda (bool): enable/disable cuda acceleration (if available)
• model_name (str): ddcolor models
  • ddcolor_paper: original model from scientific paper.
  • ddcolor_paper_tiny: lightweight version of ddcolor model, using the same training scheme as ddcolor_paper.
  • ddcolor_modelscope: model trained using the same data cleaning scheme as BigColor, it can get the best qualitative results with little degrading FID performance.
  • ddcolor_artistic: model trained with an extended dataset containing many high-quality artistic images. Also, colorfulness loss is not used during training, so there may be fewer unreasonable color artifacts.
• input_size (int): image input resolution in pixels

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

# Init your workflow
wf = Workflow("Colorization workflow")

# Add algorithm
algo = wf.add_task(name="infer_ddcolor_colorization", auto_connect=True)
algo.set_parameters({
    "cuda": "True",
    "model_name": "ddcolor_paper_tiny",
    "input_size": "1024"
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_ddcolor_colorization/main/images/original.jpg")

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_ddcolor_colorization", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-hub/infer_ddcolor_colorization/main/images/original.jpg")

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