infer_depth_anything_v2

Depth Anything V2, significantly outperforms V1, is a highly practical solution for robust monocular depth estimation.

🚀 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()

# Add algorithm
algo = wf.add_task(name="infer_depth_anything_v2", auto_connect=True)

# Run directly on your image
wf.run_on(url="https://github.com/Ikomia-dev/notebooks/blob/main/examples/img/img_dog.png?raw=true")

# Display the results
display(algo.get_output(0).get_image()) # Colormap
display(algo.get_output(1).get_image()) # Grayscale

☀️ 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

• model_name (str) - default 'vits': Name of the ViT pre-trained model.
  • 'vits' ; Param: 24.8M
  • 'vitm' ; Param: 97.5M
  • 'vitl' ; Param: 335.3M
• input_size (str) - default '640': Size of the input image.
• cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.

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_depth_anything_v2", auto_connect=True)

algo.set_parameters({
        'model_name':'vits',
        'input_size': '640',
        'cuda': 'True'})

# Run directly on your image
wf.run_on(url="https://github.com/Ikomia-dev/notebooks/blob/main/examples/img/img_dog.png?raw=true")

# Display the results
display(algo.get_output(0).get_image())
display(algo.get_output(1).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_depth_anything", auto_connect=True)

# Run on your image  
wf.run_on(url="https://github.com/Ikomia-dev/notebooks/blob/main/examples/img/img_dog.png?raw=true")

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