Image segmentation is a critical task in Computer Vision, enabling machines to understand and analyze the contents of images at a pixel level. The Segment Anything Model (SAM) is a groundbreaking instance segmentation model developed by Meta Research, which has taken the field by storm since its release in April 2023.
SAM offers unparalleled versatility and efficiency in image analysis tasks, making it a powerful tool for a wide range of applications.
SAM was specifically designed to address the limitations of existing image segmentation models and to introduce new capabilities that revolutionize the field.
One of SAM's standout features is its promptable segmentation task, which allows users to generate valid segmentation masks by providing prompts such as spatial or text clues (feature not yet released at the time of writing) that identify specific objects within an image.
This flexibility empowers users to obtain precise and tailored segmentation results effortlessly:
1. Generate segmentation masks for all objects SAM can detect.
2. Provide boxes to guide SAM in generating a mask for specific objects in an image.
3. Provide a box and a point to guide SAM in generating a mask with an area to exclude.
At the core of SAM lies its advanced architecture, which comprises three key components: an image encoder, a prompt encoder, and a lightweight mask decoder. This design enables SAM to perform real-time mask computation, adapt to new image distributions and tasks without prior knowledge, and exhibit ambiguity awareness in segmentation tasks.
By leveraging these capabilities, SAM offers remarkable flexibility and adaptability, setting new standards in image segmentation models.
A fundamental factor contributing to SAM's exceptional performance is the SA-1B dataset, the largest segmentation dataset to date, introduced by the Segment Anything project. With over 1 billion masks spread across 11 million carefully curated images, the SA-1B dataset provides SAM with a diverse and extensive training data source.
This abundance of high-quality training data equips SAM with a comprehensive understanding of various object categories, enhancing its ability to generalize and perform accurately across different segmentation tasks.
One of SAM's most impressive attributes is its zero-shot transfer capability. SAM has been trained to achieve outstanding zero-shot performance, surpassing previous fully supervised results in numerous cases.
Zero-shot transfer refers to SAM's ability to adapt to new tasks and object categories without requiring explicit training or prior exposure to specific examples. This feature allows users to leverage SAM for diverse applications with minimal need for prompt engineering, making it a truly versatile and ready-to-use tool.
With its numerous applications and innovative features, SAM unlocks new possibilities in the field of image segmentation. As a zero-shot detection model, SAM can be paired with object detection models to assign labels to specific objects accurately. Additionally, SAM serves as an annotation assistant, supporting the annotation process by generating masks for objects that require manual labeling.
Moreover, SAM can be used as a standalone tool for feature extraction. It allows users to extract object features or remove backgrounds from images effortlessly.
In conclusion, the Segment Anything Model represents a significant leap forward in the field of image segmentation. With its promptable segmentation task, advanced architecture, zero-shot transfer capability, and access to the SA-1B dataset, SAM offers unparalleled versatility and performance.
As the capabilities of Computer Vision continue to expand, SAM paves the way for cutting-edge applications and facilitates breakthroughs in various industries.
Inpainting refers to the process of restoring or repairing an image by filling in missing or damaged parts. It is a valuable technique widely used in image editing and restoration, enabling the removal of flaws and unwanted objects to achieve a seamless and natural-looking final image. Inpainting finds applications in film restoration, photo editing, and digital art, among others.
Stable Diffusion Inpainting is a specific type of inpainting technique that leverages the properties of heat diffusion to fill in missing or damaged areas of an image. It accomplishes this by applying a heat diffusion process to the surrounding pixels.
During this process, values are assigned to these pixels based on their proximity to the affected area. The heat equation is then utilized to redistribute intensity values, resulting in a seamless and natural patch. The repetition of this equation ensures the complete filling of the image patch, ultimately creating a smooth and seamless result that blends harmoniously with the rest of the image.
Stable Diffusion Inpainting sets itself apart from other inpainting techniques due to its notable stability and smoothness. Unlike slower or less reliable alternatives that can produce visible artifacts, Stable Diffusion Inpainting guarantees a stable and seamless patch. It excels particularly in handling images with complex structures, including textures, edges, and sharp transitions.
Stable Diffusion Inpainting finds practical applications in various fields.
it proves valuable for removing unwanted objects or blemishes from images.
it aids in repairing damaged or missing frames.
It benefits from Stable Diffusion Inpainting by removing artifacts or enhancing scan quality.
It can be utilized to create seamless compositions or eliminate undesired elements.
To achieve optimal inpainting results, consider the following tips:
Stable Diffusion Inpainting stands out as an advanced and effective image processing technique for restoring or repairing missing or damaged parts of an image. Its applications include film restoration, photography, medical imaging, and digital art.
With the Ikomia API, creating a workflow using Segment Anything Model (SAM) for segmentation followed by Stable diffusion inpainting becomes effortless, requiring only a few lines of code. To get started, you need to install the API in a virtual environment.
How to install a virtual environment
You can also charge directly the open-source notebook we have prepared.
For a step-by-step guide with detailed information on the algorithm's parameters, refer to this section.
Note: The workflow bellow requires 6.1 GB of GPU RAM. However, by choosing the smallest SAM model, the memory usage can be decreased to 4.9 GB of GPU RAM.
In this section, we will demonstrate how to utilize the Ikomia API to create a workflow for segmentation and diffusion inpainting as presented above.
We initialize a workflow instance. The “wf” object can then be used to add tasks to the workflow instance, configure their parameters, and run them on input data.
- ViT-H offers significant improvements over ViT-B, though the gains over ViT-L are minimal.
- Based on our tests, ViT-L presents the best balance between performance and accuracy. While ViT-H is the most accurate, it's also the slowest, and ViT-B is the quickest but sacrifices accuracy.
You can apply the workflow to your image using the ‘run_on()’ function. In this example, we use the image path:
Finally, you can display our image results using the display function:
First, we show the segmentation mask output from the Segment Anything Model. Then, display the stable diffusion inpainting output.
Here are some more stable diffusion inpainting outputs (prompts: ‘dog’, ‘fox’, ‘lioness’, ‘tiger’, ‘white cat’):
In this tutorial, we have explored the process of creating a workflow for image segmentation with SAM, followed by stable diffusion inpainting.
The Ikomia API simplifies the development of Computer Vision workflows and provides easy experimentation with different parameters to achieve optimal results.
To learn more about the API, refer to the documentation. You may also check out the list of state-of-the-art algorithms on Ikomia HUB and try out Ikomia STUDIO, which offers a friendly UI with the same features as the API.