Computer vision is making noteworthy strides in the field of Artificial intelligence and Machine Learning. Its features, like object detection and image recognition, make it useful for extracting meaningful information from images and videos. Its applications are diverse, ranging from healthcare and automotive to security systems. One of the key challenges in computer vision is visualizing and rendering complex scenes accurately and efficiently. For that, recently, a new Python framework called Blendify has been released, which enables creating and rendering scenes in Blender.
Blender is a popular open-source 3D creation software that is used for modeling, animation, and rendering. Developed by Ph.D. students Vladimir Guzov and Ilya Petrov, Blendify aims to simplify the process of using Blender for computer vision visualizations by providing a user-friendly interface without the need to interact directly with the Blender API. The user doesn’t have to delve into the complexities of the Blender API to achieve the desired visualizations.
Blendify has a simple, user-friendly interface for executing common visualization tasks. It smoothly integrates with development scripts by implementing commonly used routines and functions. These amazing functions which Blendify supports are –
- Point clouds, meshes, and primitives: It supports rendering of different types of 3D objects, such as point clouds, which are a collection of points in 3D space, meshes which are the surfaces defined by vertices and polygons; and primitives, which are the basic geometric shapes like spheres, cubes, etc.
- Per-vertex colors and textures: Blendify allows users to specify colors and textures for individual vertices of objects, enabling more detailed and visually appealing renderings.
- Advanced shadows with shadow catcher object: It supports the creation of shadows in scenes, including the use of shadow catcher objects, which allow shadows to be cast on them, making it easier to integrate 3D elements into real-world footage or images.
Blendify also includes the functionality of importing and exporting .blend format files: This allows users to easily exchange scene data with other Blender users or incorporate existing .blend files into their visualization workflows. Blendify can be integrated into existing Python projects without requiring a standalone installation of Blender itself. Users can simply install Blendify in their Python environment by running the command ‘pip install Blendify.’ The researchers have shared examples on their GitHub page of a Cornell box, mesh with texture, camera-colored paint cloud, NURBS trajectory, etc.
In conclusion, this lightweight Python framework provides a high-level API for creating and rendering scenes with Blender. It simplifies access to selected Blender functions and objects, which makes Blendify a great addition to Computer Vision applications.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.