3D Depth Mapping via MiDaS

Monocular Depth Estimation with a Single Image (MiDaS)

Table of Contents

📝 About
💻 How to build
🔧 Tools used
👤 Contact

📝About

Using PyTorch's MiDaS model and Open3D's point cloud to map a scene in 3D.

main.py

• Entry point
• Integrates the below 3 functionalities

depth_estimation

• Core logic for depth estimation (depth_estimation/depthmap.py)
• DepthMapper class responsible for setting up and utilizing a depth estimation model.
• Loads a pre-trained model (MiDaS model variants) based on the specified accuracy level, performs image transformations, and estimates the depth map from an input image.

imaging

• Handles image processing tasks (image_processing/image_process.py)
• ImageProcessorclass loads, validates, and manipulates image data.
• It includes functionalities such as loading images from disk, applying color maps, and displaying images.
• This class is utilized to handle the input and output images in the depth mapping process.

point_cloud

• Renders point clouds from the depth data generated by the depth mapping process (point_cloud/cloudrender.py).
• CloudRenderer class in processes the depth data to generate 3D points and renders them as a point cloud or voxel grid.
• This visualization helps in understanding the spatial relationships in the scene represented by the depth map.

💻 How to build

Requirements

pip install -r requirements.txt

Deploy

When running the model on a chosen image, swap out the PHOTO placeholder with the complete file path and extension of the target image. For the --accuracy_level setting, select an integer from 1 to 3 (where 1 delivers the quickest inference speed but with less accuracy, and 3 ensures the highest accuracy, albeit with a slower inference speed).

python3 main.py --accuracy_level [1|2|3] --input_img PHOTO

🔧Tools Used

👤Contact