3d_bbox

3D Bounding Box Estimation Using Deep Learning and Geometry

Input

(Image from https://github.com/skhadem/3D-BoundingBox/tree/master/eval/image_2)

Ailia input shape: (1, 3, 224, 224)

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 3d_bbox.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 3d_bbox.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 3d_bbox.py --video VIDEO_PATH

The default setting is to use the optimized model and weights, but you can also switch to the normal model by using the --normal option.

Reference

3D Bounding Box Estimation Using Deep Learning and Geometry

Framework

Pytorch

Model Format

ONNX opset = 10

Netron

3d_bbox.onnx.prototxt