(Image from kitti dataset http://www.cvlibs.net/datasets/kitti/raw_data.php)
Shape : (1, h, w, 3)
Shape : (1, h, w)
Automatically downloads the tflite file on the first run. It is necessary to be connected to the Internet while downloading.
For the sample image,
$ python3 midas.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 midas.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 midas.py --video VIDEO_PATH
By adding the -v21
option, you can use version 2.1 model.
(default use version 2.0 model)
If you use the version 2.1 model, you can use the small model with the --model_type small
option.
$ python3 midas.py -v21 --model_type small
Two versions of the model are provided: full integer quantization (8-bit) and full precision floating point (32-bit). By default, the full integer quantization is used but the user can select the other version by passing the --float flag.
$ python3 midas.py --float
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
Tensorflow 2.12.0
midas_v2.1_quant_recalib.tflite