MiDaS-master/tf/README.md

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

TensorFlow inference using .pb and .onnx models

1. Run inference on TensorFlow-model by using TensorFlow

2. Run inference on ONNX-model by using TensorFlow

3. Make ONNX model from downloaded Pytorch model file

Run inference on TensorFlow-model by using TensorFlow

1. Download the model weights model-f6b98070.pb and model-small.pb and place the file in the /tf/ folder.

2. Set up dependencies:

# install OpenCV
pip install --upgrade pip
pip install opencv-python

# install TensorFlow
pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0

Usage

1. Place one or more input images in the folder tf/input.

2. Run the model:

   python tf/run_pb.py
   

   Or run the small model:

   python tf/run_pb.py --model_weights model-small.pb --model_type small
   

3. The resulting inverse depth maps are written to the tf/output folder.

Run inference on ONNX-model by using ONNX-Runtime

1. Download the model weights model-f6b98070.onnx and model-small.onnx and place the file in the /tf/ folder.

2. Set up dependencies:

# install OpenCV
pip install --upgrade pip
pip install opencv-python

# install ONNX
pip install onnx==1.7.0

# install ONNX Runtime
pip install onnxruntime==1.5.2

Usage

1. Place one or more input images in the folder tf/input.

2. Run the model:

   python tf/run_onnx.py
   

   Or run the small model:

   python tf/run_onnx.py --model_weights model-small.onnx --model_type small
   

3. The resulting inverse depth maps are written to the tf/output folder.

Make ONNX model from downloaded Pytorch model file

1. Download the model weights model-f6b98070.pt and place the file in the root folder.

2. Set up dependencies:

# install OpenCV
pip install --upgrade pip
pip install opencv-python

# install PyTorch TorchVision
pip install -I torch==1.7.0 torchvision==0.8.0

# install TensorFlow
pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0

# install ONNX
pip install onnx==1.7.0

# install ONNX-TensorFlow
git clone https://github.com/onnx/onnx-tensorflow.git
cd onnx-tensorflow 
git checkout 095b51b88e35c4001d70f15f80f31014b592b81e 
pip install -e .

Usage

1. Run the converter:

   python tf/make_onnx_model.py
   

2. The resulting model-f6b98070.onnx file is written to the /tf/ folder.

Requirements

The code was tested with Python 3.6.9, PyTorch 1.5.1, TensorFlow 2.2.0, TensorFlow-addons 0.8.3, ONNX 1.7.0, ONNX-TensorFlow (GitHub-master-17.07.2020) and OpenCV 4.3.0.

Citation

Please cite our paper if you use this code or any of the models:

@article{Ranftl2019,
    author    = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
    title     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},
    journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
    year      = {2020},
}

License

MIT License