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## 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](#run-inference-on-tensorflow-model-by-using-tensorFlow)
2. [Run inference on ONNX-model by using TensorFlow](#run-inference-on-onnx-model-by-using-tensorflow)
3. [Make ONNX model from downloaded Pytorch model file](#make-onnx-model-from-downloaded-pytorch-model-file)
### Run inference on TensorFlow-model by using TensorFlow
1) Download the model weights [model-f6b98070.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pb) and [model-small.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.pb) and place the file in the `/tf/` folder.
2) Set up dependencies:
```shell # 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:
```shell python tf/run_pb.py ```
Or run the small model:
```shell 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](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.onnx) and [model-small.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.onnx) and place the file in the `/tf/` folder.
2) Set up dependencies:
```shell # 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:
```shell python tf/run_onnx.py ```
Or run the small model:
```shell 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](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the file in the root folder.
2) Set up dependencies:
```shell # 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:
```shell 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