| # Tensorflow Lite MiDaS iOS Example | |
| ### Requirements | |
| - XCode 11.0 or above | |
| - iOS 12.0 or above, [iOS 14 breaks the NPU Delegate](https://github.com/tensorflow/tensorflow/issues/43339) | |
| - TensorFlow 2.4.0, TensorFlowLiteSwift -> 0.0.1-nightly | |
| ## Quick Start with a MiDaS Example | |
| MiDaS is a neural network to compute depth from a single image. It uses TensorFlowLiteSwift / C++ libraries on iOS. The code is written in Swift. | |
| Paper: https://arxiv.org/abs/1907.01341 | |
| > Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer | |
| > René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun | |
| ### Install TensorFlow | |
| Set default python version to python3: | |
| ``` | |
| echo 'export PATH=/usr/local/opt/python/libexec/bin:$PATH' >> ~/.zshenv | |
| echo 'alias python=python3' >> ~/.zshenv | |
| echo 'alias pip=pip3' >> ~/.zshenv | |
| ``` | |
| Install TensorFlow | |
| ```shell | |
| pip install tensorflow | |
| ``` | |
| ### Install TensorFlowLiteSwift via Cocoapods | |
| Set required TensorFlowLiteSwift version in the file (`0.0.1-nightly` is recommended): https://github.com/isl-org/MiDaS/blob/master/mobile/ios/Podfile#L9 | |
| Install: brew, ruby, cocoapods | |
| ``` | |
| ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" | |
| brew install mc rbenv ruby-build | |
| sudo gem install cocoapods | |
| ``` | |
| The TensorFlowLiteSwift library is available in [Cocoapods](https://cocoapods.org/), to integrate it to our project, we can run in the root directory of the project: | |
| ```ruby | |
| pod install | |
| ``` | |
| Now open the `Midas.xcworkspace` file in XCode, select your iPhone device (XCode->Product->Destination->iPhone) and launch it (cmd + R). If everything works well, you should see a real-time depth map from your camera. | |
| ### Model | |
| The TensorFlow (TFlite) model `midas.tflite` is in the folder `/Midas/Model` | |
| To use another model, you should convert it from TensorFlow saved-model to TFlite model (so that it can be deployed): | |
| ```python | |
| saved_model_export_dir = "./saved_model" | |
| converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_export_dir) | |
| tflite_model = converter.convert() | |
| open(model_tflite_name, "wb").write("model.tflite") | |
| ``` | |
| ### Setup XCode | |
| * Open directory `.xcworkspace` from the XCode | |
| * Press on your ProjectName (left-top corner) -> change Bundle Identifier to `com.midas.tflite-npu` or something like this (it should be unique) | |
| * select your Developer Team (your should be signed-in by using your AppleID) | |
| * Connect your iPhone (if you want to run it on real device instead of simulator), select your iPhone device (XCode->Product->Destination->iPhone) | |
| * Click in the XCode: Product -> Run | |
| * On your iPhone device go to the: Settings -> General -> Device Management (or Profiles) -> Apple Development -> Trust Apple Development | |
| ---- | |
| Original repository: https://github.com/isl-org/MiDaS | |
| ### Examples: | |
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| ## LICENSE | |
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
| ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE | |
| LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | |
| CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | |
| SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
| INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
| CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | |
| ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
| POSSIBILITY OF SUCH DAMAGE. | |