DenseDepth

Modify from DenseDepth and Android Object Detection

Main Contributions:

1. Porting source from tensorflow1.x to tensorflow2.2
2. Convert pre-trained keras model(.h5) to tensorflow2.2(.pb) and tensorflow-lite(.tflite)
3. Android app with the tensorflow-lite quantized model

Difficulties in tflite convertion:

1. The keras model can't be loaded from TFLiteConverter.from_keras_model() because custom_objects parsing isn't supported
2. The keras model can't be loaded from TFLiteConverter.from_saved_model() because dynamic input shape [NONE, NONE, NONE, NONE] is not supported

Solution Steps:

1. After loading .pb, converting its input shape to fixed shape [1, 480, 640, 3] in the session graph.
2. Resave .pb from session graph. (Very improtant! converting and inferring directly without resave won't succeed...)
3. Load .pb again for inference.

Results

• Comparision of Keras, Tensorflow2.2 and Tensorflow-lite model over NYU Depth V2 <img width="500" src="result.png"><img width="180" src="android.jpg">

Requirements

• Install Tensorflow 2.2, python3.6
• Download and put cudart64_101.dll under your cuda bin directory (ex: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin)
• Download Keras model based on NYU Depth V2 and put it under the project

Converted Models

Over NYU Depth V2

• Tensorflow2.2 (162 MB)
• Tensorflow-lite (162 MB)
• Tensorflow-lite quantized (41 MB)

Run Demo

After download and put the pre-trained keras model(nyu.h5) in the project

1. Run python convert.py. to generate tensorflow2.2 and lite models with its corresponding depth map.
2. Rename nyuQuan.tflite with nyu.tflite and put it on Android/app/src/main/assets
3. Build and run Android with android studio

you can also download and install DenseDepth apk with the Tensorflow-lite quantized model

Reference

Thanks for the authors. If using the code, please cite their paper:

@article{Alhashim2018,
  author    = {Ibraheem Alhashim and Peter Wonka},
  title     = {High Quality Monocular Depth Estimation via Transfer Learning},
  journal   = {arXiv e-prints},
  volume    = {abs/1812.11941},
  year      = {2018},
  url       = {https://arxiv.org/abs/1812.11941},
  eid       = {arXiv:1812.11941},
  eprint    = {1812.11941}
}