Repository for the Deep Learning course project:

• Milad Samimifar
• Pooria Ashrafian

Object detection method that can simultaneously estimate the positions and depth of the objects from images. (Based on NYU-Depth V2 dataset)

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect.

• Labeled dataset (~2.8 GB)

1. Download yolov3 weights and put it in Object_detection_web_app_streamlit folder:

wget -nc https://pjreddie.com/media/files/yolov3.weights -P Object_detection_web_app_streamlit

2. Download our Unet depth model weights and put it in ./models_depth/Unet folder:

• Weighs: https://drive.google.com/file/d/1fvzPVqKj46WjaYw6OUr1a38KblWkGC4W/view?usp=sharing

• Model (Json file): https://drive.google.com/file/d/1g685-v1qGv6NBE7nhY0LSQDgUemJXarc/view?usp=sharing

Shell commmand:

gdown --id 1fvzPVqKj46WjaYw6OUr1a38KblWkGC4W -O ./models_depth/Unet/2.h5 # weights

gdown --id 1g685-v1qGv6NBE7nhY0LSQDgUemJXarc -O ./models_depth/Unet/2.json # model(json file)

3. install requirements:

pip install streamlit opencv-python black

4. Execute:

cd Object_detection_web_app_streamlit
streamlit run ./src/app.py

1. Download yolov3 weights and put it in Object_detection_web_app folder:

wget -nc https://pjreddie.com/media/files/yolov3.weights -P Object_detection_web_app

2. Install requirements:

pip install -r ./Object_detection_web_app/requirements.txt

3. Execute the code below: (Only once)

cd Object_detection_web_app
python manage.py collectstatic

4. Execute:

python manage.py runserver

Joint_notebook.ipynb:

• Download dataset and preprocessing
• Train Unet model for depth estimation [1]

• Yolov3 Object detection

• Joint model and test

[1] Ibraheem Alhashim and Peter Wonka. High quality monocular depth estimation via transfer learning. arXiv preprint arXiv:1812.11941, 2018.