DL-ODT-for-UAV

Deep Learning based Object detection and tracking for UAV videos

• UBUNTU

TODO

• Create mockup screens - https://drive.google.com/file/d/1yy86lZZ4gLDnyp_oiNmoiufngoSfyGVv/view?usp=sharing

Steps

1. Make sure "pipenv" and "virtualenv" is installed pip install --user pipenv pip install virtualenv or sudo apt install virtualenv

2. cd into the DL-ODT-for-UAV folder

3. create a new virtual environment virtualenv -p /usr/bin/python2.7 venv

4. Activate the environment source venv/bin/activate

5. Install tensorflow . pip install tensorflow sudo apt-get install python-tk `pip install pandas'

6. To install the dependencies, pip install -r requirements.txt

7. Make the run.py file executable chmod +x app.py

8. Download the yolo weight file from https://drive.google.com/open?id=1BAxKaRz-qkp4SZwY_xePFKsJ1HOADTq1 and place it at /DL-ODT-for-UAV/rolo/weights/

9. Download https://drive.google.com/open?id=1R3_mUWD_tzLt2jBeakWdiZei9NJldxJB and place it at /DL-ODT-for-UAV/rolo/output/rolo_model/ https://drive.google.com/drive/folders/1jwlw4kfceFfYQvJixKGZNQJ-_R8vihpF?usp=sharing

10. install sudo apt install protobuf-compiler , pip install pillow,pip install lxml, pip install Cython,pip install contextlib2,pip install jupyter,pip install matplotlib,pip install pandas, pip install opencv-python

11. set the path by export PYTHONPATH=/home/ancy/PycharmProjects/DL-ODT-for-UAV/models:/home/ancy/PycharmProjects/DL-ODT-for-UAV/models/research:/home/ancy/PycharmProjects/DL-ODT-for-UAV/models/research/slim:/usr/bin

12. cd models, cd research leads to DL-ODT-for-UAV/model/research/

13. compile the protobuf files protoc --python_out=. ./object_detection/protos/anchor_generator.proto ./object_detection/protos/argmax_matcher.proto ./object_detection/protos/bipartite_matcher.proto ./object_detection/protos/box_coder.proto ./object_detection/protos/box_predictor.proto ./object_detection/protos/eval.proto ./object_detection/protos/faster_rcnn.proto ./object_detection/protos/faster_rcnn_box_coder.proto ./object_detection/protos/grid_anchor_generator.proto ./object_detection/protos/hyperparams.proto ./object_detection/protos/image_resizer.proto ./object_detection/protos/input_reader.proto ./object_detection/protos/losses.proto ./object_detection/protos/matcher.proto ./object_detection/protos/mean_stddev_box_coder.proto ./object_detection/protos/model.proto ./object_detection/protos/optimizer.proto ./object_detection/protos/pipeline.proto ./object_detection/protos/post_processing.proto ./object_detection/protos/preprocessor.proto ./object_detection/protos/region_similarity_calculator.proto ./object_detection/protos/square_box_coder.proto ./object_detection/protos/ssd.proto ./object_detection/protos/ssd_anchor_generator.proto ./object_detection/protos/string_int_label_map.proto ./object_detection/protos/train.proto ./object_detection/protos/keypoint_box_coder.proto ./object_detection/protos/multiscale_anchor_generator.proto ./object_detection/protos/graph_rewriter.proto ./object_detection/protos/calibration.proto ./object_detection/protos/flexible_grid_anchor_generator.proto This creates a name_pb2.py file from every name.proto file in the models/research/object_detection/protos folder.

14. Run the following commands from the models/research/ directory: python setup.py build python setup.py install

15. To run, ./app.py

16. To deactivate, deactivate