jjsjunior / uav_network_analitycs

UAV Network Analytics

Libraries dependancies:

• Tensorflow
• Numpy
• cv2
• imutils

You can run the demo by running "python3 finalPrototype.py"

In Yolo training folder, there are some cfg file, weights, python code we used to train our 2 yolos

In CNN training folder, there is the python code we used to train our CNN for character recognition

You can donwload pb files, yolo.weights and datasets here : https://drive.google.com/drive/folders/17gxw7tv7jy3KgJFhQiHX0IilYObFbIJp?usp=sharing

More informations : https://medium.com/@theophilebuyssens/license-plate-recognition-using-opencv-yolo-and-keras-f5bfe03afc65

### Criação de ambiente conda sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Yolov3 utilizada no projeto:

https://github.com/YunYang1994/tensorflow-yolov3.git

#criar ambiente python 3.7.10

conda create --prefix ../auv/env python=3.7.10

#ativar ambiente conda activate ../auv/env #instalar tensorflow

pip install tensorflow-gpu==1.14.0

#instalar keras

pip install keras==2.2.4

pip install opencv-python==3.4.2.17
pip install opencv-contrib-python==3.4.2.17
pip install Cython --install-option="--no-cython-compile"

#ir para a pasta do darkflow pra instala-lo:

https://github.com/thtrieu/darkflow

procedimentos pra correcao de problema na instalacao local do darkflow:

####TheophileBuy/LicensePlateRecognition#2 ####Just build the Cython extensions in place. NOTE: If installing this way you will have to use ./flow in the cloned darkflow directory instead of flow as darkflow is not installed globally. python3 setup.py build_ext --inplace
####Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect) pip install -e .
Install with pip globally
pip install .

###cd /media/jones/datarec/lpr/fontes/ocr/darkflow/darkflow APENAS COMENTADO, NAO UTILIZAR
pip install .
pip install imutils
pip install -Iv h5py==2.10.0
pip install --upgrade Pillow

lembretes:

problema ao definir o LR. encontrada issue no proprio repo:
Darkflow does not use the learning rate in .cfg. Use --lr instead.
https://github.com/thtrieu/darkflow/issues/515#issuecomment-356474112

Docker para codigo de validacao do modelo de classificação de imagens

docker build -f Dockerfile_classificador -t classificador_imagem_uav . docker run --gpus "device=0" --rm --volume /media/jones/datarec/lpr/dataset/versao_atual/preprocessados_0308/train:/home/dataset/train --volume /media/jones/datarec/lpr/dataset/versao_atual/preprocessados_0308/validation:/home/dataset/validation --volume /media/jones/datarec/lpr/dataset/versao_atual/preprocessados_0308/logscarro30:/home/logs --volume /media/jones/datarec/lpr/dataset/versao_atual/preprocessados_0308/trained_models:/home/modelos --name ped_sspgo-tensorflow-lpd nvcr.io/ped_ssp/tensorflow_114_lpd --train-dir /home/dataset/train --validate-dir /home/dataset/validation --logdir /home/logs --model models/ceia_eccv-model_dpout05_multiclass --name model_ceia_char_dopout05_car_moto_multi_26 --output-dir /home/modelos/ -op Adam -lr .001 -its 300000 -bs 32