Install Tensorflow on Raspberry pi
From
Information is taken from https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi
Contents |
[edit] Update the Raspberry Pi
sudo apt-get update sudo apt-get dist-upgrade
[edit] Install TensorFlow
sudo pip3 install setuptools --upgrade sudo apt-get install libatlas-base-dev sudo pip3 install tensorflow sudo pip3 install pillow lxml jupyter matplotlib cython sudo apt-get install python-tk
[edit] Install OpenCV
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev sudo apt-get install libxvidcore-dev libx264-dev sudo apt-get install qt4-dev-tools libatlas-base-dev sudo pip3 install opencv-python==3.4.6.27
[edit] Compile and Install Protobuf
sudo apt-get install protobuf-compiler protoc --version
[edit] Set up TensorFlow Directory Structure and PYTHONPATH Variable
mkdir tensorflow1 cd tensorflow1 git clone --depth 1 https://github.com/tensorflow/models.git sudo vi ~/.bashrc
Add:
export PYTHONPATH=$PYTHONPATH:/home/pi/tensorflow1/models/research:/home/pi/tensorflow1/models/research/slim
Save and exit the session.
After login:
cd /home/pi/tensorflow1/models/research protoc object_detection/protos/*.proto --python_out=. cd /home/pi/tensorflow1/models/research/object_detection
Download the SSDLite-MobileNet model and unpack it:
wget http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz tar -xzvf ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz wget https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi/master/Object_detection_picamera.py
python3 Object_detection_picamera.py
[edit] Use a model you trained yourself
Here's a guide that shows you how to train your own model.
By adding the frozen inference graph into the object_detection directory and changing the model path in the script.
- Copy object_detection\inference_graph directory to object_detection directory in Raspberry Pi.
- Copy object_detection\training\lablemap.pbtxt to object_detection\data\ directory in Raspberry Pi.
Then, open the Object_detection_picamera.py script in a text editor.
- Go to the line where MODEL_NAME is set and change the string to match the name of the new model folder.
- Then, on the line where PATH_TO_LABELS is set, change the name of the labelmap file to match the new label map.
- Change the NUM_CLASSES variable to the number of classes your model can identify.