Adding TensorFlow Lite Setup Guide for Linux

deploy_guides/Linux_TFLite_Guide.md

@@ -0,0 +1,69 @@
+# How to Run TensorFlow Lite Models on Linux
+This guide shows how to set up a TensorFlow Lite Runtime environment on a Linux system. We'll use [Anaconda](https://www.anaconda.com/) to create a Python environment to install the TFLite Runtime in. It's easy!
+
+## Step 1. Download and Install Anaconda
+First, install [Anaconda](https://www.anaconda.com/), which is a Python environment manager that greatly simplifies Python package management and deployment. Anaconda allows you to create Python virtual environments without interfering with existing system-wide installations of Python. Go to the [Anaconda Downloads page](https://www.anaconda.com/products/distribution) and download the latest version for Linux.
+
+Once downloaded, open a terminal and navigate to the directory where the installer is located, then run:
+
+```
+bash Anaconda3-*.sh
+```
+
+Follow the on-screen instructions to complete the installation. Restart your terminal or run:
+
+```
+source ~/.bashrc
+```
+
+## Step 2. Set Up Virtual Environment and Directory
+Open a terminal and create a new directory for TensorFlow Lite:
+
+```
+mkdir ~/tflite1
+cd ~/tflite1
+```
+
+Next, create a Python 3.9 virtual environment by issuing:
+
+```
+conda create --name tflite1-env python=3.9
+```
+
+Enter "y" when prompted to proceed. Activate the environment and install the required packages by running the commands below. We'll install TensorFlow, OpenCV, and a downgraded version of protobuf. TensorFlow is a pretty big download (about 450MB), so it will take a while.
+
+```
+conda activate tflite1-env
+pip install tensorflow opencv-python protobuf==3.20.*
+```
+
+Download the detection scripts from the repository:
+
+```
+curl -O https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/TFLite_detection_image.py
+curl -O https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/TFLite_detection_video.py
+curl -O https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/TFLite_detection_webcam.py
+curl -O https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/TFLite_detection_stream.py
+```
+
+## Step 3. Move TFLite Model into Directory
+Next, download or move the custom TFLite model that was trained and downloaded from the Colab notebook into the `~/tflite1` directory. If you downloaded it from Colab, it should be in a file called `custom_model_lite.zip`. If you haven't trained a model yet and just want to test one, download a sample model by clicking this [Dropbox link](https://www.dropbox.com/scl/fi/4fk8ls8s03c94g6sb3ngo/custom_model_lite.zip?rlkey=zqda21sowk0hrw6i3f2dgbsyy&dl=0). Move the file to `~/tflite1` and unzip it:
+
+```
+tar -xf custom_model_lite.zip
+```
+
+At this point, you should have a folder at `~/tflite1/custom_model_lite` which contains at least a `detect.tflite` and `labelmap.txt` file.
+
+## Step 4. Run TensorFlow Lite Model!
+Now that everything is set up, running the TFLite model is easy. Just call one of the detection scripts and point it at your model folder with the `--modeldir` option. For example, to run your `custom_model_lite` model on a webcam, issue:
+
+```
+python TFLite_detection_webcam.py --modeldir=custom_model_lite
+```
+
+A window will appear showing detection results drawn on the live webcam feed, make sure to accept the use of webcam.. If you're running this on a headless system, ensure you have an appropriate display setup (like X forwarding or a VNC connection). You can read more about this here: [Stack Exchange Forum](https://unix.stackexchange.com/questions/12755/how-to-forward-x-over-ssh-to-run-graphics-applications-remotely), [archlinux](https://wiki.archlinux.org/title/X11vnc).
+
+For more details on how to use the detection scripts for images, videos, or streams, please see [Step 3 in the main README page](https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi#step-3-run-tensorflow-lite-models).
+
+Have fun using TensorFlow Lite! Stay tuned for more examples on how to build cool applications around your model.