AI - Object Detector

This README provides a comprehensive guide for setting up, training, and running the Object Detection System. Follow these steps carefully to ensure successful execution.

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System Requirements

Ensure that your Python version is compatible with the TensorFlow version required for this project. TensorFlow compatibility is a common issue; please verify and fix mismatches as necessary.

Recommended Environment:

• Python Version: 3.x (preferably 3.10.11) Don't go above 3.11. Fails for that.
• TensorFlow Version: Check requirements.txt for the specific version.

Tip: Using a virtual environment is highly recommended to avoid conflicts with global Python packages.

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Setup Instructions

1. Install Dependencies: Install all required packages by running the following command:

   pip install -r requirements.txt

2. Verify Installation: Ensure that TensorFlow and other dependencies are correctly installed. If you encounter issues, double-check your Python and TensorFlow versions for compatibility.

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Required Files

Before proceeding, ensure you have the following files:

1. planesnet.json:

   • This file contains the data required to train the model.
   • Place it in the root directory of the project.

2. Folder Structure:

   Project_Directory/
   ├── AI_Models/
   │   └── model.tfl (Generated after training)
   ├── Landscape_Views/
   │   └── (Output images will be saved here)
   ├── planesnet.json (donwload this from the website I gave below)
   ├── train.py
   ├── cnn_model.py
   ├── locator.py
   ├── requirements.txt
   └── README.md
   

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Dataset Information

The latest version of planesnet.json is available through the PlanesNet Kaggle page. This page also provides detailed information about the dataset layout, including the format and contents. Be sure to download the dataset from there and place the planesnet.json file in the root directory of your project.

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Step-by-Step Usage

1. Training the Model

Run the train.py script to train the model using planesnet.json. This will generate a .tfl model file in the AI_Models/ directory.

python train.py "planesnet.json" "model.tfl"

2. Running the CNN Model Script

Once the model is trained, execute the cnn_model.py script. This script uses the trained model to prepare for object detection tasks.

python cnn_model.py

3. Executing the Locator Script

To detect planes in landscape images, run the locator.py script. Pass the trained model and the image file as arguments.

Example command:

python locator.py "model.tfl" "Landscape_Views/landscape_A.png"

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Output Location

After successful execution, the output will be saved in the Landscape_Views/ folder. The output file will include the processed image with detected planes marked.

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Troubleshooting

1. TensorFlow Version Mismatch:

   • Verify your Python version matches the TensorFlow requirements.
   • If using a virtual environment, recreate it to avoid conflicts:

     python -m venv venv
     source venv/bin/activate  # On Linux/Mac
     venv\Scripts\activate     # On Windows
     pip install -r requirements.txt

2. Missing planesnet.json:

   • Ensure the file is in the root directory.
   • Without this file, the model training will fail.

3. Model File Not Found:

   • Ensure the train.py script is executed successfully to generate the model file in the AI_Models/ directory.

4. Image File Issues:

   • Check that the input image path passed to locator.py exists and is correctly formatted.

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python model/locator.py model/model.tfl model/Landscape_Views/landscape_A.png

python locator.py "model.tfl" "Landscape_Views/landscape_A.png" "imageid1"

& C:/Users/{{User}}/AppData/Local/Programs/Python/Python310/python.exe c:/Users/{{User}}/OneDrive/Desktop/Captsone/model/object_extraction_from_db.py