> For the complete documentation index, see [llms.txt](https://htootayzaaung.gitbook.io/guardlens-real-time-weapon-detection-system/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://htootayzaaung.gitbook.io/guardlens-real-time-weapon-detection-system/getting-started.md). # Getting Started #### Project Directory Structure Here is the structure of the project directory to help you navigate the files: ```plaintext . ├── detectron2 │ └── Object_Detection │ ├── Faster_RCNN(detectron2).ipynb │ ├── output │ │ ├── events.out.tfevents.1708238712.MSI.44966.0 │ │ ├── events.out.tfevents.1708898117.96d7b304deb9.10588.0 │ │ ├── inference │ │ │ ├── coco_instances_results.json │ │ │ └── instances_predictions.pth │ │ ├── intruder_valid_coco_format.json │ │ ├── intruder_valid_coco_format.json.lock │ │ ├── last_checkpoint │ │ └── metrics.json │ └── results.txt ├── Gathering_Datasets │ └── gather_data.py ├── README.md ├── Web_App_UI │ ├── alert_images │ │ ├── alert_0_1714327026.jpg │ │ └── alert_4_1714327015.jpg │ ├── app.py │ ├── requirements.txt │ ├── camera_testing │ │ ├── camera_list.py │ │ ├── test_webcam1.py │ │ └── test_webcam.py │ ├── detect.py │ ├── face_blurring.py │ ├── models │ │ ├── Mediapipe │ │ │ └── model.tflite │ │ └── Yolov8 │ │ ├── object_detection.pt │ │ └── object_segmentation.pt │ ├── object_detection.py │ ├── __pycache__ │ │ ├── face_blurring.cpython-311.pyc │ │ ├── object_detection.cpython-311.pyc │ │ └── utility_functions.cpython-311.pyc │ ├── static │ │ ├── css │ │ │ └── styles.css │ │ └── js │ │ └── main.js │ ├── templates │ │ ├── alert_email.html │ │ └── index.html │ └── utility_functions.py └── yolov8 ├── Object_Detection │ ├── runs │ │ └── detect │ │ ├── predict │ │ │ ├── 003af63a783a1615_jpg.rf.a1604da0249e644b875324c60a0021a7.jpg │ │ │ ├── 0231d2f11eb7a483_jpg.rf.ab7f38f1316b341d3b58f7ee7e84beed.jpg │ │ │ └── ... │ │ └── train │ │ ├── args.yaml │ │ ├── confusion_matrix_normalized.png │ │ ├── confusion_matrix.png │ │ ├── events.out.tfevents.1715069234.cab5a3b51062.5982.0 │ │ ├── F1_curve.png │ │ ├── labels_correlogram.jpg │ │ ├── labels.jpg │ │ ├── P_curve.png │ │ ├── PR_curve.png │ │ ├── R_curve.png │ │ ├── results.csv │ │ ├── results.png │ │ ├── train_batch0.jpg │ │ ├── train_batch1.jpg │ │ ├── train_batch2.jpg │ │ ├── train_batch33475.jpg │ │ ├── train_batch33476.jpg │ │ ├── train_batch33477.jpg │ │ ├── val_batch0_labels.jpg │ │ ├── val_batch0_pred.jpg │ │ ├── val_batch1_labels.jpg │ │ ├── val_batch1_pred.jpg │ │ ├── val_batch2_labels.jpg │ │ ├── val_batch2_pred.jpg │ │ └── weights │ │ ├── best.pt │ │ └── last.pt │ └── Weapon_Detection(YOLOv8).ipynb └── Object_Segmentation ├── runs │ └── segment │ ├── predict │ │ ├── 1014_jpg.rf.fd987b3c771cedec550b7f27d76df1af.jpg │ │ ├── 101_jpg.rf.16908c82e05c877ca5a3045916cd31a7.jpg │ │ ├── 1023_jpg.rf.e4e5fe6e70236045274dde4b5b2c253b.jpg │ │ └── ... │ └── train │ ├── args.yaml │ ├── BoxF1_curve.png │ ├── BoxP_curve.png │ ├── BoxPR_curve.png │ ├── BoxR_curve.png │ ├── confusion_matrix_normalized.png │ ├── confusion_matrix.png │ ├── events.out.tfevents.1715018725.d418ef87afe5.12865.0 │ ├── labels_correlogram.jpg │ ├── labels.jpg │ ├── MaskF1_curve.png │ ├── MaskP_curve.png │ ├── MaskPR_curve.png │ ├── MaskR_curve.png │ ├── results.csv │ ├── results.png │ ├── train_batch0.jpg │ ├── train_batch1.jpg │ ├── train_batch2.jpg │ ├── train_batch32045.jpg │ ├── train_batch32046.jpg │ ├── train_batch32047.jpg │ ├── val_batch0_labels.jpg │ ├── val_batch0_pred.jpg │ ├── val_batch1_labels.jpg │ ├── val_batch1_pred.jpg │ ├── val_batch2_labels.jpg │ ├── val_batch2_pred.jpg │ └── weights │ ├── best.pt │ └── last.pt └── Weapon_Segmentation(YOLOv8).ipynb ``` #### Setting up the Project Environment To ensure the project runs smoothly, follow these steps to set up a Python virtual environment and install all required dependencies: 1. **Create a New Virtual Environment:** * Use Python to create a new virtual environment in your project directory: ```sh python3 -m venv guard_lens ``` 2. **Activate the Virtual Environment:** * Activate the virtual environment by running: ```sh source guard_lens/bin/activate ``` 3. **Install Required Packages:** * Install all dependencies listed in the `requirements.txt` file: ```sh pip install -r requirements.txt ``` This setup ensures that all necessary Python packages are installed within an isolated environment, reducing the risk of conflicts and issues. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://htootayzaaung.gitbook.io/guardlens-real-time-weapon-detection-system/getting-started.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.