Merge pull request #1066 from ashis2004/99999

railway_person_detection added

railway_person_detection/.gitignore

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+yolov8m.pt

railway_person_detection/README.md

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+# Railway Person Detection using YOLOv8
+
+This project aims to detect people in railway environments using YOLOv8, a state-of-the-art object detection model. The system can be deployed for real-time monitoring of railway stations, platforms, or tracks to ensure safety and security.
+
+## Requirements
+
+- Python 3.x
+- OpenCV
+- PyTorch
+- Ultralytics YOLO library
+- Pre-trained YOLOv8 model weights (`yolov8m.pt`)
+- GPU (optional, for faster inference)
+
+## Installation
+
+1. Clone this repository:
+    ```bash
+    git clone https://github.com/TAHIR0110/ThereForYou.git
+    cd THEREFORYOU
+    cd railway_person_detection
+    ```
+
+2. Install dependencies:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+
+## Usage
+
+Run the `railway_person_detection.py` script to start detecting people in railway environments. You can specify various command-line arguments to customize the detection process:
+
+- `-s, --source`: Specify the video source (default is webcam, provide the path to a video file for offline detection).
+- `-x`: X-coordinate of the bounding box.
+- `-y`: Y-coordinate of the bounding box.
+- `--height`: Height of the bounding box.
+- `--width`: Width of the bounding box.
+
+Example usage:
+```bash
+python railway_person_detection.py --source 0 -x 100 -y 100 --height 200 --width 200

railway_person_detection/railway_person_detection.py

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+from ultralytics import YOLO
+import cv2
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-s","--source",default=0)
+parser.add_argument("-x", default=0)
+parser.add_argument("-y", default=0)
+parser.add_argument("--height",default=0)
+parser.add_argument("--width",default=0)
+
+
+args = parser.parse_args()
+
+
+cap = cv2.VideoCapture(args.source)
+
+model= YOLO("yolov8m.pt")
+
+
+
+while (cap.isOpened()):
+
+
+
+    suc,frame = cap.read()
+    if(args.x == 0 and args.y == 0 and args.height == 0 and args.width == 0):
+        print("User did not mentioned the values for the bounding box, by default, analyzing the whole image")
+    else:
+
+        frame = frame[args.y:args.y + args.height, args.x:args.x+args.width]
+
+
+    if not suc:
+        print("video sequence ended.")
+        break
+
+    result = model.predict(source=frame,classes=[0])
+
+    if result[0].boxes.xyxy is not None:
+        print("alert Person Detected in the Frame.")
+
+    else:
+        print("clear for now.")
+
+

railway_person_detection/requirements.txt

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+ultralytics
+opencv-python