YOLO Football Player Detection - README

Overview

This Python script utilizes the YOLO (You Only Look Once) v8 model to detect football players in a video file. It leverages the cv2 module from OpenCV for video processing and the ultralytics package to load and run the YOLO model. The script reads frames from a video, applies the YOLO model to detect objects, and then visualizes the detections by drawing bounding boxes and class labels on the frames.

Prerequisites

Before running the script, ensure you have the following dependencies installed:

Python 3.x
OpenCV (cv2)
Ultralytics YOLO

You can install the required libraries using the following commands:

pip install opencv-python
pip install ultralytics

Usage

Model Path: Update the model_path variable with the path to your trained YOLO model (e.g., "yolov8_footballplayer_detection/best.pt").
Video File: Update the cv2.VideoCapture argument with the path to your video file (e.g., "futbol.mp4").
Threshold: Adjust the threshold variable to set the confidence level for detections.

Running the Script

Execute the script using Python:

python detect_football_players.py

Script Breakdown

Import Libraries:
```
import cv2
from ultralytics import YOLO
```

Initialize Model and Video Capture:

model_path = "yolov8_footballplayer_detection/best.pt"
cap = cv2.VideoCapture("futbol.mp4")

Check if Video is Opened Correctly:

if not cap.isOpened():
    print("Video dosyası açılamadı.")
    exit()

Load YOLO Model:
```
model = YOLO(model_path)
```

Processing Video Frames:

Read each frame from the video.
Apply the YOLO model to detect objects.
Draw bounding boxes and labels for detections with a confidence score above the threshold.

threshold = 0.5

while True:
    ret, frame = cap.read()

    if not ret:
        print("Videodan kare alınamadı veya video sonuna ulaşıldı.")
        break

    results = model(frame)[0]

    for result in results.boxes.data.tolist():
        x1, y1, x2, y2, score, class_id = result

        color = (class_id*10, class_id*100, class_id*10)

        if score > threshold:
            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 4)
            cv2.putText(frame, model.names[int(class_id)].upper(), (int(x1), int(y1) - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 1.3, color, 3, cv2.LINE_AA)

    cv2.imshow("frame", frame)

    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Exiting the Script

Press the 'q' key to stop the video processing loop and close all OpenCV windows.

Notes

Make sure your video file is accessible and the path is correct.
The color scheme for bounding boxes is generated based on the class_id, which can be customized as needed.
The threshold value can be adjusted to increase or decrease the sensitivity of the detections.

Troubleshooting

If the video file cannot be opened, ensure the file path is correct and the file is not corrupted.
If the model fails to load, verify the model path and ensure the YOLO model file exists and is properly formatted.
Ensure all dependencies are installed and compatible with your Python version.

HayatiYrtgl/Football_Player_Detection_YoloV8