app.py

import gradio as gr
import torch
import cv2
import numpy as np
import matplotlib.pyplot as plt
from yolov5 import YOLOv5

# Load YOLOv5 model (best.pt)
model = YOLOv5("best.pt")  # Adjust the path to your model file

# Function to process the video and calculate ball trajectory, speed, and visualize the pitch
def process_video(video_file):
    # Load video file using OpenCV
    video = cv2.VideoCapture(video_file.name)
    ball_positions = []
    speed_data = []

    frame_count = 0
    last_position = None

    while video.isOpened():
        ret, frame = video.read()
        if not ret:
            break
        
        frame_count += 1

        # Run YOLOv5 model on the frame to detect ball
        results = model(frame)
        
        # Extract the ball position (assuming class 0 = ball)
        ball_detections = results.pandas().xywh
        ball = ball_detections[ball_detections['class'] == 0]  # class 0 is ball, adjust as needed
        
        if not ball.empty:
            ball_x = ball.iloc[0]['xmin'] + (ball.iloc[0]['xmax'] - ball.iloc[0]['xmin']) / 2
            ball_y = ball.iloc[0]['ymin'] + (ball.iloc[0]['ymax'] - ball.iloc[0]['ymin']) / 2
            ball_positions.append((frame_count, ball_x, ball_y))  # Track position in each frame

            if last_position is not None:
                # Calculate speed based on pixel displacement between frames
                distance = np.sqrt((ball_x - last_position[1]) ** 2 + (ball_y - last_position[2]) ** 2)
                fps = video.get(cv2.CAP_PROP_FPS)  # Frames per second of the video
                speed = distance * fps  # Speed = distance / time (time between frames is 1/fps)
                speed_data.append(speed)

            last_position = (frame_count, ball_x, ball_y)  # Update last position

    video.release()

    # Ball trajectory plot
    plot_trajectory(ball_positions)
    
    # Return results
    avg_speed = np.mean(speed_data) if speed_data else 0
    return f"Average Ball Speed: {avg_speed:.2f} pixels per second"

# Function to plot ball trajectory using matplotlib
def plot_trajectory(ball_positions):
    x_positions = [pos[1] for pos in ball_positions]
    y_positions = [pos[2] for pos in ball_positions]

    plt.figure(figsize=(10, 6))
    plt.plot(x_positions, y_positions, label="Ball Trajectory", color='b')
    plt.title("Ball Trajectory on Pitch")
    plt.xlabel("X Position (pitch width)")
    plt.ylabel("Y Position (pitch length)")
    plt.grid(True)
    plt.legend()
    plt.show()

# Gradio interface for the app
iface = gr.Interface(
    fn=process_video,  # Function to call when video is uploaded
    inputs=gr.inputs.File(label="Upload a Video File"),  # File input (video)
    outputs="text",  # Output the result as text
    live=True  # Keep the interface live
)

iface.launch(debug=True)