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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)
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