Create app.py

app.py

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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)