app.py

import cv2
import numpy as np
import torch
from ultralytics import YOLO
import gradio as gr
from scipy.interpolate import interp1d
import uuid
import os

# Load the trained YOLOv8n model
model = YOLO("best.pt")

# Constants
STUMPS_WIDTH = 0.2286  # meters
BALL_DIAMETER = 0.073  # meters
FRAME_RATE = 30  # Input video frame rate
SLOW_MOTION_FACTOR = 6
CONF_THRESHOLD = 0.3
RESIZE_DIM = 640  # Resize frames for faster processing

def process_video(video_path):
    if not os.path.exists(video_path):
        return [], [], "Error: Video file not found"
    cap = cv2.VideoCapture(video_path)
    frames = []
    ball_positions = []
    debug_log = []
    frame_count = 0
    max_frames = FRAME_RATE * 3  # Limit to 3 seconds of frames

    while cap.isOpened() and frame_count < max_frames:
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        # Resize frame for faster YOLO inference
        frame_resized = cv2.resize(frame, (RESIZE_DIM, RESIZE_DIM), interpolation=cv2.INTER_AREA)
        frames.append(frame.copy())  # Store original frame
        # Detect ball
        results = model.predict(frame_resized, conf=CONF_THRESHOLD, imgsz=RESIZE_DIM)
        detections = 0
        scale_x, scale_y = frame.shape[1] / RESIZE_DIM, frame.shape[0] / RESIZE_DIM
        for detection in results[0].boxes:
            if detection.cls == 0:  # Ball class
                detections += 1
                x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
                # Scale coordinates back to original frame size
                x1, x2 = x1 * scale_x, x2 * scale_x
                y1, y2 = y1 * scale_y, y2 * scale_y
                ball_center = [(x1 + x2) / 2, (y1 + y2) / 2]
                ball_positions.append(ball_center)
                cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
        frames[-1] = frame
        debug_log.append(f"Frame {frame_count}: {detections} ball detections")
    cap.release()

    if not ball_positions:
        debug_log.append("No balls detected in any frame")
    else:
        debug_log.append(f"Total ball detections: {len(ball_positions)}")

    return frames, ball_positions, "\n".join(debug_log)

def estimate_trajectory(ball_positions, frames):
    if len(ball_positions) < 2:
        return [], [], "Error: Fewer than 2 ball detections for trajectory"
    x_coords = [pos[0] for pos in ball_positions]
    y_coords = [pos[1] for pos in ball_positions]
    times = np.arange(len(ball_positions)) / FRAME_RATE

    try:
        fx = interp1d(times, x_coords, kind='linear', fill_value="extrapolate")
        fy = interp1d(times, y_coords, kind='quadratic', fill_value="extrapolate")
    except Exception as e:
        return [], [], f"Error in trajectory interpolation: {str(e)}"

    # Interpolate for all frames and future projection
    t_all = np.linspace(0, times[-1] + 0.5, len(frames) + 10)
    x_all = fx(t_all)
    y_all = fy(t_all)
    trajectory = list(zip(x_all, y_all))
    return trajectory, t_all, "Trajectory estimated successfully"

def detect_impact_point(ball_positions, frames):
    if len(ball_positions) < 3:
        return ball_positions[-1] if ball_positions else None, len(ball_positions) - 1
    # Assume batsman is near stumps (bottom center of frame)
    frame_height, frame_width = frames[0].shape[:2]
    batsman_x = frame_width / 2
    batsman_y = frame_height * 0.8  # Approximate batsman position
    min_dist = float('inf')
    impact_idx = len(ball_positions) - 1
    impact_point = ball_positions[-1]

    # Look for sudden change in trajectory or proximity to batsman
    for i in range(1, len(ball_positions) - 1):
        x, y = ball_positions[i]
        prev_x, prev_y = ball_positions[i-1]
        next_x, next_y = ball_positions[i+1]
        # Check direction change (simplified)
        dx1, dy1 = x - prev_x, y - prev_y
        dx2, dy2 = next_x - x, next_y - y
        angle_change = abs(np.arctan2(dy2, dx2) - np.arctan2(dy1, dx1))
        dist_to_batsman = np.sqrt((x - batsman_x)**2 + (y - batsman_y)**2)
        if angle_change > np.pi/4 or dist_to_batsman < frame_width * 0.1:
            impact_idx = i
            impact_point = ball_positions[i]
            break

    return impact_point, impact_idx

def lbw_decision(ball_positions, trajectory, frames):
    if not frames:
        return "Error: No frames processed", None, None, None
    if len(ball_positions) < 2:
        return "Not enough data (insufficient ball detections)", None, None, None

    frame_height, frame_width = frames[0].shape[:2]
    stumps_x = frame_width / 2
    stumps_y = frame_height * 0.9
    stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)

    pitch_point = ball_positions[0]
    impact_point, impact_idx = detect_impact_point(ball_positions, frames)

    # Check pitching point
    pitch_x, pitch_y = pitch_point
    if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_x > stumps_x Moderation: x > stumps_x + stumps_width_pixels / 2:
        return f"Not Out (Pitched outside line at x: {pitch_x:.1f}, y: {pitch_y:.1f})", trajectory, pitch_point, impact_point

    # Check impact point
    impact_x, impact_y = impact_point
    if impact_x < stumps_x - stumps_width_pixels / 2 or impact_x > stumps_x + stumps_width_pixels / 2:
        return f"Not Out (Impact outside line at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point

    # Check trajectory hitting stumps
    for x, y in trajectory:
        if abs(x - stumps_x) < stumps_width_pixels / 2 and abs(y - stumps_y) < frame_height * 0.1:
            return f"Out (Ball hits stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
    return f"Not Out (Missing stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point

def generate_slow_motion(frames, trajectory, pitch_point, impact_point, impact_idx, output_path):
    if not frames:
        return None
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, FRAME_RATE / SLOW_MOTION_FACTOR, (frames[0].shape[1], frames[0].shape[0]))

    for i, frame in enumerate(frames):
        # Draw trajectory up to current frame
        traj_points = [p for j, p in enumerate(trajectory) if j / SLOW_MOTION_FACTOR <= i]
        for x, y in traj_points:
            cv2.circle(frame, (int(x), int(y)), 5, (255, 0, 0), -1)  # Blue dots

        # Draw pitch point in early frames
        if pitch_point and i < len(frames) // 2:
            x, y = pitch_point
            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)  # Red circle
            cv2.putText(frame, "Pitch Point", (int(x) + 10, int(y) - 10), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

        # Draw impact point around impact frame
        if impact_point and abs(i - impact_idx) < 5:
            x, y = impact_point
            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)  # Yellow circle
            cv2.putText(frame, "Impact Point", (int(x) + 10, int(y) + 20), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)

        for _ in range(SLOW_MOTION_FACTOR):
            out.write(frame)
    out.release()
    return output_path

def drs_review(video):
    frames, ball_positions, debug_log = process_video(video)
    if not frames:
        return f"Error: Failed to process video\nDebug Log:\n{debug_log}", None
    trajectory, _, trajectory_log = estimate_trajectory(ball_positions, frames)
    decision, trajectory, pitch_point, impact_point = lbw_decision(ball_positions, trajectory, frames)
    _, impact_idx = detect_impact_point(ball_positions, frames)

    output_path = f"output_{uuid.uuid4()}.mp4"
    slow_motion_path = generate_slow_motion(frames, trajectory, pitch_point, impact_point, impact_idx, output_path)

    debug_output = f"{debug_log}\n{trajectory_log}"
    return f"DRS Decision: {decision}\nDebug Log:\n{debug_output}", slow_motion_path

# Gradio interface
iface = gr.Interface(
    fn=drs_review,
    inputs=gr.Video(label="Upload Video Clip"),
    outputs=[
        gr.Textbox(label="DRS Decision and Debug Log"),
        gr.Video(label="Very Slow-Motion Replay with Ball Detection (Green), Trajectory (Blue), Pitch Point (Red), Impact Point (Yellow)")
    ],
    title="AI-Powered DRS for LBW in Local Cricket",
)

if __name__ == "__main__":
    iface.launch()