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 for LBW decision and video processing
STUMPS_WIDTH = 0.2286  # meters (width of stumps)
BALL_DIAMETER = 0.073  # meters (approx. cricket ball diameter)
FRAME_RATE = 20  # Input video frame rate (reduced to 20 FPS)
SLOW_MOTION_FACTOR = 3  # Adjusted for 20 FPS (slower playback without being too slow)
CONF_THRESHOLD = 0.25  # Confidence threshold for detection
IMPACT_ZONE_Y = 0.85  # Fraction of frame height where impact is likely (near stumps)

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 = []
    detection_frames = []  # Track frames with detections
    debug_log = []

    frame_count = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        frames.append(frame.copy())
        results = model.predict(frame, conf=CONF_THRESHOLD)
        detections = 0
        for detection in results[0].boxes:
            if detection.cls == 0:  # Assuming class 0 is the ball
                detections += 1
                x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
                ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
                detection_frames.append(frame_count - 1)  # Store frame index (0-based)
                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, detection_frames, "\n".join(debug_log)

def estimate_trajectory(ball_positions, frames):
    if len(ball_positions) < 2:
        return None, None, None, "Error: Fewer than 2 ball detections for trajectory"
    frame_height = frames[0].shape[0]

    # Extract x, y coordinates
    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

    # Find impact point (closest to batsman, near stumps)
    impact_idx = None
    for i, y in enumerate(y_coords):
        if y > frame_height * IMPACT_ZONE_Y:  # Ball is near stumps/batsman
            impact_idx = i
            break
    if impact_idx is None:
        impact_idx = len(ball_positions) - 1  # Fallback to last detection

    pitch_point = ball_positions[0]
    impact_point = ball_positions[impact_idx]

    # Use positions up to impact for interpolation
    x_coords = x_coords[:impact_idx + 1]
    y_coords = y_coords[:impact_idx + 1]
    times = times[:impact_idx + 1]

    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 None, None, None, f"Error in trajectory interpolation: {str(e)}"

    # Project trajectory (detected + future for LBW decision)
    t_full = np.linspace(times[0], times[-1] + 0.5, len(times) + 10)
    x_full = fx(t_full)
    y_full = fy(t_full)
    trajectory = list(zip(x_full, y_full))

    return trajectory, pitch_point, impact_point, "Trajectory estimated successfully"

def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
    if not frames:
        return "Error: No frames processed", None, None, None
    if not trajectory or 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  # Position of the stumps at the bottom of the frame
    stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)

    pitch_x, pitch_y = pitch_point
    impact_x, impact_y = impact_point

    # Check pitching point - the ball should land between stumps
    if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_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 - the ball should hit within the stumps area
    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, detection_frames, 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]))

    trajectory_points = np.array(trajectory[:len(detection_frames)], dtype=np.int32).reshape((-1, 1, 2))

    for i, frame in enumerate(frames):
        # Draw trajectory (blue line) only for frames with detections
        if i in detection_frames and trajectory_points.size > 0:
            cv2.polylines(frame, [trajectory_points[:detection_frames.index(i) + 1]], False, (255, 0, 0), 2)

        # Draw pitch point (red circle with label) when the ball touches the ground (y < ground threshold)
        if pitch_point and impact_point and i >= detection_frames[0]:
            x, y = pitch_point
            if y > frame.shape[0] * 0.75:  # Threshold for ground contact (adjust as necessary)
                cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)
                cv2.putText(frame, "Pitch Point", (int(x) + 10, int(y) - 10), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

        # Draw impact point (yellow circle with label) when ball is near stumps (y near bottom)
        if impact_point and i >= detection_frames[min(len(detection_frames) - 1, detection_frames.index(detection_frames[-1]))]:
            x, y = impact_point
            if y > frame.shape[0] * 0.85:  # Threshold for impact (adjust as necessary)
                cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)
                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, detection_frames, debug_log = process_video(video)
    if not frames:
        return f"Error: Failed to process video\nDebug Log:\n{debug_log}", None
    trajectory, pitch_point, impact_point, trajectory_log = estimate_trajectory(ball_positions, frames)
    decision, trajectory, pitch_point, impact_point = lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point)

    output_path = f"output_{uuid.uuid4()}.mp4"
    slow_motion_path = generate_slow_motion(frames, trajectory, pitch_point, impact_point, detection_frames, 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="Slow-Motion Replay with Ball Detection (Green), Trajectory (Blue Line), Pitch Point (Red), Impact Point (Yellow)")
    ],
    title="AI-Powered DRS for LBW in Local Cricket",
    description="Upload a video clip of a cricket delivery to get an LBW decision and slow-motion replay showing ball detection (green boxes), trajectory (blue line), pitch point (red circle), and impact point (yellow circle)."
)

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