app.py

import cv2
import numpy as np
import torch
from ultralytics import YOLO
import gradio as gr
from scipy.interpolate import interp1d
from scipy.ndimage import uniform_filter1d
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)
FRAME_RATE = 20  # Input video frame rate
SLOW_MOTION_FACTOR = 2  # Reduced for faster output
CONF_THRESHOLD = 0.3  # Increased for better detection
PITCH_ZONE_Y = 0.8  # Adjusted for pitch near stumps
IMPACT_ZONE_Y = 0.7  # Adjusted for impact near batsman leg
IMPACT_DELTA_Y = 20  # Reduced for finer impact detection
STUMPS_HEIGHT = 0.711  # meters (height of 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 = []
    debug_log = []

    frame_count = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        # Process every frame for better tracking
        frames.append(frame.copy())
        # Preprocess frame for better detection
        frame = cv2.convertScaleAbs(frame, alpha=1.2, beta=10)  # Enhance contrast
        results = model.predict(frame, conf=CONF_THRESHOLD)
        detections = [det for det in results[0].boxes if det.cls == 0]
        if len(detections) == 1:
            x1, y1, x2, y2 = detections[0].xyxy[0].cpu().numpy()
            ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
            detection_frames.append(len(frames) - 1)
            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}: {len(detections)} ball detections")
        frame_count += 1
    cap.release()

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

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

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

    # Smooth coordinates with moving average
    window_size = 3
    x_coords = uniform_filter1d([pos[0] for pos in ball_positions], size=window_size, mode='nearest')
    y_coords = uniform_filter1d([pos[1] for pos in ball_positions], size=window_size, mode='nearest')
    times = np.array([i / FRAME_RATE for i in range(len(ball_positions))])

    pitch_idx = 0
    for i, y in enumerate(y_coords):
        if y > frame_height * PITCH_ZONE_Y:
            pitch_idx = i
            break
    pitch_point = ball_positions[pitch_idx]
    pitch_frame = detection_frames[pitch_idx]

    impact_idx = None
    for i in range(1, len(y_coords)):
        if (y_coords[i] > frame_height * IMPACT_ZONE_Y and
            abs(y_coords[i] - y_coords[i-1]) > IMPACT_DELTA_Y):
            impact_idx = i
            break
    if impact_idx is None:
        impact_idx = len(y_coords) - 1
    impact_point = ball_positions[impact_idx]
    impact_frame = detection_frames[impact_idx]

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

    vis_trajectory = list(zip(x_coords, y_coords))
    t_full = np.linspace(times[0], times[-1] + 0.5, len(times) + 5)
    x_full = fx(t_full)
    y_full = fy(t_full)
    full_trajectory = list(zip(x_full, y_full))

    debug_log = (f"Trajectory estimated successfully\n"
                 f"Pitch point at frame {pitch_frame + 1}: ({pitch_point[0]:.1f}, {pitch_point[1]:.1f})\n"
                 f"Impact point at frame {impact_frame + 1}: ({impact_point[0]:.1f}, {impact_point[1]:.1f})")
    return full_trajectory, vis_trajectory, pitch_point, pitch_frame, impact_point, impact_frame, debug_log

def lbw_decision(ball_positions, full_trajectory, frames, pitch_point, impact_point):
    if not frames:
        return "Error: No frames processed", None, None, None
    if not full_trajectory or len(ball_positions) < 2:
        return "Not enough data (insufficient valid single-ball detections)", None, None, None

    frame_height, frame_width = frames[0].shape[:2]
    stumps_x = frame_width / 2
    stumps_y = frame_height * 0.8  # Adjusted to align with pitch
    stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
    batsman_area_y = frame_height * 0.7

    pitch_x, pitch_y = pitch_point
    impact_x, impact_y = impact_point

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

    if impact_y < batsman_area_y or impact_x < stumps_x - in_line_threshold or impact_x > stumps_x + in_line_threshold:
        return f"Not Out (Impact outside line or above batsman at x: {impact_x:.1f}, y: {impact_y:.1f})", full_trajectory, pitch_point, impact_point

    hit_stumps = False
    for x, y in full_trajectory:
        if (abs(x - stumps_x) < in_line_threshold and
            abs(y - stumps_y) < frame_height * 0.1):
            hit_stumps = True
            break

    if hit_stumps:
        if abs(x - stumps_x) < in_line_threshold * 0.1:
            return f"Umpire's Call - Not Out (Ball clips stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", full_trajectory, pitch_point, impact_point
        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})", full_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})", full_trajectory, pitch_point, impact_point

def generate_slow_motion(frames, vis_trajectory, pitch_point, pitch_frame, impact_point, impact_frame, detection_frames, output_path, decision):
    if not frames:
        return None
    frame_height, frame_width = frames[0].shape[:2]
    stumps_x = frame_width / 2
    stumps_y = frame_height * 0.8  # Align with pitch
    stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
    stumps_height_pixels = frame_height * (STUMPS_HEIGHT / 3.0)

    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, FRAME_RATE / SLOW_MOTION_FACTOR, (frame_width, frame_height))

    trajectory_points = np.array(vis_trajectory, dtype=np.int32).reshape((-1, 1, 2))

    for i, frame in enumerate(frames):
        # Draw stumps outline
        cv2.line(frame, (int(stumps_x - stumps_width_pixels / 2), int(stumps_y)),
                 (int(stumps_x + stumps_width_pixels / 2), int(stumps_y)), (255, 255, 255), 2)
        cv2.line(frame, (int(stumps_x - stumps_width_pixels / 2), int(stumps_y - stumps_height_pixels)),
                 (int(stumps_x - stumps_width_pixels / 2), int(stumps_y)), (255, 255, 255), 2)
        cv2.line(frame, (int(stumps_x + stumps_width_pixels / 2), int(stumps_y - stumps_height_pixels)),
                 (int(stumps_x + stumps_width_pixels / 2), int(stumps_y)), (255, 255, 255), 2)

        # Draw crease line at stumps
        cv2.line(frame, (int(stumps_x - stumps_width_pixels / 2), int(stumps_y)),
                 (int(stumps_x + stumps_width_pixels / 2), int(stumps_y)), (255, 255, 0), 2)

        if i in detection_frames and trajectory_points.size > 0:
            idx = detection_frames.index(i) + 1
            if idx <= len(trajectory_points):
                cv2.polylines(frame, [trajectory_points[:idx]], False, (0, 0, 255), 2)  # Blue trajectory

        if pitch_point and i == pitch_frame:
            x, y = pitch_point
            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 0), -1)  # Green for pitching
            cv2.putText(frame, "Pitching", (int(x) + 10, int(y) - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

        if impact_point and i == impact_frame:
            x, y = impact_point
            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)  # Red for impact
            cv2.putText(frame, "Impact", (int(x) + 10, int(y) + 20),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)

        if impact_point and i == impact_frame and "Out" in decision:
            cv2.putText(frame, "Wickets", (int(stumps_x) - 50, int(stumps_y) - 20),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 165, 255), 1)  # Orange for wickets

        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
    full_trajectory, vis_trajectory, pitch_point, pitch_frame, impact_point, impact_frame, trajectory_log = estimate_trajectory(ball_positions, detection_frames, frames)
    decision, full_trajectory, pitch_point, impact_point = lbw_decision(ball_positions, full_trajectory, frames, pitch_point, impact_point)

    output_path = f"output_{uuid.uuid4()}.mp4"
    slow_motion_path = generate_slow_motion(frames, vis_trajectory, pitch_point, pitch_frame, impact_point, impact_frame, detection_frames, output_path, decision)

    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="Optimized Slow-Motion Replay with Pitching (Green), Impact (Red), Wickets (Orange), Stumps (White), Crease (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 optimized slow-motion replay showing pitching (green circle), impact (red circle), wickets (orange text), stumps (white outline), and crease line (yellow line)."
)

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