I understand your concern. You're expecting a real-time DRS (Decision Review System) output similar to what you see in international T20 cricket matches, with precise ball tracking, pitch analysis, and wicket-hitting prediction, as used by third umpires. The current output—while functional—falls short of that professional standard in terms of accuracy, visualization, and real-time capability. Let’s break down the gap between your expectations and the current output, then outline how to achieve a more professional DRS-like system. Understanding Your Expectation In international T20 matches, the DRS (e.g., Hawk-Eye) provides: Real-Time Ball Tracking: High-speed cameras (e.g., 6–8 cameras at 500+ fps) track the ball from the bowler’s release to impact. A smooth 3D trajectory is shown, often as a curved path, accounting for swing, spin, and bounce. Pitch Analysis: The system identifies the exact pitching point (where the ball bounces) and checks if it’s in line with the stumps (inside, outside leg, or outside off). A 2D or 3D pitch map shows the pitching point relative to the stumps. Wicket-Hitting Prediction: The system predicts the ball’s path after impact (e.g., hitting the pad) to determine if it would hit the stumps. A 3D visualization shows the projected path, often with a “stump cam” view indicating whether the ball would hit, miss, or clip the stumps. Detailed Decision: The DRS provides a step-by-step breakdown: pitching (inline/outside), impact (inline/outside), and wicket-hitting (yes/no). A final decision (“Out” or “Not Out”) is shown with high accuracy (99.9% in professional systems). Professional Visualization: Smooth animations of the ball’s path (curved, not linear). 3D renderings of the pitch, stumps, and trajectory. Clear annotations (e.g., “Ball Pitching: Inline,” “Impact: Inline,” “Wickets: Hitting”). The current app output, while a starting point, has these shortcomings: Simplified Tracking: It uses basic OpenCV to detect the ball in a pre-uploaded video, not real-time camera feeds. Linear Trajectories: The red (actual) and blue (projected) paths are linear, not curved, and don’t account for swing or spin. Basic Pitch Analysis: Pitching and impact points are estimated roughly, not precisely detected. 2D Visualization: The visualization is a flat 2D canvas, lacking the 3D depth and smoothness of professional DRS. Dummy ML Model: The LBW decision uses a simplistic logistic regression model, not a robust ML system trained on real cricket data. Not Real-Time: The app processes uploaded videos, not live camera feeds. How to Achieve a Real-Time DRS-Like System To meet your expectations, we need to upgrade the app to handle real-time ball tracking, accurate pitch analysis, wicket-hitting prediction, and professional-grade visualization. Here’s a step-by-step plan, followed by updated code to move closer to your goal. Key Requirements for a DRS-Like System Real-Time Camera Integration: Use multiple high-speed cameras (e.g., 120–240 fps) to capture the ball’s movement live. Process camera feeds in real-time to track the ball. Note: Hugging Face Spaces cannot access cameras (no webcam support), so this must be done locally or on a server with camera access. Advanced Ball Tracking: Use computer vision (OpenCV) to detect the ball in each frame. Apply trajectory smoothing (e.g., spline interpolation) to create a curved path. Detect swing (lateral deviation) and spin (rotation). Pitch and Impact Detection: Identify the pitching point by detecting the ball’s bounce (sudden change in y-coordinate). Detect the impact point by identifying when the ball stops (e.g., hits the pad, often with a sudden slowdown). Use pitch markings (e.g., creases, stumps) to determine if pitching/impact is inline. Wicket-Hitting Prediction: Model the ball’s physics (swing, spin, bounce) to predict the post-impact path. Use an ML model to refine predictions based on historical data. Professional Visualization: Use a 3D rendering library (e.g., Three.js) for realistic trajectory visualization. Show a detailed breakdown (pitching, impact, wicket-hitting) with annotations. Robust ML Model: Train a deep learning model (e.g., CNN) on real cricket video data to predict LBW outcomes. Host the model on Hugging Face Model Hub for inference. Challenges Hardware: Professional DRS uses 6–8 high-speed cameras ($100,000+ setup). For a playground, 2–4 consumer cameras (e.g., GoPro, $500–$2,000 each) can work but reduce accuracy. Real-Time Processing: Processing multiple camera feeds in real-time requires significant compute power (e.g., GPU server). Hugging Face Spaces (free tier: 2 vCPUs, 8GB RAM) can’t handle this; you’ll need a local setup or cloud server (e.g., AWS). Data: Training an ML model for LBW requires labeled cricket video data (scarce publicly). You may need to collect and annotate your own dataset. Visualization: 3D rendering (e.g., with Three.js) is more complex than the current 2D Canvas and requires additional setup. Plan to Achieve DRS-Like Output Since real-time camera integration and 3D visualization are complex, let’s break this into phases. For now, we’ll enhance the app to: Improve ball tracking with smoother trajectories. Add detailed LBW analysis (pitching, impact, wicket-hitting breakdown). Upgrade visualization to show a more professional 2D output (we’ll add 3D later). Provide guidance for real-time setup (local or cloud). Updated Code app.py This version improves ball tracking, adds detailed LBW analysis, and prepares for real-time integration. python Collapse Wrap Run Copy from flask import Flask, render_template, request, jsonify import numpy as np from sklearn.linear_model import LogisticRegression import cv2 import os from werkzeug.utils import secure_filename from scipy.interpolate import splprep, splev # For smooth trajectory app = Flask(__name__) # Configure upload folder UPLOAD_FOLDER = '/tmp/uploads' os.makedirs(UPLOAD_FOLDER, exist_ok=True) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov'} # Dummy ML model for LBW decision (to be replaced with a real model) def train_dummy_model(): X = np.array([ [0.5, 0.0, 0.4, 0.5, 30, 0], # Not Out [0.5, 0.5, 0.5, 0.5, 35, 2], # Out [0.6, 0.2, 0.5, 0.6, 32, 1], # Not Out [0.5, 0.4, 0.5, 0.4, 34, 0], # Out ]) y = np.array([0, 1, 0, 1]) model = LogisticRegression() model.fit(X, y) return model model = train_dummy_model() def allowed_file(filename): return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS def smooth_trajectory(points): if len(points) < 3: return points x = [p["x"] for p in points] y = [p["y"] for p in points] tck, u = splprep([x, y], s=0) u_new = np.linspace(0, 1, 50) # Smooth with 50 points x_new, y_new = splev(u_new, tck) return [{"x": x, "y": y} for x, y in zip(x_new, y_new)] def process_video(video_path): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return None, None, "Failed to open video" actual_path = [] frame_count = 0 spin = 0 last_point = None pitching_detected = False impact_detected = False y_positions = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255)) # Adjust for your ball color contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if contours: c = max(contours, key=cv2.contourArea) x, y, w, h = cv2.boundingRect(c) center_x = x + w / 2 center_y = y + h / 2 norm_x = center_x / 1280 norm_y = center_y / 720 current_point = (norm_x, norm_y) if last_point != current_point: actual_path.append({"x": norm_x, "y": norm_y}) y_positions.append(norm_y) last_point = current_point # Detect pitching (first significant downward movement) if len(y_positions) > 2 and not pitching_detected: if y_positions[-1] < y_positions[-2] and y_positions[-2] < y_positions[-3]: pitching_detected = True pitching_x = actual_path[-2]["x"] pitching_y = actual_path[-2]["y"] # Detect impact (sudden slowdown or stop) if len(actual_path) > 2 and not impact_detected: speed_current = abs(y_positions[-1] - y_positions[-2]) speed_prev = abs(y_positions[-2] - y_positions[-3]) if speed_current < speed_prev * 0.3: # Significant slowdown impact_detected = True impact_x = actual_path[-1]["x"] impact_y = actual_path[-1]["y"] frame_count += 1 if frame_count > 50: # Process more frames for accuracy break cap.release() if not actual_path: return None, None, "No ball detected in video" if not pitching_detected: pitching_x = actual_path[len(actual_path)//2]["x"] pitching_y = actual_path[len(actual_path)//2]["y"] if not impact_detected: impact_x = actual_path[-1]["x"] impact_y = actual_path[-1]["y"] fps = cap.get(cv2.CAP_PROP_FPS) or 30 speed = (len(actual_path) / (frame_count / fps)) * 0.5 # Smooth the actual path actual_path = smooth_trajectory(actual_path) # Projected path with basic physics (linear for now, add swing/spin later) projected_path = [ {"x": impact_x, "y": impact_y}, {"x": impact_x + spin * 0.1, "y": 1.0} ] # Determine pitching and impact status pitching_status = "Inline" if 0.4 <= pitching_x <= 0.6 else "Outside Leg" if pitching_x < 0.4 else "Outside Off" impact_status = "Inline" if 0.4 <= impact_x <= 0.6 else "Outside" wicket_status = "Hitting" if 0.4 <= projected_path[-1]["x"] <= 0.6 else "Missing" return actual_path, projected_path, pitching_x, pitching_y, impact_x, impact_y, speed, spin, pitching_status, impact_status, wicket_status @app.route('/') def index(): return render_template('index.html') @app.route('/analyze', methods=['POST']) def analyze(): if 'video' not in request.files: return jsonify({'error': 'No video uploaded'}), 400 file = request.files['video'] if file.filename == '' or not allowed_file(file.filename): return jsonify({'error': 'Invalid file'}), 400 filename = secure_filename(file.filename) video_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) file.save(video_path) result = process_video(video_path) if result[0] is None: os.remove(video_path) return jsonify({'error': result[2]}), 400 actual_path, projected_path, pitching_x, pitching_y, impact_x, impact_y, speed, spin, pitching_status, impact_status, wicket_status = result features = np.array([[pitching_x, pitching_y, impact_x, impact_y, speed, spin]]) prediction = model.predict(features)[0] confidence = min(model.predict_proba(features)[0][prediction], 0.99) decision = "Out" if prediction == 1 else "Not Out" os.remove(video_path) return jsonify({ 'actual_path': actual_path, 'projected_path': projected_path, 'decision': decision, 'confidence': round(confidence, 2), 'pitching': {'x': pitching_x, 'y': pitching_y, 'status': pitching_status}, 'impact': {'x': impact_x, 'y': impact_y, 'status': impact_status}, 'wicket': wicket_status }) if __name__ == '__main__': app.run(host='0.0.0.0', port=7860, debug=True)