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app.py

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-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.
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-Understanding Your Expectation
-In international T20 matches, the DRS (e.g., Hawk-Eye) provides:
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-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:
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-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.
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-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:
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-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.
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-python
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 from flask import Flask, render_template, request, jsonify
 import numpy as np
 from sklearn.linear_model import LogisticRegression