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 app = Flask(__name__) UPLOAD_FOLDER = '/tmp/uploads' os.makedirs(UPLOAD_FOLDER, exist_ok=True) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov'} def train_dummy_model(): X = np.array([ [0.5, 0.0, 0.4, 0.5, 30, 0], [0.5, 0.5, 0.5, 0.5, 35, 2], [0.6, 0.2, 0.5, 0.6, 32, 1], [0.5, 0.4, 0.5, 0.4, 34, 0], ]) 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) 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)) 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 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"] 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: impact_detected = True impact_x = actual_path[-1]["x"] impact_y = actual_path[-1]["y"] frame_count += 1 if frame_count > 50: 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 actual_path = smooth_trajectory(actual_path) projected_path = [ {"x": impact_x, "y": impact_y}, {"x": impact_x + spin * 0.1, "y": 1.0} ] 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 }) @app.route('/analyze_data', methods=['POST']) def analyze_data(): data = request.get_json() actual_path = data['actual_path'] projected_path = data['projected_path'] pitching = data['pitching'] impact = data['impact'] speed = data['speed'] spin = data['spin'] pitching_x = pitching['x'] pitching_y = pitching['y'] impact_x = impact['x'] impact_y = impact['y'] 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" 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" 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)