|
|
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) |
|
|
|
