# Neural Network — Padel Ball Detection import torch import torch.nn as nn from ultralytics import YOLO model = YOLO("yolov8x-padel.pt") model.conf = 0.85 model.iou = 0.45 # Convolutional layers for ball tracking class BallTrackNet(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 64, 3) self.conv2 = nn.Conv2d(64, 128, 3) self.conv3 = nn.Conv2d(128, 256, 3) self.pool = nn.MaxPool2d(2) self.fc = nn.Linear(256, 2) self.relu = nn.ReLU() def forward(self, x): x = self.relu(self.conv1(x)) x = self.pool(x) x = self.relu(self.conv2(x)) x = self.pool(x) x = self.relu(self.conv3(x)) return self.fc(x) # Training pipeline optimizer = torch.optim.Adam( model.parameters(), lr=3e-4, weight_decay=1e-5 ) loss_fn = nn.MSELoss() for epoch in range(100): for batch in dataloader: frames, labels = batch pred = model(frames) loss = loss_fn(pred, labels) loss.backward() optimizer.step() # Inference — Real-time detection with torch.no_grad(): detections = model.predict( source="rtsp://cam:8554/court1", stream=True, device="cuda:0" ) # Trajectory prediction trajectory = torch.tensor([ [0.42, 0.81], [0.45, 0.73], [0.51, 0.62], [0.58, 0.55], ]) velocity = trajectory[1:] - trajectory[:-1] bounce_frame = velocity[:, 1].sign().diff().argmax() # Transformer model — shot classification class ShotClassifier(nn.Module): def __init__(self): super().__init__() self.encoder = nn.TransformerEncoder( nn.TransformerEncoderLayer( d_model=512, nhead=8, dim_feedforward=2048, dropout=0.1 ), num_layers=6 ) self.classifier = nn.Sequential( nn.Linear(512, 256), nn.ReLU(), nn.Dropout(0.2), nn.Linear(256, 7) ) # shots: smash, volley, lob, # bandeja, vibora, chiquita, drive LABELS = ["smash", "volley", "lob", "bandeja", "vibora", "chiquita", "drive"] # Heatmap generation positions = tracker.get_all_positions() heatmap = np.histogram2d( positions[:, 0], positions[:, 1], bins=50 )[0] heatmap = cv2.GaussianBlur( heatmap, (15, 15), 0 ) # Court line detection — OpenCV edges = cv2.Canny(gray, 50, 150) lines = cv2.HoughLinesP( edges, 1, np.pi/180, threshold=100, minLineLength=80 ) # Repeat for scroll model = YOLO("yolov8x-padel.pt") optimizer = torch.optim.Adam( model.parameters(), lr=3e-4 )