# 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
)
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