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import numpy as np
def predict_trajectory(detection_data, pitch_height=720, stump_zone=(280, 360)):
"""
Uses polynomial regression to predict post-impact ball trajectory.
Args:
detection_data: output from `detect_lbw_event`
pitch_height: total frame height (in pixels) to simulate stumps
stump_zone: x-coordinate range for stumps (min_x, max_x)
Returns:
dict with:
- trajectory_points: [(x, y), ...] actual + predicted
- decision: "OUT" or "NOT OUT"
"""
ball_positions = detection_data["ball_positions"]
impact_frame = detection_data["impact_frame"]
if not ball_positions or impact_frame == -1:
return {
"trajectory_points": [],
"decision": "NOT ENOUGH DATA"
}
# Extract coordinates pre-impact
xs = []
ys = []
for idx, x, y in ball_positions:
if idx <= impact_frame:
xs.append(x)
ys.append(y)
if len(xs) < 5:
return {
"trajectory_points": [],
"decision": "NOT ENOUGH POINTS"
}
# Fit polynomial regression (degree 2 for parabolic path)
coeffs = np.polyfit(xs, ys, deg=2)
poly = np.poly1d(coeffs)
# Predict future trajectory
last_x = xs[-1]
future_xs = list(range(last_x, last_x + 60, 5)) # simulate 60px ahead
future_ys = [int(poly(x)) for x in future_xs]
trajectory_points = list(zip(xs, ys)) + list(zip(future_xs, future_ys))
# OUT logic: predicted y crosses stump plane and x within stump zone
for x, y in zip(future_xs, future_ys):
if y >= pitch_height - 150: # near stump base
if stump_zone[0] <= x <= stump_zone[1]:
return {
"trajectory_points": trajectory_points,
"decision": "OUT"
}
return {
"trajectory_points": trajectory_points,
"decision": "NOT OUT"
}
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