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Create trajectory_predictor.py
Browse files- trajectory_predictor.py +63 -0
trajectory_predictor.py
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import numpy as np
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def predict_trajectory(detection_data, pitch_height=720, stump_zone=(280, 360)):
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"""
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Uses polynomial regression to predict post-impact ball trajectory.
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Args:
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detection_data: output from `detect_lbw_event`
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pitch_height: total frame height (in pixels) to simulate stumps
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stump_zone: x-coordinate range for stumps (min_x, max_x)
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Returns:
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dict with:
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- trajectory_points: [(x, y), ...] actual + predicted
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- decision: "OUT" or "NOT OUT"
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"""
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ball_positions = detection_data["ball_positions"]
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impact_frame = detection_data["impact_frame"]
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if not ball_positions or impact_frame == -1:
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return {
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"trajectory_points": [],
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"decision": "NOT ENOUGH DATA"
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}
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# Extract coordinates pre-impact
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xs = []
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ys = []
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for idx, x, y in ball_positions:
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if idx <= impact_frame:
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xs.append(x)
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ys.append(y)
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if len(xs) < 5:
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return {
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"trajectory_points": [],
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"decision": "NOT ENOUGH POINTS"
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}
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# Fit polynomial regression (degree 2 for parabolic path)
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coeffs = np.polyfit(xs, ys, deg=2)
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poly = np.poly1d(coeffs)
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# Predict future trajectory
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last_x = xs[-1]
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future_xs = list(range(last_x, last_x + 60, 5)) # simulate 60px ahead
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future_ys = [int(poly(x)) for x in future_xs]
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trajectory_points = list(zip(xs, ys)) + list(zip(future_xs, future_ys))
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# OUT logic: predicted y crosses stump plane and x within stump zone
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for x, y in zip(future_xs, future_ys):
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if y >= pitch_height - 150: # near stump base
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if stump_zone[0] <= x <= stump_zone[1]:
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return {
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"trajectory_points": trajectory_points,
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"decision": "OUT"
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}
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return {
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"trajectory_points": trajectory_points,
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"decision": "NOT OUT"
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}
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