104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
"""
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Event camera simulation utilities for ML preprocessing.
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Core logic extracted from EventCameraSimulator (test.py).
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Designed for frame-by-frame preprocessing in training pipelines.
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Output:
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events_binary: (-1, 0, +1) hard threshold decision
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events_strength: [-1, 1] continuous change intensity (clipped & normalized)
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"""
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import numpy as np
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from collections import deque
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class EventProcessor:
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"""Lightweight event computation module. No visualization, no OpenCV dependency."""
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def __init__(self, threshold=0.1, use_log=True, auto_threshold=False):
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self.threshold = threshold
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self.use_log = use_log
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self.auto_threshold = auto_threshold
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self.prev_brightness = None
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self.change_history = deque(maxlen=100)
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self.threshold_scale = 1.5
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def reset(self):
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"""Clear temporal state (call on video/reset)."""
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self.prev_brightness = None
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self.change_history.clear()
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def _to_grayscale(self, frame):
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"""Convert frame to grayscale float32."""
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if frame.ndim == 3:
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# RGB/HWC -> gray via luminance weights
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gray = 0.299 * frame[..., 0] + 0.587 * frame[..., 1] + 0.114 * frame[..., 2]
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else:
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gray = frame
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return gray.astype(np.float32)
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def _compute_change(self, brightness):
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"""Compute log or linear brightness change."""
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if self.use_log:
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eps = 1e-3
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return np.log(brightness + eps) - np.log(self.prev_brightness + eps)
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else:
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return brightness - self.prev_brightness
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def _update_auto_threshold(self, change):
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"""Adapt threshold based on global change statistics."""
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abs_change = np.abs(change)
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mean_change = np.mean(abs_change)
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self.change_history.append(mean_change)
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if len(self.change_history) > 10:
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avg_change = np.mean(self.change_history)
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new_threshold = max(avg_change * self.threshold_scale, 0.01)
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self.threshold = self.threshold * 0.9 + new_threshold * 0.1
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if self.use_log:
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self.threshold = np.clip(self.threshold, 0.01, 0.5)
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else:
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self.threshold = np.clip(self.threshold, 1, 50)
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def __call__(self, frame):
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"""
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Process a single frame.
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Args:
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frame: np.ndarray, shape (H, W) or (H, W, C), uint8 or float.
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Returns:
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events_binary: np.ndarray (H, W), values in {-1, 0, +1}
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events_strength: np.ndarray (H, W), values in [-1, 1]
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event_count: int, number of non-zero events
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"""
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brightness = self._to_grayscale(frame)
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# First frame — initialise, no events
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if self.prev_brightness is None:
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self.prev_brightness = brightness
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h, w = brightness.shape
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return np.zeros((h, w), dtype=np.int8), np.zeros((h, w), dtype=np.float32), 0
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change = self._compute_change(brightness)
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if self.auto_threshold:
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self._update_auto_threshold(change)
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# Binary events
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events_binary = np.zeros_like(brightness, dtype=np.int8)
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events_binary[change > self.threshold] = 1
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events_binary[change < -self.threshold] = -1
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# Continuous strength: clip to [-threshold, threshold] then normalise to [-1, 1]
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events_strength = np.clip(change, -self.threshold, self.threshold) / self.threshold
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event_count = int(np.count_nonzero(events_binary))
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self.prev_brightness = brightness
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return events_binary, events_strength, event_count
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