303 lines
10 KiB
Python
303 lines
10 KiB
Python
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import time
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import json
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import numpy as np
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class RadiometricController:
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def __init__(
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self,
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client,
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enabled=True,
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config_json_path=None,
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interval_s=0.5,
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strip_y0_pct=0.95,
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strip_y1_pct=1.0,
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patch_x0_pct=0.35,
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patch_x1_pct=0.75,
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target_mean=0.70,
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deadband=0.03,
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alpha=0.20,
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exp_min_us=100,
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exp_max_us=80000,
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gain_min=1.0,
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gain_max=8.0,
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exp_step_gain=0.65,
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prefer_exposure=True,
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verbose=False,
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):
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self.client = client
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cfg = self._load_config_json(config_json_path)
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interval_s = cfg.get("interval_s", interval_s)
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strip_y0_pct = cfg.get("strip_y0_pct", strip_y0_pct)
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strip_y1_pct = cfg.get("strip_y1_pct", strip_y1_pct)
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patch_x0_pct = cfg.get("patch_x0_pct", patch_x0_pct)
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patch_x1_pct = cfg.get("patch_x1_pct", patch_x1_pct)
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target_mean = cfg.get("target_mean", target_mean)
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deadband = cfg.get("deadband", deadband)
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alpha = cfg.get("alpha", alpha)
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exp_min_us = cfg.get("exp_min_us", exp_min_us)
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exp_max_us = cfg.get("exp_max_us", exp_max_us)
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gain_min = cfg.get("gain_min", gain_min)
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gain_max = cfg.get("gain_max", gain_max)
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exp_step_gain = cfg.get("exp_step_gain", exp_step_gain)
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prefer_exposure = cfg.get("prefer_exposure", prefer_exposure)
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verbose = cfg.get("verbose", verbose)
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exp_apply_threshold_us = cfg.get("exp_apply_threshold_us", 50)
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gain_apply_threshold = cfg.get("gain_apply_threshold", 0.02)
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self.enabled = bool(enabled)
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self.interval_s = float(interval_s)
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self.strip_y0_pct = float(strip_y0_pct)
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self.strip_y1_pct = float(strip_y1_pct)
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self.patch_x0_pct = float(patch_x0_pct)
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self.patch_x1_pct = float(patch_x1_pct)
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self.target_mean = float(target_mean)
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self.deadband = float(deadband)
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self.alpha = float(alpha)
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self.exp_min_us = int(exp_min_us)
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self.exp_max_us = int(exp_max_us)
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self.gain_min = float(gain_min)
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self.gain_max = float(gain_max)
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self.exp_step_gain = float(exp_step_gain)
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self.prefer_exposure = bool(prefer_exposure)
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self.verbose = bool(verbose)
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self.exp_apply_threshold_us = int(exp_apply_threshold_us)
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self.gain_apply_threshold = float(gain_apply_threshold)
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self.last_update_ts = 0.0
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self.last_result = {}
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self.state = {
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"cam0": {"exp": 15000, "gain": 1.0},
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"cam1": {"exp": 15000, "gain": 1.0},
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"cam2": {"exp": 15000, "gain": 1.0},
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}
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self._ae_disabled = set()
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self._last_applied = {
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"cam0": {"exp": None, "gain": None},
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"cam1": {"exp": None, "gain": None},
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"cam2": {"exp": None, "gain": None},
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}
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def _load_config_json(self, path):
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if not path:
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return {}
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try:
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with open(path, "r", encoding="utf-8") as f:
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data = json.load(f)
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except Exception:
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return {}
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cfg = data.get("radiometric_config", {})
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return cfg if isinstance(cfg, dict) else {}
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def sync_from_camera_controls(self, camera_controls: dict | None):
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if not isinstance(camera_controls, dict):
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return
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for cam_id, ctrl in camera_controls.items():
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if cam_id not in self.state:
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continue
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exp = ctrl.get("exposure_time_us")
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gain = ctrl.get("analogue_gain")
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if exp is not None:
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self.state[cam_id]["exp"] = int(exp)
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if gain is not None:
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self.state[cam_id]["gain"] = float(gain)
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def update(self, decoded: dict, meta: dict | None = None):
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if not self.enabled:
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return None
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now = time.perf_counter()
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if now - self.last_update_ts < self.interval_s:
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return None
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self.last_update_ts = now
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results = {}
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for cam_id in ("cam2", "cam0", "cam1"):
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if cam_id not in decoded:
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continue
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img = decoded[cam_id].get("image")
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if img is None:
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continue
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metrics = self.measure_reference_patch(img)
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decision = self.compute_control(cam_id, metrics)
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apply_resp = self.apply_control(cam_id, decision)
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results[cam_id] = {
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"metrics": metrics,
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"decision": decision,
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"apply": apply_resp,
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}
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self.last_result = results
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return results
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def measure_reference_patch(self, img01: np.ndarray) -> dict:
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if img01.ndim == 3:
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# RGB: usa luminância simples
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img_gray = (
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0.299 * img01[:, :, 0] +
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0.587 * img01[:, :, 1] +
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0.114 * img01[:, :, 2]
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).astype(np.float32)
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else:
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img_gray = img01.astype(np.float32)
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h, w = img_gray.shape[:2]
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y0 = int(h * self.strip_y0_pct)
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y1 = int(h * self.strip_y1_pct)
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x0 = int(w * self.patch_x0_pct)
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x1 = int(w * self.patch_x1_pct)
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y0 = max(0, min(h - 1, y0))
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y1 = max(y0 + 1, min(h, y1))
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x0 = max(0, min(w - 1, x0))
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x1 = max(x0 + 1, min(w, x1))
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patch = img_gray[y0:y1, x0:x1]
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arr = patch.reshape(-1)
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return {
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"valid": arr.size > 0,
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"mean": float(arr.mean()) if arr.size else 0.0,
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"p05": float(np.percentile(arr, 5)) if arr.size else 0.0,
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"p95": float(np.percentile(arr, 95)) if arr.size else 0.0,
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"sat_pct": float((arr >= 0.98).mean() * 100.0) if arr.size else 0.0,
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"dark_pct": float((arr <= 0.02).mean() * 100.0) if arr.size else 0.0,
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"roi": [x0, y0, x1, y1],
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}
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def compute_control(self, cam_id: str, metrics: dict) -> dict:
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st = self.state.setdefault(cam_id, {"exp": 15000, "gain": 1.0})
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old_exp = int(st["exp"])
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old_gain = float(st["gain"])
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if not metrics.get("valid"):
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return {
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"action": "hold",
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"reason": "patch inválido",
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"old_exp": old_exp,
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"new_exp": old_exp,
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"old_gain": old_gain,
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"new_gain": old_gain,
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}
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mean = float(metrics["mean"])
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p95 = float(metrics["p95"])
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sat_pct = float(metrics["sat_pct"])
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error = self.target_mean - mean
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new_exp = old_exp
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new_gain = old_gain
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action = "hold"
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reason = "dentro da faixa morta"
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# Proteção contra saturação
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if sat_pct > 1.0 or p95 > 0.96:
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desired_exp = max(self.exp_min_us, int(old_exp * 0.85))
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new_exp = self._smooth_int(old_exp, desired_exp)
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action = "decrease_exposure"
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reason = f"saturação detectada: sat={sat_pct:.2f}% p95={p95:.3f}"
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elif abs(error) > self.deadband:
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factor = 1.0 + self.exp_step_gain * error
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factor = max(0.70, min(1.35, factor))
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if self.prefer_exposure:
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desired_exp = int(old_exp * factor)
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desired_exp = self._clamp(desired_exp, self.exp_min_us, self.exp_max_us)
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new_exp = self._smooth_int(old_exp, desired_exp)
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# Se exposição bateu limite e ainda precisa clarear/escurecer, mexe no ganho
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if desired_exp in (self.exp_min_us, self.exp_max_us):
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desired_gain = old_gain * factor
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desired_gain = self._clamp(desired_gain, self.gain_min, self.gain_max)
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new_gain = self._smooth_float(old_gain, desired_gain)
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action = "increase_exposure" if error > 0 else "decrease_exposure"
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reason = f"corrigindo erro radiométrico: error={error:.3f}"
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else:
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desired_gain = old_gain * factor
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desired_gain = self._clamp(desired_gain, self.gain_min, self.gain_max)
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new_gain = self._smooth_float(old_gain, desired_gain)
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action = "increase_gain" if error > 0 else "decrease_gain"
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reason = f"corrigindo ganho: error={error:.3f}"
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new_exp = int(self._clamp(new_exp, self.exp_min_us, self.exp_max_us))
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new_gain = float(self._clamp(new_gain, self.gain_min, self.gain_max))
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return {
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"action": action,
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"reason": reason,
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"mean": mean,
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"target_mean": self.target_mean,
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"error": error,
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"old_exp": old_exp,
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"new_exp": new_exp,
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"old_gain": old_gain,
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"new_gain": new_gain,
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}
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def apply_control(self, cam_id: str, decision: dict):
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new_exp = int(decision["new_exp"])
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new_gain = float(decision["new_gain"])
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self.state[cam_id]["exp"] = new_exp
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self.state[cam_id]["gain"] = new_gain
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responses = {}
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last = self._last_applied.setdefault(cam_id, {"exp": None, "gain": None})
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try:
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if cam_id not in self._ae_disabled:
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responses["ae"] = self.client.svc.set_ae_enable(cam_id, False)
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if cam_id == "cam2":
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responses["awb"] = self.client.svc.set_awb_enable(cam_id, False)
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self._ae_disabled.add(cam_id)
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if last["exp"] is None or abs(new_exp - last["exp"]) >= self.exp_apply_threshold_us:
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responses["exposure"] = self.client.svc.set_exposure_time(cam_id, new_exp)
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last["exp"] = new_exp
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if last["gain"] is None or abs(new_gain - last["gain"]) >= self.gain_apply_threshold:
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responses["gain"] = self.client.svc.set_analogue_gain(cam_id, new_gain)
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last["gain"] = new_gain
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except Exception as e:
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responses["error"] = str(e)
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if self.verbose:
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print(f"[RAD] {cam_id}: {json.dumps(decision, ensure_ascii=False)} | apply={responses}")
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return responses
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def _smooth_int(self, old, desired):
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return int(round((1.0 - self.alpha) * old + self.alpha * desired))
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def _smooth_float(self, old, desired):
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return float((1.0 - self.alpha) * old + self.alpha * desired)
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@staticmethod
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def _clamp(v, lo, hi):
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return max(lo, min(hi, v))
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