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#!/usr/bin/env python3
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"""
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target_math.py — 纯 numpy 的"像素 -> 三维目标"定位核心（不依赖 ROS，方便离线测试）。
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输入：一帧点云（雷达系）+ 查询像素
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输出：目标在相机系/雷达系下的坐标、距离、置信度、是否有多个深度层等
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"""
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import numpy as np
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import calib
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def _fit_plane_ray(P, ray, max_residual=0.05):
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"""PCA 拟合平面，再让射线与平面求交。返回 (点, 残差)。"""
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if P.shape[0] < 6:
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return None, None
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c = P.mean(axis=0)
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_, _, vt = np.linalg.svd(P - c)
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n = vt[2]
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denom = float(n @ ray)
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if abs(denom) < 1e-6:
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return None, None
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lam = float(n @ c) / denom
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if lam <= 0:
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return None, None
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p = lam * ray
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residual = float(np.sqrt(np.mean(((P - c) @ n) ** 2)))
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if residual > max_residual:
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return None, residual
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return p, residual
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def locate(xyz, query_u, query_v, conf=None, radius=10.0, depth_band=0.6,
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fit_plane=True, max_residual=0.05, min_conf=None, min_z=0.05):
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"""在点云里找查询像素方向上最近的可见点。
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参数
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xyz : (N,3) 雷达系点云
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query_u/v : 查询像素
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conf : (N,) 置信度，可选
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radius : 候选像素半径
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返回 dict（未找到时 found=False）
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"""
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xyz = np.asarray(xyz, dtype=np.float64)
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good = np.isfinite(xyz).all(axis=1) & (np.abs(xyz).sum(axis=1) > 1e-6)
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if conf is not None and min_conf is not None:
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good &= np.asarray(conf) >= min_conf
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idx = np.where(good)[0]
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pts = xyz[good]
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p_cam = calib.lidar_to_cam(pts)
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u, v, ok = calib.project_cam_to_pixel(p_cam)
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dpx = np.hypot(u - query_u, v - query_v)
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cand = ok & (dpx <= radius) & (p_cam[:, 2] > min_z)
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rng = np.linalg.norm(p_cam, axis=1)
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res = {
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'found': False, 'n_valid': int(pts.shape[0]), 'n_cand': int(cand.sum()),
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'radius': float(radius), 'query_u': float(query_u), 'query_v': float(query_v),
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}
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if not cand.any():
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return res
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cand_idx = np.where(cand)[0]
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order = cand_idx[np.argsort(rng[cand_idx])]
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sel = int(order[0])
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ray = calib.pixel_to_ray(query_u, query_v)
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p_near = p_cam[sel]
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# 深度分层：候选距离跨度大 => 可能擦过边缘
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r_sorted = np.sort(rng[cand_idx])
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multi = bool(len(r_sorted) > 3 and (r_sorted[-1] - r_sorted[0]) > 2.0)
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p_final = p_near
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plane_used = False
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residual = None
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if fit_plane:
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band = p_cam[cand_idx][rng[cand_idx] <= rng[sel] + depth_band]
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p_fit, residual = _fit_plane_ray(band, ray, max_residual)
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if p_fit is not None:
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p_final = p_fit
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plane_used = True
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res.update({
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'found': True,
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'p_cam': p_final,
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'range': float(np.linalg.norm(p_final)),
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'z': float(p_final[2]),
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'p_lidar': calib.cam_to_lidar(p_final)[0],
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'hit_u': float(u[sel]),
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'hit_v': float(v[sel]),
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'px_err': float(dpx[sel]),
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'lidar_index': int(idx[sel]),
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'plane_used': plane_used,
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'plane_residual': residual,
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'multi_layer': multi,
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'cand_ranges': rng[cand_idx],
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'confidence': float(conf[idx[sel]]) if conf is not None else None,
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})
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return res
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class TargetIndex:
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"""一帧点云的「像素格子 -> 点索引」倒排表。
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为什么需要：
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locate() 每次点击都会把 N 个点全量投影一遍。N≈5 万时一次几毫秒到
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几十毫秒，点一次无所谓；但同一帧反复点、或在板子上跑时，这部分是
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重复劳动。这里在【每帧】只做一次全量投影 + 分桶，之后每次点击只
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检查查询像素周围几个格子里的点，平面拟合也只在局部候选里做。
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用法：
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idx = TargetIndex(xyz, conf=conf) # 每帧建一次
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res = idx.locate(u, v, radius=10) # 每次点击，返回结构和 locate 一样
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注意：
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- 桶按点的像素坐标分，格子越大，查询时扫到的点越多；默认 32 px。
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- 只索引落在图像内的点；图像外的点本来也不会被点到。
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"""
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def __init__(self, xyz, conf=None, cell=32, min_z=0.05):
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xyz = np.asarray(xyz, dtype=np.float64)
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good = np.isfinite(xyz).all(axis=1) & (np.abs(xyz).sum(axis=1) > 1e-6)
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self.idx = np.where(good)[0]
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self.xyz = xyz[good]
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self.conf = None if conf is None else np.asarray(conf)[good]
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self.p_cam = calib.lidar_to_cam(self.xyz)
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self.u, self.v, ok = calib.project_cam_to_pixel(self.p_cam)
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self.rng = np.linalg.norm(self.p_cam, axis=1)
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self.ok = ok & (self.p_cam[:, 2] > float(min_z))
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self.cell = max(4, int(cell))
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self.nx = int(np.ceil(calib.IMAGE_WIDTH / float(self.cell)))
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self.ny = int(np.ceil(calib.IMAGE_HEIGHT / float(self.cell)))
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sel = np.where(self.ok)[0]
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if sel.size:
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cx = np.clip((self.u[sel] // self.cell).astype(np.int64),
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0, self.nx - 1)
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cy = np.clip((self.v[sel] // self.cell).astype(np.int64),
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0, self.ny - 1)
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keys = cy * self.nx + cx
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order = np.argsort(keys, kind='stable')
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keys = keys[order]
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sel = sel[order]
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uniq, starts = np.unique(keys, return_index=True)
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ends = np.append(starts[1:], keys.size)
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self.buckets = {int(k): sel[s:e]
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for k, s, e in zip(uniq, starts, ends)}
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else:
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self.buckets = {}
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def _gather(self, query_u, query_v, radius):
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c = self.cell
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x0 = max(0, int(np.floor((query_u - radius) / c)))
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x1 = min(self.nx - 1, int(np.floor((query_u + radius) / c)))
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y0 = max(0, int(np.floor((query_v - radius) / c)))
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y1 = min(self.ny - 1, int(np.floor((query_v + radius) / c)))
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parts = []
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for cy in range(y0, y1 + 1):
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base = cy * self.nx
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for cx in range(x0, x1 + 1):
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arr = self.buckets.get(base + cx)
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if arr is not None and arr.size:
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parts.append(arr)
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if not parts:
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return np.empty(0, dtype=np.int64)
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return np.concatenate(parts)
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def locate(self, query_u, query_v, radius=10.0, depth_band=0.6,
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fit_plane=True, max_residual=0.05, min_conf=None):
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"""局部查找，返回与 locate() 相同的字段。"""
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query_u = float(query_u)
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query_v = float(query_v)
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res = {
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'found': False, 'n_valid': int(self.idx.size),
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'n_cand': 0, 'radius': float(radius),
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'query_u': query_u, 'query_v': query_v,
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'indexed': True,
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}
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cand = self._gather(query_u, query_v, radius)
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if cand.size == 0:
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return res
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dpx = np.hypot(self.u[cand] - query_u, self.v[cand] - query_v)
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keep = dpx <= radius
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if min_conf is not None and self.conf is not None:
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keep &= self.conf[cand] >= min_conf
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cand = cand[keep]
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dpx = dpx[keep]
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res['n_cand'] = int(cand.size)
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if cand.size == 0:
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return res
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# 距离最近的点
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order = np.argsort(self.rng[cand])
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cand = cand[order]
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dpx = dpx[order]
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sel = int(cand[0])
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ray = calib.pixel_to_ray(query_u, query_v)
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r_sorted = np.sort(self.rng[cand])
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multi = bool(r_sorted.size > 3 and (r_sorted[-1] - r_sorted[0]) > 2.0)
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p_final = self.p_cam[sel]
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plane_used = False
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residual = None
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if fit_plane:
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band = self.p_cam[cand[self.rng[cand] <= self.rng[sel] + depth_band]]
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p_fit, residual = _fit_plane_ray(band, ray, max_residual)
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if p_fit is not None:
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p_final = p_fit
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plane_used = True
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res.update({
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'found': True,
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'p_cam': p_final,
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'p_lidar': calib.cam_to_lidar(p_final)[0],
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'hit_u': float(self.u[sel]),
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'hit_v': float(self.v[sel]),
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'px_err': float(dpx[0]),
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'lidar_index': int(self.idx[sel]),
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'plane_used': plane_used,
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'plane_residual': residual,
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'multi_layer': multi,
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'confidence': None if self.conf is None else float(self.conf[sel]),
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})
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return res
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