import numpy as np
from scipy.spatial import ConvexHull
from scipy.optimize import linprog

# ---------- Small helpers ----------

def orthonormal_basis_from_up(up):
    up = np.asarray(up, dtype=float)
    up = up / (np.linalg.norm(up) + 1e-12)
    # pick any vector not parallel to up
    t = np.array([1.0, 0.0, 0.0]) if abs(up[0]) < 0.9 else np.array([0.0, 1.0, 0.0])
    u0 = t - (t @ up) * up
    u0 /= (np.linalg.norm(u0) + 1e-12)
    v0 = np.cross(up, u0)
    v0 /= (np.linalg.norm(v0) + 1e-12)
    return u0, v0, up

def project_to_plane(points, u0, v0):
    U = np.stack([u0, v0], axis=1)            # 3x2
    return points @ U                          # (N,2), coordinates in (u0,v0)

def hull_halfspaces_2d(P2):
    hull = ConvexHull(P2)
    # equations: for 2D, each row [a,b,c] with a*x + b*y + c == 0 on edge, <= 0 inside
    A = hull.equations[:, :2]
    b = -hull.equations[:, 2]
    return A, b, hull

def solve_rect_lp(A, b, R2, alpha):
    """
    Maximize alpha^T h, subject to A c + |A R2| h <= b, h>=0.
    Vars: [c_x, c_y, h_x, h_y]
    """
    S = np.abs(A @ R2)                  # (m,2)
    A_ub = np.hstack([A, S])            # (m,4)
    b_ub = b.copy()
    bounds = [(-np.inf, np.inf), (-np.inf, np.inf), (0, np.inf), (0, np.inf)]
    c_vec = np.array([0.0, 0.0, -alpha[0], -alpha[1]])
    res = linprog(c=c_vec, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method="highs")
    if not res.success:
        return None
    c2 = res.x[:2]
    h2 = res.x[2:]
    return c2, h2

def rect_vertices_3d(center3, hx, hy, u_dir, v_dir):
    signs = np.array([[-1,-1],[ -1, 1],[ 1, 1],[ 1,-1]], float)
    verts2 = signs * np.array([hx, hy])
    verts3 = center3[None,:] + verts2[:,0:1]*u_dir[None,:] + verts2[:,1:2]*v_dir[None,:]
    return verts3  # 4x3 (rectangle corners in plane, no vertical thickness)

# ---------- Main: max inscribed rectangle footprint + height ----------

def max_inscribed_rectangle_2d_box(
    centers_np,
    up=np.array([0.0, -1.0, 0.0]),
    ground_level=0.0,
    n_angles=181,           # sample 0..90° inclusive (exploits symmetry); doubled internally
    n_weights=16,           # LPs per angle (sweep trade-offs)
    seed=0
):
    """
    Finds the maximum-area rectangle (any in-plane orientation) inside the 2D projection
    of `centers_np` onto the plane orthogonal to `up`. Height is then set from ground_level
    to the highest camera along +up.

    Returns dict with:
      center (3,), half_sizes (hx,hy,hz/2), sizes (3,),
      R (3x3), quat_xyzw (4,), footprint_vertices (4,3), box_vertices (8,3), area, volume
    """
    rng = np.random.default_rng(seed)
    u0, v0, up = orthonormal_basis_from_up(up)

    # 1) 2D projection and hull halfspaces in (u0, v0) coords
    P2 = project_to_plane(centers_np, u0, v0)     # (N,2)
    A2, b2, hull2 = hull_halfspaces_2d(P2)

    # 2) Search over angles theta in [0, pi/2) due to rectangle symmetry
    thetas = np.linspace(0.0, 0.5*np.pi, n_angles)
    best = {"area": -1.0}

    # Simple Dirichlet weights over 2 dims => Beta; add a couple of axis-focused weights
    weights = list(rng.dirichlet(np.ones(2), size=n_weights))
    weights += [np.array([1.0, 0.0]), np.array([0.0, 1.0]), np.array([0.5, 0.5])]

    for theta in thetas:
        # local rectangle axes in (u0,v0) coordinates
        ct, st = np.cos(theta), np.sin(theta)
        R2 = np.array([[ct, -st],
                       [st,  ct]], dtype=float)   # maps local (x,y) to (u0,v0)

        for alpha in weights:
            sol = solve_rect_lp(A2, b2, R2, alpha)
            if sol is None:
                continue
            c2, h2 = sol
            area = float(4.0 * h2[0] * h2[1])
            if area > best["area"]:
                best = {"area": area, "theta": theta, "c2": c2, "h2": h2}

    if best["area"] <= 0:
        raise RuntimeError("Failed to inscribe a rectangle; check point configuration.")

    # 3) Build 3D pose from best solution
    theta = best["theta"]; ct, st = np.cos(theta), np.sin(theta)
    u_dir =  ct * u0 + st * v0          # rectangle local X in world
    v_dir = -st * u0 + ct * v0          # rectangle local Y in world (right-handed with up)
    c2 = best["c2"]; hx, hy = best["h2"]
    center_plane = c2[0]*u0 + c2[1]*v0

    # Height: ground -> highest camera
    cam_h = centers_np @ up
    H = float(np.max(cam_h) - ground_level)
    H = max(H, 1e-9)
    center3 = center_plane + (ground_level + 0.5*H) * up

    # Rotation matrix with columns = local axes (X=u_dir, Y=v_dir, Z=up)
    R_box = np.column_stack([u_dir, v_dir, up])
    # Quaternion xyzw (from rotmat)
    # Manual conversion (no SciPy quaternion dependency):
    def rotmat_to_quat_xyzw(M):
        t = np.trace(M)
        if t > 0:
            s = np.sqrt(t+1.0)*2
            w = 0.25*s
            x = (M[2,1]-M[1,2])/s
            y = (M[0,2]-M[2,0])/s
            z = (M[1,0]-M[0,1])/s
        else:
            i = np.argmax([M[0,0], M[1,1], M[2,2]])
            if i == 0:
                s = np.sqrt(1.0 + M[0,0] - M[1,1] - M[2,2]) * 2
                w = (M[2,1] - M[1,2]) / s
                x = 0.25 * s
                y = (M[0,1] + M[1,0]) / s
                z = (M[0,2] + M[2,0]) / s
            elif i == 1:
                s = np.sqrt(1.0 + M[1,1] - M[0,0] - M[2,2]) * 2
                w = (M[0,2] - M[2,0]) / s
                x = (M[0,1] + M[1,0]) / s
                y = 0.25 * s
                z = (M[1,2] + M[2,1]) / s
            else:
                s = np.sqrt(1.0 + M[2,2] - M[0,0] - M[1,1]) * 2
                w = (M[1,0] - M[0,1]) / s
                x = (M[0,2] + M[2,0]) / s
                y = (M[1,2] + M[2,1]) / s
                z = 0.25 * s
        return np.array([x, y, z, w], dtype=float)

    quat_xyzw = rotmat_to_quat_xyzw(R_box)

    # Vertices (footprint & full 3D box)
    footprint4 = rect_vertices_3d(center_plane, hx, hy, u_dir, v_dir)  # 4x3 at ground plane height=0 (in plane coords)
    # 8 box corners:
    rect4_top = footprint4 + H * up
    verts8 = np.vstack([footprint4, rect4_top])

    sizes3 = np.array([2*hx, 2*hy, H], dtype=float)
    return {
        "center": center3,
        "half_sizes": np.array([hx, hy, 0.5*H], dtype=float),
        "sizes": sizes3,
        "R": R_box,
        "quat_xyzw": quat_xyzw,
        "footprint_vertices": footprint4,  # 4x3 (bottom rectangle)
        "box_vertices": verts8,            # 8x3
        "area": float(4*hx*hy),
        "volume": float((2*hx)*(2*hy)*H),
        "up": up,
        "u_dir": u_dir,
        "v_dir": v_dir,
    }

# -------- Example usage --------
# result = max_inscribed_rectangle_2d_box(
#     centers_np,
#     up=np.array([0, 1, 0]),  # your "up"
#     ground_level=0.0,        # if your ground plane is y=0 (for example)
#     n_angles=181,
#     n_weights=24,
#     seed=42
# )
# c = result["center"]; sizes = result["sizes"]; R = result["R"]; q = result["quat_xyzw"]
# print("center:", c, "sizes (W,D,H):", sizes)

def point_in_convex_hull_2d(points_xy, query_xy, tol=1e-12):
    """
    points_xy: (N,2) cloud
    query_xy:  (...,2) points to test
    Returns: boolean array with shape query_xy.shape[:-1]
    """
    hull = ConvexHull(points_xy)
    # hull.equations: rows [a, b, c] with a*x + b*y + c == 0 on edge, <= 0 inside
    A = hull.equations[:, :2]
    c = hull.equations[:, 2]
    q = np.atleast_2d(query_xy)            # (M,2)
    vals = (A @ q.T) + c[:, None]          # (num_edges, M)
    inside = np.all(vals <= tol, axis=0)   # inside if all halfspaces satisfied
    return inside.reshape(query_xy.shape[:-1])