"""LC2WF-inspired 3D line cloud wireframe module.

Instead of lifting individual 2D corners to 3D via a single depth sample,
this module:

1. Extracts 2D line segments from gestalt edge masks (eave/ridge/rake/etc).
2. Samples many depth values along each 2D segment.
3. Fits a robust 3D line through the unprojected samples (RANSAC).
4. Merges similar 3D lines across views (direction + proximity).
5. Computes closest-point intersections of 3D line pairs → vertex candidates.

The resulting vertices average over many depth samples, cancelling noise
that single-pixel corner depth estimates cannot. The 3D line intersections
give overdetermined vertex positions.

Entry points:
    extract_3d_lines(entry) → list[Line3D]
    intersect_lines_to_vertices(lines, ...) → np.ndarray
    predict_wireframe_lines(entry) → (vertices, edges)
"""

from __future__ import annotations

import numpy as np
import cv2
from dataclasses import dataclass

from hoho2025.example_solutions import (
    convert_entry_to_human_readable,
    empty_solution,
    point_to_segment_dist,
)
from hoho2025.color_mappings import gestalt_color_mapping

try:
    from mvs_utils import collect_views, project_world_to_image
except ImportError:
    from submission.mvs_utils import collect_views, project_world_to_image


EDGE_CLASSES = ['eave', 'ridge', 'rake', 'valley', 'hip']
VERTEX_CLASSES = ['apex', 'eave_end_point', 'flashing_end_point']


@dataclass
class Line3D:
    """A 3D line segment fitted from depth samples."""
    point: np.ndarray       # (3,) — a point on the line
    direction: np.ndarray   # (3,) — unit direction vector
    p1: np.ndarray          # (3,) — endpoint 1
    p2: np.ndarray          # (3,) — endpoint 2
    length: float
    n_inliers: int
    edge_class: str
    view_id: str


# ---------------------------------------------------------------------------
# Step 1-2: Extract 2D segments, sample depth, fit 3D lines
# ---------------------------------------------------------------------------

def _unproject_pixel(u, v, depth, K_inv, R_t_inv, t_world):
    """Unproject a single pixel (u, v) at the given depth to world coords.

    K_inv : (3,3) — inverse intrinsics
    R_t_inv : (3,3) — R^T (inverse rotation)
    t_world : (3,) — camera centre in world = -R^T @ t
    """
    z = float(depth)
    if z <= 0.01 or z > 80.0:
        return None
    cam = K_inv @ np.array([u * z, v * z, z])
    world = R_t_inv @ cam + t_world
    return world


def _fit_3d_line_ransac(
    pts3d: np.ndarray,
    n_iter: int = 100,
    inlier_th: float = 0.3,
    min_inliers: int = 5,
) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
    """RANSAC-fit a 3D line through a set of 3D points.

    Returns (point_on_line, unit_direction, inlier_pts) or None.
    """
    n = len(pts3d)
    if n < 2:
        return None

    best_inliers = None
    best_dir = None
    best_pt = None
    best_count = 0

    for _ in range(n_iter):
        idx = np.random.choice(n, 2, replace=False)
        p1, p2 = pts3d[idx[0]], pts3d[idx[1]]
        d = p2 - p1
        length = np.linalg.norm(d)
        if length < 0.05:
            continue
        d = d / length
        # Distance from each point to the line (p1, d)
        rel = pts3d - p1
        proj = rel @ d
        perp = rel - proj[:, None] * d
        dists = np.linalg.norm(perp, axis=1)
        inlier_mask = dists <= inlier_th
        count = int(inlier_mask.sum())
        if count > best_count:
            best_count = count
            best_inliers = inlier_mask
            best_dir = d
            best_pt = p1

    if best_count < min_inliers or best_inliers is None:
        return None

    # Refit on inliers using PCA
    inlier_pts = pts3d[best_inliers]
    centroid = inlier_pts.mean(axis=0)
    _, _, Vt = np.linalg.svd(inlier_pts - centroid)
    direction = Vt[0]
    if np.dot(direction, best_dir) < 0:
        direction = -direction

    return centroid, direction, inlier_pts


def extract_3d_lines_single_view(
    gest_np: np.ndarray,
    depth_np: np.ndarray,
    view_info: dict,
    n_samples: int = 30,
    min_line_px: int = 20,
) -> list[Line3D]:
    """Extract 3D lines from a single view's gestalt + depth."""
    H, W = depth_np.shape[:2]
    K = view_info['K']
    R = view_info['R']
    t = view_info['t']
    K_inv = np.linalg.inv(K)
    R_inv = R.T
    cam_center = -R_inv @ t

    lines: list[Line3D] = []
    view_id = view_info['image_id']

    for edge_class in EDGE_CLASSES:
        color = np.array(gestalt_color_mapping[edge_class])
        mask = cv2.inRange(gest_np, color - 0.5, color + 0.5)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))
        if mask.sum() == 0:
            continue

        _, labels, stats, _ = cv2.connectedComponentsWithStats(mask, 8, cv2.CV_32S)
        for lbl in range(1, labels.max() + 1):
            area = stats[lbl, cv2.CC_STAT_AREA]
            if area < min_line_px:
                continue

            ys, xs = np.where(labels == lbl)
            if len(xs) < 3:
                continue

            # Fit 2D line to get direction + endpoints
            pts2d = np.column_stack([xs, ys]).astype(np.float32)
            line_params = cv2.fitLine(pts2d, cv2.DIST_L2, 0, 0.01, 0.01)
            vx, vy, x0, y0 = line_params.ravel()
            proj = (xs - x0) * vx + (ys - y0) * vy
            t_min, t_max = float(proj.min()), float(proj.max())

            # Sample N points along the 2D line
            ts = np.linspace(t_min, t_max, n_samples)
            pts3d_list = []
            for t_val in ts:
                u = x0 + t_val * vx
                v_px = y0 + t_val * vy
                ui, vi = int(round(u)), int(round(v_px))
                if 0 <= ui < W and 0 <= vi < H:
                    d = depth_np[vi, ui]
                    p = _unproject_pixel(u, v_px, d, K_inv, R_inv, cam_center)
                    if p is not None:
                        pts3d_list.append(p)

            if len(pts3d_list) < 5:
                continue

            pts3d = np.array(pts3d_list, dtype=np.float64)
            result = _fit_3d_line_ransac(pts3d, n_iter=50, inlier_th=0.3, min_inliers=5)
            if result is None:
                continue

            centroid, direction, inlier_pts = result
            # Endpoints: project inliers onto direction, take extremes
            s = (inlier_pts - centroid) @ direction
            p1 = centroid + float(s.min()) * direction
            p2 = centroid + float(s.max()) * direction
            length = float(np.linalg.norm(p2 - p1))
            if length < 0.3:
                continue

            lines.append(Line3D(
                point=centroid,
                direction=direction,
                p1=p1, p2=p2,
                length=length,
                n_inliers=len(inlier_pts),
                edge_class=edge_class,
                view_id=view_id,
            ))

    return lines


# ---------------------------------------------------------------------------
# Step 1-2 entry: all views
# ---------------------------------------------------------------------------

def extract_3d_lines(entry) -> tuple[list[Line3D], dict]:
    """Extract 3D lines from all views.

    Returns (all_lines, good_entry).
    """
    good = convert_entry_to_human_readable(entry)
    colmap_rec = good.get('colmap') or good.get('colmap_binary')
    if colmap_rec is None:
        return [], good

    views = collect_views(colmap_rec, good['image_ids'])
    all_lines: list[Line3D] = []

    for gest, depth, img_id in zip(good['gestalt'], good['depth'], good['image_ids']):
        info = views.get(img_id)
        if info is None:
            continue
        depth_np = np.array(depth).astype(np.float64) / 1000.0
        H, W = depth_np.shape[:2]
        gest_np = np.array(gest.resize((W, H))).astype(np.uint8)

        # Affine depth calibration using COLMAP sparse depth (same as pipeline)
        try:
            from hoho2025.example_solutions import get_sparse_depth, get_house_mask
            from sklearn_submission import fit_affine_ransac
            depth_sparse, found, _, _ = get_sparse_depth(colmap_rec, img_id, depth_np)
            if found:
                _, _, depth_np = fit_affine_ransac(depth_np, depth_sparse,
                                                    get_house_mask(good['ade'][good['image_ids'].index(img_id)]))
        except Exception:
            pass  # use raw depth if calibration fails

        view_lines = extract_3d_lines_single_view(gest_np, depth_np, info)
        all_lines.extend(view_lines)

    return all_lines, good


# ---------------------------------------------------------------------------
# Step 3: Merge similar 3D lines across views
# ---------------------------------------------------------------------------

def merge_3d_lines(
    lines: list[Line3D],
    direction_cos: float = 0.95,
    midpoint_dist: float = 1.0,
) -> list[Line3D]:
    """Merge 3D lines that have similar direction and nearby midpoints.

    Uses greedy clustering: each line is assigned to the first compatible
    cluster. The cluster representative is recomputed as the mean of its
    members (direction via PCA, endpoints via extremal projections).
    """
    if len(lines) <= 1:
        return lines

    clusters: list[list[int]] = []
    reps: list[Line3D] = []

    for i, line in enumerate(lines):
        matched = False
        for ci, rep in enumerate(reps):
            cos = abs(float(np.dot(line.direction, rep.direction)))
            if cos < direction_cos:
                continue
            mid_d = float(np.linalg.norm(
                (line.p1 + line.p2) / 2 - (rep.p1 + rep.p2) / 2
            ))
            if mid_d > midpoint_dist:
                continue
            clusters[ci].append(i)
            # Recompute representative
            members = [lines[j] for j in clusters[ci]]
            all_pts = np.vstack([np.vstack([m.p1, m.p2]) for m in members])
            centroid = all_pts.mean(axis=0)
            _, _, Vt = np.linalg.svd(all_pts - centroid)
            direction = Vt[0]
            if np.dot(direction, rep.direction) < 0:
                direction = -direction
            s = (all_pts - centroid) @ direction
            new_p1 = centroid + float(s.min()) * direction
            new_p2 = centroid + float(s.max()) * direction
            reps[ci] = Line3D(
                point=centroid, direction=direction,
                p1=new_p1, p2=new_p2,
                length=float(np.linalg.norm(new_p2 - new_p1)),
                n_inliers=sum(m.n_inliers for m in members),
                edge_class=members[0].edge_class,
                view_id='merged',
            )
            matched = True
            break
        if not matched:
            clusters.append([i])
            reps.append(Line3D(
                point=line.point.copy(), direction=line.direction.copy(),
                p1=line.p1.copy(), p2=line.p2.copy(),
                length=line.length, n_inliers=line.n_inliers,
                edge_class=line.edge_class, view_id=line.view_id,
            ))

    return reps


# ---------------------------------------------------------------------------
# Step 4: Intersect pairs of 3D lines → vertex candidates
# ---------------------------------------------------------------------------

def closest_point_on_two_lines(
    p1: np.ndarray, d1: np.ndarray,
    p2: np.ndarray, d2: np.ndarray,
) -> tuple[np.ndarray, float] | None:
    """Find the closest point between two 3D lines.

    Returns (midpoint_of_closest_approach, distance_between_lines) or None
    if the lines are nearly parallel.
    """
    w0 = p1 - p2
    a = float(np.dot(d1, d1))
    b = float(np.dot(d1, d2))
    c = float(np.dot(d2, d2))
    d = float(np.dot(d1, w0))
    e = float(np.dot(d2, w0))

    denom = a * c - b * b
    if abs(denom) < 1e-8:
        return None  # parallel

    sc = (b * e - c * d) / denom
    tc = (a * e - b * d) / denom

    closest_on_1 = p1 + sc * d1
    closest_on_2 = p2 + tc * d2
    midpoint = (closest_on_1 + closest_on_2) / 2.0
    dist = float(np.linalg.norm(closest_on_1 - closest_on_2))

    return midpoint, dist


def intersect_lines_to_vertices(
    lines: list[Line3D],
    max_dist: float = 0.5,
    parallel_cos: float = 0.95,
    segment_margin: float = 0.5,
) -> np.ndarray:
    """Generate vertex candidates from 3D line intersections.

    For each pair of non-parallel lines:
    - compute the closest approach point;
    - accept if the distance between the lines at that point is ≤ max_dist;
    - accept only if the closest point is within ``segment_margin`` of
      both line segments (not too far outside the actual edge extent).
    """
    if len(lines) < 2:
        return np.empty((0, 3), dtype=np.float64)

    vertices: list[np.ndarray] = []
    for i in range(len(lines)):
        for j in range(i + 1, len(lines)):
            cos = abs(float(np.dot(lines[i].direction, lines[j].direction)))
            if cos >= parallel_cos:
                continue

            result = closest_point_on_two_lines(
                lines[i].point, lines[i].direction,
                lines[j].point, lines[j].direction,
            )
            if result is None:
                continue
            midpoint, dist = result
            if dist > max_dist:
                continue

            # Check that the intersection is near both line segments
            ok = True
            for line in (lines[i], lines[j]):
                s = float(np.dot(midpoint - line.point, line.direction))
                s_min = float(np.dot(line.p1 - line.point, line.direction))
                s_max = float(np.dot(line.p2 - line.point, line.direction))
                if s < s_min - segment_margin or s > s_max + segment_margin:
                    ok = False
                    break
            if ok:
                vertices.append(midpoint)

    if not vertices:
        return np.empty((0, 3), dtype=np.float64)
    return np.array(vertices, dtype=np.float64)


# ---------------------------------------------------------------------------
# Step 5: Integration helper
# ---------------------------------------------------------------------------

def snap_vertices_to_lines(
    vertices: np.ndarray,
    lines: list[Line3D],
    snap_radius: float = 0.4,
    min_line_inliers: int = 10,
    segment_margin: float = 0.3,
    require_agree: int = 1,
) -> tuple[np.ndarray, np.ndarray]:
    """Snap each vertex to the nearest 3D line if the line is trustworthy
    and the vertex sits within ``snap_radius`` perpendicular distance.

    The snap is a perpendicular projection of the vertex onto the line. If
    the projected point falls outside the segment ``[p1, p2]`` by more than
    ``segment_margin``, we clamp it to the nearest endpoint (so we never
    slide a vertex off the ends of the real edge).

    A line is considered "trustworthy" if it has ≥ ``min_line_inliers``
    depth samples (the more, the better the depth-noise averaging).

    When ``require_agree`` ≥ 2 we only snap if the vertex is within
    ``snap_radius`` of **multiple** independent lines and they all agree
    on roughly the same 3D location — this is a "consensus" mode that
    avoids snapping to a single noisy line.

    Returns
    -------
    refined : (N, 3) float64 — refined vertex positions
    snapped : (N,)  bool    — which vertices were moved
    """
    verts = np.asarray(vertices, dtype=np.float64)
    refined = verts.copy()
    snapped = np.zeros(len(verts), dtype=bool)

    if len(verts) == 0 or not lines:
        return refined, snapped

    # Pre-filter trustworthy lines
    trusted = [ln for ln in lines if ln.n_inliers >= min_line_inliers]
    if not trusted:
        return refined, snapped

    for i, v in enumerate(verts):
        # Compute perpendicular distance and projected point for each line
        candidates: list[tuple[float, np.ndarray, Line3D]] = []
        for ln in trusted:
            rel = v - ln.point
            s = float(np.dot(rel, ln.direction))
            projected = ln.point + s * ln.direction
            perp = float(np.linalg.norm(v - projected))
            if perp > snap_radius:
                continue
            # Clamp projection to segment
            s_min = float(np.dot(ln.p1 - ln.point, ln.direction))
            s_max = float(np.dot(ln.p2 - ln.point, ln.direction))
            if s_min > s_max:
                s_min, s_max = s_max, s_min
            if s < s_min - segment_margin:
                projected = ln.point + (s_min - segment_margin) * ln.direction
            elif s > s_max + segment_margin:
                projected = ln.point + (s_max + segment_margin) * ln.direction
            candidates.append((perp, projected, ln))

        if len(candidates) < require_agree:
            continue

        if require_agree >= 2:
            # Consensus: keep only if ≥2 candidates agree within snap_radius.
            candidates.sort(key=lambda c: c[0])
            best_proj = candidates[0][1]
            agree = 0
            for _, cp, _ in candidates:
                if np.linalg.norm(cp - best_proj) <= snap_radius:
                    agree += 1
            if agree < require_agree:
                continue
            # Snap to the mean of agreeing projections
            agreeing = [c[1] for c in candidates
                        if np.linalg.norm(c[1] - best_proj) <= snap_radius]
            refined[i] = np.mean(agreeing, axis=0)
            snapped[i] = True
        else:
            # Single-line snap: pick the closest
            candidates.sort(key=lambda c: c[0])
            refined[i] = candidates[0][1]
            snapped[i] = True

    return refined, snapped


def line_based_vertices(
    entry,
    max_intersection_dist: float = 0.5,
    merge_radius: float = 0.4,
) -> np.ndarray:
    """High-level: extract 3D lines, merge, intersect → vertex candidates.

    Returns (K, 3) array of deduplicated vertex positions.
    """
    lines, good = extract_3d_lines(entry)
    if not lines:
        return np.empty((0, 3), dtype=np.float64)

    merged_lines = merge_3d_lines(lines)
    if len(merged_lines) < 2:
        return np.empty((0, 3), dtype=np.float64)

    raw_verts = intersect_lines_to_vertices(
        merged_lines, max_dist=max_intersection_dist,
    )
    if len(raw_verts) == 0:
        return np.empty((0, 3), dtype=np.float64)

    # Simple NMS merge
    from scipy.spatial import cKDTree
    tree = cKDTree(raw_verts)
    clusters = tree.query_ball_point(raw_verts, merge_radius)
    used = set()
    out = []
    for i, cl in enumerate(clusters):
        if i in used:
            continue
        members = [j for j in cl if j not in used]
        if not members:
            continue
        out.append(raw_verts[members].mean(axis=0))
        used.update(members)

    return np.array(out, dtype=np.float64) if out else np.empty((0, 3), dtype=np.float64)