evaluate.py

#!/usr/bin/python3
# _*_ coding: utf-8 _*_
# ---------------------------------------------------
# @Time    : 2026-03-10 8:58 p.m.
# @Author  : shangfeng
# @Organization: University of Calgary
# @File    : evaluate.py.py
# @IDE     : PyCharm
# -----------------Evaluation TASK---------------------
# Evaluation
# 1. Chamfer Distance (CD): Measures the geometric discrepancy between the predicted mesh and the ground-truth mesh, reflecting the overall reconstruction accuracy.
#
# 2. Edge Chamfer Distance (ECD): Evaluates the geometric similarity between the edges of the reconstructed mesh and those of the ground-truth mesh, serving as an indicator of edge sharpness and structural fidelity.
#
# 3. Normal Consistency (NC): Assesses the alignment between surface normals of the predicted mesh and the ground-truth mesh, indicating the consistency of local surface orientation.
#
# 4. V_Ratio: Defined as the ratio between the number of vertices in the predicted mesh and that of the ground-truth mesh, reflecting changes in geometric complexity.
#
# 5. F_Ratio: Defined as the ratio between the number of faces in the predicted mesh and that of the ground-truth mesh, indicating variations in mesh resolution.
# ---------------------------------------------------
import os
import trimesh
import numpy as np
from scipy.spatial import cKDTree
import faiss


# --------------------------- Load mesh using trimesh and normalization --------------------------------------
def load_mesh(p_file, gt_file):
    """

    :param p_file:

    :param gt_file:

    :return:

    """
    p_mesh = trimesh.load(p_file)
    gt_mesh = trimesh.load(gt_file)
    return p_mesh, gt_mesh

def normalization(p_mesh, gt_mesh):
    gt_vertices = np.asarray(gt_mesh.vertices)
    p_vertices = np.asarray(p_mesh.vertices)
    vert_min = gt_vertices.min(axis=0)
    vert_max = gt_vertices.max(axis=0)

    vert_center = 0.5 * (vert_min + vert_max)

    gt_vertices = gt_vertices - vert_center
    # p_vertices = p_vertices - vert_center

    vert_min = gt_vertices.min(axis=0)
    vert_max = gt_vertices.max(axis=0)
    extents = vert_max - vert_min
    scale = np.max(extents)

    gt_vertices = gt_vertices / (scale + 1e-6)
    # p_vertices = p_vertices / (scale + 1e-6)
    p_vertices = p_vertices * np.sqrt(np.sum(extents ** 2)) / (scale + 1e-6)

    return trimesh.Trimesh(vertices=p_vertices,faces=p_mesh.faces), trimesh.Trimesh(vertices=gt_vertices,faces=gt_mesh.faces)


# --------------------------- L1 Chamfer distance --------------------------------------
def chamfer_l1_distance_kdtree(p, q):
    """

    p: (N,3) prediction

    q: (M,3) ground truth

    """

    # --- Remove invalid points to ensure numerical stability
    p = p[np.isfinite(p).all(axis=1)]
    q = q[np.isfinite(q).all(axis=1)]

    # --- KDTree
    tree_p = cKDTree(p)
    tree_q = cKDTree(q)

    # --- Distance
    dist_pq, _ = tree_q.query(p) # P → Q
    dist_qp, _ = tree_p.query(q) # Q → P

    # L1 Chamfer Distance
    chamfer_distance = np.mean(dist_pq) + np.mean(dist_qp)

    return chamfer_distance

def chamfer_l1_distance_faiss(p, q, use_gpu=False):
    """

    p: (N,3) prediction

    q: (M,3) ground truth

    """

    # ---------- 1. remove invalid ----------
    p = p[np.isfinite(p).all(axis=1)]
    q = q[np.isfinite(q).all(axis=1)]

    # FAISS
    p = p.astype(np.float32)
    q = q.astype(np.float32)

    # ---------- 2. build index ----------
    index_p = faiss.IndexFlatL2(3)  # dim=3
    index_q = faiss.IndexFlatL2(3)

    # ---------- 3. optional GPU ----------
    if use_gpu:
        res = faiss.StandardGpuResources()
        index_p = faiss.index_cpu_to_gpu(res, 0, index_p)
        index_q = faiss.index_cpu_to_gpu(res, 0, index_q)

    index_p.add(p)
    index_q.add(q)

    # ---------- 4. nearest neighbor ----------
    # FAISS return square distance
    D_pq, _ = index_q.search(p, 1)  # p → q
    D_qp, _ = index_p.search(q, 1)  # q → p

    # ---------- 5. convert to L1 ----------
    dist_pq = np.sqrt(D_pq[:, 0])
    dist_qp = np.sqrt(D_qp[:, 0])

    chamfer_distance = dist_pq.mean() + dist_qp.mean()

    return float(chamfer_distance)

# --------------------------- Mesh sampling points --------------------------------------
def mesh_sample_points(p_mesh, gt_mesh, sample_points=1000000):
    """

    :param p_mesh: trimesh mesh

    :param gt_mesh: Trimesh mesh

    :param sample_points:

    :return: (sample_points, 3)

    """
    p_points = p_mesh.sample(sample_points)
    gt_points = gt_mesh.sample(sample_points)
    return p_points, gt_points

# --------------------------- Edge Chamfer L1 Distance --------------------------------------
def extract_sharp_edges(mesh, angle_threshold_deg=30.0):
    """

    Version-agnostic sharp edge extraction.

    Works with any trimesh version.

    """
    faces = np.asarray(mesh.faces)
    face_normals = np.asarray(mesh.face_normals)

    # ------------------ normalize normals ----------------------
    face_normals = face_normals / (
        np.linalg.norm(face_normals, axis=1, keepdims=True) + 1e-12
    )

    # --- Step 1: build edge -> faces mapping ---
    edge_faces = dict()

    for f_idx, face in enumerate(faces):
        edges = [
            tuple(sorted((face[0], face[1]))),
            tuple(sorted((face[1], face[2]))),
            tuple(sorted((face[2], face[0]))),
        ]
        for e in edges:
            if e not in edge_faces:
                edge_faces[e] = []
            edge_faces[e].append(f_idx)

    # --- Step 2: detect sharp edges ---
    cos_thresh = np.cos(np.deg2rad(angle_threshold_deg))
    sharp_edges = []

    for edge, f_list in edge_faces.items():
        # boundary edge → sharp
        if len(f_list) == 1:
            sharp_edges.append(edge)
            continue

        # non-manifold (>2 faces) → treat as sharp
        if len(f_list) > 2:
            sharp_edges.append(edge)
            continue

        # exactly two adjacent faces
        f1, f2 = f_list
        n1 = face_normals[f1]
        n2 = face_normals[f2]

        dot = np.dot(n1, n2)
        dot = np.clip(dot, -1.0, 1.0)
        if np.abs(dot) < cos_thresh:
            sharp_edges.append(edge)

    if len(sharp_edges) == 0:
        return np.zeros((0, 2), dtype=np.int64)

    return np.asarray(sharp_edges, dtype=np.int64)


def sample_points_on_edges_global(vertices, edges, total_samples=100000):
    """

    Sample points uniformly along edges, proportional to edge length.



    Args:

        vertices (np.ndarray): (V, 3)

        edges (np.ndarray): (E, 2)

        total_samples (int): total number of sampled points



    Returns:

        np.ndarray: (total_samples, 3)

    """

    if edges.shape[0] == 0:
        return np.zeros((0, 3), dtype=np.float32)

    # --- 1. Endpoints of edges --------------
    p1 = vertices[edges[:, 0]]  # (E, 3)
    p2 = vertices[edges[:, 1]]  # (E, 3)

    # --- 2. Calculate the length of edge --------------
    edge_lengths = np.linalg.norm(p2 - p1, axis=1)  # (E,)

    # --- 3. Calculate probability --------------
    probs = edge_lengths / (edge_lengths.sum() + 1e-12)

    # --- 4. edge weight --------------
    edge_indices = np.random.choice(len(edges), size=total_samples, p=probs)

    # --- 5. random points --------------
    t = np.random.rand(total_samples, 1)  # (N,1)

    sampled_p1 = p1[edge_indices]
    sampled_p2 = p2[edge_indices]

    points = (1 - t) * sampled_p1 + t * sampled_p2

    return points.astype(np.float32)


def compute_edge_chamfer_distance(p_mesh, gt_mesh, angle_threshold_deg=30.0):
    """

    :param p_mesh:

    :param gt_mesh:

    :param angle_threshold_deg:

    :return:

    """
    # ---------- Extract sharp edges ----------
    sharp_edges_gt = extract_sharp_edges(gt_mesh, angle_threshold_deg)
    sharp_edges_pred = extract_sharp_edges(p_mesh, angle_threshold_deg)

    # ---------- Sample points on edges ----------
    edge_pts_gt = sample_points_on_edges_global(
        gt_mesh.vertices, sharp_edges_gt
    )
    edge_pts_pred = sample_points_on_edges_global(
        p_mesh.vertices, sharp_edges_pred
    )

    # ---------- Compute ECD ----------
    ecd = chamfer_l1_distance_kdtree(edge_pts_pred, edge_pts_gt)

    return ecd



# --------------------------- Normal Consistency (NC) --------------------------------------
def normal_consistency(

    p_mesh,

    gt_mesh,

    num_samples=100000

):
    """

    mesh_gt, mesh_pred: trimesh.Trimesh

    return: NC in [0, 1]

    """

    # ---------- 1. sample surface points from GT ----------
    pts_gt, face_ids = trimesh.sample.sample_surface(gt_mesh, num_samples)
    normals_gt = gt_mesh.face_normals[face_ids]

    # ---------- 2. find closest face on pred mesh---------
    closest_points, distance, face_id = p_mesh.nearest.on_surface(pts_gt)
    normals_pred = p_mesh.face_normals[face_id]

    # ---------- 3. normalize  ----------
    normals_gt = normals_gt / np.linalg.norm(normals_gt, axis=1, keepdims=True)
    normals_pred = normals_pred / np.linalg.norm(normals_pred, axis=1, keepdims=True)

    # ---------- 4. cosine similarity ----------
    cos_sim = np.abs(np.sum(normals_gt * normals_pred, axis=1))

    return float(cos_sim.mean())

# --------------------------- V_Ratio & F_Ratio --------------------------------------
def calculate_vertices_face_ratio(p_mesh, gt_mesh):
    """

    :param p_mesh: trimesh.Trimesh

    :param gt_mesh: trimesh.Trimesh

    :return: float, float

    """
    f_ratio = len(p_mesh.faces) / len(gt_mesh.faces)
    v_ratio = len(p_mesh.vertices) / len(gt_mesh.vertices)
    return v_ratio, f_ratio


# --------------------------- Mesh Evaluation For 3rd USM3D ----------------------------
def mesh_evaluation(p_file, gt_file):
    """

    :param p_file: the path of predicted mesh

    :param gt_file: the path of ground truth mesh

    :return: mesh_chamfer_distance

    """
    # --------------- Load Mesh using trimesh & normalization----------------
    p_mesh, gt_mesh = load_mesh(p_file, gt_file)
    p_mesh, gt_mesh = normalization(p_mesh, gt_mesh)

    # ----------------------- Mesh Chamfer Distance --------------------------
    p_points, gt_points = mesh_sample_points(p_mesh, gt_mesh)
    mesh_chamfer_distance = chamfer_l1_distance_kdtree(p_points, gt_points)

    # ---------------------- Edge Chamfer Distance ---------------------------
    edge_chamfer_distance = compute_edge_chamfer_distance(p_mesh, gt_mesh, angle_threshold_deg=30.0)

    # ---------------------- Normal Consistency --------------------------
    normals_consistency = normal_consistency(p_mesh, gt_mesh)

    # ---------------------- V_ratio & F_ratio ---------------------------
    v_ratio, f_ratio = calculate_vertices_face_ratio(p_mesh, gt_mesh)

    return mesh_chamfer_distance, edge_chamfer_distance, normals_consistency,  v_ratio, f_ratio


# if __name__ == '__main__':
#     p_file = r'./pred/1a_0.obj'
#     gt_file = r'./gt/1a_0.obj'
#     print(mesh_evaluation(p_file, gt_file))