metrics.py

'''
Author: mikami520 yxiao39@jh.edu
Date: 2023-05-23 19:53:49
LastEditors: Chris Xiao yl.xiao@mail.utoronto.ca
LastEditTime: 2023-12-15 17:34:26
FilePath: /UNET/metrics.py
Description: evaluation metrics for HeartNet
I Love IU
Copyright (c) 2023 by Yuliang Xiao yl.xiao@mail.utoronto.ca, All Rights Reserved. 
'''

import monai
import torch
import numpy as np
from scipy import ndimage
import sys
from pathlib import Path
import lookup_tables
import numpy as np
import pytorch3d as p3d
from pytorch3d.ops import knn_points


def dice_score(y_pred, y_true):
        
    dice_metric = monai.metrics.DiceMetric(include_background=False, reduction='none')
    return dice_metric(y_pred, y_true)

def _assert_is_numpy_array(name, array):
    """Raises an exception if `array` is not a numpy array."""
    if not isinstance(array, np.ndarray):
        raise ValueError("The argument {!r} should be a numpy array, not a "
                        "{}".format(name, type(array)))

def _assert_is_bool_numpy_array(name, array):
    _assert_is_numpy_array(name, array)
    if array.dtype != bool:
        raise ValueError("The argument {!r} should be a numpy array of type bool, "
                        "not {}".format(name, array.dtype))

def _check_nd_numpy_array(name, array, num_dims):
    """Raises an exception if `array` is not a `num_dims`-D numpy array."""
    if len(array.shape) != num_dims:
        raise ValueError("The argument {!r} should be a {}D array, not of "
                            "shape {}".format(name, num_dims, array.shape))
        
def _check_2d_numpy_array(name, array):
    _check_nd_numpy_array(name, array, num_dims=2)
  
def _check_3d_numpy_array(name, array):
    _check_nd_numpy_array(name, array, num_dims=3)
  
def _compute_bounding_box(mask):
    """Computes the bounding box of the masks.

    This function generalizes to arbitrary number of dimensions great or equal
    to 1.

    Args:
    mask: The 2D or 3D numpy mask, where '0' means background and non-zero means
        foreground.

    Returns:
    A tuple:
        - The coordinates of the first point of the bounding box (smallest on all
        axes), or `None` if the mask contains only zeros.
        - The coordinates of the second point of the bounding box (greatest on all
        axes), or `None` if the mask contains only zeros.
    """
    num_dims = len(mask.shape)
    bbox_min = np.zeros(num_dims, np.int64)
    bbox_max = np.zeros(num_dims, np.int64)

    # max projection to the x0-axis
    proj_0 = np.amax(mask, axis=tuple(range(num_dims))[1:])
    idx_nonzero_0 = np.nonzero(proj_0)[0]
    if len(idx_nonzero_0) == 0:  # pylint: disable=g-explicit-length-test
        return None, None

    bbox_min[0] = np.min(idx_nonzero_0)
    bbox_max[0] = np.max(idx_nonzero_0)

    # max projection to the i-th-axis for i in {1, ..., num_dims - 1}
    for axis in range(1, num_dims):
        max_over_axes = list(range(num_dims))  # Python 3 compatible
        max_over_axes.pop(axis)  # Remove the i-th dimension from the max
        max_over_axes = tuple(max_over_axes)  # numpy expects a tuple of ints
        proj = np.amax(mask, axis=max_over_axes)
        idx_nonzero = np.nonzero(proj)[0]
        bbox_min[axis] = np.min(idx_nonzero)
        bbox_max[axis] = np.max(idx_nonzero)

    return bbox_min, bbox_max

def _sort_distances_surfels(distances, surfel_areas):
    """Sorts the two list with respect to the tuple of (distance, surfel_area).

    Args:
    distances: The distances from A to B (e.g. `distances_gt_to_pred`).
    surfel_areas: The surfel areas for A (e.g. `surfel_areas_gt`).

    Returns:
    A tuple of the sorted (distances, surfel_areas).
    """
    sorted_surfels = np.array(sorted(zip(distances, surfel_areas)))
    return sorted_surfels[:, 0], sorted_surfels[:, 1]

def _crop_to_bounding_box(mask, bbox_min, bbox_max):
    """Crops a 2D or 3D mask to the bounding box specified by `bbox_{min,max}`."""
    # we need to zeropad the cropped region with 1 voxel at the lower,
    # the right (and the back on 3D) sides. This is required to obtain the
    # "full" convolution result with the 2x2 (or 2x2x2 in 3D) kernel.
    # TODO:  This is correct only if the object is interior to the
    # bounding box.
    cropmask = np.zeros((bbox_max - bbox_min) + 2, np.uint8)

    num_dims = len(mask.shape)
    # pyformat: disable
    if num_dims == 2:
        cropmask[0:-1, 0:-1] = mask[bbox_min[0]:bbox_max[0] + 1,
                                    bbox_min[1]:bbox_max[1] + 1]
    elif num_dims == 3:
        cropmask[0:-1, 0:-1, 0:-1] = mask[bbox_min[0]:bbox_max[0] + 1,
                                            bbox_min[1]:bbox_max[1] + 1,
                                            bbox_min[2]:bbox_max[2] + 1]
    # pyformat: enable
    else:
        assert False

    return cropmask

def compute_surface_distances(mask_gt,
                              mask_pred,
                              spacing_mm):
    """Computes closest distances from all surface points to the other surface.

    This function can be applied to 2D or 3D tensors. For 2D, both masks must be
    2D and `spacing_mm` must be a 2-element list. For 3D, both masks must be 3D
    and `spacing_mm` must be a 3-element list. The description is done for the 2D
    case, and the formulation for the 3D case is present is parenthesis,
    introduced by "resp.".

    Finds all contour elements (resp surface elements "surfels" in 3D) in the
    ground truth mask `mask_gt` and the predicted mask `mask_pred`, computes their
    length in mm (resp. area in mm^2) and the distance to the closest point on the
    other contour (resp. surface). It returns two sorted lists of distances
    together with the corresponding contour lengths (resp. surfel areas). If one
    of the masks is empty, the corresponding lists are empty and all distances in
    the other list are `inf`.

    Args:
    mask_gt: 2-dim (resp. 3-dim) bool Numpy array. The ground truth mask.
    mask_pred: 2-dim (resp. 3-dim) bool Numpy array. The predicted mask.
    spacing_mm: 2-element (resp. 3-element) list-like structure. Voxel spacing
        in x0 anx x1 (resp. x0, x1 and x2) directions.

    Returns:
    A dict with:
    "distances_gt_to_pred": 1-dim numpy array of type float. The distances in mm
        from all ground truth surface elements to the predicted surface,
        sorted from smallest to largest.
    "distances_pred_to_gt": 1-dim numpy array of type float. The distances in mm
        from all predicted surface elements to the ground truth surface,
        sorted from smallest to largest.
    "surfel_areas_gt": 1-dim numpy array of type float. The length of the
        of the ground truth contours in mm (resp. the surface elements area in
        mm^2) in the same order as distances_gt_to_pred.
    "surfel_areas_pred": 1-dim numpy array of type float. The length of the
        of the predicted contours in mm (resp. the surface elements area in
        mm^2) in the same order as distances_gt_to_pred.

    Raises:
    ValueError: If the masks and the `spacing_mm` arguments are of incompatible
        shape or type. Or if the masks are not 2D or 3D.
    """
    # The terms used in this function are for the 3D case. In particular, surface
    # in 2D stands for contours in 3D. The surface elements in 3D correspond to
    # the line elements in 2D.



    _assert_is_bool_numpy_array("mask_gt", mask_gt)
    _assert_is_bool_numpy_array("mask_pred", mask_pred)

    if not len(mask_gt.shape) == len(mask_pred.shape) == len(spacing_mm):
        raise ValueError("The arguments must be of compatible shape. Got mask_gt "
                            "with {} dimensions ({}) and mask_pred with {} dimensions "
                            "({}), while the spacing_mm was {} elements.".format(
                                len(mask_gt.shape),
                                mask_gt.shape, len(mask_pred.shape), mask_pred.shape,
                                len(spacing_mm)))

    num_dims = len(spacing_mm)
    if num_dims == 2:
        _check_2d_numpy_array("mask_gt", mask_gt)
        _check_2d_numpy_array("mask_pred", mask_pred)

        # compute the area for all 16 possible surface elements
        # (given a 2x2 neighbourhood) according to the spacing_mm
        neighbour_code_to_surface_area = (lookup_tables.create_table_neighbour_code_to_contour_length(spacing_mm))
        kernel = lookup_tables.ENCODE_NEIGHBOURHOOD_2D_KERNEL
        full_true_neighbours = 0b1111
    elif num_dims == 3:
        _check_3d_numpy_array("mask_gt", mask_gt)
        _check_3d_numpy_array("mask_pred", mask_pred)

        # compute the area for all 256 possible surface elements
        # (given a 2x2x2 neighbourhood) according to the spacing_mm
        neighbour_code_to_surface_area = (
            lookup_tables.create_table_neighbour_code_to_surface_area(spacing_mm))
        kernel = lookup_tables.ENCODE_NEIGHBOURHOOD_3D_KERNEL
        full_true_neighbours = 0b11111111
    else:
        raise ValueError("Only 2D and 3D masks are supported, not "
                            "{}D.".format(num_dims))

    # compute the bounding box of the masks to trim the volume to the smallest
    # possible processing subvolume
    bbox_min, bbox_max = _compute_bounding_box(mask_gt | mask_pred)
    # Both the min/max bbox are None at the same time, so we only check one.
    if bbox_min is None:
        return {
            "distances_gt_to_pred": np.array([]),
            "distances_pred_to_gt": np.array([]),
            "surfel_areas_gt": np.array([]),
            "surfel_areas_pred": np.array([]),
        }

    # crop the processing subvolume.
    cropmask_gt = _crop_to_bounding_box(mask_gt, bbox_min, bbox_max)
    cropmask_pred = _crop_to_bounding_box(mask_pred, bbox_min, bbox_max)

    # compute the neighbour code (local binary pattern) for each voxel
    # the resulting arrays are spacially shifted by minus half a voxel in each
    # axis.
    # i.e. the points are located at the corners of the original voxels
    neighbour_code_map_gt = ndimage.filters.correlate(
        cropmask_gt.astype(np.uint8), kernel, mode="constant", cval=0)
    neighbour_code_map_pred = ndimage.filters.correlate(
        cropmask_pred.astype(np.uint8), kernel, mode="constant", cval=0)

    # create masks with the surface voxels
    borders_gt = ((neighbour_code_map_gt != 0) &
                (neighbour_code_map_gt != full_true_neighbours))
    borders_pred = ((neighbour_code_map_pred != 0) &
                    (neighbour_code_map_pred != full_true_neighbours))

    # compute the distance transform (closest distance of each voxel to the
    # surface voxels)
    if borders_gt.any():
        distmap_gt = ndimage.morphology.distance_transform_edt(
            ~borders_gt, sampling=spacing_mm)
    else:
        distmap_gt = np.Inf * np.ones(borders_gt.shape)

    if borders_pred.any():
        distmap_pred = ndimage.morphology.distance_transform_edt(
            ~borders_pred, sampling=spacing_mm)
    else:
        distmap_pred = np.Inf * np.ones(borders_pred.shape)

    # compute the area of each surface element
    surface_area_map_gt = neighbour_code_to_surface_area[neighbour_code_map_gt]
    surface_area_map_pred = neighbour_code_to_surface_area[
        neighbour_code_map_pred]

    # create a list of all surface elements with distance and area
    distances_gt_to_pred = distmap_pred[borders_gt]
    distances_pred_to_gt = distmap_gt[borders_pred]
    surfel_areas_gt = surface_area_map_gt[borders_gt]
    surfel_areas_pred = surface_area_map_pred[borders_pred]

    # sort them by distance
    if distances_gt_to_pred.shape != (0,):
        distances_gt_to_pred, surfel_areas_gt = _sort_distances_surfels(
            distances_gt_to_pred, surfel_areas_gt)

    if distances_pred_to_gt.shape != (0,):
        distances_pred_to_gt, surfel_areas_pred = _sort_distances_surfels(
            distances_pred_to_gt, surfel_areas_pred)

    return {
        "distances_gt_to_pred": distances_gt_to_pred,
        "distances_pred_to_gt": distances_pred_to_gt,
        "surfel_areas_gt": surfel_areas_gt,
        "surfel_areas_pred": surfel_areas_pred,
    }


def compute_surface_dice_at_tolerance(surface_distances, tolerance_mm):
    """Computes the _surface_ DICE coefficient at a specified tolerance.

    Computes the _surface_ DICE coefficient at a specified tolerance. Not to be
    confused with the standard _volumetric_ DICE coefficient. The surface DICE
    measures the overlap of two surfaces instead of two volumes. A surface
    element is counted as overlapping (or touching), when the closest distance to
    the other surface is less or equal to the specified tolerance. The DICE
    coefficient is in the range between 0.0 (no overlap) to 1.0 (perfect overlap).

    Args:
    surface_distances: dict with "distances_gt_to_pred", "distances_pred_to_gt"
        "surfel_areas_gt", "surfel_areas_pred" created by
        compute_surface_distances()
    tolerance_mm: a float value. The tolerance in mm

    Returns:
    A float value. The surface DICE coefficient in [0.0, 1.0].
    """
    distances_gt_to_pred = surface_distances["distances_gt_to_pred"]
    distances_pred_to_gt = surface_distances["distances_pred_to_gt"]
    surfel_areas_gt = surface_distances["surfel_areas_gt"]
    surfel_areas_pred = surface_distances["surfel_areas_pred"]
    overlap_gt = np.sum(surfel_areas_gt[distances_gt_to_pred <= tolerance_mm])
    overlap_pred = np.sum(surfel_areas_pred[distances_pred_to_gt <= tolerance_mm])
    surface_dice = (overlap_gt + overlap_pred) / (
        np.sum(surfel_areas_gt) + np.sum(surfel_areas_pred))
    return surface_dice

# Done how to define a spacing from predicted images
def surface_dice(y_pred, y_true, spacing):
    '''
    For 3D, both masks must be 3D and `spacing_mm` must be a 3-element list.
    '''
    y_pred = y_pred.squeeze(0).detach().cpu().numpy().astype(bool)
    y_true = y_true.squeeze(0).detach().cpu().numpy().astype(bool)
    tolerance_mm = 0.1
    surface_distances = compute_surface_distances(y_true, y_pred, spacing)
    surface_distance_dice = compute_surface_dice_at_tolerance(surface_distances, tolerance_mm=tolerance_mm)
    return surface_distance_dice

def hausdorff_distance(y_pred, y_true):
    hd_metric = monai.metrics.HausdorffDistanceMetric(include_background=False, reduction='none')
    return hd_metric(y_pred, y_true)

def average_surface_distance(y_pred, y_true):
    asd = monai.metrics.SurfaceDistanceMetric(include_background=False, reduction='none', symmetric=True)
    return asd(y_pred, y_true)

def structural_similarity_index_measure(y_true, y_pred):
    data_range = torch.max(y_true) - torch.min(y_true)
    ss_metric = monai.metrics.SSIMMetric(data_range, spatial_dims=3, reduction='none')
    return ss_metric(y_true, y_pred)

def jaccard_index(y_pred, y_true):
    jaccard_loss = monai.losses.DiceLoss(include_background=False,
        to_onehot_y=False,
        softmax=False,
        jaccard =True,
        reduction="none")
    jaccard_metric = 1-jaccard_loss(y_pred, y_true)
    
    return jaccard_metric

def chamfer_weighted_symmetric(y_true, y_pred): 
    '''
    A and B are both points 
    '''
    N1 = y_true.shape[1]
    N2 = y_pred.shape[1]
    y1 = y_true[:, :, None].repeat(1, 1, N2, 1)
    y2 = y_pred[:, None].repeat(1, N1, 1, 1)

    diff = torch.sum((y1 - y2) ** 2, dim=3)

    loss1, _ = torch.min(diff, dim=1)
    loss2, _ = torch.min(diff, dim=2) 
    loss = torch.mean(loss1) + torch.mean(loss2)
    return loss

def peak_signal_to_noise_ratio(y_true, y_pred):
    data_range = torch.max(y_true) - torch.min(y_true)
    psnr_metric = monai.metrics.PSNRMetric(data_range, reduction='none')
    return psnr_metric(y_pred, y_true)

def average_normal_error(y_true, y_pred):
    assert isinstance(y_true, p3d.structures.Meshes) and isinstance(y_pred, p3d.structures.Meshes)
    verts_true = y_true.verts_packed().unsqueeze(0)
    normal_true = y_true.verts_normals_packed()
    verts_pred = y_pred.verts_packed().unsqueeze(0).to(torch.float32)
    normal_pred = y_pred.verts_normals_packed().to(torch.float32)
    _, idx, _ = knn_points(verts_pred, verts_true)
    normal_target = normal_true[idx[0,:,0]]
    normal_error = 1 - torch.diag(normal_pred @ normal_target.t())
    return torch.mean(normal_error)

def average_normalized_lap_distance(y_pred):
    assert isinstance(y_pred, p3d.structures.Meshes)
    verts_pred = y_pred.verts_packed().to(torch.float32)
    edges_pred = y_pred.edges_packed()
    verts_edges = verts_pred[edges_pred]
    v0, v1 = verts_edges.unbind(1)
    edge_loss = (v0-v1).norm(dim=1, p=2)
    
    with torch.no_grad():
        L = y_pred.laplacian_packed()
    
    loss = L.mm(verts_pred)
    lap_loss = loss.norm(dim=1, p=2)
    normalized_lap_distance = 0
    for i in range(verts_pred.shape[0]):
        index = (edges_pred[:,0] == i).nonzero(as_tuple=False)
        if index.nelement() != 0:
            assert index.dim() == 2 and index.shape[1] == 1
            normalized_lap_distance += lap_loss[i] / (edge_loss[index[:,0]].sum() / index.shape[0])
        
    return normalized_lap_distance / verts_pred.shape[0]