eval_data.py

#!/usr/bin/python

'''
Martin Kersner, m.kersner@gmail.com
2015/11/30
Evaluation metrics for image segmentation inspired by
paper Fully Convolutional Networks for Semantic Segmentation.
'''

import numpy as np
import SimpleITK as sitk
import torch


def dice_loss(pred, target):
    """pred: tensor with first dimension as batch
       target :tensor with first dimension as batch
    """
    smooth = 1
    iflat = pred.contiguous().view(-1)
    tflat = target.contiguous().view(-1)
    intersection = (iflat * tflat).sum()

    A_sum = torch.sum(iflat * iflat)
    B_sum = torch.sum(tflat * tflat)

    #return 1-((2. * intersection+smooth)/(A_sum+B_sum+smooth))
    return (2. * intersection+smooth)/(A_sum+B_sum+smooth)

def jaccard(pred, target):
    """pred: tensor with first dimension as batch
       target :tensor with first dimension as batch
    """
    smooth = 1
    iflat = pred.contiguous().view(-1)
    tflat = target.contiguous().view(-1)
    intersection = (iflat * tflat).sum()

    A_sum = torch.sum(iflat * iflat)
    B_sum = torch.sum(tflat * tflat)

    #return 1-((2. * intersection+smooth)/(A_sum+B_sum+smooth))
    return (intersection+smooth)/(A_sum+B_sum-intersection+smooth)


def pixel_accuracy(eval_segm, gt_segm):
    '''
    sum_i(n_ii) / sum_i(t_i)
    '''

    #check_size(eval_segm, gt_segm)

    cl, n_cl = extract_classes(gt_segm)
    eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)

    sum_n_ii = 0
    sum_t_i  = 0

    for i, c in enumerate(cl):
        curr_eval_mask = eval_mask[i, :, :, :]
        curr_gt_mask = gt_mask[i, :, :, :]

        sum_n_ii += np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
        sum_t_i  += np.sum(curr_gt_mask)
 
    if (sum_t_i == 0):
        pixel_accuracy_ = 0
    else:
        pixel_accuracy_ = sum_n_ii / sum_t_i

    return pixel_accuracy_

def mean_accuracy(eval_segm, gt_segm):
    '''
    (1/n_cl) sum_i(n_ii/t_i)
    '''

    #check_size(eval_segm, gt_segm)

    cl, n_cl = extract_classes(gt_segm)
    eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)

    accuracy = list([0]) * n_cl

    for i, c in enumerate(cl):
        curr_eval_mask = eval_mask[i, :, :, :]
        curr_gt_mask = gt_mask[i, :, :, :]

        n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
        t_i  = np.sum(curr_gt_mask)
 
        if (t_i != 0):
            accuracy[i] = n_ii / t_i

    mean_accuracy_ = np.mean(accuracy)
    return mean_accuracy_

def mean_IU(eval_segm, gt_segm):
    '''
    (1/n_cl) * sum_i(n_ii / (t_i + sum_j(n_ji) - n_ii))
    '''

    #check_size(eval_segm, gt_segm)

    cl, n_cl   = union_classes(eval_segm, gt_segm)
    _, n_cl_gt = extract_classes(gt_segm)
    eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)

    IU = list([0]) * n_cl

    for i, c in enumerate(cl):
        curr_eval_mask = eval_mask[i, :, :, :]
        curr_gt_mask = gt_mask[i, :, :, :]
 
        if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
            continue

        n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
        t_i  = np.sum(curr_gt_mask)
        n_ij = np.sum(curr_eval_mask)

        IU[i] = n_ii / (t_i + n_ij - n_ii)
 
    mean_IU_ = np.sum(IU) / n_cl_gt
    return mean_IU_

def frequency_weighted_IU(eval_segm, gt_segm):
    '''
    sum_k(t_k)^(-1) * sum_i((t_i*n_ii)/(t_i + sum_j(n_ji) - n_ii))
    '''

    #check_size(eval_segm, gt_segm)

    cl, n_cl = union_classes(eval_segm, gt_segm)
    eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)

    frequency_weighted_IU_ = list([0]) * n_cl

    for i, c in enumerate(cl):
        curr_eval_mask = eval_mask[i, :, :, :]
        curr_gt_mask = gt_mask[i, :, :, :]
 
        if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
            continue

        n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
        t_i  = np.sum(curr_gt_mask)
        n_ij = np.sum(curr_eval_mask)

        frequency_weighted_IU_[i] = (t_i * n_ii) / (t_i + n_ij - n_ii)
 
    sum_k_t_k = get_pixel_area(eval_segm)
    
    frequency_weighted_IU_ = np.sum(frequency_weighted_IU_) / sum_k_t_k
    return frequency_weighted_IU_

'''
Auxiliary functions used during evaluation.
'''
def get_pixel_area(segm):
    return segm.shape[0] * segm.shape[1] * segm.shape[2]

def extract_both_masks(eval_segm, gt_segm, cl, n_cl):
    eval_mask = extract_masks(eval_segm, cl, n_cl)
    gt_mask   = extract_masks(gt_segm, cl, n_cl)

    return eval_mask, gt_mask

def extract_classes(segm):
    cl = np.unique(segm)
    n_cl = len(cl)

    return cl, n_cl

def union_classes(eval_segm, gt_segm):
    eval_cl, _ = extract_classes(eval_segm)
    gt_cl, _   = extract_classes(gt_segm)

    cl = np.union1d(eval_cl, gt_cl)
    n_cl = len(cl)

    return cl, n_cl

def extract_masks(segm, cl, n_cl):
    h, w, d  = segm_size(segm)
    masks = np.zeros((n_cl, h, w, d))

    for i, c in enumerate(cl):
        masks[i, :, :, :] = segm == c

    return masks

def segm_size(segm):
    try:
        height = segm.shape[0]
        width  = segm.shape[1]
        depth = segm.shape[2]
    except IndexError:
        raise

    return height, width, depth
    

'''def check_size(eval_segm, gt_segm):
    d_e, h_e, w_e = segm_size(eval_segm)
    d_g, h_g, w_g = segm_size(gt_segm)

    if (h_e != h_g) or (w_e != w_g):
        raise EvalSegErr("DiffDim: Different dimensions of matrices!")'''

'''
Exceptions
'''
class EvalSegErr(Exception):
    def __init__(self, value):
        self.value = value

    def __str__(self):
        return repr(self.value)


if __name__ == "__main__":
    pred = sitk.GetArrayFromImage(sitk.ReadImage(\
        ''))
    target = sitk.GetArrayFromImage(sitk.ReadImage(\
        ''))
    pred1 = pred
    target1 = target
    pred = torch.from_numpy(pred.astype(np.float32))
    target = torch.from_numpy(target.astype(np.float32))
    assert(pred.shape == target.shape)
    dice = dice_loss(pred, target)
    print(dice)
    accuracy=[]
    for i in range(pred.shape[0]):
        accuracy.append(pixel_accuracy(pred1[i,:,:], target1[i,:,:]))
    print(accuracy)