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# encoding: utf-8
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
# voc cityscapes metric
def hist_info(n_cl, pred, gt):
assert (pred.shape == gt.shape)
k = (gt >= 0) & (gt < n_cl)
labeled = np.sum(k)
correct = np.sum((pred[k] == gt[k]))
return np.bincount(n_cl * gt[k].astype(int) + pred[k].astype(int),
minlength=n_cl ** 2).reshape(n_cl,
n_cl), labeled, correct
def compute_score(hist, correct, labeled):
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
acc = np.diag(hist) / hist.sum(1)
mean_acc = np.nanmean(acc)
mean_IU = np.nanmean(iu)
mean_IU_no_back = np.nanmean(iu[1:])
freq = hist.sum(1) / hist.sum()
freq_IU = (iu[freq > 0] * freq[freq > 0]).sum()
mean_pixel_acc = correct / labeled
return iu, mean_IU, mean_IU_no_back, mean_pixel_acc, mean_acc
# ade metric
def meanIoU(area_intersection, area_union):
iou = 1.0 * np.sum(area_intersection, axis=1) / np.sum(area_union, axis=1)
meaniou = np.nanmean(iou)
meaniou_no_back = np.nanmean(iou[1:])
return iou, meaniou, meaniou_no_back
def intersectionAndUnion(imPred, imLab, numClass):
# Remove classes from unlabeled pixels in gt image.
# We should not penalize detections in unlabeled portions of the image.
imPred = imPred * (imLab >= 0)
# Compute area intersection:
intersection = imPred * (imPred == imLab)
(area_intersection, _) = np.histogram(intersection, bins=numClass,
range=(1, numClass))
# Compute area union:
(area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
(area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
area_union = area_pred + area_lab - area_intersection
return area_intersection, area_union
def mean_pixel_accuracy(pixel_correct, pixel_labeled):
mean_pixel_accuracy = 1.0 * np.sum(pixel_correct) / (
np.spacing(1) + np.sum(pixel_labeled))
return mean_pixel_accuracy
def pixelAccuracy(imPred, imLab):
# Remove classes from unlabeled pixels in gt image.
# We should not penalize detections in unlabeled portions of the image.
pixel_labeled = np.sum(imLab >= 0)
pixel_correct = np.sum((imPred == imLab) * (imLab >= 0))
pixel_accuracy = 1.0 * pixel_correct / pixel_labeled
return pixel_accuracy, pixel_correct, pixel_labeled
def compute_metrics(results, num_classes):
hist = np.zeros((num_classes, num_classes))
correct = 0
labeled = 0
count = 0
for d in results:
hist += d['hist']
correct += d['correct']
labeled += d['labeled']
count += d['count']
_, mean_IU, _, mean_pixel_acc, mean_acc = compute_score(hist, correct,
labeled)
return mean_IU, mean_pixel_acc, mean_acc |