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evaluate_seg.py
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evaluate_seg.py
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import numpy as np
from PIL import Image
import os
import pdb
from multiprocessing import Pool
from functools import partial
from datetime import datetime
# ignore 255
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.initialized = False
self.val = None
self.avg = None
self.sum = None
self.count = None
def initialize(self, val, weight):
self.val = val
self.avg = val
self.sum = val * weight
self.count = weight
self.initialized = True
def update(self, val, weight=1):
if not self.initialized:
self.initialize(val, weight)
else:
self.add(val, weight)
def add(self, val, weight):
self.val = val
self.sum += val * weight
self.count += weight
self.avg = self.sum / self.count
def value(self):
return self.val
def average(self):
return self.avg
def accuracy(preds, label):
valid = (label >= 0)
acc_sum = (valid * (preds == label)).sum()
valid_sum = valid.sum()
acc = float(acc_sum) / (valid_sum + 1e-10)
return acc, valid_sum
def intersectionAndUnion(imPred, imLab, numClass):
imPred = np.asarray(imPred).copy()
imLab = np.asarray(imLab).copy()
imPred += 1
imLab += 1
# 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 evaluate(pred_dir, gt_dir, num_class):
acc_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
names = os.listdir(pred_dir)
for name in names:
pred = Image.open('{}/{}'.format(pred_dir, name)).convert('P')
gt = Image.open('{}/{}'.format(gt_dir, name)).convert('P')
pred = pred.resize(gt.size)
pred = np.array(pred, dtype=np.int64)
gt = np.array(gt, dtype=np.int64)
gt[gt==255] = -1
acc, pix = accuracy(pred, gt)
intersection, union = intersectionAndUnion(pred, gt, num_class)
acc_meter.update(acc, pix)
intersection_meter.update(intersection)
union_meter.update(union)
iou = intersection_meter.sum / (union_meter.sum + 1e-10)
for i, _iou in enumerate(iou):
print('class [{}], IoU: {}'.format(i, _iou))
# print('[Eval Summary]:')
# print('Mean IoU: {:.4}, Accuracy: {:.2f}%'
# .format(iou.mean(), acc_meter.average() * 100))
return iou.mean(), acc_meter.average()
def evaluate_one(pp, num_class):
pred = Image.open(pp[0]).convert('P')
gt = Image.open(pp[1]).convert('P')
pred = pred.resize(gt.size)
pred = np.array(pred, dtype=np.int64)
gt = np.array(gt, dtype=np.int64)
gt[gt == 255] = -1
intersection, union = intersectionAndUnion(pred, gt, num_class)
return [intersection, union]
def evaluate_iou(pred_dir, gt_dir, num_class):
names = os.listdir(pred_dir)
paths = [('{}/{}'.format(pred_dir, name), '{}/{}'.format(gt_dir, name)) for name in names]
pool = Pool(4)
results = pool.map(partial(evaluate_one, num_class=num_class), paths)
results = np.array(results)
ins = results[:, 0, :]
uns = results[:, 1, :]
# ins = []
# uns = []
# for pp in paths:
# intersection, union = evaluate_one(pp, num_class)
# ins += [intersection]
# uns += [union]
# ins = np.array(ins)
# uns = np.array(uns)
iou = ins.sum(0) / (uns.sum(0)+1e-10)
miou = iou.mean()
# for i, _iou in enumerate(iou):
# print('class [{}], IoU: {}'.format(i, _iou))
# print('[Eval Summary]:')
# print('Mean IoU: {:.4}'
# .format(miou))
return miou
# for name in names:
# pred = Image.open('{}/{}'.format(pred_dir, name))
# gt = Image.open('{}/{}'.format(gt_dir, name)).convert('P')
# pred = pred.resize(gt.size)
# pred = np.array(pred, dtype=np.int64)
# gt = np.array(gt, dtype=np.int64)
# gt[gt==255] = -1
# intersection, union = intersectionAndUnion(pred, gt, num_class)
# intersection_meter.update(intersection)
# union_meter.update(union)
# iou = intersection_meter.sum / (union_meter.sum + 1e-10)
# for i, _iou in enumerate(iou):
# print('class [{}], IoU: {}'.format(i, _iou))
#
# print('[Eval Summary]:')
# print('Mean IoU: {:.4}'
# .format(iou.mean()))
# return iou.mean()
if __name__ == "__main__":
output_dir = '../WSLfiles/WT_densenet169/results'
gt_dir = '../data/datasets/segmentation_Dataset/VOCdevkit/VOC2012/SegmentationClass'
miou = evaluate_iou(output_dir, gt_dir, 21)
print(miou)