machineLearningTemplate/evaluate.py

"""
评估指定文件夹下的预测结果, 评价结果均不计算背景类
"""
import numpy as np
from PIL import Image
from os.path import join
from network_module.iou import IOU
from network_module.pix_acc import calculate_acc


def evalute(n_classes, dataset_path, verbose=False):
    iou = IOU(n_classes)

    test_list = open(join(dataset_path, 'test_dataset_list.txt').replace('\\', '/')).readlines()
    for ind in range(len(test_list)):
        pred = np.array(Image.open(join(dataset_path, 'prediction', test_list[ind].strip('\n')).replace('\\', '/')))
        label = np.array(Image.open(join(dataset_path, 'labels', test_list[ind].strip('\n')).replace('\\', '/')))
        if len(label.flatten()) != len(pred.flatten()):
            print('跳过{:s}: pred len {:d} != label len {:d},'.format(
                test_list[ind].strip('\n'), len(label.flatten()), len(pred.flatten())))
            continue

        iou.add_data(pred, label)

    # 必须置于iou_loss.forward前,因为forward会清除hist
    overall_acc, acc = calculate_acc(iou.hist)
    mIoU, IoUs = iou.get_miou()

    if verbose:
        for ind_class in range(n_classes):
            print('===>' + str(ind_class) + ':\t' + str(IoUs[ind_class].float()))
        print('===> mIoU: ' + str(mIoU))
        print('===> overall accuracy:', overall_acc)
        print('===> accuracy of each class:', acc)


if __name__ == "__main__":
    evalute(9, './dataset/MFNet(RGB-T)-mini', verbose=True)