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unet-master 2

于 2020-06-29 发布
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下载积分: 1 下载次数: 4
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代码说明：

说明：  使用unet对图像进行分割的源码，里面有训练集，可以根据自己的需要更换训练数据。(Use the source code of the image segmentation using UNET, which has a training set, you can change the training data according to your own needs.)

文件列表：

unet-master, 0 , 2020-06-24
unet-master\trainUnet.ipynb, 9916 , 2020-06-24
__MACOSX, 0 , 2020-06-29
__MACOSX\unet-master, 0 , 2020-06-29
__MACOSX\unet-master\._trainUnet.ipynb, 212 , 2020-06-24
unet-master\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\._.DS_Store, 120 , 2020-06-29
unet-master\dataPrepare.ipynb, 3831 , 2019-02-21
__MACOSX\unet-master\._dataPrepare.ipynb, 212 , 2019-02-21
unet-master\LICENSE, 1065 , 2019-02-21
__MACOSX\unet-master\._LICENSE, 212 , 2019-02-21
unet-master\Untitled.ipynb, 11919 , 2020-06-24
unet-master\__pycache__, 0 , 2020-06-24
unet-master\__pycache__\model.cpython-36.pyc, 2097 , 2020-06-24
unet-master\__pycache__\data.cpython-36.pyc, 3898 , 2020-06-24
unet-master\model.py, 3745 , 2019-02-21
__MACOSX\unet-master\._model.py, 212 , 2019-02-21
unet-master\README.md, 2552 , 2019-02-21
__MACOSX\unet-master\._README.md, 212 , 2019-02-21
unet-master\img, 0 , 2019-02-21
unet-master\img\0label.png, 178720 , 2019-02-21
__MACOSX\unet-master\img, 0 , 2020-06-29
__MACOSX\unet-master\img\._0label.png, 212 , 2019-02-21
unet-master\img\0test.png, 400739 , 2019-02-21
__MACOSX\unet-master\img\._0test.png, 212 , 2019-02-21
unet-master\img\u-net-architecture.png, 40580 , 2019-02-21
__MACOSX\unet-master\img\._u-net-architecture.png, 212 , 2019-02-21
__MACOSX\unet-master\._img, 212 , 2019-02-21
unet-master\.ipynb_checkpoints, 0 , 2020-06-24
unet-master\.ipynb_checkpoints\trainUnet-checkpoint.ipynb, 9802 , 2020-06-24
unet-master\.ipynb_checkpoints\Untitled-checkpoint.ipynb, 72 , 2020-06-24
unet-master\main.py, 821 , 2019-02-21
__MACOSX\unet-master\._main.py, 212 , 2019-02-21
unet-master\data, 0 , 2020-06-24
unet-master\data\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data, 0 , 2020-06-29
__MACOSX\unet-master\data\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane, 0 , 2020-06-24
unet-master\data\membrane\.DS_Store, 8196 , 2020-06-29
__MACOSX\unet-master\data\membrane, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\test, 0 , 2020-06-29
unet-master\data\membrane\test\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\test, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\test\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\test\0_predict.png, 48695 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._0_predict.png, 212 , 2019-02-21
unet-master\data\membrane\test\1_predict.png, 54547 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._1_predict.png, 212 , 2019-02-21
unet-master\data\membrane\test\1.png, 213325 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._1.png, 212 , 2019-02-21
unet-master\data\membrane\test\0.png, 214932 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\._test, 212 , 2020-06-29
unet-master\data\membrane\test-volume.tif, 7871660 , 2019-02-21
__MACOSX\unet-master\data\membrane\._test-volume.tif, 212 , 2019-02-21
unet-master\data\membrane\train-volume.tif, 7870730 , 2019-02-21
__MACOSX\unet-master\data\membrane\._train-volume.tif, 212 , 2019-02-21
unet-master\data\membrane\train-labels.tif, 7869573 , 2019-02-21
__MACOSX\unet-master\data\membrane\._train-labels.tif, 212 , 2019-02-21
unet-master\data\membrane\train, 0 , 2020-06-24
unet-master\data\membrane\train\.DS_Store, 10244 , 2020-06-29
__MACOSX\unet-master\data\membrane\train, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\aug, 0 , 2020-06-29
unet-master\data\membrane\train\aug\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\aug, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\aug\._.DS_Store, 120 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\._aug, 212 , 2020-06-29
unet-master\data\membrane\train\label, 0 , 2020-06-29
unet-master\data\membrane\train\label\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\label, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\label\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\label\4.png, 14312 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._4.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\2.png, 14052 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._2.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\3.png, 13829 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._3.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\1.png, 13977 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._1.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\0.png, 14322 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\._label, 212 , 2020-06-29
unet-master\data\membrane\train\image, 0 , 2020-06-29
unet-master\data\membrane\train\image\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\image, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\image\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\image\4.png, 189054 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._4.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\2.png, 188971 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._2.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\3.png, 187963 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._3.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\1.png, 188189 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._1.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\0.png, 187651 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\._image, 212 , 2020-06-29
__MACOSX\unet-master\data\membrane\._train, 212 , 2020-06-24

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发表评论

0 个回复

  • FSIM
    Zhang等人 利用相位一致性信息的特性，提出了 特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息 作为它的两个特征，得到了较好的结果。 (A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA s demonstrate that FSIM can achieve much higher consistency with the subjective uations than state-of-the-art IQA metrics.)
    2015-09-16 19:18:45下载
    积分：1
  • kmeans
    kmeans算法实现，对图像处理。亲自使用过，可用，供参考。(Kmeans algorithm for image processing.)
    2013-10-17 10:57:41下载
    积分：1
  • lenaentropy
    说明：  求lena图像的信息熵，图像大小为256X256(For lena image information entropy, image size is 256x256)
    2008-11-18 18:58:53下载
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  • shutu1
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    2020-03-19 11:47:30下载
    积分：1
  • 图像分割
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    2017-08-14 10:54:46下载
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    k-均值算法 做的图象分割实例 （将图片像素聚成三类）(k-means algorithm to do the image segmentation example (poly-pixel pictures into three categories))
    2008-03-13 14:16:47下载
    积分：1
  • PSFgenerate
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    2021-04-22 15:18:48下载
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  • 打码平台 远程打码 实例源码
    C# 远程打码 实例项目源码
    2013-06-13下载
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  • classification
    说明：  改进LBP纹理提取以及纹理分类，主要包括三部分测试：纹理提取、分类和分割(Improved LBP texture extraction and texture classification, including three parts of testing: texture extraction, classification and segmentation)
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