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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

  • 图像分割中常用的水平集方法的matlab源代码
    图像分割是图像分析的关键步骤，而水平集方法是图像分割的一项热门方法，它有着许多算法不具有的优点，尤其可用于分割背景复杂信息难以提取的医学图像。这里是一些水平集分割图像的matlab的实现例子，希望可以帮到大家。(Image segmentation is the key step of image analysis, while the level set method is a popular image segmentation method, it has many advantages that the algorithm does not have, especially can be used for medical image segmentation of complex background information is difficult to extract. Here is some level set segmentation image matlab example, I hope we can help you.)
    2017-08-27 10:44:26下载
    积分：1
  • yundongguji1
    这是图像运动补偿中的进行运动补偿估计的常用程序算法matlab实现(This is the image motion compensation in the exercise of compensation is estimated to achieve common procedures algorithm matlab)
    2009-01-08 20:38:15下载
    积分：1
  • key
    实现了基于关键像素点的FLICM图像分割算法，关键像素由局部最大值方法提取。(A FLICM image segmentation algorithm based on key pixels is implemented. Key pixels are extracted by local maximum method.)
    2020-06-25 09:20:01下载
    积分：1
  • Image-Restoration-by-total-variation
    基于全变分（TV）的多光谱遥感图像恢复。当多光谱图像中一个通道模糊时，利用其它通道的图像特征辅助进行去模糊或反卷积，效果不错！！附带有对应的文章做参考。(This is a new image restoration or deconvolution method, which uses one of clear band image to constraint Total Variation de-blurring of degraded band image in multispectral image. It tested with different noise with different regularization parameters. It also compared with otherhods.)
    2013-08-30 22:13:45下载
    积分：1
  • discrim_rf
    斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )
    2012-10-30 16:17:30下载
    积分：1
  • ViBe-master
    前景检测 对动态背景进行多角度监测 动态背景检测(Foreground detection is used to monitor dynamic background from different angles)
    2020-11-17 21:19:39下载
    积分：1
  • ENVI
    ENVI是比较常用的遥感图像处理软件，使用该插件，可以使ENVI软件支持HDF5格式的遥感影像，HDF5数据格式是科学计算一体化数据格式，常用于卫星遥感影像的外部存储。(Envi is more commonly used in remote sensing image processing software, use the plugin can enable envi software support the HDF5 format of remote sensing images, the HDF5 data format is scientific computing integration data format, commonly used in satellite remote sensing image of the external storage. )
    2016-07-22 10:52:38下载
    积分：1
  • target
    MATLAB编写的角点检测程序，用于对目标的检测和稳定的跟踪。很适合对于运动目标的精确跟踪。(Corner detection procedures written in MATLAB for target detection and tracking. Very suitable for the accurate tracking of moving targets.)
    2012-07-04 18:17:55下载
    积分：1
  • attack
    为数字图像水印提供多种攻击方式，其中包括JPEG压缩、高斯低通滤波等多达十种攻击方式。(Offers a variety of attacks of digital watermarking ,including the jpeg compression, Gaussian low-pass filtering, up to ten different attacks)
    2020-06-29 21:20:01下载
    积分：1
  • fireflies
    萤火虫算法的源代码，函数文件，可直接运行，用来寻找图像阈值分割的最优值（三阈值）。(firefly algorithm function.)
    2021-03-18 22:29:19下载
    积分：1
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