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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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    积分：1
  • yj
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    Matlab的GUI中选择图像的感兴趣区域ROI(正方形和矩形)(Matlab GUI to select the image region of interest ROI (square and rectangular))
    2012-03-28 11:28:55下载
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    高斯滤波是一种线性平滑滤波，适用于消除高斯噪声，广泛应用于图像处理的减噪过程。通俗的讲，高斯滤波就是对整幅图像进行加权平均的过程，每一个像素点的值，都由其本身和邻域内的其他像素值经过加权平均后得到。高斯滤波的具体操作是：用一个模板（或称卷积、掩模）扫描图像中的每一个像素，用模板确定的邻域内像素的加权平均灰度值去替代模板中心像素点的值。(Gaussian smoothing filter is a linear filter for the elimination of Gaussian noise, the noise reduction process is widely used in image processing. Popular speaking, a Gaussian filter to the whole image is a weighted average of the process, the value of each pixel, both by itself and other neighborhood pixel values obtained after weighted average. The specific operation Gaussian filter is: a template (or a convolution mask) to scan each pixel in the image, a weighted average gray value of the neighborhood is determined using a template to replace the pixel value of the center pixel of the template.)
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