scikit-image-0.16.2.tar.gz资源-CSDN文库

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76 浏览量 2024-02-15 14:43:03 上传评论收藏 27.6MB GZ 举报

共1035个文件

py：596个

rst：174个

c：51个

Scikit-image是一个强大的Python图像处理库，它包含了各种算法，用于图像分割、几何变换、颜色空间操作、滤波以及特征检测等。版本0.16.2是该库的一个稳定版本，提供了许多改进和新功能。 1. **安装与依赖**：安装scikit-image通常使用pip命令，`pip install scikit-image`。它依赖于其他一些库，如NumPy、SciPy和matplotlib，这些都需要预先安装。 2. **图像变换**： Scikit-image提供了多种几何变换函数，如旋转、缩放、平移和仿射变换。例如，`affine_transform()`可以实现非线性变换，而`rescale()`则用于按比例调整图像大小。 3. **滤波与降噪**：图像滤波是图像处理中的关键步骤，scikit-image提供了多种滤波器，如高斯滤波、中值滤波和双边滤波，用于平滑图像或保留边缘。`gaussian_filter()`用于应用高斯滤波，`median_filter()`执行中值滤波以去除噪声。 4. **颜色空间操作**：图像的颜色空间转换是处理颜色图像时常用的功能。scikit-image支持多种颜色空间转换，如RGB到灰度、HSV或YCbCr的转换。`color.rgb2gray()`可以将RGB图像转换为灰度图像。 5. **图像分割**：分割是将图像分成具有不同特征的区域的过程。Scikit-image提供了多种分割方法，如基于阈值的分割（`thresholding`模块）、区域生长（`morphology`模块的`regionprops`）和基于模型的分割（如Felzenszwalb和Huttenlocher的分水岭算法）。 6. **形态学操作**：形态学操作在图像处理中用于处理边界和结构。比如，膨胀和腐蚀可以用于去除噪声或突出物体边缘；骨架化可以将物体简化为单像素宽的骨架。 7. **特征检测**： Scikit-image提供了多种特征检测算法，如角点检测（`corner.harris()`）、SIFT（Scale-Invariant Feature Transform）和SURF（Speeded Up Robust Features）。这些特征可用于图像识别和匹配。 8. **测量与分析**：库中的`measure`模块包含多种工具，用于计算图像的统计属性，如区域的面积、周长、质心等，以及进行轮廓检测和形状匹配。 9. **深度学习集成**：虽然scikit-image本身不包含深度学习算法，但可以与深度学习框架如TensorFlow和PyTorch结合使用，处理预处理和后处理任务，提供数据增强等功能。 10. **文档与社区支持**： Scikit-image拥有详尽的文档和活跃的社区，用户可以通过官方文档和论坛获取帮助，快速解决问题。 scikit-image-0.16.2是一个强大的工具，适合科研和工程人员进行图像处理工作。无论是基础的图像操作还是复杂的分析任务，它都能提供高效且易于使用的接口。如果你在Python环境中进行图像处理，scikit-image是一个不可或缺的库。

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scikit-image-0.16.2.tar.gz （1035个子文件）

generic_cy.c 6.46MB

_texture.c 4.38MB

_denoise_cy.c 4.3MB

_hough_transform.c 4.3MB

_warps_cy.c 4.18MB

orb_cy.c 4.04MB

interpolation.c 3.48MB

_max_tree.c 2.82MB

percentile_cy.c 2.13MB

_marching_cubes_classic_cy.c 1.84MB

_mcp.c 1.64MB

_marching_cubes_lewiner_cy.c 1.56MB

_flood_fill_cy.c 1.47MB

core_cy.c 1.43MB

_extrema_cy.c 1.32MB

bilateral_cy.c 1.31MB

_draw.c 1.25MB

_nl_means_denoising.c 1.19MB

_slic.c 1MB

_ctmf.c 1019KB

_spath.c 1012KB

corner_cy.c 1006KB

_skeletonize_cy.c 1004KB

_watershed.c 965KB

_radon_transform.c 959KB

_seam_carving.c 956KB

_quickshift_cy.c 952KB

_find_contours_cy.c 929KB

_hoghistogram.c 926KB

_pnpoly.c 921KB

_ncut_cy.c 919KB

_multiotsu.c 911KB

_hessian_det_appx.c 903KB

_convex_hull.c 898KB

brief_cy.c 889KB

transform.c 889KB

censure_cy.c 757KB

_unwrap_3d.c 756KB

_unwrap_2d.c 755KB

_moments_cy.c 754KB

_unwrap_1d.c 737KB

_ccomp.c 568KB

_felzenszwalb_cy.c 480KB

_colormixer.c 473KB

heap.c 365KB

_histograms.c 338KB

_greyreconstruct.c 330KB

geometry.c 104KB

unwrap_3d_ljmu.c 47KB

tifffile.c 29KB

unwrap_2d_ljmu.c 26KB

setup.cfg 38B

_cascade.cpp 4.36MB

_haar.cpp 1.65MB

_skeletonize_3d_cy.cpp 1.11MB

MarchingCubes.cpp 44KB

multi.fits 264KB

simple.fits 135KB

no_time_for_that_tiny.gif 4KB

LookUpTable.h 155KB

vectorized_ops.h 2KB

fast_exp.h 1KB

conditional_omp.h 665B

navbar.html 525B

versions.html 457B

searchbox.html 345B

localtoc.html 274B

navigation.html 1B

_skeletonize_3d_cy.pyx.in 15KB

MANIFEST.in 562B

matplotlib_plugin.ini 123B

tifffile_plugin.ini 110B

qt_plugin.ini 105B

simpleitk_plugin.ini 92B

gtk_plugin.ini 91B

pil_plugin.ini 91B

gdal_plugin.ini 90B

fits_plugin.ini 89B

imageio_plugin.ini 88B

imread_plugin.ini 86B

hubble_deep_field.jpg 516KB

retina.jpg 263KB

rocket.jpg 110KB

truncated.jpg 400B

Makefile 5KB

Makefile 566B

README.md 1KB

README.md 457B

README.md 295B

astronaut_GRAY_hog_L2-Hys.npy 2.38MB

astronaut_GRAY_hog_L1.npy 2.38MB

camera_unsup2.npy 2MB

camera_rl.npy 2MB

camera_wiener.npy 2MB

camera_unsup.npy 2MB

lfw_subset.npy 977KB

bw_text_skeleton.npy 168KB

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