scikit-image-0.15.0.tar.gz资源-CSDN下载

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scikit-image

scikit_image

skimage

0.15.0

深度学习

199 浏览量 2024-02-16 14:08:53 上传评论收藏 30.79MB GZ 举报

共998个文件

py：569个

rst：165个

c：49个

Scikit-image是一个强大的开源Python库，专用于图像处理和计算机视觉任务。它的版本0.15.0在压缩包文件"scikit-image-0.15.0.tar.gz"中提供，是Python科学计算生态中的一个重要组成部分。这个库是基于NumPy数组结构，因此它与诸如Pandas、Matplotlib和SciPy等其他科学计算库有着良好的兼容性。 Scikit-image的核心功能包括图像转换、滤波、形态学操作、测量、分割、色彩空间操作和对齐等。这些功能使得它在各种应用场景中非常实用，如医学成像、生物信息学、工业检测、地理信息系统以及艺术和人文研究等。在"scikit-image-0.15.0"这个版本中，用户可以找到以下关键模块和功能： 1. **颜色模块**：提供了多种颜色空间之间的转换，如RGB到灰度，HSV到RGB等，这对于色彩分析和图像调整至关重要。 2. **转换模块**：包含几何变换，如平移、旋转、缩放，以及直方图均衡化，用于改善图像的对比度。 3. **滤波模块**：提供了各种滤波器，如高斯滤波、中值滤波、自适应阈值等，用于降噪和特征提取。 4. **几何模块**：用于图像的形状检测和分析，如边缘检测、角点检测、轮廓提取等。 5. **测量模块**：包含区域属性计算、连接组件分析，可用于对象计数和大小分析。 6. **分割模块**：提供了一系列的图像分割算法，如快速阈值分割、水平集、区域生长等，用于将图像分成不同的有意义部分。 7. **应用模块**：针对特定应用领域，如图像恢复、增强现实、生物图像分析等，提供专门的工具和算法。 8. **实验模块**：包含正在开发或测试的新功能，可能不够稳定，但为用户提供前瞻性的功能探索。深度学习是当前计算机视觉领域的热门话题，虽然scikit-image本身并不直接支持深度学习模型的训练和实现，但它可以作为预处理和后处理工具，帮助构建深度学习模型的输入和理解输出结果。例如，它可以用来清洗和标准化图像数据，或者对模型预测的结果进行可视化和分析。 scikit-image-0.15.0提供了丰富的图像处理工具，是Python用户进行图像分析和计算机视觉项目时的得力助手。无论你是初学者还是经验丰富的开发者，这个库都能为你提供强大而灵活的功能。通过深入学习和理解scikit-image，你可以提升图像处理技能，解决各种实际问题，并参与到前沿的科研和创新工作中去。

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scikit-image-0.15.0.tar.gz （998个子文件）

generic_cy.c 6.34MB

_texture.c 4.37MB

_denoise_cy.c 4.3MB

_hough_transform.c 4.29MB

orb_cy.c 4.04MB

_warps_cy.c 4.04MB

interpolation.c 3.48MB

_max_tree.c 2.81MB

percentile_cy.c 2.13MB

_marching_cubes_classic_cy.c 1.84MB

_mcp.c 1.63MB

_marching_cubes_lewiner_cy.c 1.56MB

_flood_fill_cy.c 1.47MB

core_cy.c 1.42MB

_extrema_cy.c 1.32MB

bilateral_cy.c 1.3MB

_draw.c 1.25MB

_nl_means_denoising.c 1.18MB

_slic.c 1024KB

_spath.c 1009KB

corner_cy.c 1004KB

_skeletonize_cy.c 1002KB

_watershed.c 963KB

_radon_transform.c 956KB

_seam_carving.c 955KB

_quickshift_cy.c 950KB

_hoghistogram.c 924KB

_pnpoly.c 919KB

_ncut_cy.c 917KB

_hessian_det_appx.c 901KB

_convex_hull.c 896KB

brief_cy.c 887KB

transform.c 887KB

_find_contours_cy.c 776KB

censure_cy.c 755KB

_unwrap_3d.c 754KB

_unwrap_2d.c 753KB

_moments_cy.c 751KB

_unwrap_1d.c 735KB

_ccomp.c 565KB

_felzenszwalb_cy.c 479KB

_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 1.64MB

_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 514B

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

astronaut_GRAY_hog_L2-Hys.npy 2.38MB

astronaut_GRAY_hog_L1.npy 2.38MB

camera_unsup2.npy 2MB

camera_unsup.npy 2MB

camera_rl.npy 2MB

camera_wiener.npy 2MB

lfw_subset.npy 977KB

bw_text_skeleton.npy 168KB

chessboard_RGB_U8.npy 117KB

chessboard_GRAY_U8.npy 39KB

lab_array_d50_10.npy 200B

luv_array_d55_2.npy 200B

lab_array_d75_2.npy 200B

lab_array_e_2.npy 200B

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