scikit-image-0.18.2.tar.gz_scikit-image库下载资源-CSDN文库

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共1133个文件

py：654个

rst：198个

pyx：54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0.18.2是该库的一个更新，提供了一些性能优化和新特性。这个压缩包"scikit-image-0.18.2.tar.gz"包含了源代码和其他必要的文件，用户可以下载并安装在自己的Python环境中。在深度学习领域，图像预处理是至关重要的一步，scikit-image提供了许多实用的工具来准备数据。例如，它可以调整图像大小，进行直方图均衡化以增强图像对比度，甚至可以消除噪声，这些都是训练神经网络模型前常见的步骤。关于"scikit-image"，它的功能包括： 1. **图像转换**：支持平移、旋转、缩放等几何变换，以及直方图均衡化、色彩空间转换（如RGB到灰度）等。 2. **滤波与降噪**：提供了多种滤波器，如高斯滤波、中值滤波、拉普拉斯滤波等，用于平滑图像或检测边缘。还有专门的算法如快速傅里叶变换(FFT)进行频域分析。 3. **图像分割**：包括基于阈值、区域生长、连通组件分析等多种方法，可将图像分割成不同的部分。 4. **形态学操作**：膨胀、腐蚀、开闭运算等，常用于去除噪声，分离或连接物体。 5. **特征检测**：可以检测角点、边缘、曲线、条纹等，这些特征在计算机视觉任务中非常有用。 6. **测量与分析**：可以计算图像的属性，如面积、周长、形状因子等，对于定量分析图像特征很有帮助。 7. **应用示例**：scikit-image还提供了许多示例代码，帮助用户理解和学习各种图像处理技术。在安装scikit-image-0.18.2时，用户需要先解压这个.tar.gz文件，通常使用tar命令或者图形化工具。解压后，通过Python的setuptools或pip工具进行安装。安装完成后，就可以在Python环境中导入scikit-image库，并利用其丰富的函数进行图像处理。对于深度学习开发者来说，结合scikit-image和TensorFlow、PyTorch等深度学习框架，可以构建强大的端到端的图像处理和模型训练流程。scikit-image的易用性和强大功能使其成为Python图像处理领域不可或缺的工具之一。通过不断迭代更新，如0.18.2这样的版本，scikit-image持续改进用户体验，优化性能，确保其在图像处理领域的领先地位。

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scikit-image-0.18.2.tar.gz （1133个子文件）

generic_cy.c 9.14MB

_texture.c 4.58MB

_denoise_cy.c 4.45MB

_hough_transform.c 4.44MB

_warps_cy.c 4.29MB

orb_cy.c 4.25MB

interpolation.c 3.49MB

_max_tree.c 2.92MB

percentile_cy.c 2.44MB

_mcp.c 1.88MB

_marching_cubes_lewiner_cy.c 1.78MB

_nl_means_denoising.c 1.7MB

_flood_fill_cy.c 1.66MB

_marching_cubes_classic_cy.c 1.57MB

core_cy.c 1.56MB

bilateral_cy.c 1.47MB

_extrema_cy.c 1.43MB

_draw.c 1.38MB

_slic.c 1.32MB

corner_cy.c 1.29MB

core_cy_3d.c 1.29MB

_rolling_ball_cy.c 1.22MB

_radon_transform.c 1.18MB

_skeletonize_cy.c 1.1MB

_spath.c 1.09MB

brief_cy.c 1.06MB

_sparse_cy.c 1.06MB

_watershed_cy.c 1.05MB

_quickshift_cy.c 1.04MB

_multiotsu.c 1.04MB

_pnpoly.c 1.02MB

_hoghistogram.c 1.01MB

_find_contours_cy.c 1.01MB

_ncut_cy.c 1.01MB

_convex_hull.c 1013KB

_hessian_det_appx.c 1013KB

transform.c 949KB

geometry.c 932KB

_unwrap_3d.c 925KB

censure_cy.c 924KB

_moments_cy.c 924KB

_unwrap_2d.c 923KB

_unwrap_1d.c 903KB

_ccomp.c 590KB

heap.c 546KB

_felzenszwalb_cy.c 511KB

_colormixer.c 494KB

fast_exp.c 378KB

_histograms.c 351KB

_greyreconstruct.c 336KB

unwrap_3d_ljmu.c 47KB

unwrap_2d_ljmu.c 26KB

setup.cfg 181B

_cascade.cpp 4.56MB

_remap.cpp 2.06MB

_haar.cpp 1.79MB

_skeletonize_3d_cy.cpp 1.21MB

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

unwrap_3d_ljmu.h 297B

unwrap_2d_ljmu.h 255B

navbar.html 518B

versions.html 457B

searchbox.html 345B

localtoc.html 274B

navigation.html 1B

_skeletonize_3d_cy.pyx.in 15KB

MANIFEST.in 619B

qt_plugin.ini 154B

matplotlib_plugin.ini 123B

tifffile_plugin.ini 110B

simpleitk_plugin.ini 92B

pil_plugin.ini 91B

gtk_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 719B

README.md 4KB

README.md 2KB

README.md 457B

README.md 295B

CODE_OF_CONDUCT.md 70B

astronaut_GRAY_hog_L2-Hys.npy 2.38MB

astronaut_GRAY_hog_L1.npy 2.38MB

camera_rl.npy 2MB

camera_unsup2.npy 2MB

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