scikit-image图像处理入门

AI算法与图像处理

发布于 2019-08-20 10:38:52

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发布于 2019-08-20 10:38:52

文章被收录于专栏：AI算法与图像处理AI算法与图像处理

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来源：Opencv学堂
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scikit-image概述与安装

skimage是纯python语言实现的BSD许可开源图像处理算法库，主要的优势在于：

提供一套高质量易用性强的图像算法库API
满足研究人员与学生学习图像处理算法的需要，算法API参数可调
满足工业级应用开发需求，有实际应用价值

scikit-image主要模块如下：

官方主页

https://scikit-image.org/

安装

pip install scikit-image

代码教程

导入支持的模块

from skimage import data, io, filters, feature, segmentation
from skimage import color, exposure, measure, morphology, draw
from matplotlib import pyplot as plt
from skimage import transform as tf

从data中获取测试图像与数据并显示

image = data.chelsea()
io.imshow(image)
io.show()

这个是开源作者养的宠物猫

灰度转换

gray = color.rgb2gray(image)
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()

ax[0].imshow(image)
ax[0].set_title("Input RGB")
ax[1].imshow(gray, cmap=plt.cm.gray)
ax[1].set_title("gray")

fig.tight_layout()
plt.show()

通道分离操作

hsv_img = color.rgb2hsv(image)
hue_img = hsv_img[:, :, 0]
value_img = hsv_img[:, :, 2]

fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 2))
ax0.imshow(image)
ax0.set_title("RGB image")
ax0.axis('off')
ax1.imshow(hue_img, cmap='hsv')
ax1.set_title("Hue channel")
ax1.axis('off')
ax2.imshow(value_img)
ax2.set_title("Value channel")
ax2.axis('off')

fig.tight_layout()
plt.show()

滤波操作

image = data.chelsea()
gray = color.rgb2gray(image)
blur = filters.gaussian(image, 15)
usm = filters.unsharp_mask(image, 3, 1.0)
sobel = filters.sobel(gray)
prewitt = filters.prewitt(gray)
eh = exposure.equalize_adapthist(gray)
lapl = filters.laplace(image, 3)
median = filters.median(gray)

图像二值化处理

image = io.imread("D:/images/dice.jpg")
gray = color.rgb2gray(image)
ret = filters.threshold_otsu(gray)
print(ret)

轮廓发现

binary = gray > ret
ax[0].imshow(gray > ret, cmap='gray')
ax[0].set_title("binary")
contours = measure.find_contours(binary, 0.8)
for n, contour in enumerate(contours):
         ax[1].plot(contour[:, 1], contour[:, 0], linewidth=2)
ax[1].set_title("contours")

Canny边缘

image = io.imread("D:/images/master.jpg")
gray = color.rgb2gray(image)
edge = feature.canny(gray, 3)

骨架提取

image = data.horse()
gray = color.rgb2gray(image)
ret = filters.threshold_otsu(gray)
binary = gray < ret
skele = morphology.skeletonize(binary)

harris角点检测

image = io.imread("D:/images/home.jpg")
gray = color.rgb2gray(image)
coords = feature.corner_peaks(feature.corner_harris(gray), min_distance=5)

BRIEF特征匹配

keypoints1 = corner_peaks(corner_harris(img1), min_distance=5)
keypoints2 = corner_peaks(corner_harris(img2), min_distance=5)
keypoints3 = corner_peaks(corner_harris(img3), min_distance=5)

extractor = BRIEF()
extractor.extract(img1, keypoints1)
keypoints1 = keypoints1[extractor.mask]
descriptors1 = extractor.descriptors

extractor.extract(img2, keypoints2)
keypoints2 = keypoints2[extractor.mask]
descriptors2 = extractor.descriptors

extractor.extract(img3, keypoints3)
keypoints3 = keypoints3[extractor.mask]
descriptors3 = extractor.descriptors

matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)

上述同时显示两张图像的相似代码

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()

ax[0].imshow(image)
ax[0].set_title("Input RGB")
ax[1].imshow(gray > ret, cmap='gray')
ax[1].set_title("binary")
ax[0].axis('off')
ax[1].axis('off')

fig.tight_layout()
plt.show()

完整的演示代码下载地址

https://github.com/gloomyfish1998/opencv_tutorial/blob/master/python/ski_image_demo.py