pip install scikit-image

conda install -c conda-forge scikit-image

import numpy as np
import matplotlib.pyplot as plt
from skimage import io, color, filters, transform, feature, morphology, segmentation, exposure, measure, util

image = io.imread('sample.jpg')
plt.imshow(image)
plt.axis('off')
plt.show()

gray = color.rgb2gray(image)
plt.imshow(gray, cmap='gray')

img = io.imread('image.png')
io.imsave('output.jpg', img)

resized = transform.resize(image, (200, 200))
cropped = image[50:150, 80:180]

rotated = transform.rotate(image, angle=45)
warped = transform.warp(image, transform.AffineTransform(scale=(0.8, 0.8)))

from skimage import exposure

eq = exposure.equalize_hist(gray)
plt.imshow(eq, cmap='gray')

clahe = exposure.equalize_adapthist(gray, clip_limit=0.03)

gamma_corrected = exposure.adjust_gamma(gray, 0.5)
brightened = exposure.adjust_log(gray)

from skimage import img_as_ubyte, img_as_float

img_uint8 = img_as_ubyte(gray)
img_float = img_as_float(image)

from skimage.filters import rank
from skimage.morphology import disk
blurred = rank.mean(img_as_ubyte(gray), disk(5))

gaussian = filters.gaussian(gray, sigma=2)

from skimage.restoration import denoise_bilateral, denoise_nl_means

bilateral = denoise_bilateral(image, sigma_color=0.05, sigma_spatial=15)
nlmeans = denoise_nl_means(image, h=0.1)

from skimage.filters import unsharp_mask
sharpened = unsharp_mask(gray, radius=1, amount=1.5)

edges_sobel = filters.sobel(gray)
plt.imshow(edges_sobel, cmap='gray')

edges = feature.canny(gray, sigma=2)

lap = filters.laplace(gray)

from skimage.transform import hough_line, hough_circle, hough_circle_peaks

edges = feature.canny(gray)
h, theta, d = hough_line(edges)

from skimage.filters import threshold_otsu

thresh = threshold_otsu(gray)
binary = gray > thresh

from skimage.filters import threshold_local
binary_local = gray > threshold_local(gray, block_size=35)

from skimage.measure import label, regionprops

labeled = label(binary)
regions = regionprops(labeled)

from skimage.segmentation import slic, mark_boundaries

segments = slic(image, n_segments=200, compactness=10)
plt.imshow(mark_boundaries(image, segments))

from skimage.segmentation import watershed
from scipy import ndimage as ndi

distance = ndi.distance_transform_edt(binary)
markers = ndi.label(binary)[0]
labels = watershed(-distance, markers, mask=binary)

from skimage.morphology import erosion, dilation, disk

eroded = erosion(binary, disk(3))
dilated = dilation(binary, disk(3))

opened = morphology.opening(binary, disk(3))
closed = morphology.closing(binary, disk(3))

skeleton = morphology.skeletonize(binary)

hull = morphology.convex_hull_image(binary)

labels = measure.label(binary)
plt.imshow(labels)

props = measure.regionprops_table(labels, properties=('area', 'centroid', 'bbox'))
print(props)

count = len(np.unique(labels)) - 1
total_area = sum([r.area for r in measure.regionprops(labels)])

orb = feature.ORB(n_keypoints=200)
orb.detect_and_extract(gray)
plt.imshow(feature.plot_matches(image, image, orb.keypoints, orb.keypoints))

from skimage.feature import hog

hog_vec, hog_img = hog(gray, visualize=True)
plt.imshow(hog_img, cmap='gray')

from skimage.feature import match_template

result = match_template(gray, template)
ij = np.unravel_index(np.argmax(result), result.shape)

from skimage import color

hsv = color.rgb2hsv(image)
lab = color.rgb2lab(image)
ycbcr = color.rgb2ycbcr(image)

r = image[:, :, 0]
g = image[:, :, 1]
b = image[:, :, 2]

volume = io.imread('ct_scan.tif')
from skimage.filters import sobel
edges = sobel(volume)

from skimage import measure
verts, faces, _, _ = measure.marching_cubes(volume, level=0.5)

coins = io.imread('https://scikit-image.org/docs/stable/_static/img/coins.png')
gray = color.rgb2gray(coins)
edges = filters.sobel(gray)
thresh = filters.threshold_otsu(edges)
binary = edges > thresh
labels = measure.label(binary)
count = len(measure.regionprops(labels))
print("硬幣數(shù)量：", count)

cells = io.imread('cells.png')
gray = color.rgb2gray(cells)
thresh = filters.threshold_otsu(gray)
binary = morphology.closing(gray > thresh, morphology.disk(3))
distance = ndi.distance_transform_edt(binary)
markers = ndi.label(binary)[0]
labels = watershed(-distance, markers, mask=binary)

dark = io.imread('dark_scene.jpg')
gray = color.rgb2gray(dark)
enhanced = exposure.equalize_adapthist(gray, clip_limit=0.03)
plt.imshow(enhanced, cmap='gray')

from skimage.feature import ORB, match_descriptors
from skimage.transform import ProjectiveTransform, warp

orb = ORB(n_keypoints=200)
orb.detect_and_extract(img1_gray)
keypoints1, descriptors1 = orb.keypoints, orb.descriptors

orb.detect_and_extract(img2_gray)
keypoints2, descriptors2 = orb.keypoints, orb.descriptors

matches = match_descriptors(descriptors1, descriptors2, cross_check=True)

import torch
from skimage import transform, exposure

def preprocess(img):
    img = transform.resize(img, (224, 224))
    img = exposure.equalize_adapthist(img)
    return torch.tensor(img.transpose(2,0,1)).float()

from sklearn.cluster import KMeans

features = gray.reshape(-1, 1)
kmeans = KMeans(n_clusters=2).fit(features)
segmented = kmeans.labels_.reshape(gray.shape)