skimage.segmentation.slic(image, n_segments=100, compactness=10.0, max_num_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False, start_label=1, mask=None, *, channel_axis=-1) 使用颜色-(...
skimage.segmentation.slic(image, n_segments=100, compactness=10.0, max_num_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False, start_label=1, mask=None, *, channel_axis=- 1) """ imag...
图像分割用于将图像划分为不同的区域,以便进行更深入的分析。 from skimage import segmentation, color 使用SLIC算法进行图像分割 segments = segmentation.slic(image, n_segments=100) segmented_image = color.label2rgb(segments, image, kind='avg') io.imshow(segmented_image) plt.show() 六、总结 通过skimage...
问skimage.segmentation.slic()究竟代表什么？EN我的工作是复制移动伪造检测和卡在其中一种算法。我有一...
sk_slic = skimage.segmentation.slic(src,n_segments = 256, sigma = 5) 使用以下代码生成带有超像素重心的图像。 # Measure properties of labeled image regions regions = regionprops(labels) # Scatter centroid of each superpixel plt.scatter([x.centroid[1] for x in regions], [y.centroid[0] for...
from skimage.util import img_as_float from skimage.measure import regionprops from skimage.segmentation...color']-graph.nodes[n]['mean color'] diff=np.linal...
fromskimageimportsegmentation# 图像分割segments=segmentation.slic(image,compactness=10,n_segments=400)# 显示分割后的图像io.imshow(segmentation.mark_boundaries(image,segments))io.show() 1. 2. 3. 4. 5. 6. 7. 8. 6. 图像特征提取 fromskimageimportfeature# 提取图像特征hog_features=feature.hog(gray...
import skimage.segmentation as seg import skimage.filters as filters import skimage.draw as draw import skimage.color as color 1. 2. 3. 4. 5. 6. 7. 路 图片text = data.page() image_show(text) 路 这个图像有点暗,但也许我们仍然可以选择一个值,无需任何高级算法就能给出合理的分割。现在,为...
Separate inner loop of SLIC computation Apr 3, 2013 6 Use relative imports in skimage files Dec 26, 2014 7 from ..util import img_as_float, regular_grid 8 from ..segmentation._slic import (_slic_cython, 9 _enforce_label_connectivity_cython) 10 from ..color import rgb2lab Se...
skimage.segmentation.watershed(image, markers=None, connectivity=1, offset=None, mask=None, compactness=0, watershed_line=False) 在從給定標記淹沒的圖像中查找流域盆地。 參數: image:ndarray (2-D, 3-D, ...) 首先標記最低值點的數據數組。