从skimage.segmentation模块导入slice和mark_boundaries函数,以及从skimage中导入io子模块,还有从matplotlib.pyplot导入plt子模块。 通过io.imread函数读取名为"Lenna.png"的图像并将其存储在变量img中。 使用SLIC(Simple Linear Iterative Clustering)算法对图像进行分割,n_segments参数指定了期望的分割数目,compactness参数则...
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库,Python用户可以方便地进行图像的读取、显示和处理。skimage不仅支持多种图像格式,还提供了丰富的图像处理和分析工具。从图像的...
使用skimage.segmentation.slic函数进行超像素分割。我们首先将点云数据投影到二维平面上进行处理,然后再映射回三维空间。 fromskimage.segmentationimportslicfromskimageimportcolor# 将点云数据投影到2D平面color_image=color.rgb2lab(np.zeros((points.shape[0],3)))# 这里用空图代替实际颜色# 进行超像素分割segments...
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-(...
Python Slic 实现方块效果的步骤指南 在Python中,使用slic的结果显示为方块的效果通常涉及图像处理的内容。我们将通过几个步骤来实现这个目标。以下是整个流程的概述。 流程表格 详细步骤 1. 导入必要的库 importmatplotlib.pyplotasplt# 用于绘图fromskimageimportio# 用于图像操作fromskimage.segmentationimportslic# 用于...
import numpy as np import matplotlib.pyplot as plt import skimage.data as data import skimage.segmentation as seg import skimage.filters as filters import skimage.draw as draw import skimage.color as color 绘制图像的简单函数: def image_show(image, nrows=1, ncols=1, cmap='gray'): fig, ax...
segmentation import slic from skimage.segmentation import mark_boundaries from skimage.util import img_as_float import matplotlib.pyplot as plt import cv2 as cv # load the image and convert it to a floating point data type image = img_as_float(cv.imread(".././img/base.jpg")) fig = plt...
fromskimage.colorimportlabel2rgb final_image = label2rgb(segments, coffee, kind="avg") >>>show(final_image) 让我们将此操作包装在函数中,并尝试使用更多段: fromskimage.colorimportlabel2rgb fromskimage.segmentationimportslic defsegment(image, n_segm...
importcv2importnumpyasnpimportmatplotlib.pyplotaspltfromskimage.segmentationimportslicimporttensorflowastf# 读取图像并转换为灰度img = cv2.imread('image.jpg',0)# 1. 阈值分割ret, thresh = cv2.threshold(img,127,255, cv2.THRESH_BINARY) plt.imshow(thresh, cmap='gray') ...
skimage import img_as_floatfrom skimage.morphology import skeletonizefrom skimage import data, img_as_floatimport matplotlib.pyplot as pylabfrom matplotlib import cmfrom skimage.filters import sobel, threshold_otsufrom skimage.feature import cannyfrom skimage.segmentation import felzenszwalb, slic, quick...