scikit-image

问题 I have a label_image array and I am deriving the outlines/boundaries of the objects on that array. Currently I am doing that by getting all unique labels, iterating over them and then find the contours of each object. Like in the loop below, where I am populating a dict with keys the label and values the contours import cv2 import pandas as pd import numpy as np def extract_borders(label_image): labels = np.unique(label_image[label_image > 0]) d = {} for label in labels: y = label_image ==

问题 I'm working in image proceesing and I have the following code to obtain the convex hull of a image: from skimage import io from skimage.color import rgb2gray from skimage.morphology import convex_hull_image original = io.imread('test.png') image = rgb2gray(original) chull = convex_hull_image(image) I want to crop the original image according to the convex hull in order to eliminate empty space that is in the image (original image attached), and have an image that only contains what is inside

问题 Input is a uint16 grayscale .tif-image, 512 x 512 pixels. As the title to this question implies, I would like to calculate the average pixel intensity of blobs identified by the blob_log method (see: http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.blob_log ) but am unsure how to access the pixel values of each individual blob. Average intensity values must be returned in uint16 range (0 to 65535). Below is what I have so far. Apologies in advance if I haven't been

问题 I'm trying to understand how the polygon function works with this example of the documentation: from skimage.draw import polygon img = np.zeros((10, 10), dtype=np.uint8) r = np.array([1, 2, 8, 1]) c = np.array([1, 7, 4, 1]) rr, cc = polygon(r, c) img[rr, cc] = 1 img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],

问题 I am trying to find some relative maximums of a given image. I understand that there are two possible methods, the first is using scipy.ndimage.maximum_filter() and the second using skimage.feature.peak_local_max() . In order to compare both methods I have modified an example from skimage shown here in order to compare the peaks found. from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage.feature import peak_local_max from skimage import data, img_as_float im = img_as

问题 I am trying to find some relative maximums of a given image. I understand that there are two possible methods, the first is using scipy.ndimage.maximum_filter() and the second using skimage.feature.peak_local_max() . In order to compare both methods I have modified an example from skimage shown here in order to compare the peaks found. from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage.feature import peak_local_max from skimage import data, img_as_float im = img_as

来源： https://stackoverflow.com/questions/64145300/remove-a-particular-color-from-an-image

来源： https://stackoverflow.com/questions/64145300/remove-a-particular-color-from-an-image

来源： https://stackoverflow.com/questions/53102654/converting-image-to-grayscale

来源： https://stackoverflow.com/questions/53102654/converting-image-to-grayscale