def imgTry2():
img = skimage.io.imread(r'C:\Users\Sajjad\Desktop\tree.jpg')
viewer = skimage.viewer.ImageViewer(img)
viewer.show()
img[img < 128] = 0
viewer = skimage.viewer.ImageViewer(img)
viewer.view()
import sys import numpy as np import skimage.color import skimage.io import skimage.filters import skimage.viewer # get filename, kernel size, and threshold value from command line filename = sys.argv[1] sigma = float(sys.argv[2]) # t = float(sys.argv[3]) # read and display the original image image = skimage.io.imread(fname=filename) viewer = skimage.viewer.ImageViewer(image) viewer.show() # blur and grayscale before thresholding blur = skimage.color.rgb2gray(image) blur = skimage.filters.gaussian(image, sigma=sigma) # perform inverse binary thresholding # MODIFY CODE HERE! t = skimage.filters.threshold_otsu(blur) mask = blur > t viewer = skimage.viewer.ImageViewer(mask) viewer.show() # use the mask to select the "interesting" part of the image sel = np.zeros_like(image) sel[mask] = image[mask]
#!/usr/bin/python
# Ben Chapman-Kish
# 2016-07-14
import matplotlib.pyplot as plt
import sys, skimage, skimage.viewer
imname=sys.argv[1]
image = skimage.io.imread(imname)
noise_image = skimage.util.random_noise(image, mode='poisson') # or gaussian
viewer=skimage.viewer.ImageViewer(noise_image)
viewer.show()
skimage.io.imsave(imname[:imname.index('.')]+'-new.jpg', noise_image)
