scikit-image

问题 I have an image that I found contours on with skimage.measure.find_contours() but now I want to create a mask for the pixels fully outside the largest closed contour. Any idea how to do this? Modifying the example in the documentation: import numpy as np import matplotlib.pyplot as plt from skimage import measure # Construct some test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2))) # Find contours at a constant value of 0.8 contours

问题 I have an image that I found contours on with skimage.measure.find_contours() but now I want to create a mask for the pixels fully outside the largest closed contour. Any idea how to do this? Modifying the example in the documentation: import numpy as np import matplotlib.pyplot as plt from skimage import measure # Construct some test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2))) # Find contours at a constant value of 0.8 contours

问题 I have an image that I found contours on with skimage.measure.find_contours() but now I want to create a mask for the pixels fully outside the largest closed contour. Any idea how to do this? Modifying the example in the documentation: import numpy as np import matplotlib.pyplot as plt from skimage import measure # Construct some test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2))) # Find contours at a constant value of 0.8 contours

问题 I have an image that I found contours on with skimage.measure.find_contours() but now I want to create a mask for the pixels fully outside the largest closed contour. Any idea how to do this? Modifying the example in the documentation: import numpy as np import matplotlib.pyplot as plt from skimage import measure # Construct some test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2))) # Find contours at a constant value of 0.8 contours

问题 Given is a trained classifer in scikit learn, e.g. a RandomForestClassifier . The classifier has been trained on samples of size e.g. 25x25. How can I easily apply this to all tiles/windows in a large image (e.g. 640x480)? What I could do is (slow code ahead!) x_train = np.arange(25*25*1000).reshape(25,25,1000) # just some pseudo training data y_train = np.arange(1000) # just some pseudo training labels clf = RandomForestClassifier() clf.train( ... ) #train the classifier img = np.arange(640

问题 I have a volume of image slices and their according masks. I've been trying to use skimage.segmentation library to mark the object in mind for each slice according to its mask. import numpy as np from skimage.segmentation import mark_boundaries import matplotlib.pyplot as plt def plot_marked_volume(marked_image_volume, mask): for slice in range(len(marked_image_volume)): if np.count_nonzero(mask[slice,:,:]): plt.figure(figsize=(10,10)) edges_pz = mark_boundaries(marked_image_volume[slice,:,:]

问题 I have following Image file: Image I used PIL and Skimage to open it but I get following errors First with PIL (tried with and without trucate option): Code: from PIL import Image, ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True img = Image.open("image_output.pgm") Erorr: OSError: cannot identify image file 'image_output.pgm' And with Skimage: Code: from skimage import io img = io.imread("image_output.pgm") Error: OSError: cannot identify image file <_io.BufferedReader name='image_output.pgm

问题 I have following Image file: Image I used PIL and Skimage to open it but I get following errors First with PIL (tried with and without trucate option): Code: from PIL import Image, ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True img = Image.open("image_output.pgm") Erorr: OSError: cannot identify image file 'image_output.pgm' And with Skimage: Code: from skimage import io img = io.imread("image_output.pgm") Error: OSError: cannot identify image file <_io.BufferedReader name='image_output.pgm

问题 I am trying to analyze greyscale TIFF stacks, in which a given frame will look like this. I filter it (using Gaussian blur), and then binarize it (using Otsu's method for threshold). MATLAB code, which works great: image_conncomp = bwconncomp(image_binary); # entire stack is held in image_binary for i=1:image_conncomp.NumObjects object_size = length(image_conncomp.PixelIdxList{i}); end Each white spot in the example image is picked up, and its volume (in pixels) is pretty accurately given by

问题 I am trying to analyze greyscale TIFF stacks, in which a given frame will look like this. I filter it (using Gaussian blur), and then binarize it (using Otsu's method for threshold). MATLAB code, which works great: image_conncomp = bwconncomp(image_binary); # entire stack is held in image_binary for i=1:image_conncomp.NumObjects object_size = length(image_conncomp.PixelIdxList{i}); end Each white spot in the example image is picked up, and its volume (in pixels) is pretty accurately given by