def on_press(event): if event.inaxes is None: print("none") return fig = event.inaxes.figure ax = fig.add_subplot(122) img_gray = data.clock() ax.imshow(img_gray, cmap="gray") print(event) ax1.scatter(event.xdata, event.ydata) py.axis("off") fig.canvas.draw()
def test_clock(): """ Test that "clock" image can be loaded. """ data.clock()
fig = event.inaxes.figure ax = fig.add_subplot(122) img_gray = data.clock() ax.imshow(img_gray, cmap="gray") print(event) ax1.scatter(event.xdata, event.ydata) py.axis("off") fig.canvas.draw() def button_press(event): print('button is pressed!') def draw_button(): global button # must global point = py.axes([0.3, 0.03, 0.1, 0.03]) button = Button(point, "click me") button.on_clicked(button_press) if __name__ == "__main__": img = data.clock() fig = py.figure() draw_button() fig.canvas.mpl_connect("button_press_event", on_press) ax1 = fig.add_subplot(121) ax1.imshow(img) py.axis("off") py.show()
""" Test adding list of images """ import numpy as np from skimage import data import napari list_of_images = [ data.astronaut(), data.clock(), data.coins(), data.binary_blobs(length=50, n_dim=3) ] with napari.gui_qt(): axis_labels = ['t', 'z', 'y', 'x'] viewer = napari.view_image(np.random.random((10, 6, 30, 40)), axis_labels=axis_labels)
from skimage.color import rgb2gray, gray2rgb from skimage.segmentation import mark_boundaries import time import matplotlib.image as mpimg exec(open('/Users/Salim_Andre/Desktop/IMA/PRAT/code/pd_segmentation_0.py').read()) exec(open('/Users/Salim_Andre/Desktop/IMA/PRAT/code/tree.py').read()) ### DATASET PATH_img = '/Users/Salim_Andre/Desktop/IMA/PRAT/' # path to my own images swans=mpimg.imread(PATH_img+'swans.jpg'); baby=mpimg.imread(PATH_img+'baby.jpg'); img_set = [data.astronaut(), data.camera(), data.coins(), data.checkerboard(), data.chelsea(), \ data.coffee(), data.clock(), data.hubble_deep_field(), data.horse(), data.immunohistochemistry(), \ data.moon(), data.page(), data.rocket(), swans, baby] ### IMAGE I=img_as_float(img_set[0]); ### PARAMETERS FOR 0-HOMOLOGY GROUPS mode='customized'; n_superpixels=10000; RV_epsilon=30; gauss_sigma=0.5; list_events=[800]; n_pxl_min_ = 30; density_excl=0.0;
from skimage import data from skimage.color import rgb2gray from skimage import img_as_ubyte,img_as_float gray_images = { "cat":rgb2gray(img_as_float(data.chelsea())), "astro":rgb2gray(img_as_float(data.astronaut())), "camera":data.camera(), "coin": data.coins(), "clock":data.clock(), "blobs":data.binary_blobs(), "coffee":rgb2gray(img_as_float(data.coffee())) } from numpy.linalg import svd def compress_svd(image,k): U,s,V = svd(image,full_matrices=False) reconst_matrix = np.dot(U[:,:k],np.dot(np.diag(s[:k]),V[:k,:])) return reconst_matrix,s def compress_show_gray_images(img_name,k): image=gray_images[img_name] original_shape = image.shape reconst_img,s = compress_svd(image,k) fig,axes = plt.subplots(1,2,figsize=(8,5)) axes[0].plot(s) compression_ratio =100.0* (k*(original_shape[0] + original_shape[1])+k)/(original_shape[0]*original_shape[1]) axes[1].set_title("compression ratio={:.2f}".format(compression_ratio)+"%") axes[1].imshow(reconst_img,cmap='gray') axes[1].axis('off') fig.tight_layout()
image = caller() plt.figure() plt.title(name) if image.ndim == 2: plt.imshow(image, cmap=plt.cm.gray) else: plt.imshow(image) plt.show() ############################################################################ # Thumbnail image for the gallery # sphinx_gallery_thumbnail_number = -1 fig, axs = plt.subplots(nrows=3, ncols=3) for ax in axs.flat: ax.axis("off") axs[0, 0].imshow(data.astronaut()) axs[0, 1].imshow(data.binary_blobs(), cmap=plt.cm.gray) axs[0, 2].imshow(data.brick(), cmap=plt.cm.gray) axs[1, 0].imshow(data.colorwheel()) axs[1, 1].imshow(data.camera(), cmap=plt.cm.gray) axs[1, 2].imshow(data.cat()) axs[2, 0].imshow(data.checkerboard(), cmap=plt.cm.gray) axs[2, 1].imshow(data.clock(), cmap=plt.cm.gray) further_img = np.full((300, 300), 255) for xpos in [100, 150, 200]: further_img[150 - 10:150 + 10, xpos - 10:xpos + 10] = 0 axs[2, 2].imshow(further_img, cmap=plt.cm.gray) plt.subplots_adjust(wspace=0.1, hspace=0.1)
plotComparison(space, removeNoise(space), 'Original', 'Denoised Space Image After Notch Filter') plotComparison(space, displayNoise(space, removeNoise(space)), 'Original', 'Noise Only') # In[75]: #========================Images for testing================================================================ space = io.imread('space_noise_img.png') denoised_space = removeNoise(space) checkboard = data.checkerboard() camerman = data.camera() grass = data.grass() gravel = data.gravel() clock = data.clock() coins = data.coins() # In[76]: #========================resize function================================================================ def resizeToAnyDimension(img, row, col): newImg = np.array(Image.fromarray(img).resize((row, col), Image.ANTIALIAS)) return newImg # In[77]: coins_64 = resizeToAnyDimension(coins, 64, 64) camerman_64 = resizeToAnyDimension(camerman, 64, 64)