# Import the module and function to enlarge images from skimage.____ import ____ # Import the data module from skimage import ____ # Load the image from data rocket_image = ____.____() # Enlarge the image so it is 3 times bigger enlarged_rocket_image = ____(rocket_image, ____, ____=____, multichannel=____) # Show original and resulting image show_image(rocket_image) show_image(enlarged_rocket_image, "3 times enlarged image")
# Import the otsu threshold function from skimage.____ import ____ # Obtain the optimal otsu global thresh value global_thresh = ____(page_image) # Obtain the binary image by applying global thresholding binary_global = page_image ____ ____ # Show the binary image obtained show_image(binary_global, 'Global thresholding')
# Import the necessary modules from skimage import data, ____ # Load the image original_image = ____.coffee() # Apply the adaptive equalization on the original image adapthist_eq_image = ____.____(original_image, ____=____) # Compare the original image to the equalized show_image(original_image) show_image(adapthist_eq_image, '#ImageProcessingDatacamp')
# Import the module and function from skimage.____ import ____ # Apply total variation filter denoising denoised_image = ____(____, multichannel=____) # Show the noisy and denoised images show_image(____, 'Noisy') ____(____, 'Denoised image')
# Import the otsu threshold function from skimage.____ import ____ # Make the image grayscale using rgb2gray chess_pieces_image_gray = ____(____) # Obtain the optimal threshold value with otsu thresh = ____(____) # Apply thresholding to the image binary = chess_pieces_image_gray ____ ____ # Show the image show_image(binary, 'Binary image')
# Import threshold and gray convertor functions from skimage.____ import ____ from skimage.color import ____ # Turn the image grayscale gray_tools_image = ____ # Obtain the optimal thresh thresh = ____(tools_image) # Obtain the binary image by applying thresholding binary_image = gray_tools_image > ____ # Show the resulting binary image show_image(binary_image, 'Binarized image')
# Import the slic function from segmentation module from skimage.____ import ____ # Import the label2rgb function from color module from skimage.____ import ____ # Obtain the segmentation with 400 regions segments = ____(____, ____=____) # Put segments on top of original image to compare segmented_image = ____(____, ____, kind='avg') # Show the segmented image show_image(segmented_image, "Segmented image, 400 superpixels")
# Import the module and function from skimage.____ import ____ # Add noise to the image noisy_image = ____ # Show original and resulting image show_image(____, 'Original') ____(____, 'Noisy image')
# Import the try all function from skimage.____ import ____ # Import the rgb to gray convertor function from skimage.____ import ____ # Turn the fruits image to grayscale grayscale = ____ # Use the try all method on the grayscale image fig, ax = ____(____, verbose=False) # Show the resulting plots plt.show()
