Assignment Chef icon Assignment Chef

[SOLVED] Assignment 1: image filtering and edge detection 1 image acquisition (5 points) using a cellphone camera or a standalone digital camera

5.0 1 customer review Digital download

Digital download

$25.00

Availability
In stock
Checkout
One item

Need a hand?

Message us on WhatsApp for payment or download support.

WhatsApp QR code
Using a cellphone camera or a standalone digital camera, capture two images of a household object,
where each image of the object is taken from a slightly different viewpoint. These images can be of
any size. An example of such an image pair is shown below:Please note that all assignments are to be done individually (not in groups), and so you must
acquire your own images. Do not share images or copy someone else’s images!
You will be using these images, as well as the processed images, in some future assignments, so it is
important to do all the steps correctly. Display the original images in the assignment’s Jupyter notebook.If your images are color (RGB), convert them to grayscale (monochrome) by using the python statement:
gray = np.dot(your_image[…,:3], [0.2989, 0.5870, 0.1140])
In your notebook explain what this python statement does.
Display the grayscale images in the assignment’s Jupyter notebook. Use the resize() function of scikit-image to resize your image so that the largest dimension has a size of
256 pixels.
A tutorial on how to use the resize function can be found at:
https://scikit-image.org/docs/stable/auto_examples/transform/plot_rescale.htmlWrite python code that can perform convolution on an image with a specific filter kernel.
Using the convolution code, smooth the pair of resized grayscale images using a 5×5 pixel Gaussian
kernel. Then repeat the smoothing on the original grayscale images, this time using a 15×15 Gaussian
kernel. Display the smoothed images in the notebook. Also show in the notebook the kernel values (either
as an array of numbers or as an image).Using your convolution code, compute the x and y derivative images of each of the smoothed images
using the horizontal and vertical Sobel filters. Display the derivative images in the notebook.With python, compute the edge gradient magnitude and orientation of the smoothed images using the
Sobel filter values computed in part 5. Display the magnitude and orientation images in the notebook.For
the orientation image, display the angle value using an RGB colormap, such as ‘jet’ in the imshow()
function (e.g. something like ax.imshow(data, cmap=’jet’) ).Details about matplotlib colormaps, including other colormaps you can try, can be found at:
https://matplotlib.org/stable/tutorials/colors/colormaps.html