Make sure that you put this right after your report cover page. During submission, you will merge this PDF with your report. You may find the questions here (if you are familiar with LaTex, feel free to use the following template to generate the answers). In this part of the assignment, you will be practicing filtering by hand on a given "image". The assignment Part 1: Written Questions (6 points) Note that lack of submission of either the code or the PDF will also result in loss of points. If you are using Jupyter, assuming you set things up correctly, you should be able to simply pint the notebook into a PDF file for submission. The scripts and results (as screenshots or otherwise) should be pasted into a single PDF file and clearly labeled. In this assignment, you also need to hand in scripts showing tests of your functions on all the cases specified as well as the images and other answers requested. You will lose marks for insufficient or unclear comments. To get full marks, your functions (i.e., *.py or *.ipynb files) must not only work correctly, but also must be clearly documented with sufficient comments for others to easily use and understand the code. Hand in all parts of this assignment using Canvas (both the code and PDF file as specified). In regular Python you can use img.show() and in Jupyter you can use display(img). Recall that visualizing imagies in regular Python and in Jupyter differs a bit. HINT: Review Assignment 0 for the basics of reading/writing images, converting colour to greyscale, and converting PIL images to/from Numpy arrays. If you are using Jupyter Notebook with Google Colab or on your personal machine, you will, in addition, would also want to import: For consistency (and to make life easier for the markers) you are required to import modules for this assignment exactly as follows: There are different ways to import libraries/modules into Python. The purpose of this assignment is to get some initial experience with Python and to learn the basics of constructing and using linear filters. Img = cv2.imread(img_path)/255.0 in this line what why /255.Assignment 1: Image Filtering and Hybrid ImagesÄue: At the end of the day 11:59pm, Wednsday, September 30th, 2020. Reply to this email directly, view it on GitHub You are receiving this because you commented. # noise multiplied by bottom and top half images,# whites stay white blacks black, noise is added to centerimg2 = img*2n2 = np.clip(np.where(img2 # noise multiplied by image:# whites can go to black but blacks cannot go to whitenoisy2mul = np.clip((img*(1 + noise*0.2)),0,1)noisy4mul = np.clip((img*(1 + noise*0.4)),0,1) # noise overlaid over imagenoisy = np.clip((img + noise*0.2),0,1)noisy2 = np.clip((img + noise*0.4),0,1) Img = cv2.imread(img_path)/255.0noise = np.random.normal(loc=0, scale=1, size=img.shape) Import numpy as npimport cv2import matplotlib.pyplot as plt In every case i blend in 0.2 and 0.4 of the image Noise affects mid values, white and black receiving little noise image folded over and gaussian noise multipled and added to it: peak gaussian noise multiplied then added over image: noise increases gaussian noise added over image: noise is spread throughout On Fri, at 8:30 AM Kanishk Rana commented on this gist. Is there a way to add noise to the bottom half of the image? # norm noise for viz only noise2 = ( noise - noise. # noise multiplied by bottom and top half images, # whites stay white blacks black, noise is added to center img2 = img * 2 n2 = np. # noise multiplied by image: # whites can go to black but blacks cannot go to white noisy2mul = np. Import numpy as np import cv2 import matplotlib.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |