Poopertu af Hash Functons: 1. Pre- image Resistance 2. Second - Pre umage
Q: image Resistamce 2. Second - Pre image Hash Functons: 1. Pre-
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A: ans is given below
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- Poropertu f Hash Fumctons: 1. Pre- umage Resistamce 2. Second - Pre imageclass Noiseify(ImageEffect): | def apply(pixels: np.ndarray) -> np.ndarray:Suppose we have an image of size 32X32X64. We want to collect 32X32X192 features from this image. We can do it in two different ways: A. 32X32X64 --> 1X1, 32 filters --> 5X5, 64 filters. --> --> 1X1, 16 filters --> 7X7, n filters. 32X32X192 B. 32X32X64 --> 5X5, 32 filters --> 1X1, 64 filters --> 7X7, 64 filters --> 1X1, m filters 32X32X192 What are the values of m and n? 4 ------- What will be the number of multiplication for A? What will be the number of multiplication for B? Did you have to assume any values for padding or stride? If yes, what are those values? -- 4 8 ---- 8 -----
- explain this code for me please import cv2 as cv import numpy as np from matplotlib import pyplot as plt def lanesDetection(img): height = img.shape[0] width = img.shape[1] region_of_interest_vertices = [ (200, height), (width/2, height/1.37), (width-300, height) ] gray_img = cv.cvtColor(img, cv.COLOR_RGB2GRAY) edge = cv.Canny(gray_img, 50, 100, apertureSize=3) cropped_image = region_of_interest( edge, np.array([region_of_interest_vertices], np.int32)) lines = cv.HoughLinesP(cropped_image, rho=2, theta=np.pi/180, threshold=50, lines=np.array([]), minLineLength=10, maxLineGap=30) image_with_lines = draw_lines(img, lines) return image_with_lines def region_of_interest(img, vertices): mask = np.zeros_like(img) match_mask_color = (255) cv.fillPoly(mask, vertices, match_mask_color) masked_image = cv.bitwise_and(img, mask) return masked_image def draw_lines(img, lines): img…Upsampling an Image using Interpolation TechniquesThere are a number of techniques that can be used to upsample an image.In this assignment, you are expected to implement Interpolation methods (such as Lagrangian Interpolation, Divided Difference, etc.) for three different images and compare the results. Example Code Python: orgImage = cv2.imread("dene.png")w, h, d = orgImage.shapexResized = int(w * scale);yResized = int(h * scale);....cv2.imwrite("resizedImage.png", resizedImage) Example Code Matlab:I = imread('pen.png');figure;imshow(I); [w, h, d ]= size(I)R = I(:,:,1);G = I(:,:,2);B = I(:,:,3); %for cubic spline write findcoefficients method[ax,bx,cx,dx ] = findCoefficients(R);[ay,by,cy,dy ] = findCoefficients(G);[az,bz,cz,dz ] = findCoefficients(B); ....R_new = ax(i)*i_d.^3 + bx(i)*i_d.^2 + cx(i)*i_d + dx(i);G_new = ay(i)*i_d.^3 + by(i)*i_d.^2 + cy(i)*i_d + dy(i);B_new = az(i)*i_d.^3 + bz(i)*i_d.^2 + cz(i)*i_d + dz(i);HOW TO ADD WEIGHTS TO THE EDGES FROM A DATASET import networkx as nximport pandas as pdimport matplotlib.pyplot as plt%matplotlib inline df=pd.read_csv("abc.csv")dfG=nx.from_pandas_edgelist(df, source="from",target="to")plt.figure(figsize=(12,12))nx.draw(G,with_labels=True)
- can you please solve this ? Having an image object of type BufferedImage –an image of your choice- that has the following artifacts: image has a lot of noise image is sheared towards the positive x-directions image is sheared towards the positive y-directions image has inverted colors image is zoomed in image is colored while we want it in grayscale image is to be written to the hard disk A different image is to be loaded from the hard disk Write a java code segment to resolve the above problems.1.Generate motion blurred images, apply Wiener filtering for image restoration, and display the results. I=imread('f:\1.jpg') I=rgb2gray(s) I = im2double(I); % Simulate motion blur LEN = 21; THETA = 11; PSF = fspecial('motion', LEN, THETA); blurred = imfilter(I, PSF , 'conv', 'circular'); % restore image wnr2 = deconvwnr(blurred, PSF); subplot(1,2,1);imshow(blurred);title('motion blurred image') subplot(1,2,2);imshow(wnr2);title('restore image')For a given text file Visualize the content using common libraries (e.g., term frequency histogram in matplotlib)
- Draw the Weighted Quick Union object on 0 through 10, that results from the following connectconnectcalls. Do not use path compression. Note that if we connect two sets of equal weight, by convention we make the set whose root has a smaller number the parent of the other. We use the convention that if an element is the root of the set, its array value is the weight of the set negated. connect(0, 1); connect(2, 3); connect(9, 5); connect(5, 7); connect(7, 1); connect(4, 2); connect(3, 1);procedure generate( G : group; P : property; s : 1..k+l; i : 1..k+1;[)'1, T2 ..... )'i-1] : initial segment of base image;var K : group );(* Generate the elements of G (s) whose base image startwith 1(1, T2 ..... 1I/-1 and that may have property P.If one is found then extend K, the subgroup of G (s) of elementswith property P that have already been found, and return to search. *) Algorithm to Backtrack Search Restricting Choice of Base ImagesComputer Science For an image represented by a given large 3d numpy array, use numpy to get a neighborhood filter using the mean of the pixels. For example, look through each of the image’s 3×3 window of pixels and replace outlier pixels with the mean value of the others in the 3×3 window. Set the resulting pixel value to be the mean value of a pixel and its eight neighbors. For pixels on an edge, use the mean value of the pixel and its five neighbors. For pixels on corners, use the mean value of the pixel and its three neighbors. If you loop over every pixel then you must store your input and output arrays separately