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 = np.copy(img)     blank_image = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)     for line in lines:         for x1, y1, x2, y2 in line:             cv.line(blank_image, (x1, y1), (x2, y2), (0, 255, 0), 2)     img = cv.addWeighted(img, 0.8, blank_image, 1, 0.0)     return img def videoLanes():     cap = cv.VideoCapture('./img/Lane.mp4')     while(cap.isOpened()):         ret, frame = cap.read()         frame = lanesDetection(frame)         cv.imshow('Lanes Detection', frame)         if cv.waitKey(1) & 0xFF == ord('q'):             break     cap.release()     cv.destroyAllWindows() if __name__ == "__main__":     videoLanes()

Autoencoder and VAE a) Explain how autoencoder works and mention some of its applications b) Explain the following code 1 input layers. Input (shape=(28, 28, 1)) 2 3 x = layers.Conv2D(32, (3, 3), activation='relu", padding="same") (input) 4 x = layers.MaxPooling20((2, 2), padding="same") (x) 5 x = layers.Conv2D(32, (3, 3), activation="relu", padding="same") (x) x = layers.MaxPooling20((2, 2), padding="same") (x) 7 8 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu", padding="same") (x) 9 x = layers.Conv2DTranspose (32, (3, 3), strides=2, activation='relu', padding="same") (x) 10 I x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same") (x) 11 12 autoencoder Model (input, x) 13 autoencodex.compile(optimizer="adan", loss="binary_crossentropy") 14 autoencoder.summary()

import javax.swing.*; import java.util.ArrayList; import java.util.Collections; import java.util.Random; import java.awt.*; import java.awt.event.*; public class memory extends JFrame implements ActionListener {     private JButton[] cards;     private ImageIcon[] icons;     private int[] iconIDs;     private JButton firstButton;     private ImageIcon firstIcon;     private int numMatches;     public memory() {         setTitle("Memory Matching Game");         setSize(800, 600);         setLayout(new BorderLayout());         JPanel boardPanel = new JPanel(new GridLayout(4, 4));         add(boardPanel, BorderLayout.CENTER);         icons = new ImageIcon[8];         for (int i = 1; i <= 8; i++) {             icons[i-1] = new ImageIcon("image" + i + ".png");         }         iconIDs = new int[16];         for (int i = 0; i < 8; i++) {             iconIDs[2*i] = i;             iconIDs[2*i+1] = i;         }         Random rand = new Random();         for (int i = 0; i < 16;…

2. Problem 2: In this problem you will test our "in class" version of Gaussian elimination against the version in numpy. Use both codes to solve some randomly chosen linear systems of size N 10, 20, 40, 80, 160, 320, 640, 1280, 2560, and 5120. Depending on your computer, the last two may or may not work. If they don't, just say so in your report. If you computer can do N- 10240 then try this as well. Note that in cach case N 10 * 2k for k = 1,..., 10. That is, each time we are doubling the size of the matrix. abone Plot the results as two curves. Our code in red and numPy in bluc. (Or whatever colors you prefer). Can you guess the function which governs the runtime? Based on this guess, how long would it take to do N 40960 using numPy? N=81920?