python using numpy as np, matplotlib.pyplot as plt create an new image consisting of a 2×2 grid with images[0] in the top-left position, images[1] in the top-right position, images[2] in the bottom-left position, and images[3] in the bottom-right position. Your starter code is: images = enter code
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In python using numpy as np, matplotlib.pyplot as plt create an new image consisting of a 2×2 grid with images[0] in the top-left position, images[1] in the top-right position, images[2] in the bottom-left position, and images[3] in the bottom-right position. Your starter code is:
images = enter code
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- Make the photo below into a grayscale using python. Let us assume that the photo below is located in a folder name 'pic' and the name of the photo is Bird.jpgCode for gray: def grayscale(image): for y in range(image.getHeight()): for x in range(image.getWidth()): (r, g, b) = image.getPixel(x, y) r = int(r * 0.299) g= int(g * 0.587) b = int(b * 0.114) lum = r + g + b image.setPixels(x, y, (lum, lum, lum))#this is a python programtopic: operation overloading, Encapsulation Please find the attached imageIn python, using numpy and pandas take the reshaped images array and create a new image consisting of a 2×2 grid with images[0] in the top-left position, images[1] in the top-right position, images[2] in the bottom-left position, and images[3] in the bottom-right position. Show using plt.imshow(image)
- The image as attached is my question, I already have the answer with red text. Also I implement a python code for this question as follow: And all you need to do is help me check the python code. If there is any error, please help me correct it, thank you. # import the librariesimport numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport sklearnfrom sklearn import preprocessingfrom sklearn.preprocessing import LabelEncoderfrom sklearn.naive_bayes import GaussianNB #0.2 Assigning features and label variables ID = ['1234', '6754', '2345','2457','3567','7532','1456','1468','8989','9876']Temp = ['H','H','N','H','N','N','N','H','H','H']BP = ['H','N','H','H','H','H','N','N','N','N']Test1 = ['P','N','P','P','P','N','N','N','N','N']Test2 = ['N','P','P','N','P','N','N','N','P','N']Health = ['D','D','D','D','D','H','H','H','H','H'] #0.3 Creating labelEncoder and Converting string labels into numbers. le = preprocessing.LabelEncoder()Temp_encoded=le.fit_transform(Temp)print("The…implement a grid transformation that reduces the size of the grid, while retaining the information in it in python 3 __init__(self, size: int): create a square 2-D grid of ints with size rows and size columns. Initialize all values to 0. set_value(self, i: int, j: int, val: int): set the value in the i-th row and j-th column to val. squish(self, k:int): Sum up the elements of k by k blocks of the grid. This method does not return anything, but rather modifies the grid object. The size of the modified grid is n / k where n was the original size. You can assume that the grid's size is divisible by k.Write a program in python to illustrate 17 random polygons with 4-12 sides, each side ranging in length from 10-50, located at random position (x, y). Select a color at random from red, green, blue, black, or yellow. Produce the code (screenshot) and the output in .ipynb and .html.
- Please can you help me solve this problem in Python. Darkening an image requires adjusting its pixels toward black as a limit, whereas lightening an image requires adjusting them toward white as a limit. Because black is RGB (0, 0, 0) and white is RGB (255, 255, 255), adjusting the three RGB values of each pixel by the same amount in either direction will have the desired effect. Of course, the algorithms must avoid exceeding either limit during the adjustments. Lightening and darkening are actually special cases of a process known as color filtering. A color filter is any RGB triple applied to an entire image. The filtering algorithm adjusts each pixel by the amounts specified in the triple. For example, you can increase the amount of red in an image by applying a color filter with a positive red value and green and blue values of 0. The filter (20, 0, 0) would make an image’s overall color slightly redder. Alternatively, you can reduce the amount of red by applying a color filter…Implement image segmentation method into biomedical image data (any data, for example cancer image) in phyton notebook/google collab (.ipynb). Include the code and give report about the code and the result of image segmentation. Segmentation method must not be deep learning method, only computer vision and image processing!Use Python Codehow to filter dataframes using python . For example I have a student grade csv table, first, I need to read the csv using pandas library then I want to visualize my student scores (using matplotlib, seaborn, etc) but I only want to visualize those whose scores are 60 and above. so how do i do the code in python.Exampe of Student grades table (csv)(Student name and exam date): Student name 4/7/20 Jojo 90 Lili 65 Bibi 50 Sarah 40 Rajah 100 Albert 80 Cherry 75 Jamie 85 So , based on the table above, what doesn't need to be visualized is Bibi and Sarah . Hope you can help me :)
- Modify the SmallO.java program (image attached) so that it: a) Keeps and displays a running total and the average so far of the enterednumbers. b) Displays the largest and the smallest numbers so far of the enterednumbers.Write a program that draws two circles withradius 15 pixels, centered at random locations, with a line connecting the twocircles. The distance between the two centers is displayed on the line, as shownin Figure .Can someone get this Python code to work in Google Colab? import turtle screen = turtle.Screen() # background color screen.bgcolor("lightpink") # turtle object y = turtle.Turtle() # define function # for drawing rays of Sun def drawFourRays(t, length, radius): for i in range(4): t.penup() t.forward(radius) t.pendown() t.forward(length) t.penup() t.backward(length + radius) t.left(90) # Draw circle # to make sun y.penup() y.goto(85, 110) y.fillcolor("yellow") y.pendown() y.begin_fill() y.circle(45) y.end_fill() # Use the defined # function to draw rays y.penup() y.goto(85, 169) y.pendown() drawFourRays(y, 85, 54) y.right(45) drawFourRays(y, 85, 54) y.left(45) # To keep the # output window active turtle.done()