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13.5.Write a MATLAB script (or function) that extracts
the connected components of a binary image and displays
the results using different colors for each component and
overlays a cross-shaped symbol on top of each component’s center of gravity
https://uk.mathworks.com/help/images/label-and-measure-objects-in-a-binary-image.html
I = imread('morph.bmp');
cc = bwconncomp(BW)
labeled = labelmatrix(cc);
RGB_label = label2rgb(labeled, @copper, 'c', 'shuffle');
imshow(RGB_label,'InitialMagnification','fit')
14.1.Write a MATLAB script to generate a test image containing an ideal edge and
plot the intensity profile and the first and second derivatives along a horizontal line
of the image.
https://uk.mathworks.com/help/images/intensity-profile-of-images.html
https://uk.mathworks.com/help/images/edge-detection.html
https://courses.engr.illinois.edu/cs543/sp2011/lectures/Lecture%2008%20-%20Finding%20Edges%20and%20Straight%20Lines%20-%20Vision_Spring2011.pdf
https://uk.mathworks.com/help/matlab/ref/diff.html
https://web.njit.edu/~cliu/Courses/Fall2001/images_tb.pdf
I = imread('morph.bmp');
improfile
first order
BW = edge(I,'Prewitt')
improfile
16.6 Use the edge function inMATLAB and write a script to compute and display
the edges of a color image for the following cases:
(a) RGB image, combining the edges from each color channel by adding them
up.
(b) RGB image, combining the edges from each color channel with a logical
OR operation.
(c) YIQ image, combining the edges from each color channel by adding them
up.
(d) YIQ image, combining the edges from each color channel with a logical
OR operation.
6. Log the results to Table 17.2.
7. Write code to calculate and display the RMS error and the PSNR (dB) between
the original image and each of the lower quality resulting images.
8. Show the results inTable 17.3 and compare them with your subjective evaluation
of image quality.
https://uk.mathworks.com/matlabcentral/fileexchange/22924-count-of-coins
https://uk.mathworks.com/matlabcentral/fileexchange/25157-image-segmentation-tutorial
import networkx as nx
import matplotlib.pyplot as plt
G = nx.DiGraph()
G.add_edges_from(
[('A', 'B'), ('A', 'C'), ('D', 'B'), ('E', 'C'), ('E', 'F'),
('B', 'H'), ('B', 'G'), ('B', 'F'), ('C', 'G')])
val_map = {'A': 1.0,
'D': 0.5714285714285714,
'H': 0.0}
values = [val_map.get(node, 0.25) for node in G.nodes()]
# Specify the edges you want here
red_edges = [('A', 'C'), ('E', 'C')]
edge_colours = ['black' if not edge in red_edges else 'red'
for edge in G.edges()]
black_edges = [edge for edge in G.edges() if edge not in red_edges]
# Need to create a layout when doing
# separate calls to draw nodes and edges
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap('jet'),
node_color = values, node_size = 500)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edges(G, pos, edgelist=red_edges, edge_color='r', arrows=True)
nx.draw_networkx_edges(G, pos, edgelist=black_edges, arrows=False)
plt.show()
https://stackoverflow.com/questions/20133479/how-to-draw-directed-graphs-using-networkx-in-python
clc;
clear all;
Ps= input('enter the LED output power:');
PsdBm=10*log 10(ps/0.001);
disp=(PsdBm);
C = input('enter the number of connectors:');
Closs=input ('enter the loss at eacch connectors:');
Lc=C*Closs;
L=input('enter the length:');
a=('enter the attenuation:');
CL=L*a;
S=input('enter the number of splices:');
Sloss=input('enter the loss at each splices:');
Ls=S*Sloss;
SM=input('enter the system margin: ');
TL=CL+Lc+Ls+SM;
Pr=input('enter the receiver sensitivity:');
fprintf('\total loss :%f',TL);
fprintf('\npower receieved :%f\n',pr);
Pt=PsdBm-Pr;
fprintf('totalloss:%f',pt);
