Please run this code on python and give me output of it i couldnt managed that

Code:import cv$2$

import numpy as np

# Define color ranges for segmentation $($in HSV color space$)$

color$\_$ranges $=\{$

'red': $[(0,70,50),(10,255,255),(170,70,50),(180,255,255)],$ # Red has two ranges in HSV

'yellow': $[(20,100,100),(30,255,255)]$

$\}$

def color$\_$segmentation$($frame$)$:

hsv $=$ cv$2.$cvtColor$($frame$,$ cv$2.$COLOR$\_$BGR$2$HSV$)$

mask $=$ np$.$zeros$($hsv$.$shape$[$:$2],$ dtype$=$np$.$uint$8)$

for color, ranges in color$\_$ranges.items$()$:

for i in range$(0,$ len$($ranges$),2)$:

mask $+=$ cv$2.$inRange$($hsv$,$ np$.$array$($ranges$[$i$]),$ np$.$array$($ranges$[$i $+1]))$

return mask

#code for edge detection

def edge$\_$detection$($roi$)$:

return cv$2.$Canny$($roi$,100,200)$

# Load template images and convert them to grayscale

templates $=\{$

'stop': cv$2.$imread$(\text{'}$stop$\_$template.jpg$\text{'},$ cv$2.$IMREAD$\_$GRAYSCALE$),$

'caution': cv$2.$imread$(\text{'}$caution$\_$template.jpg$\text{'},$ cv$2.$IMREAD$\_$GRAYSCALE$)$

$\}$

#code for Task$3$: template matching

# Resize templates for scale invariance

for key in templates:

templates$[$key$]=$ cv$2.$resize$($templates$[$key$],(50,50))$

def template$\_$matching$($edges$)$:

matches $=[]$

for label, template in templates.items$()$:

res $=$ cv$2.$matchTemplate$($edges$,$ template, cv$2.$TM$\_$CCOEFF$\_$NORMED$)$

loc $=$ np$.$where$($res $>=0\mathrm{.}5)$ # Adjust threshold as needed

for pt in zip$(*$loc$[$::$-1])$:

matches.append$(($label$,$ pt$,$ res$[$pt$[1],$ pt$[0]]))$

return matches

#code for Task$4$: shape analysis

def shape$\_$analysis$($contour$)$:

approx $=$ cv$2.$approxPolyDP$($contour$,0\mathrm{.}02*$ cv$2.$arcLength$($contour$,$ True$),$ True$)$

if len$($approx$)==8$:

return 'stop'

elif len$($approx$)==4$:

return 'caution'

return None

# Main code for real$-$time detection

def main$()$:

cap $=$ cv$2.$VideoCapture$(0)$ # Capture video from the first camera

while True:

ret, frame $=$ cap.read$()$

if not ret:

break

mask $=$ color$\_$segmentation$($frame$)$

contours, $\_=$ cv$2.$findContours$($mask$,$ cv$2.$RETR$\_$TREE, cv$2.$CHAIN$\_$APPROX$\_$SIMPLE$)$

for contour in contours:

x$,$ y$,$ w$,$ h $=$ cv$2.$boundingRect$($contour$)$

roi $=$ frame$[$y:y$+$h$,$ x:x$+$w$]$

edges $=$ edge$\_$detection$($roi$)$

matches $=$ template$\_$matching$($edges$)$

for label, pt$,$ score in matches:

if shape$\_$analysis$($contour$)==$ label:

cv$2.$rectangle$($frame$,($x$,$ y$),($x $+$ w$,$ y $+$ h$),(0,255,0),2)$

cv$2.$putText$($frame$,$ label, $($x$,$ y $-10),$ cv$2.$FONT$\_$HERSHEY$\_$SIMPLEX, $0\mathrm{.}9,(36,255,12),2)$

cv$2.$imshow$(\text{'}$Traffic Sign Detection', frame$)$

if cv$2.$waitKey$(1)$ & $0$xFF $==$ ord$(\text{'}$q$\text{'})$:

break

cap.release$()$

cv$2.$destroyAllWindows$()$

if $\_\_$name$\_\_=="\_\_$main$\_\_"$:

main$()$