Download the Tomatoes$\_$Ripe.jpg file from the assignment page.

Run the code below a few times and play with some of the variable values and see what happens to the output! When you have a good idea of how the program works, complete the final part of this project.

import cv$2$

import numpy as np

from matplotlib import pyplot as plt

from google.colab.patches import cv$2\_$imshow

import os

# Use some exception handling to observe best practices

try:

# Open and resize image to height $=1000$ width $=1000$

width$\_$height $=(1000,1000)$

img $=$ cv$2.$imread$(\text{'}/$content$/$drive$/$MyDrive$/$Tomatoes$\_$Ripe.jpg$\text{'})$

img $=$ cv$2.$resize$($img$,$ width$\_$height, interpolation$=$cv$2.$INTER$\_$AREA$)$

# Convert BGR to HSV

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

original$\_$img $=$ cv$2.$cvtColor$($img$,$ cv$2.$COLOR$\_$BGR$2$RGB$)$

# Define HSV ranges for 'red'

# Adjust for desired red hue$/$saturation

# $([$ H S V $])$

lower$\_$red $=$ np$.$array$([0,100,100])$

upper$\_$red $=$ np$.$array$([10,255,255])$

# $([$ H S V $])$

# Use min$/$max of Hue in the HSV image to get the 'red mask'

red$\_$mask $=$ cv$2.$inRange$($hsv$,$ lower$\_$red, upper$\_$red$)$

# Use mask to draw contours

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

# Overlay contours on a copy of the original image

blue$\_$outline$\_$color $=(255,0,0)$

# Copy the image so we can display an original copy of it at the end

blue$\_$outline $=$ img.copy$()$

# This takes the image copy and draws the contours with a line weight of $2.$

# Change the last number and the outline color variable to see how it changes the output

# The $-1$ indicates that all of the contours will be drawn, other funtions of this argument

# can be used to draw contours at specific locations on the image

cv$2.$drawContours$($blue$\_$outline, contours, $-1,$ blue$\_$outline$\_$color, $2)$

outline$\_$reds $=$ cv$2.$cvtColor$($blue$\_$outline, cv$2.$COLOR$\_$BGR$2$RGB$)$

# Show original image, mask, and outline of red images in $3$ x $3$ subplot

fig $=$ plt$.$figure$($figsize$=(14,8))$

ax$1=$ fig.add$\_$subplot$(1,3,1)$

ax$2=$ fig.add$\_$subplot$(1,3,2)$

ax$3=$ fig.add$\_$subplot$(1,3,3)$

ax$1.$imshow$($original$\_$img$),$ ax$1.$set$\_$title$(\text{'}$Original Image'$),$ ax$1.$axis$(\text{'}$off$\text{'})$

ax$2.$imshow$($red$\_$mask, cmap$=$'gray'$),$ ax$2.$set$\_$title$(\text{'}$HSV Mask'$),$ ax$2.$axis$(\text{'}$off$\text{'})$

ax$3.$imshow$($outline$\_$reds$),$ ax$3.$set$\_$title$(\text{'}$Outline of Detected Red$\backslash \text{'}$s$\text{'}),$ ax$3.$axis$(\text{'}$off$\text{'})$

plt$.$tight$\_$layout$()$

plt$.$show$()$

# Just in case image isnt processed correctly tell us why

except Exception as e:

print$($f$\text{'}$Error processing $\{$img$\}\text{'})$