Implement the lab sheets given below

Motion Detection using Frame Differencing

import cv$2$

import numpy as np

# I n i t i a l i z e video capture

# Capture video from a f i l e or a devi c e

cap $=$ cv$2.$ VideoCapture $($ path$\_$to$\_$video $.$mp$4)$

# Read two c ons e cut i v e frames to i n i t i a l i z e background

ret $,$ frame$1=$ cap $.$ read $()$

ret $,$ frame$2=$ cap $.$ read $()$

whi l e True :

# Perform frame d i f f e r e n c i n g

d i f f $=$ cv$2.$ a b s d i f f $($ frame$1,$ frame$2)$

gray $=$ cv$2.$ cvtColor $($ d i f f $,$ cv$2.$COLOR$\_$BGR$2$GRAY$)$

blur $=$ cv$2.$ GaussianBlur $($ gray $,(5,5),0)$

$\_,$ thr e sh $=$ cv$2.$ thr e sho ld $($ blur $,20,255,$ cv$2.$THRESH$\_$BINARY$)$

d i l a t e d $=$ cv$2.$ d i l a t e $($ thresh $,$ None $,$ i t e r a t i o n s $=3)$

contours $,\_=$ cv$2.$ findContours $($ di l a t ed $,$ cv$2.$RETR$\_$TREE, cv$2.$CHAIN$\_$APPROX$\_$SIMPLE

$)$

# Draw r e c t a n g l e s around moving o b j e c t s

f o r contour in contour s :

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

i f cv$2.$ contourArea $($ contour $)<900$: # Adjust area thr e sho ld as needed

cont inue

cv$2.$ r e c t a n g l e $($ frame$1,($x $,$ y $),($ x $+$ w$,$ y $+$ h$),(0,255,0),2)$

# Display r e s u l t

cv$2.$ imshow$($Motion De t e c t ion $,$ frame$1)$

# Update frames

frame$1=$ frame$2$

ret $,$ frame$2=$ cap $.$ read $()$

# Exit on $$ q $$ pr e s s

i f cv$2.$ waitKey $(1)$ & $0$xFF $==$ ord $($ q $)$ :

break

# Re l eas e capture and c l o s e windows

cap $.$ r e l e a s e $()$

cv$2.$ destroyAllWindows $()$

$2\mathrm{.}1$ Lucas$-$Kanade method for motion estimation

import cv$2$

import numpy as np

# Capture video from a f i l e or a devi c e

cap $=$ cv$2.$ VideoCapture $($ path$\_$to$\_$video $.$mp$4)$

# Read the f i r s t frame

ret $,$ prev$\_$frame $=$ cap $.$ read $()$

# Convert frame to g r a y s c a l e

prev$\_$gray $=$ cv$2.$ cvtColor $($ prev$\_$frame $,$ cv$2.$COLOR$\_$BGR$2$GRAY$)$

# Create Lucas$$Kanade parameters

lk$\_$params $=$ d i c t $($ winSize$=(15,15),$

maxLevel$=2,$

c r i t e r i a $=($cv$2.$TERM$\_$CRITERIA$\_$EPS $|$ cv$2.$TERM$\_$CRITERIA$\_$COUNT,

$10,0.03))$

whi l e True :

# Read the cur r ent frame

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

i f not r e t :

break

# Convert frame to g r a y s c a l e

frame$\_$gray $=$ cv$2.$ cvtColor $($ frame $,$ cv$2.$COLOR$\_$BGR$2$GRAY$)$

# Ca l cul a t e o p t i c a l flow us ing Lucas$$Kanade method

# Find point cor r e spondenc e s between pr evi ous and cur r ent frames

p$1,$ p$2,\_=$ cv$2.$ calcOpticalFlowPyrLK $($ prev$\_$gray $,$ frame$\_$gray $,$ None $,$ None $,**$

lk$\_$params $)$

# S e l e c t good po int s

good$\_$new $=$ p$2[$ p$1$ i s not None $]$

good$\_$old $=$ p$1[$ p$1$ i s not None $]$

# Draw motion v e c t o r s

f o r i $,($new $,$ old $)$ in enumerate $($ z ip $($ good$\_$new $,$ good$\_$old $))$ :

a $,$ b $=$ new. r a v e l $().$ astype $($ i n t $)$

c $,$ d $=$ old $.$ r a v e l $().$ astype $($ i n t $)$

frame $=$ cv$2.$ l i n e $($ frame $,($ a $,$ b$),($ c $,$ d$),(0,255,0),2)$

frame $=$ cv$2.$ c i r c l e $($ frame $,($ a $,$ b$),5,(0,0,255),1)$

# Display the r e s u l t i n g frame with motion v e c t o r s

cv$2.$ imshow$($ Opt i cal Flow $,$ frame $)$

# Exit on $$ q $$ pr e s s

i f cv$2.$ waitKey $(1)$ & $0$xFF $==$ ord $($ q $)$ :

break

# Update pr evi ous frame and pr evi ous po int s

prev$\_$gray $=$ frame$\_$gray $.$ copy $()$

# Re l eas e capture and c l o s e windows

cap $.$ r e l e a s e $()$

cv$2.$ destroyAllWindows $()$

Video Segmentation:import cv$2$

# Capture the video f e ed

cap $=$ cv$2.$ VideoCapture $($ s u r v e i l l a n c e$\_$v i d e o $.$mp$4)$

# I n i t i a l i z e the background subt r a c t o r

fgbg $=$ cv$2.$ createBackgroundSubtractorMOG$2()$

whi l e True :

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

i f not r e t :

break

# Apply background subt r a c t i on

fgmask $=$ fgbg $.$ apply $($ frame $)$

# Thresholding

$\_,$ thr e sh $=$ cv$2.$ thr e sho ld $($ fgmask $,127,255,$ cv$2.$THRESH$\_$BINARY$)$

# Display the o r i g i n a l frame and the r e s u l t

cv$2.$ imshow$($ Or i g ina l Frame $,$ frame $)$

cv$2.$ imshow$($ Foreground Mask $,$ thr e sh $)$

i f cv$2.$ waitKey $(30)$ & $0$xFF $==27$ :

break

cap $.$ r e l e a s e $()$

cv$2.$ destroyAllWindows $()$

Instructions:

You are free to use Tensorflow$/$Keras for the implementation.

Use one video file which is $1$min or less.

Section A:

List all the dependencies necessary for running the code and specify their versions, in the very beginning of the notebook.

Module $2$ Motion Detection and Motion Estimation code

Module $4$ Video Segmentation code

write code for implementing Module $5$ Motion Tracking in Video code

Module $6$ Video Indexing code

Module $7$ Video Summarization code