Opencv

Opencv project link

https://www.kaggle.com/code/bulentsiyah/learn-opencv-by-examples-with-python/notebook

Dataset link

https://www.kaggle.com/code/bulentsiyah/learn-opencv-by-examples-with-python/input 

Visual story telling of netflix link

https://www.kaggle.com/code/subinium/storytelling-with-data-netflix-ver

Resize image

import cv2

import numpy as np

import matplotlib.pyplot as plt

image = cv2.imread(r"D:\sims\eb\sim21\EB-ML-06-10-2022-Test-Output-15\PERFORATION\Overkill\Fail\Blister 1 2022-03-12 12-59-43.859 T0 M0 G0 3 PERFORATION Mono.bmp", 1)

# Loading the image

half = cv2.resize(image, (0, 0), fx = 0.1, fy = 0.1)

bigger = cv2.resize(image, (1050, 1610))

stretch_near = cv2.resize(image, (780, 540), 

               interpolation = cv2.INTER_LINEAR)

Titles =["Original", "Half", "Bigger", "Interpolation Nearest"]

images =[image, half, bigger, stretch_near]

count = 4

for i in range(count):

    plt.subplot(2, 2, i + 1)

    plt.title(Titles[i])

    plt.imshow(images[i])

plt.show()

Foreground Extraction of image

# Python program to illustrate foreground extraction using GrabCut algorithm

# organize imports

import numpy as np

import cv2

from matplotlib import pyplot as plt

# path to input image specified and

# image is loaded with imread command

image = cv2.imread('/content/images.jpg')

# create a simple mask image similar

# to the loaded image, with the

# shape and return type

mask = np.zeros(image.shape[:2], np.uint8)

# specify the background and foreground model

# using numpy the array is constructed of 1 row

# and 65 columns, and all array elements are 0

# Data type for the array is np.float64 (default)

backgroundModel = np.zeros((1, 65), np.float64)

foregroundModel = np.zeros((1, 65), np.float64)

# define the Region of Interest (ROI)

# as the coordinates of the rectangle

# where the values are entered as

# (startingPoint_x, startingPoint_y, width, height)

# these coordinates are according to the input image

# it may vary for different images

rectangle = (20, 100, 150, 150)

# apply the grabcut algorithm with appropriate

# values as parameters, number of iterations = 3

# cv2.GC_INIT_WITH_RECT is used because

# of the rectangle mode is used

cv2.grabCut(image, mask, rectangle,

      backgroundModel, foregroundModel,

      3, cv2.GC_INIT_WITH_RECT)

# In the new mask image, pixels will

# be marked with four flags

# four flags denote the background / foreground

# mask is changed, all the 0 and 2 pixels

# are converted to the background

# mask is changed, all the 1 and 3 pixels

# are now the part of the foreground

# the return type is also mentioned,

# this gives us the final mask

mask2 = np.where((mask == 2)|(mask == 0), 0, 1).astype('uint8')

# The final mask is multiplied with

# the input image to give the segmented image.

image_segmented = image * mask2[:, :, np.newaxis]

# output segmented image with colorbar

plt.subplot(1, 2, 1)

plt.title('Original Image')

plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

plt.axis('off')

# Display the segmented image

plt.subplot(1, 2, 2)

plt.title('Segmented Image')

plt.imshow(cv2.cvtColor(image_segmented, cv2.COLOR_BGR2RGB))

plt.axis('off')

plt.show()

Cornor Detection

# Python program to illustrate

# corner detection with

# Shi-Tomasi Detection Method

 

# organizing imports

import cv2

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

# path to input image specified and

# image is loaded with imread command

img = cv2.imread('/content/polygon.jpg')

# convert image to grayscale

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Shi-Tomasi corner detection function

# We are detecting only 100 best corners here

# You can change the number to get desired result.

corners = cv2.goodFeaturesToTrack(gray_img, 100, 0.01, 10)

# convert corners values to integer

# So that we will be able to draw circles on them

corners = np.int0(corners)

# draw red color circles on all corners

for i in corners:

  x, y = i.ravel()

  cv2.circle(img, (x, y), 3, (255, 0, 0), -1)

# resulting image

plt.imshow(img)

# De-allocate any associated memory usage

if cv2.waitKey(0) & 0xff == 27:

  cv2.destroyAllWindows()

Extract images from video file

# Importing all necessary libraries

import cv2

import os

# Read the video from specified path

cam = cv2.VideoCapture("/content/bandicam 2024-07-22 15-00-39-968.mp4")

try:

 

  # creating a folder named data

  if not os.path.exists('data'):

    os.makedirs('data')

# if not created then raise error

except OSError:

  print ('Error: Creating directory of data')

# frame

currentframe = 0

while(True):

 

  # reading from frame

  ret,frame = cam.read()

  if ret:

    # if video is still left continue creating images

    name = './data/frame' + str(currentframe) + '.jpg'

    print ('Creating...' + name)

    # writing the extracted images

    cv2.imwrite(name, frame)

    # increasing counter so that it will

    # show how many frames are created

    currentframe += 1

  else:

    break

# Release all space and windows once done

cam.release()

cv2.destroyAllWindows()

Graphics Design using turtle library

import turtle
colors = [ "pink","yellow","blue","green","white","red"]
sketch = turtle.Pen()
turtle.bgcolor("black")
for i in range(200):
   sketch.pencolor(colors[i % 6])
   sketch.width(i/100 + 1)
   sketch.forward(i)
   sketch.left(59)

Draw a PANDA using graphics design TURTLE library

# Draw a Panda using Turtle Graphics
# Import turtle package
import turtle

# Creating a turtle object(pen)
pen = turtle.Turtle()

# Defining method to draw a colored circle 
# with a dynamic radius
def ring(col, rad):

    # Set the fill
    pen.fillcolor(col)

    # Start filling the color
    pen.begin_fill()

    # Draw a circle
    pen.circle(rad)

    # Ending the filling of the color
    pen.end_fill()

##########################Main Section#############################

# pen.up           --> move turtle to air
# pen.down      --> move turtle to ground
# pen.setpos        --> move turtle to given position
# ring(color, radius) --> draw a ring of specified color and radius
###################################################################

##### Draw ears #####
# Draw first ear
pen.up()
pen.setpos(-35, 95)
pen.down
ring('black', 15)

# Draw second ear
pen.up()
pen.setpos(35, 95)
pen.down()
ring('black', 15)

##### Draw face #####
pen.up()
pen.setpos(0, 35)
pen.down()
ring('white', 40)

##### Draw eyes black #####

# Draw first eye
pen.up()
pen.setpos(-18, 75)
pen.down
ring('black', 8)

# Draw second eye
pen.up()
pen.setpos(18, 75)
pen.down()
ring('black', 8)

##### Draw eyes white #####

# Draw first eye
pen.up()
pen.setpos(-18, 77)
pen.down()
ring('white', 4)

# Draw second eye
pen.up()
pen.setpos(18, 77)
pen.down()
ring('white', 4)

##### Draw nose #####
pen.up()
pen.setpos(0, 55)
pen.down
ring('black', 5)

##### Draw mouth #####
pen.up()
pen.setpos(0, 55)
pen.down()
pen.right(90)
pen.circle(5, 180)
pen.up()
pen.setpos(0, 55)
pen.down()
pen.left(360)
pen.circle(5, -180)
pen.hideturtle()