Computer Vision 2- Image Basics with OpenCV

Learn A-Z Computer Vision In 15 Days 

In this lecture, we will about  Image basics with Opencv.

Install OpenCV in python

!pip install opencv-python

-Read, Display & Write Image

Read the Image

import cv2
image = cv2.imread("Shape.png") # Give the image path

Display the Image

cv2.imshow("image_window",image)
cv2.waitKey(0)
cv2.destroyAllWindows()

OR

import matplotlib.pyplot as plt
%matplotlib inline

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

   

Read Grayscale(black-white) Image

import cv2

image = cv2.imread("Shape.png",0)

OR

image = cv2.imread("Shape.png")
image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

plt.imshow(image,cmap='gray')

Save the Image

cv2.imwrite('New_Grayscale_Picture.jpg',image)

Increase Display Size

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(image,cmap='gray')

Draw Images

import cv2
import numpy as np

cv2.imshow("black_bg",np.zeros(shape=(512,512,3),dtype=np.int8))
cv2.waitKey(0)
cv2.destroyAllWindows()

OR

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(np.zeros(shape=(512,512,3),dtype=np.int8))

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(np.ones(shape=(512,512,3),dtype=np.int8)*255)

Draw the Line on Black Bg

# Draw a diagonal Green line of thickness of 5 pixels
image = np.zeros((450,750,3), np.uint8)  # (height,width)
cv2.line(image, (0,0), (750, 450), (0,255,0), 5)

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

Draw Rectangle On Black Background

image = np.zeros((450,750,3), np.uint8)  #(height,width)
cv2.rectangle(image,(150,100), (500, 300), (0,255,0), 5)

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

Draw Circle On Black Background

image = np.zeros((450,750,3), np.uint8)  #(height,width)
cv2.circle(image,(350,200), 100, (0,255,0), 5)

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

Draw Circle OnClick

import cv2
import numpy as np

# Create a function based on a CV2 Event 
def draw_circle(event,x,y,flags,param):
    if event == cv2.EVENT_LBUTTONDOWN: #(Left button click)
        cv2.circle(img,(x,y),50,(0,255,0),5)
    elif event == cv2.EVENT_RBUTTONDOWN: #(Right button click)
        cv2.circle(img,(x,y),50,(0,0,255),5)
        

# Create a black image
img = np.zeros((450,750,3), np.uint8)
# This names the window so we can reference it 
cv2.namedWindow(winname='my_drawing')
# Connects the mouse button to our callback function
cv2.setMouseCallback('my_drawing',draw_circle)

while True: #Runs forever until we break with Esc key on keyboard
    # Shows the image window
    cv2.imshow('my_drawing',img)
    if cv2.waitKey(20) & 0xFF == 27:
        break
# Once script is done, its usually good practice to call this line
# It closes all windows (just in case you have multiple windows called)
cv2.destroyAllWindows()

Write Name Black Bg

image = np.zeros((450,750,3), np.uint8)
cv2.putText(image, 'Hello Mayank!', (200,200), cv2.FONT_HERSHEY_SIMPLEX, 2, (0,255,0), 3)

fig = plt.figure(figsize=(10,8))

ax = fig.add_subplot(111)
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

Color Space

- BGR format generates the color by combining Blue, Green & Red. Opencv stores color in BRG. 

- RGR format generates the color by combining Blue, Green & Red. PIL stores color in BRG.

a = 1

import cv2
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

# Read image in BGR format
image = cv2.imread("target.jpg")
cv2.imshow("image_window",image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Convert the BGR to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow()

# Read image in BGR format
image = cv2.imread("target.jpg")

B, G, R = cv2.split(image)

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


cv2.imshow("Blue", cv2.merge([B, zeros, zeros]))
cv2.imshow("Green", cv2.merge([zeros, G, zeros]))
cv2.imshow("Red", cv2.merge([zeros, zeros, R])) 

cv2.waitKey(0)
cv2.destroyAllWindows()

fig,axes = plt.subplots(nrows= 1, ncols = 3,dpi=300 )
axes[0].imshow(cv2.cvtColor(cv2.merge([B, zeros, zeros]), cv2.COLOR_BGR2RGB))
axes[1].imshow(cv2.cvtColor( cv2.merge([zeros, G, zeros]), cv2.COLOR_BGR2RGB))
axes[2].imshow(cv2.cvtColor( cv2.merge([zeros, zeros, R]), cv2.COLOR_BGR2RGB))

for ax in axes:
    ax.set_xticks([])
    ax.set_yticks([])

# Read image in BGR format
image = cv2.imread("target.jpg")

image[:,:,0] = 0    # Zero out contribution from blue
image[:,:,1] = 1    # Zero out contribution from green

# Matplot reuires RGB format 
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

-HSV format closely aligns with the way human vision perceives color-making attributes. It has:

• Hue - color value(0-179)

• Saturation- Gray color intensity (0-255)

• Value - Brightness (0-255)

Color filtering Using HSV

import cv2
import numpy as np
image = cv2.imread('target.jpg')

hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Look the Color to Define the Range the Blue Color
# lower range for blue color is 95. So the lower limit for HSV is (95 ,0 ,0)
# Upper range for blue color is 95. So the lower limit for HSV is (120 ,255 ,255)
mask = cv2.inRange(hsv, (95,0,0), (120,255,255))

fig,axes = plt.subplots(nrows= 1 , ncols = 2,dpi=200 )
axes[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
axes[1].imshow(cv2.cvtColor(mask, cv2.COLOR_BGR2RGB))

                          Original Image                                                 Segment Image for Blue   

In the next blog, we will discuss the Read, Show & Write Image. 
https://sngurukuls247.blogspot.com/2020/05/computer-vision-3-image-basics-with.html

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