EXP:01 Image-Handling-and-Pixel-Transformations-Using-OpenCV

Name:Ananda Rakshan K V

Reg.No:212223230014

AIM:

Write a Python program using OpenCV that performs the following tasks:

1. Read and Display an Image.
2. Adjust the brightness of an image.
3. Modify the image contrast.
4. Generate a third image using bitwise operations.

Software Required:

• Anaconda - Python 3.7
• Jupyter Notebook (for interactive development and execution)

Algorithm:

Step 1:

Load an image from your local directory and display it.

Step 2:

Create a matrix of ones (with data type float64) to adjust brightness.

Step 3:

Create brighter and darker images by adding and subtracting the matrix from the original image.
Display the original, brighter, and darker images.

Step 4:

Modify the image contrast by creating two higher contrast images using scaling factors of 1.1 and 1.2 (without overflow fix).
Display the original, lower contrast, and higher contrast images.

Step 5:

Split the image (boy.jpg) into B, G, R components and display the channels

Program Developed By:

• Name: Ananda Rakshan K V

• Register Number: 212223230014

  Ex. No. 01

1. Read the image ('Eagle_in_Flight.jpg') using OpenCV imread() as a grayscale image.

import cv2
import numpy as np
import matplotlib.pyplot as plt
img_bgr =cv2.imread('Eagle_in_Flight.jpg',0)

2. Print the image width, height & Channel.

img_bgr.shape

3. Display the image using matplotlib imshow().

plt.imshow(img_bgr,cmap='gray')
plt.title('BGR Image')
plt.axis('on')
plt.show()

4. Save the image as a PNG file using OpenCV imwrite().

img=cv2.imread('Eagle_in_Flight.jpg')
cv2.imwrite('Eagle.png',img)

5. Read the saved image above as a color image using cv2.cvtColor().

img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

6. Display the Colour image using matplotlib imshow() & Print the image width, height & channel.

plt.imshow(img_rgb)
plt.title("RGB IMAGE")
plt.axis("on")  
plt.show()
img_rgb.shape

7. Crop the image to extract any specific (Eagle alone) object from the image.

cropped=img_rgb[20:420,200:550]
plt.imshow(cropped[:,:,::-1])
plt.title("CROPPED IMAGE")
plt.axis("off")
plt.show()

8. Resize the image up by a factor of 2x.

res= cv2.resize(cropped,(200*2, 200*2))

9. Flip the cropped/resized image horizontally.

flipped_img = cv2.flip(cropped, 1)
plt.imshow(flipped_img[:,:,::-1])
plt.title('Flipped Horizontal')
plt.axis('off')
plt.show()

10. Read in the image ('Apollo-11-launch.jpg').

img=cv2.imread('Apollo-11-launch.jpg',cv2.IMREAD_COLOR)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

11. Add the following text to the dark area at the bottom of the image (centered on the image):

text = cv2.putText(img_rgb, "Apollo 11 Saturn V Launch, July 16, 1969", (300, 700),cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)  
plt.imshow(text, cmap='gray')  
plt.title("TEXT IMAGE")
plt.show()  

12. Draw a magenta rectangle that encompasses the launch tower and the rocket.

rcol= (255, 0, 255)
cv2.rectangle(img_rgb, (400, 100), (800, 650), rcol, 3)

13. Display the final annotated image.

plt.title("Annotated image")
plt.imshow(img_rgb)
plt.show()

14. Read the image ('Boy.jpg').

img =cv2.imread('boy.jpg',cv2.IMREAD_COLOR)
img_rgb= cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 

15. Adjust the brightness of the image.

m = np.ones(img_rgb.shape, dtype="uint8") * 50

16. Create brighter and darker images.

img_brighter = cv2.add(img_rgb, m)  
img_darker = cv2.subtract(img_rgb, m)  

17. Display the images (Original Image, Darker Image, Brighter Image).

plt.figure(figsize=(10,5))
plt.subplot(1,3,1), plt.imshow(img_rgb), plt.title("Original Image"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow(img_brighter), plt.title("Brighter Image"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow(img_darker), plt.title("Darker Image"), plt.axis("off")
plt.show()

18. Modify the image contrast.

matrix1 = np.ones(img_rgb.shape) * 0.8
matrix2 = np.ones(img_rgb.shape,) * 1.2
img_lower = np.uint8(cv2.multiply(np.float64(img_rgb),matrix1))
img_higher = np.uint8(cv2.multiply(np.float64(img_rgb),matrix2))

19. Display the images (Original, Lower Contrast, Higher Contrast).

plt.figure(figsize=[18,5])
plt.subplot(131), plt.imshow(img_rgb), plt.title("Original Image"), plt.axis("off")
plt.subplot(132), plt.imshow(img_lower), plt.title("Lower Contrast"), plt.axis("off")
plt.subplot(133), plt.imshow(img_higher), plt.title("Higher Contrast"), plt.axis("off")
plt.show()

20. Split the image (boy.jpg) into the B,G,R components & Display the channels.

b, g, r = cv2.split(img)
plt.figure(figsize=(10,5))
plt.subplot(1,3,1), plt.imshow(b), plt.title("Blue Channel"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow(g), plt.title("Green Channel"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow(r), plt.title("Red Channel"), plt.axis("off")
plt.show()

21. Merged the R, G, B , displays along with the original image

merged_rgb = cv2.merge([r, g, b])
plt.figure(figsize=(5,5))
plt.imshow(merged_rgb)
plt.title("Merged RGB Image")
plt.axis("off")
plt.show()

22. Split the image into the H, S, V components & Display the channels.

hsv_img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
h, s, v = cv2.split(hsv_img)
plt.figure(figsize=(10,5))
plt.subplot(1,3,1), plt.imshow(h), plt.title("Hue Channel"), plt.axis("off")
plt.subplot(1,3,2), plt.imshow(s), plt.title("Saturation Channel"), plt.axis("off")
plt.subplot(1,3,3), plt.imshow(v), plt.title("Value Channel"), plt.axis("off")
plt.show()

23. Merged the H, S, V, displays along with original image.

merged_hsv = cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2RGB)
combined = np.concatenate((img_rgb, merged_hsv), axis=1)
plt.figure(figsize=(10, 5))
plt.imshow(combined)
plt.title("Original Image  &  Merged HSV Image")
plt.axis("off")
plt.show()

Output:

• i) Read and Display an Image.

  1.Read 'Eagle_in_Flight.jpg' as grayscale and display:

2.Save image as PNG and display:

3.Cropped image:

4.Resize and flip Horizontally:

5.Read 'Apollo-11-launch.jpg' and Display the final annotated image:

• ii) Adjust Image Brightness.

  1.Create brighter and darker images and display:

  

• iii) Modify Image Contrast.

• iv) Generate Third Image Using Bitwise Operations.

  1.Split 'Boy.jpg' into B, G, R components and display:

2.Merge the R, G, B channels and display:

3.Split the image into H, S, V components and display:

4.Merge the H, S, V channels and display:

Result:

Thus, the images were read, displayed, brightness and contrast adjustments were made, and bitwise operations were performed successfully using the Python program.