├── README.md
└── Image processing using open cv.py


/README.md:
--------------------------------------------------------------------------------
1 | # image-processing


--------------------------------------------------------------------------------
/Image processing using open cv.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | # coding: utf-8
  3 | 
  4 | # In[9]:
  5 | 
  6 | 
  7 | get_ipython().system('pip install opencv-python numpy matplotlib')
  8 | 
  9 | 
 10 | # In[12]:
 11 | 
 12 | 
 13 | import cv2
 14 | import numpy as np
 15 | import matplotlib.pyplot as plt
 16 | 
 17 | 
 18 | # In[13]:
 19 | 
 20 | 
 21 | def load_image(image_path):
 22 |     return cv2.imread(image_path, cv2.IMREAD_COLOR)
 23 | 
 24 | 
 25 | # In[14]:
 26 | 
 27 | 
 28 | def convert_to_grayscale(image):
 29 |     return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 30 | 
 31 | 
 32 | # In[15]:
 33 | 
 34 | 
 35 | def rotate_image(image, angle):
 36 |     (h, w) = image.shape[:2]
 37 |     center = (w // 2, h // 2)
 38 |     matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
 39 |     return cv2.warpAffine(image, matrix, (w, h))
 40 | 
 41 | 
 42 | # In[16]:
 43 | 
 44 | 
 45 | def crop_border(image, border_thickness):
 46 |     """
 47 |     Crop the border of the image with the specified thickness from all sides.
 48 |     
 49 |     Parameters:
 50 |         image (numpy.ndarray): The input image.
 51 |         border_thickness (int): The thickness of the border to crop from all sides.
 52 |     
 53 |     Returns:
 54 |         numpy.ndarray: The image with the border removed.
 55 |     """
 56 |     if border_thickness >= min(image.shape[:2]) // 2:
 57 |         raise ValueError("Border thickness is too large for the given image dimensions.")
 58 |     
 59 |     (h, w) = image.shape[:2]
 60 |     start_row = border_thickness
 61 |     start_col = border_thickness
 62 |     end_row = h - border_thickness
 63 |     end_col = w - border_thickness
 64 |     return image[start_row:end_row, start_col:end_col]
 65 | 
 66 | 
 67 | # In[30]:
 68 | 
 69 | 
 70 | def blur_image(image, kernel_size=(1, 1)):
 71 |     return cv2.GaussianBlur(image, kernel_size, 0)
 72 | 
 73 | 
 74 | # In[18]:
 75 | 
 76 | 
 77 | def get_image_info(image):
 78 |     dimensions = image.shape
 79 |     number_of_pixels = image.size
 80 |     return dimensions, number_of_pixels
 81 | 
 82 | 
 83 | # In[31]:
 84 | 
 85 | 
 86 | image_path = "C:/Users/muthu/OneDrive/Desktop/nehru.jpg"
 87 | 
 88 | # Load the image
 89 | image = load_image(image_path)
 90 | 
 91 | # Convert to grayscale
 92 | gray_image = convert_to_grayscale(image)
 93 | 
 94 | # Rotate the image by 45 degrees
 95 | rotated_image = rotate_image(gray_image, 45)
 96 | 
 97 | # Crop the image (region from (50, 50) to (150, 150))
 98 | cropped_image = crop_image(rotated_image, 10, 10, 150, 150)
 99 | 
100 | # Blur the image
101 | blurred_image = blur_image(cropped_image)
102 | 
103 | # Get image dimensions and number of pixels
104 | dimensions, number_of_pixels = get_image_info(blurred_image)
105 | 
106 | # Display the original and processed images
107 | plt.figure(figsize=(12, 6))
108 | 
109 | plt.subplot(2, 3, 1)
110 | plt.title('Original Image')
111 | plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
112 | 
113 | plt.subplot(2, 3, 2)
114 | plt.title('Grayscale Image')
115 | plt.imshow(gray_image, cmap='gray')
116 | 
117 | plt.subplot(2, 3, 3)
118 | plt.title('Rotated Image')
119 | plt.imshow(rotated_image, cmap='gray')
120 | 
121 | plt.subplot(2, 3, 4)
122 | plt.title('Cropped Image')
123 | plt.imshow(cropped_image, cmap='gray')
124 | 
125 | plt.subplot(2, 3, 5)
126 | plt.title('Blurred Image')
127 | plt.imshow(blurred_image, cmap='gray')
128 | 
129 | plt.show()
130 | 
131 | print(f'Dimensions of the image: {dimensions}')
132 | print(f'Number of pixels: {number_of_pixels}')
133 | 
134 | 


--------------------------------------------------------------------------------