Basic Operations on Images

Goals

• Accessing and modifying pixel values
• Accessing image properties
• Setting the Image Region ( area ) (ROI)
• Splitting and Merging Images

Almost all the operations in this section are mainly related to Numpy rather than OpenCV. Good knowledge of Numpy is required to write better optimized code with OpenCV.

Accessing and Modifying Pixel Values

Let’s load a color image first:

>>> import cv2
>>> import numpy as np

>>> img = cv2.imread('messi5.jpg')

You can access a pixel value by its row and column coordinates. For a BGR image, it returns an array of Blue, Green, Red values. For a grayscale image, only the corresponding intensity is returned.

>>> px = img[100,100]
>>> print(px)
[157 166 200]

# accessing only blue pixel
>>> blue = img[100,100,0]
>>> print(blue)
157

You can modify the pixel values in the same way.

>>> img[100,100] = [255,255,255]
>>> print(img[100,100])
[255 255 255]

Warning

Numpy is a library optimized for fast array calculations. Therefore, accessing and modifying each pixel value individually will be very slow.

Note

The method mentioned above is normally used to select a region of the array, for example the first 5 rows and the last 3 columns and similar things. For individual pixel access, Numpy array methods, array.item () and array.itemset () are considered better. But they always return a scalar. Therefore, if you want to access all B, G, R values, you need to call array.item () separately for each one.

A better method for pixel access and editing:

# accessing RED value
>>> img.item(10,10,2)
59
# modifying RED value
>>> img.itemset((10,10,2),100)
>>> img.item(10,10,2)
100

Accessing Image Properties

Image properties include the number of rows, columns and channels, image data type, number of pixels, etc.

The shape of the image is accessed by img.shape. It returns a tuple of a number of rows, columns and channels (if the image is color).

>>> print(img.shape)
(342, 548, 3)

Note

If the image is grayscale, the returned tuple contains only the number of rows and columns. This is therefore a good method for checking whether a loaded image is grayscale or a color image.

Total number of pixels is accessed by img.size:

>>> print(img.size)
562248

Image data type is obtained by image.dtype:

>>> print(img.dtype)
uint8

Note

img.dtype is very important when debugging, because a large number of errors in OpenCV-Python code are caused by invalid data types.

Image ROI

Sometimes you will have to play with certain regions of images. For eye detection in images, first face detection is done over the whole image. When a face is obtained, we select the face region alone and search for eyes inside it. This approach improves accuracy.

Here I will select the ball and copy it to another region in the image:

>>> ball = img[280:340, 330:390]
>>> img[273:333, 100:160] = ball

Splitting and Merging Image Channels

The B, G, R channels of an image can be split into their individual planes when needed. Then, the individual channels can be merged back to form a BGR image again.

>>> b,g,r = cv2.split(img)
>>> img = cv2.merge((b,g,r))
# or
>>> b = img[:,:,0]
# Suppose you want to set all red pixels to zero - you don't need to split
# like this. You can simply use Numpy, which is faster.

>>> img[:,:,2] = 0

Note

cv2.split() is a costly operation (in terms of time). So use it only if necessary. Numpy is much more efficient.

Making Borders for Images (Padding)

If you want to create a border around an image, something like a photo frame, you can use cv2.copyMakeBorder() function. But it has more applications for convolution operation, zero padding etc. This function takes following arguments:

• src - input image
• top, bottom, left, right - border width in number of pixels in corresponding directions
• borderType - Flag defining what kind of border to be added. It can be following types:
• _cv2.BORDER_CONSTAN_T - Adds a constant colored border. The value should be given as next argument.
• cv2.BORDER_REFLECT - Border will be a mirror reflection of the border elements, like this: fedcba | abcdefgh | hgfedcb
• cv2.BORDER_REFLECT_101 or cv2.BORDER_DEFAULT - Same as above, but with a slight change, like this: gfedcb | abcdefgh | gfedcba
• cv2.BORDER_REPLICATE - Last element is replicated throughout, like this: aaaaaa | abcdefgh | hhhhhhh
• cv2.BORDER_WRAP - Can’t explain, it will look like this: cdefgh | abcdefgh | abcdefg
• value - Border color if border type is cv2.BORDER_CONSTANT

Below is a sample code demonstrating all these border types for better understanding:

import cv2
import numpy as np
from matplotlib import pyplot as plt

BLUE = [255,0,0]

img1 = cv2.imread('opencv_logo.png')

replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP)
constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE)

plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101')
plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP')
plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT')

plt.show()

See the result below ( The image is displayed with matplotlib, so RED and BLUE channels are swapped ):