Hello Everyone, I want to tell you about how to make digital imaging with python using OpenCV Package. But first, you must have PYCharm or NetBeans to run Digital Imaging Program.
Okay, here I'm using PYCharm, follow my instructions.
1 . The first program we make Zoom program, this is tutorial how to make zoom with Python using OpenCV and NumPY packages.
import numpy as np
import cv2 as cv
img = cv.imread('test.jpg',1)
sizex, sizey = img.shape[0]*1, img.shape[0]*1
img_new =cv.resize(img, (sizex, sizey))
cv.imshow('Old Image', img)
cv.imshow('Image After Resize', img_new)
cv.waitKey(0)
cv.destroyAllWindows()
Explanation Code:
import numpy as np
import cv2 as cv
To called OpenCV and NumPY package on this program.
img = cv.imread('test.jpg',1)
The img variable saves the result of the image file invocation performed by the function cv.imread
sizex, sizey = img.shape[0]*1, img.shape[0]*1
img_new =cv.resize(img, (sizex, sizey))
To know the dimensions or the physical size of the image and the number of channels in it you can use the shape, this code img.shape img.shape[0]1, img.shape[0]1 width and height will multiply 1. And then, resize() function for change size image.
cv.imshow('Old Image', img)
To show image file default, you can use cv.imshow('Title window', variable image file)
cv.imshow('Image After Resize', img_new) this code to show image file after zoomed, because at there you call variable which image has zoomed.
2 . The Second program about how to blur an image with python using OpenCV and NumPY Packages.
On this program, we will use the Averaging technique.
import numpy as np
import cv2 as cv
img = cv.imread('test.jpg',1)
blur = cv.blur(img, (10,10))
cv.imshow('Old Image', img)
cv.imshow('Image After Blur', blur)
cv.waitKey(0)
cv.destroyAllWindows()
Explanation Code:
blur = cv.blur(img, (10,10))
This code has two arguments are img to called a variable where image file place and (10,10) where the intent of 2 argument that is to regulate how high rate of bleeding to be obtained. The high value will result in the higher blur effect.
3 . This third program if you want to clear the noise image, can use technique Median Blur
import numpy as np
import cv2 as cv
img = cv.imread('noise.jpg',1)
blur = cv.medianBlur(img,5)
cv.imshow('Old Image', img)
cv.imshow('Image After Gaussian Blur', blur)
cv.waitKey(0)
cv.destroyAllWindows()
Explanation Code:
blur = cv.medianBlur(img,5) Just use the two arguments src as img variable where the image file is stored, and its value is 5 to set the strength of the resulting blur.
4 . The fourth program uses the Edge Detection technique which only detects the line of objects in each image
import numpy as np
import cv2 as cv
img = cv.imread('test.jpg',1)
blur = cv.Canny(img,50,60)
cv.imshow('Old Image', img)
cv.imshow('Image After Edge Detection', blur)
cv.waitKey(0)
cv.destroyAllWindows()
Explanation Code:
blur = cv.Canny(img,50,60)
Value 50 as minVal.
Value 60 as maxVal.
minVal and maxVal which means as the value that governs the intensity of the gradient on the object.
If the value is changed to be smaller, then the line will be much more detailed.