Opencv basic use Mat operation [10]

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• 🍊 # Column: Image processing

• 🍊 This blog post continues with the previous article # OpenCV histogram and Image Composition [9]

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This post continues to test some of the OpencV Python sample code

• Direct pull test of this post is opencv – master4.5.1, then in opencv/samples/python/tutorial_code/testing different module py file directory

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[linear addition of images]

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Run the code:

cdopencv/samples/python/tutorial_code/core/AddingImages cp .. /.. /.. /.. /data/LinuxLogo.jpg . cp .. /.. /.. /.. /data/WindowsLogo.jpg . python adding_images.pyCopy the code

The effect is as follows:

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The DFT of an image is taken and it’s power spectrum is displayed.【 DFT 】

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Run code 1:

cdopencv/samples/python/tutorial_code/core/discrete_fourier_transform cp .. /.. /.. /.. /data/lena.jpg . python discrete_fourier_transform.py lena.jpgCopy the code

The running effect is as follows:

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FileStorage you can serialize objects in OpenCV

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Run code 1:

cd opencv/samples/python/tutorial_code/core/file_input_output

python3 file_input_output.py moli.json
Copy the code

The running effect is as follows:

cat moli.json 
The file contains the following contents, of course, if you want to understand, you need to look at the code:
# It is important for me to know that there is such a file input that the generated code can be used for reference later

{
    "iterationNr": 100,
    "strings": [
        "image1.jpg"."Awesomeness".".. /data/baboon.jpg"]."Mapping": {
        "One": 1,
        "Two": 2}."R_MAT": {
        "type_id": "opencv-matrix"."rows": 3."cols": 3."dt": "d"."data": [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]}."T_MAT": {
        "type_id": "opencv-matrix"."rows": 3."cols": 1,
        "dt": "d"."data": [0.0, 0.0, 0.0]},"MyData": { "A": 97, "X": 3.1415926535897931 e+00,"name": "mydata1234"}}Copy the code

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【 Mat_mask_operations 】

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Run code 1:

cd. /mat_mask_operations/ cp .. /.. /.. /.. /data/lena.jpg . python3 file_input_output.py moli.jsonCopy the code

The running effect is as follows:

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Mat_operations.py [common OpenCV image manipulation function usage example]

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Run code 1:

cd ../mat_operations/

cat mat_operations.py 

Copy the code

Examples of common OpenCV image manipulation functions are provided in the mat_operations.py code

from __future__ import division
import cv2 as cv
import numpy as np

# Snippet code for Operations with images tutorial (not intended to be run)

def load() :
    # Input/Output
    filename = 'img.jpg'
    ## [Load an image from a file]
    img = cv.imread(filename)
    ## [Load an image from a file]

    ## [Load an image from a file in grayscale]
    img = cv.imread(filename, cv.IMREAD_GRAYSCALE)
    ## [Load an image from a file in grayscale]

    ## [Save image]
    cv.imwrite(filename, img)
    ## [Save image]

def access_pixel() :
    # Accessing pixel intensity values
    img = np.empty((4.4.3), np.uint8)
    y = 0
    x = 0
    ## [Pixel access 1]
    _intensity = img[y,x]
    ## [Pixel access 1]

    ## [Pixel access 3]
    _blue = img[y,x,0]
    _green = img[y,x,1]
    _red = img[y,x,2]
    ## [Pixel access 3]

    ## [Pixel access 5]
    img[y,x] = 128
    ## [Pixel access 5]

def reference_counting() :
    # Memory management and reference counting
    ## [Reference counting 2]
    img = cv.imread('image.jpg')
    _img1 = np.copy(img)
    ## [Reference counting 2]

    ## [Reference counting 3]
    img = cv.imread('image.jpg')
    _sobelx = cv.Sobel(img, cv.CV_32F, 1.0)
    ## [Reference counting 3]

def primitive_operations() :
    img = np.empty((4.4.3), np.uint8)
    ## [Set image to black]
    img[:] = 0
    ## [Set image to black]

    ## [Select ROI]
    _smallImg = img[10:110.10:110]
    ## [Select ROI]

    ## [BGR to Gray]
    img = cv.imread('image.jpg')
    _grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    ## [BGR to Gray]

    src = np.ones((4.4), np.uint8)
    ## [Convert to CV_32F]
    _dst = src.astype(np.float32)
    ## [Convert to CV_32F]

def visualize_images() :
    ## [imshow 1]
    img = cv.imread('image.jpg')
    cv.namedWindow('image', cv.WINDOW_AUTOSIZE)
    cv.imshow('image', img)
    cv.waitKey()
    ## [imshow 1]

    ## [imshow 2]
    img = cv.imread('image.jpg')
    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    sobelx = cv.Sobel(grey, cv.CV_32F, 1.0)
    # find minimum and maximum intensities
    minVal = np.amin(sobelx)
    maxVal = np.amax(sobelx)
    draw = cv.convertScaleAbs(sobelx, alpha=255.0/(maxVal - minVal), beta=-minVal * 255.0/(maxVal - minVal))
    cv.namedWindow('image', cv.WINDOW_AUTOSIZE)
    cv.imshow('image', draw)
    cv.waitKey()
    ## [imshow 2]

Copy the code

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