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本博文接上一篇博文，继续进行部分 opencv Python 示例代码运行 效果测试
- 本博文的测试是直接拉取 opencv-master4.5.1，然后在opencv/samples/python/tutorial_code/ 目录下对不同模块 py 文件进行测试
AddingImages【图片线性相加】
运行该代码：
cd opencv/samples/python/tutorial_code/core/AddingImages
cp ../../../../data/LinuxLogo.jpg .
cp ../../../../data/WindowsLogo.jpg .
python adding_images.py
效果如下：
The dft of an image is taken and it's power spectrum is displayed.【离散傅里叶变换（DFT）】
运行代码一：
cd opencv/samples/python/tutorial_code/core/discrete_fourier_transform
cp ../../../../data/lena.jpg .
python discrete_fourier_transform.py lena.jpg
运行效果如下：
FileStorage you can serialize objects in OpenCV【序列化】
运行代码一：
cd opencv/samples/python/tutorial_code/core/file_input_output
python3 file_input_output.py moli.json
运行效果如下：
cat moli.json
# 文件内容如下，当然如果要理解，需要结合代码来看：
# 对我而言，重要的是我知道有这样一份 文件输入生成的代码以后可以用来借鉴
{
"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": 3.1415926535897931e+00, "name": "mydata1234" }
}
mat_mask_operations【灰度图 sharpen 处理】
运行代码一：
cd ../mat_mask_operations/
cp ../../../../data/lena.jpg .
python3 file_input_output.py moli.json
运行效果如下：
mat_operations.py【常见OpenCV 图像操作函数使用示例】
运行代码一：
cd ../mat_operations/
cat mat_operations.py
mat_operations.py 代码中 提供了常见的 OpenCV 图像操作函数使用 示例
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]
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