ubuntu22.04@laptop OpenCV Get Started: 003_image

本文主要是介绍ubuntu22.04@laptop OpenCV Get Started: 003_image_resizing，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

ubuntu22.04@laptop OpenCV Get Started: 003_image_resizing

1. 源由
2. resize应用Demo
3 image_resize
- 3.1 C++应用Demo
- 3.2 Python应用Demo
- 3.3 重点过程分析
- - 3.3.1 根据宽高调整大小
  - 3.3.2 根据比例调整大小
  - 3.3.3 根据插值方式调整大小
4. 总结
5. 参考资料

1. 源由

在OpenCV中调整图像大小：

要记住图像的原始纵横比（即宽高比）
减小图像的大小，需要对像素缩减采样值。
增加图像的大小，需要对新像素进行插值。

各种插值技术开始发挥作用来完成上述操作。

2. resize应用Demo

003_image_resizing是OpenCV调整图像比例的示例程序。

确认OpenCV安装路径：

$ find /home/daniel/ -name "OpenCVConfig.cmake"
/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/
/home/daniel/OpenCV/opencv/build/OpenCVConfig.cmake
/home/daniel/OpenCV/opencv/build/unix-install/OpenCVConfig.cmake$ export OpenCV_DIR=/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/

3 image_resize

3.1 C++应用Demo

C++应用Demo工程结构：

003_image_resizing/CPP$ tree .
.
├── CMakeLists.txt
├── image.jpg
└── image_resize.cpp0 directories, 3 files

C++应用Demo工程编译执行：

$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config Release
$ cd ..
$ ./build/image_resize

3.2 Python应用Demo

Python应用Demo工程结构：

003_image_resizing/Python$ tree .
.
├── image.jpg
├── image_resize.py
└── requirements.txt0 directories, 3 files

Python应用Demo工程执行：

$ workoncv-4.9.0
$ python image_resize.py

3.3 重点过程分析

resize(src, dsize[, dst[, fx[, fy[, interpolation]]]])

src: It is the required input image, it could be a string with the path of the input image (eg: ‘test_image.png’).
dsize: It is the desired size of the output image, it can be a new height and width.
fx: Scale factor along the horizontal axis.
fy: Scale factor along the vertical axis.
interpolation: It gives us the option of different methods of resizing the image.

3.3.1 根据宽高调整大小

C++:

// Set rows and columns
int up_width = 600;
int up_height = 400;
Mat resized_up;
//resize up
resize(image, resized_up, Size(up_width, up_height), INTER_LINEAR);

Python:

# Set rows and columns
up_width = 600
up_height = 400
up_points = (up_width, up_height)
# resize the image
resized_up = cv2.resize(image, up_points, interpolation = cv2.INTER_LINEAR)

3.3.2 根据比例调整大小

C++:

// Scaling Up the image 1.2 times by specifying both scaling factors
double scale_up_x = 1.2;
double scale_up_y = 1.2;
// Scaling Down the image 0.6 times specifying a single scale factor.
double scale_down = 0.6;
Mat scaled_f_up, scaled_f_down;
//resize 
resize(image,scaled_f_down, Size(), scale_down, scale_down, INTER_LINEAR);
resize(image, scaled_f_up, Size(), scale_up_x, scale_up_y, INTER_LINEAR);

Python:

# Scaling Up the image 1.2 times by specifying both scaling factors
scale_up_x = 1.2
scale_up_y = 1.2
# Scaling Down the image 0.6 times specifying a single scale factor.
scale_down = 0.6scaled_f_down = cv2.resize(image, None, fx= scale_down, fy= scale_down, interpolation= cv2.INTER_LINEAR)
scaled_f_up = cv2.resize(image, None, fx= scale_up_x, fy= scale_up_y, interpolation= cv2.INTER_LINEAR)

3.3.3 根据插值方式调整大小

INTER_AREA: INTER_AREA uses pixel area relation for resampling. This is best suited for reducing the size of an image (shrinking). When used for zooming into the image, it uses the INTER_NEAREST method.
INTER_CUBIC: This uses bicubic interpolation for resizing the image. While resizing and interpolating new pixels, this method acts on the 4×4 neighboring pixels of the image. It then takes the weights average of the 16 pixels to create the new interpolated pixel.
INTER_LINEAR: This method is somewhat similar to the INTER_CUBIC interpolation. But unlike INTER_CUBIC, this uses 2×2 neighboring pixels to get the weighted average for the interpolated pixel.
INTER_NEAREST: The INTER_NEAREST method uses the nearest neighbor concept for interpolation. This is one of the simplest methods, using only one neighboring pixel from the image for interpolation.

C++:

# Scaling Down the image 0.6 using different Interpolation Method
Mat res_inter_linear, res_inter_nearest, res_inter_area;
resize(image, res_inter_linear, Size(), scale_down, scale_down, INTER_LINEAR);
resize(image, res_inter_nearest, Size(), scale_down, scale_down, INTER_NEAREST);
resize(image, res_inter_area, Size(), scale_down, scale_down, INTER_AREA);

Python:

# Scaling Down the image 0.6 times using different Interpolation Method
res_inter_nearest = cv2.resize(image, None, fx= scale_down, fy= scale_down, interpolation= cv2.INTER_NEAREST)
res_inter_linear = cv2.resize(image, None, fx= scale_down, fy= scale_down, interpolation= cv2.INTER_LINEAR)
res_inter_area = cv2.resize(image, None, fx= scale_down, fy= scale_down, interpolation= cv2.INTER_AREA)