使用OpenCV进行读/写/展示图片
总览
Python
# import the cv2 library
import cv2
# The function cv2.imread() is used to read an image.
img_grayscale = cv2.imread('test.jpg',0)
# The function cv2.imshow() is used to display an image in a window.
cv2.imshow('graycsale image',img_grayscale)
# waitKey() waits for a key press to close the window and 0 specifies indefinite loop
cv2.waitKey(0)
# cv2.destroyAllWindows() simply destroys all the windows we created.
cv2.destroyAllWindows()
# The function cv2.imwrite() is used to write an image.
cv2.imwrite('grayscale.jpg',img_grayscale)
C++
//Include Libraries
#include<opencv2/opencv.hpp>
#include<iostream>
// Namespace nullifies the use of cv::function();
using namespace std;
using namespace cv;
// Read an image
Mat img_grayscale = imread("test.jpg", 0);
// Display the image.
imshow("grayscale image", img_grayscale); // 无需在他们的前面加上命名空间，例如cv::imshow()
// Wait for a keystroke.
waitKey(0);
// Destroys all the windows created
destroyAllWindows();
// Write the image in the same directory
imwrite("grayscale.jpg", img_grayscale);
1. imread() 读取图片
-
语法imread(filename, flags)
第一个参数: 图像名 第二个参数:[optional flag] cv2.IMREAD_UNCHANGED or -1 cv2.IMREAD_GRAYSCALE or 0 cv2.IMREAD_COLOR or 1 # 这个是默认值，读取图片作为彩色图片 -
注意:
OpenCV读取出来的是BGR格式，但是其他cv库使用的是RGB格式(所以有时候需要转换格式) 例如: from matplotlib import pyplot as plt plt.axis("off") plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)) plt.show()
2. imshow() 在窗口展示图片
-
语法imshow(window_name, image)
第一个参数是窗口名 第二个是需要展示的图片 要一次显示多个图像，请为要显示的每个图像指定一个新窗口名称。 该函数一般和waitKey(),destroyAllWindows() / destroyWindow()一起使用 waitKey()是键盘响应函数，它需要一个参数: 显示窗口的时间(单位毫秒)，如果是0则无限期等待击键。 还可以设置该功能以检测键盘上的 Q 键或 ESC 键等特定击键，从而更明确地告诉哪个键应该触发哪个行为 -
案例
Python
#Displays image inside a window cv2.imshow('color image',img_color) cv2.imshow('grayscale image',img_grayscale) cv2.imshow('unchanged image',img_unchanged) # Waits for a keystroke cv2.waitKey(0) # 0表示一直等待，也可以填具体时间单位是毫秒。可以通过返回值来判断是q or ESC # Destroys all the windows created cv2.destroyAllwindows()C++
// Create a window. namedWindow( "color image", WINDOW_AUTOSIZE ); namedWindow( "grayscale image", WINDOW_AUTOSIZE ); namedWindow( "unchanged image", WINDOW_AUTOSIZE ); // Show the image inside it. imshow( "color image", img_color ); imshow( "grayscale image", img_grayscale ); imshow( "unchanged image", img_unchanged ); // Wait for a keystroke. waitKey(0); // Destroys all the windows created destroyAllWindows();
3. imwrite() 写文件到文件目录
-
语法imwrite(filename, image)
第一个参数是文件名，必须包括文件扩展名(.png, .jpg etc) 第二个参数是保存的图片，如果保存成功返回True -
案例
Python
cv2.imwrite('grayscale.jpg',img_grayscale)
C++
imwrite("grayscale.jpg", img_grayscale);
使用OpenCV读/写视频
1. Reading Videos
part1: Reading Video From a file
Python
import cv2
# Create a video capture object, in this case we are reading the video from a file
vid_capture = cv2.VideoCapture('Resources/Cars.mp4') # 创建一个视频捕捉对象，有助于流式传输或显示视频
if (vid_capture.isOpened() == False): # isOpened()方法判断视频文件是否打开正确
print("Error opening the video file")
# Read fps and frame count
else:
# Get frame rate information
# You can replace 5 with CAP_PROP_FPS as well, they are enumerations
fps = vid_capture.get(5) # get() 方法得到视频流的元数据，注意该方法不适合web cameras.# 5代表frame rate(帧率fps)
print('Frames per second : ', fps,'FPS')
# Get frame count
# You can replace 7 with CAP_PROP_FRAME_COUNT as well, they are enumerations
frame_count = vid_capture.get(7) # 7代表帧数(frame count)
print('Frame count : ', frame_count)
while(vid_capture.isOpened()):
# vid_capture.read() methods returns a tuple, first element is a bool
# and the second is frame
# 一帧一帧的读取
ret, frame = vid_capture.read() # read()放回元组，第一个是boolean[True表示视频流包含要读取的帧], 第二个是实际的视频帧
if ret == True:
cv2.imshow('Frame',frame)
# 20 is in milliseconds, try to increase the value, say 50 and observe
key = cv2.waitKey(20)
if key == ord('q'):
break
else:
break
# Release the video capture object
vid_capture.release() # 释放视频捕捉对象
cv2.destroyAllWindows() # 关闭所有窗口
C++
// Include Libraries
#include<opencv2/opencv.hpp>
#include<iostream>
// Namespace to nullify use of cv::function(); syntax
using namespace std;
using namespace cv;
int main()
{
// initialize a video capture object
VideoCapture vid_capture("Resources/Cars.mp4");
// Print error message if the stream is invalid
if (!vid_capture.isOpened())
{
cout << "Error opening video stream or file" << endl;
}
else
{
// Obtain fps and frame count by get() method and print
// You can replace 5 with CAP_PROP_FPS as well, they are enumerations
int fps = vid_capture.get(5);
cout << "Frames per second :" << fps;
// Obtain frame_count using opencv built in frame count reading method
// You can replace 7 with CAP_PROP_FRAME_COUNT as well, they are enumerations
int frame_count = vid_capture.get(7);
cout << " Frame count :" << frame_count;
}
// Read the frames to the last frame
while (vid_capture.isOpened())
{
// Initialise frame matrix
Mat frame;
// Initialize a boolean to check if frames are there or not
bool isSuccess = vid_capture.read(frame);
// If frames are present, show it
if(isSuccess == true)
{
//display frames
imshow("Frame", frame);
}
// If frames are not there, close it
if (isSuccess == false)
{
cout << "Video camera is disconnected" << endl;
break;
}
//wait 20 ms between successive frames and break the loop if key q is pressed
int key = waitKey(20);
if (key == 'q')
{
cout << "q key is pressed by the user. Stopping the video" << endl;
break;
}
}
// Release the video capture object
vid_capture.release();
destroyAllWindows();
return 0;
}
Part2: Reading an Image Sequence
Python
vid_capture = cv2.VideoCapture('Resources/Image_sequence/Cars%04d.jpg')
C++
VideoCapture vid_capture("Resources/Image_sequence/Cars%04d.jpg");
+ 注意: 该数据的形式是: (Race_Cars_01.jpg, Race_Cars_02.jpg, Race_Cars_03.jpg, etc…)
Part3: Reading Video from a Webcam(摄像头)
- 如果是系统内置的摄像头，设备的索引可以写成0
- 如果系统连接多个摄像头，索引可以增加(e.g. 1, 2, etc)
Python
vid_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW) # CAP_DSHOW是一个可选项，通过视频输入直接显示的缩写
C++
VideoCapture vid_capture(0);
2. Writing videos
预览
import cv2
vid_capture = cv2.VideoCapture("video/test.mp4")
frame_width = int(vid_capture.get(3))
frame_height = int(vid_capture.get(4))
frame_size = (frame_width, frame_height)
output = cv2.VideoWriter("video/out_video.avi",
cv2.VideoWriter_fourcc('M','J','P','G'),
20,
frame_size
)
while(vid_capture.isOpened()):
ret, frame = vid_capture.read()
if ret == True:
output.write(frame)
else:
print("Stream disconnected")
break
vid_capture.release()
output.release()
- 前置知识: 获取视频帧的width和height
Python
# Obtain frame size information using get() method
frame_width = int(vid_capture.get(3))
frame_height = int(vid_capture.get(4))
frame_size = (frame_width,frame_height)
fps = 20
C++
// Obtain frame size information using get() method
Int frame_width = static_cast<int>(vid_capture.get(3));
int frame_height = static_cast<int>(vid_capture.get(4));
Size frame_size(frame_width, frame_height);
int fps = 20;
- 语法: VideoWriter(filename, apiPreference, fourcc, fps, frameSize[, isColor])
filename: 输出文件的路径
apiPreference: API后端标识符
fourcc: 编解码器的 4 字符代码，用于压缩帧
fps: 创建的视频流的帧率
fram_size: 视频帧的大小
isCOlor: 如果不为零，编码器将期望并编码彩色帧。 否则它将适用于灰度帧（该标志目前仅在 Windows 上受支持）
-
一个特殊的便利函数用于检索四字符编解码器，需要作为视频写入器对象 cv2 的第二个参数
VideoWriter_fourcc('M', 'J', 'P', 'G')in Python.VideoWriter::fourcc('M', 'J', 'P', 'G')in C++.
-
视频编解码器指定如何压缩视频流。 它将未压缩的视频转换为压缩格式，反之亦然。 要创建 AVI 或 MP4 格式，请使用以下fourcc规范
- AVI:
cv2.VideoWriter_fourcc('M','J','P','G') - MP4:
cv2.VideoWriter_fourcc(*'XVID')
- AVI:
Python
# Initialize video writer object
output = cv2.VideoWriter('Resources/output_video_from_file.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 20, frame_size)
C++
//Initialize video writer object
VideoWriter output("Resources/output.avi", VideoWriter::fourcc('M', 'J', 'P', 'G'),frames_per_second, frame_size);
- 下面以每秒 20 帧的速度将 AVI 视频文件写入磁盘
Python
while(vid_capture.isOpened()):
# vid_capture.read() methods returns a tuple, first element is a bool
# and the second is frame
ret, frame = vid_capture.read()
if ret == True:
# Write the frame to the output files
output.write(frame)
else:
print(‘Stream disconnected’)
break
C++
while (vid_capture.isOpened())
{
// Initialize frame matrix
Mat frame;
// Initialize a boolean to check if frames are there or not
bool isSuccess = vid_capture.read(frame);
// If frames are not there, close it
if (isSuccess == false)
{
cout << "Stream disconnected" << endl;
break;
}
// If frames are present
if(isSuccess == true)
{
//display frames
output.write(frame);
// display frames
imshow("Frame", frame);
// wait for 20 ms between successive frames and break
// the loop if key q is pressed
int key = waitKey(20);
if (key == ‘q’)
{
cout << "Key q key is pressed by the user.
Stopping the video" << endl;
break;
}
}
}
- 最后，释放video capture和video-writer
Python
# Release the objects
vid_capture.release()
output.release()
C++
// Release the objects
vid_capture.release();
output.release();
使用OpenCV进行标注图片
- 在图片中添加信息
- 在对象检测的情况下在对象周围绘制边界框
- 用不同颜色突出显示像素以进行图像分割
用彩色线条标注图像
- 语法 line(image, start_point, end_point, color, thickness)
Python
# Import dependencies
import cv2
# Read Images
img = cv2.imread('sample.jpg')
# Display Image
cv2.imshow('Original Image',img)
cv2.waitKey(0)
# Print error message if image is null
if img is None:
print('Could not read image')
# Draw line on image
imageLine = img.copy() # 进行拷贝图片
# Draw the image from point A to B
pointA = (200,80)
pointB = (450,80)
# 左上角是原始的点，x 轴代表图像的水平方向 y轴代表图像的垂直方向
# 其中imageLine是原始的图片， 开始点，结束点，线条的颜色，线条粗细，
cv2.line(imageLine, pointA, pointB, (255, 255, 0), thickness=3, lineType=cv2.LINE_AA)
cv2.imshow('Image Line', imageLine)
cv2.waitKey(0)
C++
// Import dependencies
#include <opencv2/opencv.hpp>
#include <iostream>
// Using namespaces to nullify use of c::function(); syntax and std::function(); syntax
using namespace std;
using namespace cv;
int main()
{
// Read Images
Mat img = imread("sample.jpg");
// Display Image
imshow("Original Image", img);
waitKey();
// Print Error message if image is null
if (img.empty())
{
cout << "Could not read image" << endl;
}
// Draw line on image
Mat imageLine = img.clone(); // 进行拷贝图片
Point pointA(200,80);
Point pointB(450,80);
line(imageLine, pointA, pointB, Scalar(255, 255, 0), 3, 8, 0);
imshow("Lined Image", imageLine);
waitKey();
}
画个圆
- 语法: circle(image, center_coordinates, radius, color, thickness)
- 参数分别是图片，中心点，半径，颜色，线条粗细
Python
# Make a copy of image
imageCircle = img.copy()
# define the center of circle
circle_center = (415,190)
# define the radius of the circle
radius =100
# Draw a circle using the circle() Function
cv2.circle(imageCircle, circle_center, radius, (0, 0, 255), thickness=3, lineType=cv2.LINE_AA)
# Display the result
cv2.imshow("Image Circle",imageCircle)
cv2.waitKey(0)
C++
// Make a copy of image
Mat circle_image = img.clone();
// define the center of circle
Point circle_center(415,190);
// define the radius of circle
int radius = 100;
// Draw a circle using the circle() Function
circle(circle_image, circle_center, radius, Scalar(0, 0, 255), 3, 8, 0);
// Display the result
imshow("Circle on Image", circle_image);
waitKey();
画个实心圆
Python
# make a copy of the original image
imageFilledCircle = img.copy()
# define center of the circle
circle_center = (415,190)
# define the radius of the circle
radius =100
# draw the filled circle on input image (这里thickness=-1表示画实心圆)
cv2.circle(imageFilledCircle, circle_center, radius, (255, 0, 0), thickness=-1, lineType=cv2.LINE_AA)
# display the output image
cv2.imshow('Image with Filled Circle',imageFilledCircle)
cv2.waitKey(0)
C++
// make a copy of the original image
Mat Filled_circle_image = img.clone();
// define the center of circle
Point circle_center(415,190);
// define the radius of the circle
int radius = 100;
//Draw a Filled Circle using the circle() Function
circle(Filled_circle_image, circle_center, radius, Scalar(255, 0, 0), -1, 8, 0);
// display the output image
imshow("Circle on Image", circle_image);
waitKey();
画一个矩形
- 语法: rectangle(image, start_point, end_point, color, thickness)
- start_point: (top, left) end_point: (bottom, right)
Python
# make a copy of the original image
imageRectangle = img.copy()
# define the starting and end points of the rectangle
start_point =(300,115)
end_point =(475,225)
# draw the rectangle
cv2.rectangle(imageRectangle, start_point, end_point, (0, 0, 255), thickness= 3, lineType=cv2.LINE_8)
# display the output
cv2.imshow('imageRectangle', imageRectangle)
cv2.waitKey(0)
C++
// make a copy of the original image
Mat rect_image = image.clone();
// Define the starting and end points for the rectangle
Point start_point(300,115);
Point end_point(475,225);
// Draw a rectangle using the rectangle() function
rectangle(rect_image, start_point, end_point, Scalar(0,0,255), 3, 8, 0);
imshow("Rectangle on Image", rect_image);
waitKey();
添加文本
- 语法: putText(image, text, org, font, fontScale, color)
- image: 原始图片 text: 需要标注的文本
- org: 文本处在的(top, left)坐标
- fontFace: OpenCV 支持 Hershey 字体集合中的几种字体样式，以及斜体字体。 如下:
- FONT_HERSHEY_SIMPLEX = 0,
- FONT_HERSHEY_PLAIN = 1,
- FONT_HERSHEY_DUPLEX = 2,
- FONT_HERSHEY_COMPLEX = 3,
- FONT_HERSHEY_TRIPLEX = 4,
- FONT_HERSHEY_COMPLEX_SMALL = 5,
- FONT_HERSHEY_SCRIPT_SIMPLEX = 6,
- FONT_HERSHEY_SCRIPT_COMPLEX = 7,
- FONT_ITALIC = 16
- fontScale: 字体比例是一个浮点值，用于向上或向下缩放字体的基本大小。 根据图像的分辨率，选择适当的字体比例。
- color: 这里是一个BGR元组(B, G, R)
Python
# make a copy of the original image
imageText = img.copy()
#let's write the text you want to put on the image
text = 'I am a Happy dog!'
#org: Where you want to put the text
org = (50,350)
# write the text on the input image
# 原始图片 文本 左上角 字体样式 字体缩放比例 字体颜色
cv2.putText(imageText, text, org, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=1.5, color=(250,225,100))
# display the output image with text over it
cv2.imshow("Image Text",imageText)
cv2.waitKey(0)
cv2.destroyAllWindows()
C++
// make a copy of the original image
Mat imageText = img.clone();
// Write text using putText() function
putText(imageText, "I am a Happy dog!", Point(50,350), FONT_HERSHEY_COMPLEX, 1.5, Scalar(250,225,100));
imshow("Text on Image", imageText);
waitKey(0);
使用OpenCV进行缩放图片
-
在调整大小的图像中也保持相同，请务必记住图像的原始纵横比（即宽度与高度）。
-
减小图像的大小将需要对像素进行重新采样。
-
增加图像的大小需要重建图像。 这意味着您需要插入新像素。(可以使用图像插值技术)
Image resizing with a custom Width and Height
Python
# let's start with the Imports
import cv2
import numpy as np
# Read the image using imread function
image = cv2.imread('image.jpg')
cv2.imshow('Original Image', image)
# let's downscale the image using new width and height
down_width = 300 # 可以通过h,w,c=image.shape返回图片height,wight, number of channels
down_height = 200
down_points = (down_width, down_height)
resized_down = cv2.resize(image, down_points, interpolation= cv2.INTER_LINEAR)
# let's upscale the image using new width and height
up_width = 600
up_height = 400
up_points = (up_width, up_height)
resized_up = cv2.resize(image, up_points, interpolation= cv2.INTER_LINEAR)
# Display images
cv2.imshow('Resized Down by defining height and width', resized_down)
cv2.waitKey()
cv2.imshow('Resized Up image by defining height and width', resized_up)
cv2.waitKey()
#press any key to close the windows
cv2.destroyAllWindows()
C++
// let's start with including libraries
#include<opencv2/opencv.hpp>
#include<iostream>
// Namespace to nullify use of cv::function(); syntax
using namespace std;
using namespace cv;
int main()
{
// Read the image using imread function
Mat image = imread("image.jpg");
imshow("Original Image", image);
// let's downscale the image using new width and height
int down_width = 300;
int down_height = 200;
Mat resized_down;
//resize down
resize(image, resized_down, Size(down_width, down_height), INTER_LINEAR);
// let's upscale the image using new width and height
int up_width = 600;
int up_height = 400;
Mat resized_up;
//resize up
resize(image, resized_up, Size(up_width, up_height), INTER_LINEAR);
// Display Images and press any key to continue
imshow("Resized Down by defining height and width", resized_down);
waitKey();
imshow("Resized Up image by defining height and width", resized_up);
waitKey();
destroyAllWindows();
return 0;
}
Resizing an image with a Scaling factor
INTER_AREA: 使用像素区域关系进行重采样。 这最适合减小图像的大小（缩小）。 当用于放大图像时，它使用 INTER_NEAREST 方法。INTER_CUBIC: 这使用双三次插值来调整图像大小。 在调整大小和插入新像素时，此方法作用于图像的 4×4 相邻像素。 然后取 16 个像素的权重平均值来创建新的插值像素。INTER_LINEAR: 这种方法有点类似于 INTER_CUBIC 插值。 但与 INTER_CUBIC 不同，它使用 2×2 相邻像素来获得插值像素的加权平均值。INTER_NEAREST: INTER_NEAREST 方法使用最近邻概念进行插值。 这是最简单的方法之一，仅使用图像中的一个相邻像素进行插值。
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)
# Concatenate images in horizontal axis for comparison
vertical= np.concatenate((res_inter_nearest, res_inter_linear, res_inter_area), axis = 0)
# Display the image Press any key to continue
cv2.imshow('Inter Nearest :: Inter Linear :: Inter Area', vertical)
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);
Mat a,b,c;
vconcat(res_inter_linear, res_inter_nearest, a);
vconcat(res_inter_area, res_inter_area, b);
vconcat(a, b, c);
// Display the image Press any key to continue
imshow("Inter Linear :: Inter Nearest :: Inter Area :: Inter Area", c);
使用OpenCV裁剪图片
Cropping using OpenCV
Python
# Import packages
import cv2
import numpy as np
img = cv2.imread('test.jpg')
print(img.shape) # Print image shape
cv2.imshow("original", img)
# Cropping an image cropped=img[start_row:end_row, start_col:end_col]
cropped_image = img[80:280, 150:330]
# Display cropped image
cv2.imshow("cropped", cropped_image)
# Save the cropped image
cv2.imwrite("Cropped Image.jpg", cropped_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
C++
// Include Libraries
#include<opencv2/opencv.hpp>
#include<iostream>
// Namespace nullifies the use of cv::function();
using namespace std;
using namespace cv;
int main()
{
// Read image
Mat img = imread("test.jpg");
cout << "Width : " << img.size().width << endl;
cout << "Height: " << img.size().height << endl;
cout<<"Channels: :"<< img.channels() << endl;
// Crop image
Mat cropped_image = img(Range(80,280), Range(150,330));
//display image
imshow(" Original Image", img);
imshow("Cropped Image", cropped_image);
//Save the cropped Image
imwrite("Cropped Image.jpg", cropped_image);
// 0 means loop infinitely
waitKey(0);
destroyAllWindows();
return 0;
}
Diving an Image into Small Patches
Python
img = cv2.imread("test_cropped.jpg")
image_copy = img.copy()
imgheight=img.shape[0]
imgwidth=img.shape[1]
M = 76
N = 104
x1 = 0
y1 = 0
for y in range(0, imgheight, M):
for x in range(0, imgwidth, N):
if (imgheight - y) < M or (imgwidth - x) < N:
break
y1 = y + M
x1 = x + N
# check whether the patch width or height exceeds the image width or height
if x1 >= imgwidth and y1 >= imgheight:
x1 = imgwidth - 1
y1 = imgheight - 1
#Crop into patches of size MxN
tiles = image_copy[y:y+M, x:x+N]
#Save each patch into file directory
cv2.imwrite('saved_patches/'+'tile'+str(x)+'_'+str(y)+'.jpg', tiles)
cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1)
elif y1 >= imgheight: # when patch height exceeds the image height
y1 = imgheight - 1
#Crop into patches of size MxN
tiles = image_copy[y:y+M, x:x+N]
#Save each patch into file directory
cv2.imwrite('saved_patches/'+'tile'+str(x)+'_'+str(y)+'.jpg', tiles)
cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1)
elif x1 >= img # when patch width exceeds the image width
x1 = imgwidth - 1
#Crop into patches of size MxN
tiles = image_copy[y:y+M, x:x+N]
#Save each patch into file directory
cv2.imwrite('saved_patches/'+'tile'+str(x)+'_'+str(y)+'.jpg', tiles)
cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1)
else:
#Crop into patches of size MxN
tiles = image_copy[y:y+M, x:x+N]
#Save each patch into file directory
cv2.imwrite('saved_patches/'+'tile'+str(x)+'_'+str(y)+'.jpg', tiles)
cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1)
#Save full image into file directory
cv2.imshow("Patched Image",img)
cv2.imwrite("patched.jpg",img)
cv2.waitKey()
cv2.destroyAllWindows()
C++
Mat img = imread("test_cropped.jpg");
Mat image_copy = img.clone();
int imgheight = img.rows;
int imgwidth = img.cols;
int M = 76;
int N = 104;
int x1 = 0;
int y1 = 0;
for (int y = 0; y<imgheight; y=y+M)
{
for (int x = 0; x<imgwidth; x=x+N)
{
if ((imgheight - y) < M || (imgwidth - x) < N)
{
break;
}
y1 = y + M;
x1 = x + N;
string a = to_string(x);
string b = to_string(y);
if (x1 >= imgwidth && y1 >= imgheight)
{
x = imgwidth - 1;
y = imgheight - 1;
x1 = imgwidth - 1;
y1 = imgheight - 1;
// crop the patches of size MxN
Mat tiles = image_copy(Range(y, imgheight), Range(x, imgwidth));
//save each patches into file directory
imwrite("saved_patches/tile" + a + '_' + b + ".jpg", tiles);
rectangle(img, Point(x,y), Point(x1,y1), Scalar(0,255,0), 1);
}
else if (y1 >= imgheight)
{
y = imgheight - 1;
y1 = imgheight - 1;
// crop the patches of size MxN
Mat tiles = image_copy(Range(y, imgheight), Range(x, x+N));
//save each patches into file directory
imwrite("saved_patches/tile" + a + '_' + b + ".jpg", tiles);
rectangle(img, Point(x,y), Point(x1,y1), Scalar(0,255,0), 1);
}
else if (x1 >= imgwidth)
{
x = imgwidth - 1;
x1 = imgwidth - 1;
// crop the patches of size MxN
Mat tiles = image_copy(Range(y, y+M), Range(x, imgwidth));
//save each patches into file directory
imwrite("saved_patches/tile" + a + '_' + b + ".jpg", tiles);
rectangle(img, Point(x,y), Point(x1,y1), Scalar(0,255,0), 1);
}
else
{
// crop the patches of size MxN
Mat tiles = image_copy(Range(y, y+M), Range(x, x+N));
//save each patches into file directory
imwrite("saved_patches/tile" + a + '_' + b + ".jpg", tiles);
rectangle(img, Point(x,y), Point(x1,y1), Scalar(0,255,0), 1);
}
}
}
imshow("Patched Image", img);
imwrite("patched.jpg",img);
waitKey();
destroyAllWindows();
使用 OpenCV 进行图像旋转和平移
使用OpenCV进行图像旋转
Python
import cv2
# Reading the image
image = cv2.imread('image.jpg')
# dividing height and width by 2 to get the center of the image
height, width = image.shape[:2]
# get the center coordinates of the image to create the 2D rotation matrix
center = (width/2, height/2)
# using cv2.getRotationMatrix2D() to get the rotation matrix(得到2D选择矩阵)
rotate_matrix = cv2.getRotationMatrix2D(center=center, angle=45, scale=1)
# rotate the image using cv2.warpAffine(得到旋转的图像)
rotated_image = cv2.warpAffine(src=image, M=rotate_matrix, dsize=(width, height))
cv2.imshow('Original image', image)
cv2.imshow('Rotated image', rotated_image)
# wait indefinitely, press any key on keyboard to exit
cv2.waitKey(0)
# save the rotated image to disk
cv2.imwrite('rotated_image.jpg', rotated_image)
C++
#include <iostream>
#include<opencv2/opencv.hpp>
using namespace cv;
int main(int, char**)
{
Mat image = imread("image.jpg");
imshow("image", image);
waitKey(0);
double angle = 45;
// get the center coordinates of the image to create the 2D rotation matrix
Point2f center((image.cols - 1) / 2.0, (image.rows - 1) / 2.0);
// using getRotationMatrix2D() to get the rotation matrix
Mat rotation_matix = getRotationMatrix2D(center, angle, 1.0);
// we will save the resulting image in rotated_image matrix
Mat rotated_image;
// rotate the image using warpAffine
warpAffine(image, rotated_image, rotation_matix, image.size());
imshow("Rotated image", rotated_image);
// wait indefinitely, press any key on keyboard to exit
waitKey(0);
// save the rotated image to disk
imwrite("rotated_im.jpg", rotated_image);
return 0;
}
使用OpenCV进行图像的平移
Python
import cv2
import numpy as np
# read the image
image = cv2.imread('image.jpg')
# get the width and height of the image
height, width = image.shape[:2]
# get tx and ty values for translation
# you can specify any value of your choice
tx, ty = width / 4, height / 4
# create the translation matrix using tx and ty, it is a NumPy array
translation_matrix = np.array([
[1, 0, tx],
[0, 1, ty]
], dtype=np.float32)
# apply the translation to the image
translated_image = cv2.warpAffine(src=image, M=translation_matrix, dsize=(width, height))
# display the original and the Translated images
cv2.imshow('Translated image', translated_image)
cv2.imshow('Original image', image)
cv2.waitKey(0)
# save the translated image to disk
cv2.imwrite('translated_image.jpg', translated_image)
C++
#include "opencv2/opencv.hpp"
using namespace cv
// read the image
Mat image = imread("image.jpg");
// get the height and width of the image
int height = image.cols;
int width = image.rows;
// get tx and ty values for translation
float tx = float(width) / 4;
float ty = float(height) / 4;
// create the translation matrix using tx and ty
float warp_values[] = { 1.0, 0.0, tx, 0.0, 1.0, ty };
Mat translation_matrix = Mat(2, 3, CV_32F, warp_values);
// save the resulting image in translated_image matrix
Mat translated_image;
// apply affine transformation to the original image using the translation matrix
warpAffine(image, translated_image, translation_matrix, image.size());
//display the original and the Translated images
imshow("Translated image", translated_image);
imshow("Original image", image);
waitKey(0);
// save the translated image to disk
imwrite("translated_image.jpg", translated_image);