裁剪是为了从图像中删除所有不需要的物体或区域。甚至突出显示图像的特定功能。
使用OpenCV裁剪没有特定的功能，NumPy数组切片是工作。读取的每个图像都存储在2D数组中（对于每个颜色通道）。只需指定要裁剪区域的高度和宽度（以像素为单位），就可以完成
1.使用OpenCV裁剪
以下代码片段展示了如何使用Python和C++裁剪图像。在例子的进一步，您将详细了解这些。
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_image = img[400:1200, 350:700] # 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(400,1200), Range(350,700)); //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; }
上面的代码读取并显示图像及其尺寸。尺寸不仅包括二维矩阵的宽度和高度，还包括通道的数量（例如，RGB图像有3个通道——红色、绿色和蓝色）。
让我们尝试裁剪图像中包含美女的部分。
Python
cropped_image = img[400:1200, 350:700] # Slicing to crop the image # Display the cropped image cv2.imshow("cropped", cropped_image) cv2.waitKey(0) cv2.destroyAllWindows()
C++
Mat crop = img(Range(400,1200),Range(350,700)); // Slicing to crop the image // Display the cropped image imshow("Cropped Image", crop); waitKey(0); destroyAllWindows(); return 0;
在Python中，您可以使用与NumPy数组切片相同的方法裁剪图像。要切片数组，您需要指定第一维和第二维的开始和结束索引。
- 第一个维度总是行数或图像的高度。
- 第二个维度是列数或图像的宽度。
如何剪切图像的NumPy数组？查看此示例中的语法：
cropped = img[start_row:end_row, start_col:end_col]
在C++中，我们使用Range()函数裁剪图像。
Python同理一样，它也应用切片。
在这里，图像也按照上述相同的约定作为二维矩阵读取。
以下是裁剪图像的C++语法：
img(Range(start_row, end_row), Range(start_col, end_col)) • 1
2.使用裁剪功能对图像进行划分
在OpenCV中裁剪的一个实际应用可以是将图像划分为大小相同图像块。使用循环从图像中裁剪片段。首先从图像的形状中获取所需图像块的高度和宽度
Python
img = cv2.imread("test_cropped.jpg") image_copy = img.copy() imgheight=img.shape[0] imgwidth=img.shape[1]
C++
Mat img = imread("test_cropped.jpg"); Mat image_copy = img.clone(); int imgheight = img.rows; int imgwidth = img.cols;
加载高度和宽度，以指定需要裁剪较小图像块的范围。为此，使用Python中的range()函数。现在，使用两个循环裁剪：
宽度范围
高度范围
已知原图像瘩高度宽度为(1350,1080),我们使用的图像块的高度和宽度分别为(270,216）。内外循环的步幅（我们在图像中移动的像素数）也就是划分下来，有25个图像块。（拼图一样）
Python
M = 216 N = 270 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(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(str(x) + '_' + str(y) + '.jpg', tiles) cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1) elif x1 >= imgwidth: # 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(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(str(x) + '_' + str(y) + '.jpg', tiles) cv2.rectangle(img, (x, y), (x1, y1), (0, 255, 0), 1)
C++
int M = 216; int N = 270; 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); } } }
接下来，使用imshow()函数显示图像块拼图。使用imwrite()函数将其保存到文件目录中。
Python
#Save full image into file directory cv2.imshow("Patched Image",img) cv2.imwrite("patched.jpg",img) cv2.waitKey() cv2.destroyAllWindows()
C++
imshow("Patched Image", img); imwrite("patched.jpg",img); waitKey(); destroyAllWindows();
Python
C++
