如何在C++中用文字制作图片的白色背景

Posted 2023-02-22

【中文标题】如何在C++中用文字制作图片的白色背景

【英文标题】：How can make white background of the picture with texts in C++

【发布时间】：2021-04-26 14:02:15

【问题描述】：

我想裁剪 1280x720 图像上的文本并将文本放回 1280x720 白色图像上。我希望文字之外的地方是白色的。

其实我把图片中的所有文字都剪掉了拼贴，但是文字的大小变了，我不要这个。

我希望图片中文字的位置和大小不要改变。

如何在 Opencv 中做到这一点？ c++ 或 python

等

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/opencv_modules.hpp>

#include <cmath>
#include <fstream>

using namespace std;
using namespace cv;

Mat detect_text(Mat input) 
    Mat large = input;
    Mat rgb;
    // downsample and use it for processing
    pyrDown(large, rgb);
    Mat small;
    cvtColor(rgb, small, COLOR_BGR2GRAY);
    // morphological gradient
    Mat grad;
    Mat morphKernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
    morphologyEx(small, grad, MORPH_GRADIENT, morphKernel);
    // binarize
    Mat bw;
    threshold(grad, bw, 0.0, 255.0, THRESH_BINARY | THRESH_OTSU);
    // connect horizontally oriented regions
    Mat connected;
    morphKernel = getStructuringElement(MORPH_RECT, Size(9, 1));
    morphologyEx(bw, connected, MORPH_CLOSE, morphKernel);
    // find contours
    Mat mask = Mat::zeros(bw.size(), CV_8UC1);
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(connected, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE,
                 Point(0, 0));
    // filter contours
    cv::Mat croppedImage;

    Mat temp_Image(input.size(), CV_8UC3, Scalar(255, 255, 255));

    for(int idx = 0; idx >= 0; idx = hierarchy[idx][0]) 
        Rect rect = boundingRect(contours[idx]);
        Mat maskROI(mask, rect);

        maskROI = Scalar(0, 0, 0);
        // fill the contour
        drawContours(mask, contours, idx, Scalar(255, 255, 255), FILLED);
        // ratio of non-zero pixels in the filled region
        double r = (double)countNonZero(maskROI) / (rect.width * rect.height);

        // assume at least 45% of the area is filled if it contains text
        if(r > 0.45 &&
           (rect.height > 8 && rect.width > 8) // constraints on region size
           // these two conditions alone are not very robust. better to use
           // something
           // like the number of significant peaks in a horizontal projection as a
           // third condition
        ) 
            rect.x = rect.x;
            rect.y = rect.y;
            rect.height = rect.height + 10;
            rect.width = rect.width + 10;

            rectangle(rgb, rect, Scalar(0, 255, 0), 2);
            croppedImage = rgb(rect);

            //
            //  to put cropped texts in tem_image
            //
            // imwrite(string("test_text_contours.jpg"), temp_image);
        
    
    // imwrite(string("test_text_contours.jpg"), croppedImage);

    return croppedImage;


int main(int argc, char *argv[])

    Mat img = cv.imread("1.jpg");
    detect_text(img);
    return 0;

【参考方案1】：

这里是python代码：

import cv2
import numpy as np

def get_white_image(img, crops):
    image_sizey, image_sizex, c = img.shape
    white_image = np.zeros([image_sizey, image_sizex, 3], dtype=np.uint8)
    white_image.fill(255)
    for crop in crops:
        white_image[crop[0]:crop[1], crop[2]:crop[3]] = img[crop[0]:crop[1], crop[2]:crop[3]]
    return white_image


image = cv2.imread("baldo.jpg")
cv2.imshow("image", image)
crops = []
crops.append([35, 80, 70, 140])
crops.append([85, 95, 50, 160])
crops.append([115, 145, 50, 90])
crops.append([220, 240, 80, 125])
result = get_white_image(image, crops=crops)
cv2.imshow("result", result)
cv2.waitKey(0)

结果如下： Image and white image