对灰度图像进行基于边像强度和积分图文本定位并用框框出文本c语言

好的，下面是基于边缘强度和积分图的文本定位的C语言代码示例：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define WIDTH 640
#define HEIGHT 480

unsigned char input_img[HEIGHT][WIDTH]; // 输入灰度图像
unsigned char binary_img[HEIGHT][WIDTH]; // 二值化图像
unsigned char edge_img[HEIGHT][WIDTH]; // 边缘强度图像
unsigned int integral_img[HEIGHT][WIDTH]; // 积分图像

int main()
{
    // 读入灰度图像
    FILE *fp = fopen("input_gray.raw", "rb");
    fread(input_img, sizeof(unsigned char), WIDTH * HEIGHT, fp);
    fclose(fp);

    // 对灰度图像进行二值化处理
    for (int i = 0; i < HEIGHT; i++) {
        for (int j = 0; j < WIDTH; j++) {
            if (input_img[i][j] > 128) {
                binary_img[i][j] = 255;
            } else {
                binary_img[i][j] = 0;
            }
        }
    }

    // 计算边缘强度图像
    int sobel_x[3][3] = {{-1, 0, 1}, {-2, 0, 2}, {-1, 0, 1}};
    int sobel_y[3][3] = {{-1, -2, -1}, {0, 0, 0}, {1, 2, 1}};
    for (int i = 1; i < HEIGHT - 1; i++) {
        for (int j = 1; j < WIDTH - 1; j++) {
            int gx = 0, gy = 0;
            for (int k = -1; k <= 1; k++) {
                for (int l = -1; l <= 1; l++) {
                    gx += sobel_x[k + 1][l + 1] * binary_img[i + k][j + l];
                    gy += sobel_y[k + 1][l + 1] * binary_img[i + k][j + l];
                }
            }
            int edge = sqrt(gx * gx + gy * gy);
            if (edge > 255) {
                edge = 255;
            }
            edge_img[i][j] = edge;
        }
    }

    // 计算积分图像
    for (int i = 0; i < HEIGHT; i++) {
        for (int j = 0; j < WIDTH; j++) {
            if (i == 0 && j == 0) {
                integral_img[i][j] = edge_img[i][j];
            } else if (i == 0) {
                integral_img[i][j] = integral_img[i][j - 1] + edge_img[i][j];
            } else if (j == 0) {
                integral_img[i][j] = integral_img[i - 1][j] + edge_img[i][j];
            } else {
                integral_img[i][j] = integral_img[i - 1][j] + integral_img[i][j - 1] - integral_img[i - 1][j - 1] + edge_img[i][j];
            }
        }
    }

    // 计算文本区域并绘制边框
    int min_size = 10; // 最小文本区域大小
    int max_size = 1000; // 最大文本区域大小
    int threshold = 100; // 文本区域平均强度阈值
    for (int i = 1; i < HEIGHT - 1; i++) {
        for (int j = 1; j < WIDTH - 1; j++) {
            for (int k = i + min_size; k < HEIGHT; k++) {
                for (int l = j + min_size; l < WIDTH; l++) {
                    int size = (k - i) * (l - j);
                    if (size > min_size && size < max_size) {
                        int sum = integral_img[k][l] - integral_img[k][j - 1] - integral_img[i - 1][l] + integral_img[i - 1][j - 1];
                        int mean = sum / size;
                        if (mean > threshold) {
                            // 绘制文本区域边框
                            for (int x = i; x <= k; x++) {
                                input_img[x][j] = 255;
                                input_img[x][l] = 255;
                            }
                            for (int y = j; y <= l; y++) {
                                input_img[i][y] = 255;
                                input_img[k][y] = 255;
                            }
                        }
                    }
                }
            }
        }
    }

    // 保存结果图像
    fp = fopen("output_text_detection.raw", "wb");
    fwrite(input_img, sizeof(unsigned char), WIDTH * HEIGHT, fp);
    fclose(fp);

    return 0;
}

需要注意的是，此代码仅作为示例，实际应用中还需要根据具体情况进行参数调整和优化。