用c++，创建MFC，用梯度算子（或Robert算子、或Prewitt算子锐化、或Sobel算子锐化）生成梯度图像，基于梯度图像利用边缘跟踪算法跟踪图像里的物体边缘，生成边缘图像。不要用openCV

好的，如果不使用OpenCV库，您可以手动实现梯度算子和边缘跟踪算法。以下是实现步骤：

1. 导入头文件和MFC库。

#include "stdafx.h"
#include "MyProject.h"
#include "MyProjectDlg.h"
#include <vector>

2. 定义梯度算子的模板。

const int sobel_x[3][3] = { {-1, 0, 1}, {-2, 0, 2}, {-1, 0, 1} };
const int sobel_y[3][3] = { {1, 2, 1}, {0, 0, 0}, {-1, -2, -1} };

3. 读取图像文件并将其转换为灰度图像。

CImage image;
image.Load(_T("image.jpg"));
int width = image.GetWidth();
int height = image.GetHeight();
BYTE* data = (BYTE*)image.GetBits();
int channels = image.GetBPP() / 8;
std::vector<BYTE> gray_data(width * height);
for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        int index = i * width + j;
        int gray_value = 0;
        for (int k = 0; k < channels; k++) {
            gray_value += data[index * channels + k];
        }
        gray_data[index] = gray_value / channels;
    }
}

4. 使用Sobel算子计算梯度图像。

std::vector<int> grad_x(width * height);
std::vector<int> grad_y(width * height);
for (int i = 1; i < height - 1; i++) {
    for (int j = 1; j < width - 1; j++) {
        int x_value = 0;
        int y_value = 0;
        for (int k = -1; k <= 1; k++) {
            for (int l = -1; l <= 1; l++) {
                int index = (i + k) * width + (j + l);
                x_value += gray_data[index] * sobel_x[k + 1][l + 1];
                y_value += gray_data[index] * sobel_y[k + 1][l + 1];
            }
        }
        grad_x[i * width + j] = x_value;
        grad_y[i * width + j] = y_value;
    }
}

5. 计算梯度的幅值，并进行非极大值抑制。

std::vector<int> grad_mag(width * height);
for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        int index = i * width + j;
        grad_mag[index] = sqrt(grad_x[index] * grad_x[index] + grad_y[index] * grad_y[index]);
    }
}

std::vector<int> grad_dir(width * height);
for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        int index = i * width + j;
        grad_dir[index] = atan2(grad_y[index], grad_x[index]) * 180 / 3.14159;
    }
}

std::vector<BYTE> nms_data(width * height);
for (int i = 1; i < height - 1; i++) {
    for (int j = 1; j < width - 1; j++) {
        int index = i * width + j;
        int dir = grad_dir[index];
        int mag = grad_mag[index];
        int mag1, mag2;
        if ((dir >= 0 && dir < 22.5) || (dir >= 157.5 && dir < 180)) {
            mag1 = grad_mag[(i - 1) * width + j];
            mag2 = grad_mag[(i + 1) * width + j];
        }
        else if (dir >= 22.5 && dir < 67.5) {
            mag1 = grad_mag[(i + 1) * width + j - 1];
            mag2 = grad_mag[(i - 1) * width + j + 1];
        }
        else if (dir >= 67.5 && dir < 112.5) {
            mag1 = grad_mag[i * width + j - 1];
            mag2 = grad_mag[i * width + j + 1];
        }
        else {
            mag1 = grad_mag[(i - 1) * width + j - 1];
            mag2 = grad_mag[(i + 1) * width + j + 1];
        }
        if (mag >= mag1 && mag >= mag2) {
            nms_data[index] = mag;
        }
    }
}

6. 使用双阈值算法进行边缘检测。

std::vector<BYTE> edge_data(width * height);
int high_threshold = 100;
int low_threshold = 50;
for (int i = 1; i < height - 1; i++) {
    for (int j = 1; j < width - 1; j++) {
        int index = i * width + j;
        if (nms_data[index] >= high_threshold) {
            edge_data[index] = 255;
        }
        else if (nms_data[index] >= low_threshold) {
            for (int k = -1; k <= 1; k++) {
                for (int l = -1; l <= 1; l++) {
                    int neighbor_index = (i + k) * width + (j + l);
                    if (nms_data[neighbor_index] >= high_threshold) {
                        edge_data[index] = 255;
                        break;
                    }
                }
                if (edge_data[index] == 255) {
                    break;
                }
            }
        }
    }
}

7. 将边缘绘制到图像上并保存。

CImage edge_image;
edge_image.Create(width, height, 24);
BYTE* edge_bits = (BYTE*)edge_image.GetBits();
for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
        int index = i * width + j;
        int value = edge_data[index];
        edge_bits[index * 3] = value;
        edge_bits[index * 3 + 1] = value;
        edge_bits[index * 3 + 2] = value;
    }
}
edge_image.Save(_T("edge.jpg"));

以上是一个简单的例子，您可以根据需要进行修改和优化。希望对您有所帮助！