vivado hls::mat
时间: 2023-10-01 21:09:44 浏览: 173
Vivado HLS is a high-level synthesis tool from Xilinx that allows designers to convert C, C++, or SystemC code into RTL (Register Transfer Level) code. The "mat" in "vivado hls::mat" might refer to matrix operations or matrix-related functions in Vivado HLS. It's used to perform matrix computations efficiently by leveraging the hardware capabilities of FPGA devices.
相关问题
Vivado HLS 实现车牌识别算法
Vivado HLS是一个高级综合工具,可以使用C/C++等高级语言编写代码,并将其转换为可综合的硬件描述语言。下面是一个简单的车牌识别算法的示例:
1. 读入图片并进行预处理
```c++
void plate_recognition(unsigned char *input_image, unsigned char *output_image)
{
// 读入图片并进行预处理
cv::Mat in_img = cv::imread(input_image);
cv::Mat gray_img;
cv::cvtColor(in_img, gray_img, CV_BGR2GRAY);
cv::Mat blur_img;
cv::GaussianBlur(gray_img, blur_img, cv::Size(5, 5), 0);
cv::Mat canny_img;
cv::Canny(blur_img, canny_img, 50, 150, 3);
cv::Mat dilate_img;
cv::dilate(canny_img, dilate_img, cv::Mat(), cv::Point(-1, -1), 1, 1, 1);
cv::Mat erode_img;
cv::erode(dilate_img, erode_img, cv::Mat(), cv::Point(-1, -1), 1, 1, 1);
// 将处理后的图片转换为灰度图像
int width = erode_img.cols;
int height = erode_img.rows;
unsigned char *gray_data = (unsigned char *)malloc(width * height);
for (int row = 0; row < height; row++) {
for (int col = 0; col < width; col++) {
gray_data[row * width + col] = erode_img.at<unsigned char>(row, col);
}
}
```
2. 进行车牌定位
```c++
// 进行车牌定位
int plate_x = 0;
int plate_y = 0;
int plate_width = 0;
int plate_height = 0;
bool found_plate = false;
for (int row = 0; row < height - 100; row++) {
for (int col = 0; col < width - 200; col++) {
int sum = 0;
for (int i = 0; i < 100; i++) {
for (int j = 0; j < 200; j++) {
sum += gray_data[(row + i) * width + col + j];
}
}
if (sum > 100000 && sum < 150000) {
plate_x = col;
plate_y = row;
plate_width = 200;
plate_height = 100;
found_plate = true;
break;
}
}
if (found_plate) {
break;
}
}
```
3. 对车牌进行字符分割和识别
```c++
// 对车牌进行字符分割和识别
if (found_plate) {
cv::Mat plate_img = in_img(cv::Rect(plate_x, plate_y, plate_width, plate_height));
cv::Mat gray_plate;
cv::cvtColor(plate_img, gray_plate, CV_BGR2GRAY);
cv::Mat thresh_plate;
cv::adaptiveThreshold(gray_plate, thresh_plate, 255, cv::ADAPTIVE_THRESH_GAUSSIAN_C, cv::THRESH_BINARY, 11, 2);
std::vector<std::vector<cv::Point>> contours;
cv::findContours(thresh_plate, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
std::vector<cv::Rect> rects;
for (int i = 0; i < contours.size(); i++) {
cv::Rect rect = cv::boundingRect(contours[i]);
if (rect.width > 10 && rect.height > 10 && rect.width < plate_width / 2 && rect.height < plate_height) {
rects.push_back(rect);
}
}
std::sort(rects.begin(), rects.end(), [](cv::Rect a, cv::Rect b) { return a.x < b.x; });
for (int i = 0; i < rects.size(); i++) {
cv::Mat char_img = gray_plate(rects[i]);
cv::resize(char_img, char_img, cv::Size(32, 32));
// 将字符图像转换为一维数组,供后面的神经网络使用
float *char_data = (float *)malloc(32 * 32 * sizeof(float));
for (int row = 0; row < 32; row++) {
for (int col = 0; col < 32; col++) {
char_data[row * 32 + col] = char_img.at<unsigned char>(row, col) / 255.0;
}
}
// 调用神经网络进行字符识别
int char_label = neural_network(char_data);
printf("%c", char_label);
free(char_data);
}
}
```
4. 将识别结果输出到控制台
```c++
// 将识别结果输出到控制台
printf("\n");
free(gray_data);
}
```
以上是一个简单的车牌识别算法的示例,可以通过Vivado HLS将其转换为可综合的硬件描述语言,并在FPGA上进行加速。需要注意的是,在实现过程中需要考虑硬件资源的限制和优化算法以提高性能。
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