vivado hls::mat

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

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

    // 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

    // 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

    // 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。