一段有趣的代码，喂仓鼠
由于图像车牌识别是一个比较复杂的任务，需要使用大量的图像处理算法和机器学习算法，因此单纯用 C 语言来实现这个任务是非常困难的。但是，我们可以使用 C 语言来实现一些基本的图像处理算法，以便进行车牌识别的前置处理，例如图像二值化、边缘检测等等。下面给出一个简单的示例代码： ```c #include <stdio.h> #include <stdlib.h> #include <string.h> #include <math.h> #define WIDTH 640 #define HEIGHT 480 typedef struct { unsigned char r, g, b; } RGB; typedef struct { int x, y; } Point; void read_image(char *filename, RGB *image) { FILE *fp = fopen(filename, "rb"); if (!fp) { printf("Error opening file %sn", filename); exit(1); } unsigned char header[54]; fread(header, sizeof(unsigned char), 54, fp); int width = *(int *)&header[18]; int height = *(int *)&header[22]; if (width != WIDTH || height != HEIGHT) { printf("Error: image dimension does not match (%dx%d)n", width, height); exit(1); } unsigned char *data = (unsigned char *)malloc(width * height * 3); fread(data, sizeof(unsigned char), width * height * 3, fp); fclose(fp); for (int i = 0; i < width * height; i++) { image[i].b = data[i * 3]; image[i].g = data[i * 3 + 1]; image[i].r = data[i * 3 + 2]; } free(data); } void write_image(char *filename, RGB *image) { FILE *fp = fopen(filename, "wb"); if (!fp) { printf("Error opening file %sn", filename); exit(1); } unsigned char header[54] = { 0x42, 0x4d, 0x36, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0xe0, 0x01, 0x00, 0x00, 0x01, 0x00, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x13, 0x0b, 0x00, 0x00, 0x13, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 }; *(int *)&header[18] = WIDTH; *(int *)&header[22] = HEIGHT; fwrite(header, sizeof(unsigned char), 54, fp); unsigned char *data = (unsigned char *)malloc(WIDTH * HEIGHT * 3); for (int i = 0; i < WIDTH * HEIGHT; i++) { data[i * 3] = image[i].b; data[i * 3 + 1] = image[i].g; data[i * 3 + 2] = image[i].r; } fwrite(data, sizeof(unsigned char), WIDTH * HEIGHT * 3, fp); fclose(fp); free(data); } void rgb2gray(RGB *input, unsigned char *output) { for (int i = 0; i < WIDTH * HEIGHT; i++) { output[i] = (unsigned char)(0.299 * input[i].r + 0.587 * input[i].g + 0.114 * input[i].b); } } void binarize(unsigned char *input, unsigned char *output, unsigned char threshold) { for (int i = 0; i < WIDTH * HEIGHT; i++) { output[i] = (input[i] > threshold) ? 255 : 0; } } void sobel(unsigned char *input, unsigned char *output) { int x_kernel[3][3] = {{-1, 0, 1}, {-2, 0, 2}, {-1, 0, 1}}; int y_kernel[3][3] = {{-1, -2, -1}, {0, 0, 0}, {1, 2, 1}}; for (int y = 1; y < HEIGHT - 1; y++) { for (int x = 1; x < WIDTH - 1; x++) { int gx = 0, gy = 0; for (int j = -1; j <= 1; j++) { for (int i = -1; i <= 1; i++) { int idx = (y + j) * WIDTH + (x + i); gx += x_kernel[j + 1][i + 1] * input[idx]; gy += y_kernel[j + 1][i + 1] * input[idx]; } } int idx = y * WIDTH + x; output[idx] = (unsigned char)round(sqrt(gx * gx + gy * gy)); } } } void hough(unsigned char *input, unsigned char *output) { int max_rho = (int)round(sqrt(WIDTH * WIDTH + HEIGHT * HEIGHT)); int max_theta = 180; int *accumulator = (int *)calloc(max_rho * max_theta, sizeof(int)); for (int y = 0; y < HEIGHT; y++) { for (int x = 0; x < WIDTH; x++) { if (input[y * WIDTH + x] > 0) { for (int theta = 0; theta < max_theta; theta++) { double rho = x * cos(theta * M_PI / 180.0) + y * sin(theta * M_PI / 180.0); int idx = (int)round(rho) * max_theta + theta; accumulator[idx]++; } } } } int max_count = 0, max_rho_idx = 0, max_theta_idx = 0; for (int rho = 0; rho < max_rho; rho++) { for (int theta = 0; theta < max_theta; theta++) { int idx = rho * max_theta + theta; if (accumulator[idx] > max_count) { max_count = accumulator[idx]; max_rho_idx = rho; max_theta_idx = theta; } } } double rho = max_rho_idx; double theta = max_theta_idx * M_PI / 180.0; for (int y = 0; y < HEIGHT; y++) { for (int x = 0; x < WIDTH; x++) { double r = x * cos(theta) + y * sin(theta); if (fabs(r - rho) < 1.0) { output[y * WIDTH + x] = 255; } } } free(accumulator); } int main(int argc, char **argv) { if (argc != 3) { printf("Usage: %s <input_image> <output_image>n", argv[0]); exit(1); } RGB *image = (RGB *)malloc(WIDTH * HEIGHT * sizeof(RGB)); read_image(argv[1], image); unsigned char *gray = (unsigned char *)malloc(WIDTH * HEIGHT); rgb2gray(image, gray); unsigned char *binarized = (unsigned char *)malloc(WIDTH * HEIGHT); binarize(gray, binarized, 100); unsigned char *edge = (unsigned char *)malloc(WIDTH * HEIGHT); sobel(binarized, edge); unsigned char *houghed = (unsigned char *)malloc(WIDTH * HEIGHT); hough(edge, houghed); write_image(argv[2], image); free(image); free(gray); free(binarized); free(edge); free(houghed); return 0; } ``` 这段代码可以读入一个彩色图像，然后将其转换为灰度图像，进行二值化、边缘检测、霍夫变换等操作，最后将结果保存为一个彩色图像。这些操作只是车牌识别的一部分，需要更多的算法和技术来完成整个任务。
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