写一个C语言实现的物体检测算法

物体检测是计算机视觉领域中的一个热门研究方向，以下是一个基于C语言实现的物体检测算法的示例代码：

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

#define IMG_SIZE 224
#define NUM_CLASSES 10

// 定义卷积层结构体
typedef struct ConvLayer {
    int kernel_size; // 卷积核大小
    int num_kernels; // 卷积核个数
    int stride; // 步长
    float **kernels; // 卷积核数组
} ConvLayer;

// 定义池化层结构体
typedef struct PoolingLayer {
    int pool_size; // 池化大小
    int stride; // 步长
} PoolingLayer;

// 定义全连接层结构体
typedef struct DenseLayer {
    int input_size; // 输入大小
    int output_size; // 输出大小
    float **weights; // 权重矩阵
    float *biases; // 偏置向量
} DenseLayer;

// 初始化卷积层
ConvLayer* init_conv_layer(int kernel_size, int num_kernels, int stride) {
    ConvLayer *conv_layer = (ConvLayer*)malloc(sizeof(ConvLayer));
    conv_layer->kernel_size = kernel_size;
    conv_layer->num_kernels = num_kernels;
    conv_layer->stride = stride;
    conv_layer->kernels = (float**)malloc(num_kernels * sizeof(float*));
    for (int i = 0; i < num_kernels; i++) {
        conv_layer->kernels[i] = (float*)malloc(kernel_size * kernel_size * sizeof(float));
        for (int j = 0; j < kernel_size * kernel_size; j++) {
            conv_layer->kernels[i][j] = (float)rand() / RAND_MAX - 0.5; // 随机初始化卷积核
        }
    }
    return conv_layer;
}

// 初始化池化层
PoolingLayer* init_pooling_layer(int pool_size, int stride) {
    PoolingLayer *pooling_layer = (PoolingLayer*)malloc(sizeof(PoolingLayer));
    pooling_layer->pool_size = pool_size;
    pooling_layer->stride = stride;
    return pooling_layer;
}

// 初始化全连接层
DenseLayer* init_dense_layer(int input_size, int output_size) {
    DenseLayer *dense_layer = (DenseLayer*)malloc(sizeof(DenseLayer));
    dense_layer->input_size = input_size;
    dense_layer->output_size = output_size;
    dense_layer->weights = (float**)malloc(output_size * sizeof(float*));
    for (int i = 0; i < output_size; i++) {
        dense_layer->weights[i] = (float*)malloc(input_size * sizeof(float));
        for (int j = 0; j < input_size; j++) {
            dense_layer->weights[i][j] = (float)rand() / RAND_MAX - 0.5; // 随机初始化权重
        }
    }
    dense_layer->biases = (float*)malloc(output_size * sizeof(float));
    for (int i = 0; i < output_size; i++) {
        dense_layer->biases[i] = (float)rand() / RAND_MAX - 0.5; // 随机初始化偏置
    }
    return dense_layer;
}

// 卷积操作
float conv(float **input, int input_size, float **kernel, int kernel_size, int i, int j) {
    float sum = 0.0;
    for (int m = 0; m < kernel_size; m++) {
        for (int n = 0; n < kernel_size; n++) {
            sum += input[i + m][j + n] * kernel[m][n];
        }
    }
    return sum;
}

// ReLU激活函数
float relu(float x) {
    return fmaxf(0.0, x);
}

// softmax激活函数
void softmax(float *x, int size) {
    float max_val = x[0];
    for (int i = 1; i < size; i++) {
        if (x[i] > max_val) {
            max_val = x[i];
        }
    }
    float sum = 0.0;
    for (int i = 0; i < size; i++) {
        x[i] = expf(x[i] - max_val);
        sum += x[i];
    }
    for (int i = 0; i < size; i++) {
        x[i] /= sum;
    }
}

// 前向传播
void forward(float **input, ConvLayer *conv_layer, PoolingLayer *pooling_layer, DenseLayer *dense_layer, float *output) {
    int conv_output_size = (IMG_SIZE - conv_layer->kernel_size) / conv_layer->stride + 1;
    float **conv_output = (float**)malloc(conv_output_size * sizeof(float*));
    for (int i = 0; i < conv_output_size; i++) {
        conv_output[i] = (float*)malloc(conv_output_size * sizeof(float));
        for (int j = 0; j < conv_output_size; j++) {
            conv_output[i][j] = 0.0;
            for (int k = 0; k < conv_layer->num_kernels; k++) {
                conv_output[i][j] += conv(input, IMG_SIZE, conv_layer->kernels[k], conv_layer->kernel_size, i * conv_layer->stride, j * conv_layer->stride);
            }
            conv_output[i][j] = relu(conv_output[i][j]); // 对卷积结果进行ReLU激活
        }
    }

    int pooling_output_size = (conv_output_size - pooling_layer->pool_size) / pooling_layer->stride + 1;
    float **pooling_output = (float**)malloc(pooling_output_size * sizeof(float*));
    for (int i = 0; i < pooling_output_size; i++) {
        pooling_output[i] = (float*)malloc(pooling_output_size * sizeof(float));
        for (int j = 0; j < pooling_output_size; j++) {
            float max_val = -INFINITY;
            for (int m = 0; m < pooling_layer->pool_size; m++) {
                for (int n = 0; n < pooling_layer->pool_size; n++) {
                    max_val = fmaxf(max_val, conv_output[i * pooling_layer->stride + m][j * pooling_layer->stride + n]);
                }
            }
            pooling_output[i][j] = max_val; // 对池化结果取最大值
        }
    }

    float *dense_input = (float*)malloc(pooling_output_size * pooling_output_size * sizeof(float));
    for (int i = 0; i < pooling_output_size; i++) {
        for (int j = 0; j < pooling_output_size; j++) {
            dense_input[i * pooling_output_size + j] = pooling_output[i][j];
        }
    }

    for (int i = 0; i < dense_layer->output_size; i++) {
        output[i] = 0.0;
        for (int j = 0; j < dense_layer->input_size; j++) {
            output[i] += dense_layer->weights[i][j] * dense_input[j];
        }
        output[i] += dense_layer->biases[i]; // 对全连接层的输出加上偏置
    }

    softmax(output, NUM_CLASSES); // 对全连接层的输出进行softmax激活
}

int main() {
    // 读取输入图像
    float **input = (float**)malloc(IMG_SIZE * sizeof(float*));
    for (int i = 0; i < IMG_SIZE; i++) {
        input[i] = (float*)malloc(IMG_SIZE * sizeof(float));
    }
    FILE *fp = fopen("input.txt", "r");
    for (int i = 0; i < IMG_SIZE; i++) {
        for (int j = 0; j < IMG_SIZE; j++) {
            fscanf(fp, "%f", &input[i][j]);
        }
    }
    fclose(fp);

    // 初始化卷积层、池化层、全连接层
    ConvLayer *conv_layer = init_conv_layer(3, 16, 1);
    PoolingLayer *pooling_layer = init_pooling_layer(2, 2);
    DenseLayer *dense_layer = init_dense_layer(28 * 28 * 16, NUM_CLASSES);

    // 前向传播
    float *output = (float*)malloc(NUM_CLASSES * sizeof(float));
    forward(input, conv_layer, pooling_layer, dense_layer, output);

    // 输出预测结果
    int max_index = 0;
    float max_val = output[0];
    for (int i = 1; i < NUM_CLASSES; i++) {
        if (output[i] > max_val) {
            max_index = i;
            max_val = output[i];
        }
    }
    printf("Predicted class: %d\n", max_index);

    // 释放内存
    for (int i = 0; i < conv_layer->num_kernels; i++) {
        free(conv_layer->kernels[i]);
    }
    free(conv_layer->kernels);
    free(conv_layer);
    free(pooling_layer);
    for (int i = 0; i < dense_layer->output_size; i++) {
        free(dense_layer->weights[i]);
    }
    free(dense_layer->weights);
    free(dense_layer->biases);
    free(dense_layer);
    free(output);
    for (int i = 0; i < IMG_SIZE; i++) {
        free(input[i]);
    }
    free(input);
    return 0;
}

这个示例代码实现了一个简单的卷积神经网络，包含一个卷积层、一个池化层和一个全连接层，用于对输入图像进行物体检测。其中，卷积操作、ReLU激活函数、softmax激活函数等都是常见的计算机视觉算法中使用的基本操作。