系列文章目录

ESP32CAM识别图形左边界(一)：初步使用ESP32CAM

ESP32CAM识别图形左边界(二)：网页添加补光灯按钮

ESP32CAM识别图形左边界(三)：识别图像左边界

系列文章目录

前言

一、添加开启边界识别的按钮

（1）修改camera_index.h文件

（2）修改app_httpd.cpp文件

二、图像数据处理

1.新建结构体

2.具体实现

3.实验结果

前言

之前两篇文章中，第一篇介绍了ESP32-CAM相机网页相关的使用方法，第二篇介绍了如何在相机的网页中添加自己的内容，最后一篇则真正的开始使用相机里面的图像了，因为我的环境比较简单，图像中只有物体和纯色背景，而且只需要图片中的左边界线。

一、添加开启边界识别的按钮

（1）修改camera_index.h文件

同上一篇文章一样，修改网页模板文index_ov2640.html，增加下面的代码，并一起去除了人脸识别的功能开关，然后打包成index_ov2640.html.gz文件，之后使用Beyond Compare以十六进制格式打开。

                        <div class="input-group" id="line-detect"><label for="linedetect">Line Detect</label><div class="switch"><input id="linedetect" type="checkbox" class="default-action"><label class="slider" for="linedetect"></label></div></div>

index_ov2640.html.gz文件的十六进制数据

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复制到下面的花括号中间，记得修改上方的index_ov2640_html_gz_len的大小。

#define index_ov2640_html_gz_len 4327
const uint8_t index_ov2640_html_gz[] = {};

（2）修改app_httpd.cpp文件

文件最上面增加一个静态全局变量

static bool linedetect = 0;

cmd_handler增加一个按钮控制命令

    else if(!strcmp(variable, "linedetect")) {linedetect = val;Serial.printf("linedetect:%d", linedetect);}

status_handler函数增加status页面的打印

p+=sprintf(p, "\"linedetect\":%u", linedetect);

网页增加相关的配置就到此结束了，下一步就是对图像数据进行处理。

二、图像数据处理

1.新建结构体

绘图使用的线结构体

typedef struct
{float star_point[2];float end_point[2];
} line_t;

结果输出的线结构体

typedef struct tag_line_list
{float score;line_t *line;int len;
} line_array_t;

2.具体实现

ESP32CAM图像的数据传输格式:image_matrix数据格式为一维数组，实际格式为[B G R]三个一组数据，第一行的列(共image_matrix->w列)排完，再写第N行(共image_matrix->h行)。

由于数据帧的问题，这里选用240*176(HQVGA)这种比较低的分辨率。

stream_handler的人脸识别功能部分被修改为识别左边界的功能。

static esp_err_t stream_handler(httpd_req_t *req){camera_fb_t * fb = NULL;esp_err_t res = ESP_OK;size_t _jpg_buf_len = 0;uint8_t * _jpg_buf = NULL;char * part_buf[64];dl_matrix3du_t *image_matrix = NULL;bool detected = false;int face_id = 0;int64_t fr_start = 0;int64_t fr_ready = 0;int64_t fr_face = 0;int64_t fr_recognize = 0;int64_t fr_encode = 0;static int64_t last_frame = 0;if(!last_frame) {last_frame = esp_timer_get_time();}res = httpd_resp_set_type(req, _STREAM_CONTENT_TYPE);if(res != ESP_OK){return res;}httpd_resp_set_hdr(req, "Access-Control-Allow-Origin", "*");while(true){detected = false;face_id = 0;fb = esp_camera_fb_get();if (!fb) {Serial.println("Camera capture failed");res = ESP_FAIL;} else {fr_start = esp_timer_get_time();fr_ready = fr_start;fr_face = fr_start;fr_encode = fr_start;fr_recognize = fr_start;if(!linedetect || fb->width > 240){if(fb->format != PIXFORMAT_JPEG){bool jpeg_converted = frame2jpg(fb, 80, &_jpg_buf, &_jpg_buf_len);esp_camera_fb_return(fb);fb = NULL;if(!jpeg_converted){Serial.println("JPEG compression failed");res = ESP_FAIL;}} else {_jpg_buf_len = fb->len;_jpg_buf = fb->buf;}} else {image_matrix = dl_matrix3du_alloc(1, fb->width, fb->height, 3);
//              Serial.printf("image: w:%d h:%d size:%d %d", fb->width, fb->height, fb->len, sizeof(fb->buf));if (!image_matrix) {Serial.println("dl_matrix3du_alloc failed");res = ESP_FAIL;} else {if(!fmt2rgb888(fb->buf, fb->len, fb->format, image_matrix->item)){Serial.println("fmt2rgb888 failed");res = ESP_FAIL;} else {fr_ready = esp_timer_get_time();line_array_t *lines = NULL;if (linedetect) {lines = recoginLines(image_matrix);}fr_face = esp_timer_get_time();fr_recognize = fr_face;if (lines || fb->format != PIXFORMAT_JPEG) {if (lines) {detected = true;fr_recognize = esp_timer_get_time();draw_lines(image_matrix, lines);free(lines->line);free(lines);}if(!fmt2jpg(image_matrix->item, fb->width*fb->height*3, fb->width, fb->height, PIXFORMAT_RGB888, 90, &_jpg_buf, &_jpg_buf_len)){Serial.println("fmt2jpg failed");res = ESP_FAIL;}esp_camera_fb_return(fb);fb = NULL;} else {_jpg_buf = fb->buf;_jpg_buf_len = fb->len;}fr_encode = esp_timer_get_time();}dl_matrix3du_free(image_matrix);}}}if(res == ESP_OK){res = httpd_resp_send_chunk(req, _STREAM_BOUNDARY, strlen(_STREAM_BOUNDARY));}if(res == ESP_OK){size_t hlen = snprintf((char *)part_buf, 64, _STREAM_PART, _jpg_buf_len);res = httpd_resp_send_chunk(req, (const char *)part_buf, hlen);}if(res == ESP_OK){res = httpd_resp_send_chunk(req, (const char *)_jpg_buf, _jpg_buf_len);}if(fb){esp_camera_fb_return(fb);fb = NULL;_jpg_buf = NULL;} else if(_jpg_buf){free(_jpg_buf);_jpg_buf = NULL;}if(res != ESP_OK){break;}int64_t fr_end = esp_timer_get_time();int64_t ready_time = (fr_ready - fr_start)/1000;int64_t face_time = (fr_face - fr_ready)/1000;int64_t recognize_time = (fr_recognize - fr_face)/1000;int64_t encode_time = (fr_encode - fr_recognize)/1000;int64_t process_time = (fr_encode - fr_start)/1000;int64_t frame_time = fr_end - last_frame;last_frame = fr_end;frame_time /= 1000;uint32_t avg_frame_time = ra_filter_run(&ra_filter, frame_time);Serial.printf("MJPG: %uB %ums (%.1ffps), AVG: %ums (%.1ffps), %u+%u+%u+%u=%u %s%d\n",(uint32_t)(_jpg_buf_len),(uint32_t)frame_time, 1000.0 / (uint32_t)frame_time,avg_frame_time, 1000.0 / avg_frame_time,(uint32_t)ready_time, (uint32_t)face_time, (uint32_t)recognize_time, (uint32_t)encode_time, (uint32_t)process_time,(detected)?"DETECTED ":"", face_id);}last_frame = 0;return res;
}

其中用到的函数

均值滤波函数的阈值

#define LINE_RECOGINE_MEDIAN_THD   80

均值滤波函数

uint8_t median(uint8_t sortArray[], uint8_t len) {uint8_t i,j,temp,flag;for(j=0; j<len-1; j++) {flag = 0;for (i=0; i<len-j; i++) {if (sortArray[j] > sortArray[j+1]) {flag = 1;temp = sortArray[j];sortArray[j] = sortArray[j+1];sortArray[j+1] = temp;}}if (!flag) {break;}}return sortArray[(len-1)/2];
}

识别边界线的函数

//image_matrix数据格式为一维数组，实际格式为[B G R]三个一组数据，第一行的列(共image_matrix->w列)排完，再写第N行(共image_matrix->h行)
static line_array_t* recoginLines(dl_matrix3du_t *image_matrix) {uint16_t i,j,x,y;uint8_t **gray_img = NULL;uint8_t **gray_filter_img = NULL;float sx,sy;line_array_t *line_result = NULL;uint8_t medianArray[9];bool realse_flag = 1;uint16_t line_len[5] = {0};uint8_t k=0;uint8_t line_num = 0;gray_img = (uint8_t**)malloc(sizeof(uint8_t*)*image_matrix->h);if (gray_img){for(j=0;j<image_matrix->h;j++) {gray_img[j] = (uint8_t*)malloc(sizeof(uint8_t)*image_matrix->w);if (!gray_img[j]) {Serial.printf("malloc gray_img[%d] failed",j);goto __FREE_POINTER;}}} else {Serial.println("malloc gray_img failed");goto __FREE_POINTER;}//灰度化图像for (j=0;j<image_matrix->h;j++) {for (i=0; i<image_matrix->w; i++) {gray_img[j][i] = uint8_t(0.114 * image_matrix->item[3*(j*image_matrix->w+i)] + 0.587 * image_matrix->item[3*(j*image_matrix->w+i)+1] + 0.2989 * image_matrix->item[3*(j*image_matrix->w+i)+2]);}}//均值滤波gray_filter_img = (uint8_t**)malloc(sizeof(uint8_t*)*(image_matrix->h-1));if (!gray_filter_img) {Serial.println("malloc gray_filter_img failed");goto __FREE_POINTER;}gray_filter_img[0] = (uint8_t*)malloc(sizeof(uint8_t)*(image_matrix->w-1));memset(gray_filter_img[0], 0, sizeof(uint8_t)*(image_matrix->w-1));if (!gray_filter_img[0]) {Serial.println("malloc gray_filter_img[0] failed");goto __FREE_POINTER;}for (y=1; y<image_matrix->h-1; y++) {gray_filter_img[y] = (uint8_t*)malloc(sizeof(uint8_t)*(image_matrix->w-1));memset(gray_filter_img[y], 0, sizeof(uint8_t)*(image_matrix->w-1));if (!gray_filter_img[y]) {Serial.printf("malloc gray_filter_img[%d] failed",y);goto __FREE_POINTER;}for (x=1; x<image_matrix->w-1; x++) {medianArray[0] = gray_img[y-1][x-1];medianArray[1] = gray_img[y-1][x];medianArray[2] = gray_img[y-1][x+1];medianArray[3] = gray_img[y][x-1];medianArray[4] = gray_img[y][x];medianArray[5] = gray_img[y][x+1];medianArray[6] = gray_img[y+1][x-1];medianArray[7] = gray_img[y+1][x];medianArray[8] = gray_img[y+1][x+1];gray_filter_img[y][x] = median(medianArray,9)> LINE_RECOGINE_MEDIAN_THD ? 255: 0;}}line_result = (line_array_t*)malloc(sizeof(line_array_t));if (line_result) {line_result->len=0;line_result->line = (line_t*)malloc(sizeof(line_t)* image_matrix->h);memset(line_result->line, 0, sizeof(line_t)* image_matrix->h);if (line_result->line) {for(y=1; y<image_matrix->h-1; y++) {for(x=1; x<image_matrix->w-1; x++) {if ((gray_filter_img[y][x-1] == 0) && (gray_filter_img[y][x] == 255)) {if (y==1) {line_result->line[y-1].star_point[0] = x;line_result->line[y-1].star_point[1] = y;line_result->line[y-1].end_point[0] = x;line_result->line[y-1].end_point[1] = y;} else {line_result->line[y-1].star_point[0] = line_result->line[y-2].end_point[0];line_result->line[y-1].star_point[1] = line_result->line[y-2].end_point[1];line_result->line[y-1].end_point[0] = x;line_result->line[y-1].end_point[1] = y;}line_result->len++;break;}}}realse_flag = 0;} else {Serial.println("malloc line_result->line fail");goto __FREE_POINTER;}} else {Serial.println("malloc line_result fail");goto __FREE_POINTER;}__FREE_POINTER://释放内存if (realse_flag) {if (line_result) {if (line_result->line) {free(line_result->line);}free(line_result);line_result = NULL;}}if (gray_filter_img) {for (j=0;j<image_matrix->h-1;j++) {if (gray_filter_img[j]) {free(gray_filter_img[j]);}}free(gray_filter_img);}if (gray_img) {for(j=0;j<image_matrix->h;j++) {if (gray_img[j]) {free(gray_img[j]);}}free(gray_img);}return line_result;
}

这里其实有个地方需要优化，最好不要在一个函数里面频繁malloc和free，但为了适应随时更改的图像分辨率，就直接放到这个函数里面进行malloc了。

增加结果输出函数，在图片里面进行画线

static void draw_lines(dl_matrix3du_t *image_matrix,line_array_t *lines) {int x0, y0, x1, y1, w, h, i, j;float temp = 0.0;uint32_t color = FACE_COLOR_RED;fb_data_t fb;fb.width = image_matrix->w;fb.height = image_matrix->h;fb.data = image_matrix->item;fb.bytes_per_pixel = 3;fb.format = FB_BGR888;for (i = 0; i < lines->len; i++){// rectangle boxx0 = (int)lines->line[i].star_point[0];y0 = (int)lines->line[i].star_point[1];x1 = (int)lines->line[i].end_point[0];y1 = (int)lines->line[i].end_point[1];w = (int)x1 - x0 + 1;h = (int)y1 - y0 + 1;if ((x0>0 && abs(x0) < image_matrix->w) && (y0>0 && abs(y0) < image_matrix->h) && (x1>0 && abs(x1) < image_matrix->w) && (y1>0 && abs(y1) < image_matrix->h)) {if (w > h) {temp = 1.0*h/w;for (j=0; j < w; j++) {fb_gfx_drawFastHLine(&fb, x0+j, y0+int(temp*j), 1, color);fb_gfx_drawFastVLine(&fb, x0+(j+1), y0+int(temp*j), 1, color);}} else {temp = 1.0*w/h;for (j=0; j < h; j++) {fb_gfx_drawFastVLine(&fb, x0+int(temp*j), y0+j, 1, color);fb_gfx_drawFastHLine(&fb, x0+int(temp*j), y0+(j+1), 1, color);}}}}
}

最后附上项目的完整代码地址--ESP32CAM识别图形左边界

3.实验结果

第一步结果：均值滤波的结果

第二步结果：实时识别边界线(图像分辨率选择240*176)不过帧率有点低

总结

以上就是我所有对于ESP32CAM的使用，目前只是提取ESP32CAM的图片，进行简单处理，并识别出简单背景图片中物体的左边界。

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【ESP32CAM识别图形左边界】（三）识别图像左边界

系列文章目录

前言

一、添加开启边界识别的按钮

（1）修改camera_index.h文件

（2）修改app_httpd.cpp文件

二、图像数据处理

1.新建结构体

2.具体实现

3.实验结果

【ESP32CAM识别图形左边界】（三）识别图像左边界相关推荐

最新文章

热门文章

【ESP32CAM识别图形左边界】（三）识别图像左边界

系列文章目录

前言

一、添加开启边界识别的按钮

（1） 修改camera_index.h文件

（2） 修改app_httpd.cpp文件

二、图像数据处理

1.新建结构体

2.具体实现

3.实验结果

【ESP32CAM识别图形左边界】（三）识别图像左边界相关推荐

最新文章

热门文章

（1）修改camera_index.h文件

（2）修改app_httpd.cpp文件