本文主要是介绍瑞芯微RV1126——人脸识别源码分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
本节内容主要分为3部分，第一部分是流程结构图;第二部分为人脸识别代码流程;第三部分为具体的代码分析。
1.流程结构图
2.人脸识别代码流程
1、人脸数据的初始化：
init_all_rockx_face_data();init_face_data();
2、创建rtsp会话，这里包括发送码流数据，得客户端,也就是我们在windows上用ffplay去拉流得时候，才会发送码流数据给客户端：
create_rtsp_demo(554);
rtsp_new_session
rtsp_set_video
rtsp_sync_video_ts
**
3、初始化vi通道属性**
VI_CHN_ATTR_S vi_chn_attr;
4、初始化视频处理属性
RGA_ATTR_S stRgaAttr;
5、初始化编码通道属性：
VENC_CHN_ATTR_S venc_chn_attr;
6、绑定数据源：
RK_MPI_SYS_Bind
7、开始捕获码流：
RK_MPI_VI_StartStream
8、执行三个对应的线程：
- pthread_create(&rockx_pid, NULL, rockx_vi_detect_thread, NULL);
- pthread_create(&venc_pid, NULL, rockx_vi_face_recognize_venc_thread, NULL);
- pthread_create(&rtsp_pid, NULL, rockx_venc_rtsp_thread, NULL);
**9、销毁申请的系统资源; **
3.核心代码分析
初始化vi通道属性；初始化视频处理属性；初始化编码通道属性；绑定数据源；开始捕获码流：
//初始化vi通道属性VI_CHN_ATTR_S vi_chn_attr;vi_chn_attr.pcVideoNode = pDeviceName;vi_chn_attr.u32BufCnt = u32BufCnt;vi_chn_attr.u32Width = u32Width;vi_chn_attr.u32Height = u32Height;//你的摄像头分辨率大小不要超过vencvi_chn_attr.enPixFmt = IMAGE_TYPE_NV12;vi_chn_attr.enBufType = VI_CHN_BUF_TYPE_MMAP;vi_chn_attr.enWorkMode = VI_WORK_MODE_NORMAL;ret = RK_MPI_VI_SetChnAttr(s32CamId, 0, &vi_chn_attr);//设置vi通道属性ret |= RK_MPI_VI_EnableChn(s32CamId, 0);//使能vi通道属性，让其生效if (ret){printf("ERROR: create rkisp0 VI[0] error! ret=%d\n", ret);return 0;}//初始化rga属性RGA_ATTR_S stRgaAttr;stRgaAttr.bEnBufPool = RK_TRUE;stRgaAttr.u16BufPoolCnt = 2;stRgaAttr.u16Rotaion = 0;stRgaAttr.stImgIn.u32X = 0;stRgaAttr.stImgIn.u32Y = 0;stRgaAttr.stImgIn.imgType = IMAGE_TYPE_NV12;stRgaAttr.stImgIn.u32Width = u32Width;stRgaAttr.stImgIn.u32Height = u32Height;stRgaAttr.stImgIn.u32HorStride = u32Width;stRgaAttr.stImgIn.u32VirStride = u32Height;stRgaAttr.stImgOut.u32X = 0;stRgaAttr.stImgOut.u32Y = 0;stRgaAttr.stImgOut.imgType = IMAGE_TYPE_NV12;stRgaAttr.stImgOut.u32Width = disp_width;stRgaAttr.stImgOut.u32Height = disp_height;stRgaAttr.stImgOut.u32HorStride = disp_width;stRgaAttr.stImgOut.u32VirStride = disp_height;ret = RK_MPI_RGA_CreateChn(0, &stRgaAttr);//rga通道if (ret){printf("ERROR: Create rga[0] falied! ret=%d\n", ret);return -1;}//初始化编码属性VENC_CHN_ATTR_S venc_chn_attr;memset(&venc_chn_attr, 0, sizeof(VENC_CHN_ATTR_S));venc_chn_attr.stVencAttr.u32PicWidth = disp_width;venc_chn_attr.stVencAttr.u32PicHeight = disp_height;venc_chn_attr.stVencAttr.u32VirWidth = disp_width;venc_chn_attr.stVencAttr.u32VirHeight = disp_height;venc_chn_attr.stVencAttr.imageType = IMAGE_TYPE_NV12;venc_chn_attr.stVencAttr.enType = RK_CODEC_TYPE_H264;venc_chn_attr.stVencAttr.u32Profile = 66;venc_chn_attr.stRcAttr.enRcMode = VENC_RC_MODE_H264CBR;//恒定的编码码率类型venc_chn_attr.stRcAttr.stH264Cbr.u32Gop = 30;venc_chn_attr.stRcAttr.stH264Cbr.u32BitRate = disp_width * disp_height * 3;venc_chn_attr.stRcAttr.stH264Cbr.fr32DstFrameRateDen = 1;venc_chn_attr.stRcAttr.stH264Cbr.fr32DstFrameRateNum = 25;venc_chn_attr.stRcAttr.stH264Cbr.u32SrcFrameRateDen = 1;venc_chn_attr.stRcAttr.stH264Cbr.u32SrcFrameRateNum = 25;ret = RK_MPI_VENC_CreateChn(0, &venc_chn_attr);//创建编码通道if (ret){printf("ERROR: Create venc failed!\n");exit(0);}//初始化mpp通道MPP_CHN_S vi_chn;MPP_CHN_S rga_chn;vi_chn.enModId = RK_ID_VI;vi_chn.s32ChnId = 0;rga_chn.enModId = RK_ID_RGA;rga_chn.s32ChnId = 0;ret = RK_MPI_SYS_Bind(&vi_chn, &rga_chn);//绑定vi和rga通道if (ret != 0){printf("[VI] vi id: %d bind venc id: %d, ret: %d error\n", vi_chn.s32ChnId, rga_chn.s32ChnId, ret);return -1;}初始化三个线程
//初始化三个线程idpthread_t rockx_pid;pthread_t venc_pid;pthread_t rtsp_pid;//创建人脸检测线程pthread_create(&rockx_pid, NULL, rockx_vi_detect_thread, NULL);//人脸识别线程pthread_create(&venc_pid, NULL, rockx_vi_face_recognize_venc_thread, NULL);//人脸编码rtsp传输线程pthread_create(&rtsp_pid, NULL, rockx_venc_rtsp_thread, NULL);
人脸检测线程
void *rockx_vi_detect_thread(void *args)
{//自动释放线程资源pthread_detach(pthread_self());//创建一个类对象thread_mapS_THREAD_MAP thread_map;//对thread_map进行初始化get_thread_map(0, &thread_map);//定义了一个map类型database_face_map对象map<string, rockx_face_feature_t> database_face_map = thread_map.thread_map;//定义迭代器database_itermap<string, rockx_face_feature_t>::iterator database_iter;//定义一个缓冲区MEDIA_BUFFER src_mb = NULL;//人脸模式枚举变量定义rockx_module_t data_version;data_version = ROCKX_MODULE_FACE_DETECTION_V2;//定义一个人脸执行返回结果变量rockx_ret_t rockx_ret;//定义人脸检测处理指针变量rockx_handle_t face_det_handle;//定义人脸识别特征提取处理指针变量rockx_handle_t face_recognize_handle;//定义人脸特征点定位处理指针变量rockx_handle_t face_5landmarks_handle;//定义了人脸标记检测处理指针变量rockx_handle_t face_masks_det_handle;//定义人脸配置结构体指针变量rockx_config_t *config = rockx_create_config();//获取人脸配置值//添加配置人脸模型存放路径，这里是存放在共享目录下：/mnt/nfs/rockx_data/rockx_add_config(config, ROCKX_CONFIG_DATA_PATH, "/mnt/nfs/rockx_data/");//创建使用人脸模型数据，来处理人脸检测rockx_ret = rockx_create(&face_det_handle, data_version, config,sizeof(rockx_config_t));//判断是否创建使用人脸模型数据 来处理人脸检测是否成功 if (rockx_ret != ROCKX_RET_SUCCESS){printf("init face_detect error %d\n", rockx_ret);return NULL;}//使用人脸模型数据来人脸识别特征提取rockx_ret = rockx_create(&face_recognize_handle, ROCKX_MODULE_FACE_RECOGNIZE,config, sizeof(rockx_config_t));//识别是否成功 if (rockx_ret != ROCKX_RET_SUCCESS){printf("init face_recognize error %d\n", rockx_ret);return NULL;}//使用模型算法数据来做人脸特征点定位处理rockx_ret = rockx_create(&face_5landmarks_handle,ROCKX_MODULE_FACE_LANDMARK_5, config, 0);//判断是否处理成功 if (rockx_ret != ROCKX_RET_SUCCESS){printf("init rockx module ROCKX_MODULE_FACE_LANDMARK_68 error %d\n",rockx_ret);}// rockx_handle_t face_masks_det_handle;进行标记处理rockx_ret = rockx_create(&face_masks_det_handle,ROCKX_MODULE_FACE_MASKS_DETECTION, config, 0);if (rockx_ret != ROCKX_RET_SUCCESS){printf("init rockx module ROCKX_MODULE_FACE_MASKS_DETECTION error %d\n",rockx_ret);}//定义人脸图片结构体变量，并进行成员赋值rockx_image_t input_image;input_image.width = 1920;input_image.height = 1080;input_image.pixel_format = ROCKX_PIXEL_FORMAT_YUV420SP_NV12;bool is_recognize = false;string predict = "";//rockx_face_result_t face_result;int ret;//做轮询操作while (!quit){
#if 1//从指定通道中获取数据缓冲区src_mb = RK_MPI_SYS_GetMediaBuffer(RK_ID_VI, 0, -1);if (!src_mb){printf("ERROR: RK_MPI_SYS_GetMediaBuffer get null buffer!\n");break;}//从指定的MEDIA_BUFFER中获取缓冲区数据大小input_image.size = RK_MPI_MB_GetSize(src_mb);input_image.data = (unsigned char *)RK_MPI_MB_GetPtr(src_mb);//从指定的MEDIA_BUFFER中获取缓冲区数据指针
#endif#if 1//rockx_face_result_group_t face_result_group;//memset(&face_result_group, 0, sizeof(face_result_group));//定义人脸处理结果结构体变量rockx_object_array_t face_array;memset(&face_array, 0, sizeof(face_array));//开始人脸检测rockx_ret = rockx_face_detect(face_det_handle, &input_image, &face_array, NULL);if (rockx_ret != ROCKX_RET_SUCCESS){printf("rockx_face_detect ERROR %d\n", rockx_ret);}//进行互斥处理set_rockx_face_array(face_array);//判断检测人脸特征数量值是否大于0if (face_array.count > 0){//rockx_queue->putRockxFaceArray(face_array);printf("face_count : %d\n", face_array.count);for (int i = 0; i < face_array.count; i++){if (1){int is_false_face;//进行人脸过滤处理ret = rockx_face_filter(face_5landmarks_handle, &input_image,&face_array.object[i].box, &is_false_face);if (ret != ROCKX_RET_SUCCESS){printf("rockx_face_filter error %d\n", ret);}if (is_false_face)continue;}
#if 1//人脸检测结果（包括人脸、车牌、头部、物体等）变量定义rockx_object_t max_face;rockx_object_t cur_face = face_array.object[i];//进行人脸区域计算处理操作int cur_face_box_area = (cur_face.box.right - cur_face.box.left) *(cur_face.box.bottom - cur_face.box.top);int max_face_box_area = (max_face.box.right - max_face.box.left) *(max_face.box.bottom - max_face.box.top);if (cur_face_box_area > max_face_box_area){max_face = cur_face;}//检测输出处理rockx_image_t out_img;memset(&out_img, 0, sizeof(rockx_image_t));//进行面部矫正对齐ret = rockx_face_align(face_5landmarks_handle, &input_image,&(max_face.box), NULL, &out_img);if (ret != ROCKX_RET_SUCCESS){printf("face_align failed\n");}//人脸特征结果变量定义rockx_face_feature_t out_feature;//获取人脸特征rockx_face_recognize(face_recognize_handle, &out_img, &out_feature);for (database_iter = database_face_map.begin();database_iter != database_face_map.end(); database_iter++){float similarity;//比较两个人脸特征的相似性ret = rockx_face_feature_similarity(&database_iter->second,&out_feature, &similarity);printf("simple_value = %lf\n", similarity);//判断预测精度if (similarity <= 1.0){is_recognize = true;//predict_name_bak = database_iter->first;predict = database_iter->first;break;}else{is_recognize = false;predict = "";continue;}}if (is_recognize == true){predict = database_iter->first;}else{predict = "";}set_rockx_prdict_name(predict);#endif}}
#endif//释放缓冲区RK_MPI_MB_ReleaseBuffer(src_mb);src_mb = NULL;}//释放相关人脸处理数据模块rockx_destroy(face_det_handle);rockx_destroy(face_recognize_handle);rockx_destroy(face_5landmarks_handle);return NULL;
}人脸识别线程
void *rockx_vi_face_recognize_venc_thread(void *args)
{pthread_detach(pthread_self());//线程资源自动释放MEDIA_BUFFER mb = NULL; //媒体缓存区int ret;float x_rate = (float)1280 / 1920;float y_rate = (float)720 / 1080;while (!quit){//从指定通道中获取数据缓冲区mb = RK_MPI_SYS_GetMediaBuffer(RK_ID_RGA, 0, -1);if (!mb){printf("ERROR: RK_MPI_SYS_GetMediaBuffer get null buffer!\n");break;}//获取人脸处理结果rockx_object_array_t face_array = get_rockx_face_array();//创建mat对象，并创建了720x1080的像素块，每个像素每个通道的位数都是8位，一个字节的。上述CV_8UC3中的8表示8位、UC表示uchar类型、1表示1个通道Mat tmp_img = Mat(720, 1280, CV_8UC1, RK_MPI_MB_GetPtr(mb));
#if 1//对人脸x,y，w,h进行处理for (int i = 0; i < face_array.count; i++){int x = face_array.object[i].box.left * x_rate;int y = face_array.object[i].box.top * y_rate;int w = (face_array.object[i].box.right - face_array.object[i].box.left) * x_rate;int h = (face_array.object[i].box.bottom - face_array.object[i].box.top) * y_rate;if (x < 0)x = 0;if (y < 0)y = 0;while ((uint32_t)(x + w) >= 1280){w -= 16;}while ((uint32_t)(y + h) >= 720){h -= 16;}//获取人脸预测名字string predict_name = get_rockx_prdict_name();printf("predict_name = %s\n", predict_name.c_str());nv12_border((char *)RK_MPI_MB_GetPtr(mb), 1280, 720, x, y, w, h, 255, 0, 255);int baseline;//计算人脸名字文本大小Size text_size = getTextSize(predict_name, 2, 2, 2, &baseline);Point origin;origin.x = tmp_img.cols / 4 - text_size.width / 4;origin.y = tmp_img.rows / 4 + text_size.height / 4;//把名字字符填充到文本框里面去cv::putText(tmp_img, predict_name, origin, cv::FONT_HERSHEY_COMPLEX, 1, cv::Scalar(255, 0, 255), 3);}
#endif//释放对应的资源RK_MPI_SYS_SendMediaBuffer(RK_ID_VENC, 0, mb);RK_MPI_MB_ReleaseBuffer(mb);mb = NULL;}return NULL;
}人脸编码rtsp传输线程
void *rockx_venc_rtsp_thread(void *args)
{pthread_detach(pthread_self());MEDIA_BUFFER mb = NULL;while (!quit){mb = RK_MPI_SYS_GetMediaBuffer(RK_ID_VENC, 0, -1);if (!mb){printf("ERROR: RK_MPI_SYS_GetMediaBuffer get null buffer!\n");break;}//rtsp来传输码流rtsp_tx_video(g_rtsp_session, (unsigned char *)RK_MPI_MB_GetPtr(mb), RK_MPI_MB_GetSize(mb), RK_MPI_MB_GetTimestamp(mb));RK_MPI_MB_ReleaseBuffer(mb);rtsp_do_event(g_rtsplive);}return NULL;
}这篇关于瑞芯微RV1126——人脸识别源码分析的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
