概述
SAM 是一种先进的人工智能模型,已经证明了在分割复杂和多样化图像方面具有优异的表现。该模型是计算机视觉和图像分割领域的一个重大突破。 SAM 的架构旨在处理各种图像分割任务,包括对象检测、实例分割和全景分割。这意味着该模型可以应用于各种用例,从医学图像分析到自主驾驶。
SAM 的独特之处之一是它具有执行全景分割的能力,这涉及将实例分割和语义分割相结合。实例分割涉及识别和划分图像内每个物体实例,而语义分割涉及为图像中的每个像素标记相应的类别标签。全景分割将这两种方法结合起来,以提供对图像更全面的理解。
SAM 的另一个关键特点是其灵活性。该模型可以针对特定的用例和领域进行微调,使其高度适应性。 SAM 的架构也非常高效,使其能够实时处理大量数据。这使其非常适合需要快速准确的图像分割的应用,例如安全监控、工业自动化和机器人技术。
SAM 如何运作:模型架构
SAM(Segment Anything Model)是用于图像分割任务的先进深度学习模型。 SAM 使用卷积神经网络(CNN)和基于 Transformer 的架构结合在一起以分层和多尺度的方式处理图像。以下是 SAM 如何工作的高级概述:
- 骨干网络:SAM 使用预训练的 Vision Transformer,即 ViT 作为其骨干网络。骨干网络用于从输入图像中提取特征。
- 特征金字塔网络(FPN):SAM 使用特征金字塔网络(FPN)在多个尺度上生成特征映射。 FPN 是一系列卷积层,它们在不同尺度上运作,以从骨干网络的输出中提取特征。 FPN 确保 SAM 可以在不同细节层次上识别物体和边界。
- 解码器网络:SAM 使用解码器网络为输入图像生成分割掩模。解码器网络接受 FPN 的输出并将其上采样到原始图像大小。上采样过程使模型能够生成具有与输入图像相同分辨率的分割掩模。
- 基于 Transformer 的架构:SAM 还使用基于 Transformer 的架构来改进分割结果。 Transformer 是一种神经网络架构,非常有效地处理序列数据,例如文本或图像。使用基于 Transformer 的架构通过从输入图像中获取上下文信息来改进分割结果。
- 自监督学习:SAM 利用自监督学习从未标记的数据中学习。这涉及在大型未标记图像数据集上训练模型,以学习图像中的常见模式和特征。学习到的特征可以用于改善模型在特定图像分割任务上的性能。
- 全景分割:SAM 可以执行全景分割,这涉及结合实例和语义分割。实例分割涉及识别和划分图像内每个物体实例,而语义分割涉及为图像中的每个像素标记相应的类别标签。全景分割将这两种方法结合起来,以提供对图像更全面的理解。
SAM 的潜在用例
SAM(Segment Anything Model)是一种高度通用的图像分割模型,可应用于各种用例。以下是 SAM 的五个潜在用例:
- 自动驾驶车辆:SAM 可用于自动驾驶车辆中,以识别和分割环境中的不同物体,例如车辆、行人和路标。这些信息可用于帮助车辆做出有根据的导航和安全决策。
- 医学影像:SAM 可用于医学影像中,以分割图像中的不同结构和组织,例如肿瘤、血管和器官。这些信息可用于协助医生进行诊断和治疗计划。
- 对象检测:SAM 可用于识别和分割图像中的对象,用于对象检测任务。这可以在安全监控、工业自动化和机器人应用中很有用。
- 农业:SAM 可用于农业中,以监测作物的健康和生长情况。通过对田地或作物的不同区域进行分割,SAM 可以识别需要关注的区域,例如害虫侵害或营养不足的区域。
- 建筑工地监测:SAM 可用于监测建筑工地的进度,通过分割工地的不同组件,例如建筑物、设备和材料。这些信息可用于跟踪项目进度,确保项目按计划进行。
C++推理
ncnn
NCNN是一个为移动和嵌入式设备设计的高性能神经网络推理库,由腾讯的优图实验室(YouTu Lab)开发并开源。以下是对NCNN的简要概述:
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目标:NCNN旨在提供快速、轻量级的深度学习模型部署方案,特别优化了在资源受限的设备上的性能。
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性能优化:NCNN利用了多种硬件加速技术,包括NEON、Metal、OpenGL等,以实现在不同平台上的最优性能。
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跨平台:支持跨平台使用,包括但不限于Android、iOS、Linux、Windows等操作系统。
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模型支持:支持多种深度学习框架的模型转换,例如Caffe、TensorFlow等,方便开发者将不同来源的模型集成到NCNN中。
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轻量化设计:NCNN的库文件体积小,适合移动设备和嵌入式设备,减少存储和内存占用。
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灵活性:提供了灵活的输入输出接口,可以轻松地与现有的应用程序或系统进行集成。
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易用性:NCNN提供了简洁的API,使得模型的加载、运行和推理过程简单明了。
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硬件兼容性:针对不同的硬件平台进行了优化,包括CPU、GPU和DSP等,以充分利用各种硬件的计算能力。
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社区支持:作为一个开源项目,NCNN拥有活跃的社区支持,不断有新的功能和优化被加入。
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应用场景:适用于实时性要求高的场景,如视频流处理、图像识别、语音识别等。
NCNN的设计哲学是“小而美”,它专注于推理(inference)而非训练(training),并且特别注重在移动和嵌入式设备上的性能和效率。这使得NCNN成为在边缘设备上部署深度学习模型的理想选择。
C++ 推理
#include "pipeline.h"
#include <iostream>
namespace sam{
PipeLine::~PipeLine()
{}
int PipeLine::Init(const std::string& image_encoder_param, const std::string& image_encoder_bin, const std::string& mask_decoder_param,const std::string& mask_decoder_bin)
{sam_ = std::make_shared<SegmentAnything>();int ret = sam_->Load(image_encoder_param,image_encoder_bin,mask_decoder_param,mask_decoder_bin);return ret;
}int PipeLine::ImageEmbedding(const cv::Mat& bgr, pipeline_result_t& pipeline_result)
{std::cout << "start image encoder..." << std::endl;sam_->ImageEncoder(bgr, pipeline_result.image_embeddings, pipeline_result.image_info);std::cout << "finish image encoder..." << std::endl;return 0;
}int PipeLine::AutoPredict(const cv::Mat& bgr, pipeline_result_t& pipeline_result, int n_per_side)
{pipeline_result.prompt_info.prompt_type = PromptType::Point;//generate grid pointsstd::vector<float> points_xy_vec;get_grid_points(points_xy_vec, n_per_side);std::vector<sam_result_t> proposals;for(int i = 0; i < n_per_side; ++i) {std::vector<sam_result_t> objects;for(int j = 0; j < n_per_side; ++j) {pipeline_result.prompt_info.points.clear();pipeline_result.prompt_info.labels.clear();pipeline_result.prompt_info.points.push_back(points_xy_vec[i * n_per_side * 2 + 2 * j] * pipeline_result.image_info.img_w);pipeline_result.prompt_info.points.push_back(points_xy_vec[i * n_per_side * 2 + 2 * j + 1] * pipeline_result.image_info.img_h);pipeline_result.prompt_info.points.push_back(0);pipeline_result.prompt_info.points.push_back(0);pipeline_result.prompt_info.labels.push_back(1);pipeline_result.prompt_info.labels.push_back(-1);sam_->MaskDecoder(pipeline_result.image_embeddings, pipeline_result.image_info, pipeline_result.prompt_info, objects);}proposals.insert(proposals.end(), objects.begin(), objects.end());std::cout<<"processing: "<< i <<"/"<<n_per_side<<std::endl;}std::vector<int> picked;sam_->NMS(bgr, proposals, picked);int num_picked = picked.size();for(int j = 0; j < num_picked; ++j){pipeline_result.sam_result.push_back(proposals[picked[j]]);}return 0;
}int PipeLine::Predict(const cv::Mat& bgr, pipeline_result_t& pipeline_result)
{sam_->MaskDecoder(pipeline_result.image_embeddings, pipeline_result.image_info, pipeline_result.prompt_info, pipeline_result.sam_result);return 0;
}void PipeLine::Draw(const cv::Mat& bgr, const pipeline_result_t& pipeline_result)
{static const unsigned char colors[81][3] = {{56, 0, 255},{226, 255, 0},{0, 94, 255},{0, 37, 255},{0, 255, 94},{255, 226, 0},{0, 18, 255},{255, 151, 0},{170, 0, 255},{0, 255, 56},{255, 0, 75},{0, 75, 255},{0, 255, 169},{255, 0, 207},{75, 255, 0},{207, 0, 255},{37, 0, 255},{0, 207, 255},{94, 0, 255},{0, 255, 113},{255, 18, 0},{255, 0, 56},{18, 0, 255},{0, 255, 226},{170, 255, 0},{255, 0, 245},{151, 255, 0},{132, 255, 0},{75, 0, 255},{151, 0, 255},{0, 151, 255},{132, 0, 255},{0, 255, 245},{255, 132, 0},{226, 0, 255},{255, 37, 0},{207, 255, 0},{0, 255, 207},{94, 255, 0},{0, 226, 255},{56, 255, 0},{255, 94, 0},{255, 113, 0},{0, 132, 255},{255, 0, 132},{255, 170, 0},{255, 0, 188},{113, 255, 0},{245, 0, 255},{113, 0, 255},{255, 188, 0},{0, 113, 255},{255, 0, 0},{0, 56, 255},{255, 0, 113},{0, 255, 188},{255, 0, 94},{255, 0, 18},{18, 255, 0},{0, 255, 132},{0, 188, 255},{0, 245, 255},{0, 169, 255},{37, 255, 0},{255, 0, 151},{188, 0, 255},{0, 255, 37},{0, 255, 0},{255, 0, 170},{255, 0, 37},{255, 75, 0},{0, 0, 255},{255, 207, 0},{255, 0, 226},{255, 245, 0},{188, 255, 0},{0, 255, 18},{0, 255, 75},{0, 255, 151},{255, 56, 0},{245, 255, 0}};cv::Mat img = bgr.clone();for(size_t n = 0; n < pipeline_result.sam_result.size(); ++n){for (int y = 0; y < img.rows; ++y) {uchar* image_ptr = img.ptr(y);const uchar* mask_ptr = pipeline_result.sam_result[n].mask.ptr<uchar>(y);for (int x = 0; x < img.cols; ++x) {if (mask_ptr[x] > 0){image_ptr[0] = cv::saturate_cast<uchar>(image_ptr[0] * 0.5 + colors[n][0] * 0.5);image_ptr[1] = cv::saturate_cast<uchar>(image_ptr[1] * 0.5 + colors[n][1] * 0.5);image_ptr[2] = cv::saturate_cast<uchar>(image_ptr[2] * 0.5 + colors[n][2] * 0.5);}image_ptr += 3;}}//cv::rectangle(img, pipeline_result.sam_result[n].box, cv::Scalar(0,255,0), 2, 8,0);switch(pipeline_result.prompt_info.prompt_type){case PromptType::Point:for(int i = 0; i < pipeline_result.prompt_info.points.size() / 2; ++i){cv::circle(img, cv::Point(pipeline_result.prompt_info.points[2 * i], pipeline_result.prompt_info.points[2 * i + 1]), 5, cv::Scalar(255,255,0),2,8);}break;case PromptType::Box:cv::rectangle(img, cv::Rect(cv::Point(pipeline_result.prompt_info.points[0], pipeline_result.prompt_info.points[1]), cv::Point(pipeline_result.prompt_info.points[2], pipeline_result.prompt_info.points[3])), cv::Scalar(255,255,0),2,8);break;default:break;}}cv::imshow("dst", img);//cv::imshow("mask", pipeline_result.sam_result.mask);cv::imwrite("dst.jpg",img);cv::waitKey();
}void PipeLine::get_grid_points(std::vector<float>& points_xy_vec, int n_per_side)
{float offset = 1.f / (2 * n_per_side);float start = offset;float end = 1 - offset;float step = (end - start) / (n_per_side - 1);std::vector<float> points_one_side;for (int i = 0; i < n_per_side; ++i) {points_one_side.push_back(start + i * step);}points_xy_vec.resize(n_per_side * n_per_side * 2);for (int i = 0; i < n_per_side; ++i) {for (int j = 0; j < n_per_side; ++j) {points_xy_vec[i * n_per_side * 2 + 2 * j + 0] = points_one_side[j];points_xy_vec[i * n_per_side * 2 + 2 * j + 1] = points_one_side[i];}}
}}
#include "segment_anything.h"namespace sam
{
SegmentAnything::~SegmentAnything()
{image_encoder_net_.clear();mask_decoder_net_.clear();
}static inline float intersection_area(const sam_result_t& a, const sam_result_t& b)
{cv::Rect_<float> inter = a.box & b.box;return inter.area();
}static void qsort_descent_inplace(std::vector<sam_result_t>& faceobjects, int left, int right)
{int i = left;int j = right;float p = faceobjects[(left + right) / 2].iou_pred;while (i <= j){while (faceobjects[i].iou_pred > p)i++;while (faceobjects[j].iou_pred < p)j--;if (i <= j){// swapstd::swap(faceobjects[i], faceobjects[j]);i++;j--;}}#pragma omp parallel sections{#pragma omp section{if (left < j) qsort_descent_inplace(faceobjects, left, j);}#pragma omp section{if (i < right) qsort_descent_inplace(faceobjects, i, right);}}
}static void qsort_descent_inplace(std::vector<sam_result_t>& faceobjects)
{if (faceobjects.empty())return;qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}static void nms_sorted_bboxes(const cv::Mat& bgr,const std::vector<sam_result_t>& faceobjects, std::vector<int>& picked, float nms_threshold)
{picked.clear();const int n = faceobjects.size();std::vector<float> areas(n);for (int i = 0; i < n; i++){areas[i] = faceobjects[i].box.area();}cv::Mat img = bgr.clone();for (int i = 0; i < n; i++){const sam_result_t& a = faceobjects[i];int keep = 1;for (int j = 0; j < (int)picked.size(); j++){const sam_result_t& b = faceobjects[picked[j]];// intersection over unionfloat inter_area = intersection_area(a, b);float union_area = areas[i] + areas[picked[j]] - inter_area;// float IoU = inter_area / union_areaif (inter_area / union_area > nms_threshold){keep = 0;}}if (keep)picked.push_back(i);}
}
int SegmentAnything::NMS(const cv::Mat& bgr, std::vector<sam_result_t>& proposals, std::vector<int>& picked, float nms_threshold)
{qsort_descent_inplace(proposals);nms_sorted_bboxes(bgr, proposals, picked, nms_threshold);return 0;
}int SegmentAnything::Load(const std::string& image_encoder_param, const std::string& image_encoder_bin, const std::string& mask_decoder_param, const std::string& mask_decoder_bin)
{int ret = 0;ret = image_encoder_net_.load_param(image_encoder_param.c_str());if (ret < 0)return -1;ret = image_encoder_net_.load_model(image_encoder_bin.c_str());if (ret < 0)return -1;ret = mask_decoder_net_.load_param(mask_decoder_param.c_str());if (ret < 0)return -1;ret = mask_decoder_net_.load_model(mask_decoder_bin.c_str());if (ret < 0)return -1;return 0;
}
int SegmentAnything::ImageEncoder(const cv::Mat& bgr, ncnn::Mat& image_embeddings, image_info_t& image_info)
{const int target_size = 1024;int img_w = bgr.cols;int img_h = bgr.rows;int w = img_w;int h = img_h;float scale = 1.f;if (w > h){scale = (float)target_size / w;w = target_size;h = h * scale;}else{scale = (float)target_size / h;h = target_size;w = w * scale;}ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);int wpad = target_size - w;int hpad = target_size - h;ncnn::Mat in_pad;ncnn::copy_make_border(in, in_pad, 0, hpad, 0, wpad, ncnn::BORDER_CONSTANT, 0.f);in_pad.substract_mean_normalize(means_, norms_);ncnn::Extractor image_encoder_ex = image_encoder_net_.create_extractor();image_encoder_ex.input("image", in_pad);image_encoder_ex.extract("image_embeddings", image_embeddings);image_info.img_h = img_h;image_info.img_w = img_w;image_info.pad_h = h;image_info.pad_w = w;image_info.scale = scale;return 0;
}int SegmentAnything::embed_masks(const prompt_info_t& prompt_info, ncnn::Mat& mask_input, ncnn::Mat& has_mask)
{mask_input = ncnn::Mat(256, 256, 1);mask_input.fill(0.f);has_mask = ncnn::Mat(1);has_mask.fill(0.f);return 0;
}
int SegmentAnything::transform_coords(const image_info_t& image_info, ncnn::Mat& point_coords)
{for(int h = 0; h < point_coords.h; ++h){float* ptr = point_coords.row(h);ptr[0] *= image_info.scale;ptr[1] *= image_info.scale;}return 0;
}
int SegmentAnything::embed_points(const prompt_info_t& prompt_info, std::vector<ncnn::Mat>& point_labels, ncnn::Mat& point_coords)
{int num_points = prompt_info.points.size() / 2;point_coords = ncnn::Mat(num_points * 2, (void*)prompt_info.points.data()).reshape(2, num_points).clone();ncnn::Mat point_labels1 = ncnn::Mat(256, num_points);ncnn::Mat point_labels2 = ncnn::Mat(256, num_points);ncnn::Mat point_labels3 = ncnn::Mat(256, num_points);ncnn::Mat point_labels4 = ncnn::Mat(256, num_points);ncnn::Mat point_labels5 = ncnn::Mat(256, num_points);ncnn::Mat point_labels6 = ncnn::Mat(256, num_points);point_labels1.row_range(0, num_points - 1).fill(1.f);point_labels1.row_range(num_points - 1, 1).fill(0.f);for (int i = 0; i < num_points - 1; ++i) {if (prompt_info.labels[i] == -1)point_labels2.row_range(i, 1).fill(1.f);elsepoint_labels2.row_range(i, 1).fill(0.f);}point_labels2.row_range(num_points - 1, 1).fill(1.f);for (int i = 0; i < num_points - 1; ++i) {if (prompt_info.labels[i] == 0)point_labels3.row_range(i, 1).fill(1.f);elsepoint_labels3.row_range(i, 1).fill(0.f);}point_labels3.row_range(num_points - 1, 1).fill(0.f);for (int i = 0; i < num_points - 1; ++i) {if (prompt_info.labels[i] == 1)point_labels4.row_range(i, 1).fill(1.f);elsepoint_labels4.row_range(i, 1).fill(0.f);}point_labels4.row_range(num_points - 1, 1).fill(0.f);for (int i = 0; i < num_points - 1; ++i) {if (prompt_info.labels[i] == 2)point_labels5.row_range(i, 1).fill(1.f);elsepoint_labels5.row_range(i, 1).fill(0.f);}point_labels5.row_range(num_points - 1, 1).fill(0.f);for (int i = 0; i < num_points - 1; ++i) {if (prompt_info.labels[i] == 3)point_labels6.row_range(i, 1).fill(1.f);elsepoint_labels6.row_range(i, 1).fill(0.f);}point_labels6.row_range(num_points - 1, 1).fill(0.f);point_labels.push_back(point_labels1);point_labels.push_back(point_labels2);point_labels.push_back(point_labels3);point_labels.push_back(point_labels4);point_labels.push_back(point_labels5);point_labels.push_back(point_labels6);return 0;
}
int SegmentAnything::MaskDecoder(const ncnn::Mat& image_embeddings, image_info_t& image_info, const prompt_info_t& prompt_info, std::vector<sam_result_t>& sam_results, float pred_iou_thresh, float stability_score_thresh)
{std::vector<ncnn::Mat> point_labels;ncnn::Mat point_coords;embed_points(prompt_info, point_labels, point_coords);transform_coords(image_info, point_coords);ncnn::Mat mask_input, has_mask;embed_masks(prompt_info, mask_input, has_mask);ncnn::Extractor mask_decoder_ex = mask_decoder_net_.create_extractor();mask_decoder_ex.input("mask_input", mask_input);mask_decoder_ex.input("point_coords", point_coords);mask_decoder_ex.input("point_labels1", point_labels[0]);mask_decoder_ex.input("point_labels2", point_labels[1]);mask_decoder_ex.input("point_labels3", point_labels[2]);mask_decoder_ex.input("point_labels4", point_labels[3]);mask_decoder_ex.input("point_labels5", point_labels[4]);mask_decoder_ex.input("point_labels6", point_labels[5]);mask_decoder_ex.input("image_embeddings", image_embeddings);mask_decoder_ex.input("has_mask_input", has_mask);ncnn::Mat scores;mask_decoder_ex.extract("scores", scores);ncnn::Mat masks;mask_decoder_ex.extract("masks", masks);//postprocessstd::vector<std::pair<float, int>> scores_vec;for (int i = 1; i < scores.w; ++i) {scores_vec.push_back(std::pair<float, int>(scores[i], i));}std::sort(scores_vec.begin(), scores_vec.end(), std::greater<std::pair<float, int>>());if (scores_vec[0].first > pred_iou_thresh) {sam_result_t sam_result;ncnn::Mat mask = masks.channel(scores_vec[0].second);cv::Mat cv_mask_32f = cv::Mat::zeros(cv::Size(mask.w, mask.h), CV_32F);std::copy((float*)mask.data, (float*)mask.data + mask.w * mask.h, (float*)cv_mask_32f.data);cv::Mat single_mask_32f;cv::resize(cv_mask_32f(cv::Rect(0, 0, image_info.pad_w, image_info.pad_h)), single_mask_32f, cv::Size(image_info.img_w,image_info.img_h), 0, 0, 1);float stable_score = calculate_stability_score(single_mask_32f);if (stable_score < stability_score_thresh)return -1;single_mask_32f = single_mask_32f > 0;single_mask_32f.convertTo(sam_result.mask, CV_8UC1, 1, 0);if (postprocess_mask(sam_result.mask, sam_result.box) < 0)return -1;sam_results.push_back(sam_result);}else {return -1;}return 0;
}
int SegmentAnything::postprocess_mask(cv::Mat& mask, cv::Rect& box)
{std::vector<std::vector<cv::Point>> contours;std::vector<cv::Vec4i> hierarchy;cv::findContours(mask.clone(), contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);if(contours.size() == 0)return -1;if (contours.size() > 1) {float max_area = 0;int max_idx = 0;std::vector<std::pair<float,int>> areas;for (size_t i = 0; i < contours.size(); ++i) {float area = cv::contourArea(contours[i]);if (area > max_area) {max_idx = i;max_area = area;}areas.push_back(std::pair<float,int>(area,i));}for (size_t i = 0; i < areas.size(); ++i) {//if (i == max_idx)// continue;//else {// cv::drawContours(mask, contours, i, cv::Scalar(0), -1);//}if(areas[i].first < max_area * 0.3){cv::drawContours(mask, contours, i, cv::Scalar(0), -1);}else{box = box | cv::boundingRect(contours[i]);}}}else {box = cv::boundingRect(contours[0]);}return 0;
}
float SegmentAnything::calculate_stability_score(cv::Mat& mask, float mask_threshold, float stable_score_offset)
{float intersections = (float)cv::countNonZero(mask > (mask_threshold + stable_score_offset));float unions = (float)cv::countNonZero(mask > (mask_threshold - stable_score_offset));return intersections / unions;
}
}
调用模型
#include "pipeline.h"
#include <iostream>int main()
{int type = 1;cv::Mat bgr = cv::imread("2.jpg");std::shared_ptr<sam::PipeLine> pipe(new sam::PipeLine());pipe->Init("models/encoder-matmul.param","models/encoder-matmul.bin", "models/decoder.param", "models/decoder.bin");pipeline_result_t pipe_result;pipe->ImageEmbedding(bgr, pipe_result);switch (type){case 1://automatic maskpipe_result.sam_result.clear();pipe_result.prompt_info.points.clear();pipe_result.prompt_info.labels.clear();pipe->AutoPredict(bgr, pipe_result);pipe->Draw(bgr, pipe_result);break;case 2://prompt input: pointspipe_result.prompt_info.prompt_type = PromptType::Point;pipe_result.prompt_info.points.push_back(497);pipe_result.prompt_info.points.push_back(220);pipe_result.prompt_info.points.push_back(455);pipe_result.prompt_info.points.push_back(294);pipe_result.prompt_info.points.push_back(0);pipe_result.prompt_info.points.push_back(0);pipe_result.prompt_info.labels.push_back(1);pipe_result.prompt_info.labels.push_back(1);pipe_result.prompt_info.labels.push_back(-1);pipe->Predict(bgr, pipe_result);pipe->Draw(bgr, pipe_result);break;case 3://prompt input: boxpipe_result.prompt_info.prompt_type = PromptType::Box;pipe_result.prompt_info.points.push_back(344);pipe_result.prompt_info.points.push_back(144);pipe_result.prompt_info.points.push_back(607);pipe_result.prompt_info.points.push_back(582);pipe_result.prompt_info.points.push_back(0);pipe_result.prompt_info.points.push_back(0);pipe_result.prompt_info.labels.push_back(2);pipe_result.prompt_info.labels.push_back(3);pipe_result.prompt_info.labels.push_back(-1);pipe->Predict(bgr, pipe_result);pipe->Draw(bgr, pipe_result);break;default:break;}return 0;
}
点选择:
矩形选择: