OpenVINO 2022.3实战二：Window 10 环境下用 OpenVINO 2022.3部署yolov5-seg 7.0

1 配置OpenVINO C++开发环境

见 OpenVINO 2022.3实战一：Window 10 环境下用 OpenVINO 2022.3部署yolov5 7.0_mingo_敏的博客-CSDN博客

2 下载并转换YOLOv5-Seg预训练模型

下载yolov5代码 ultralytics/yolov5

python export.py --weights yolov5s-seg.pt --include torchscript onnx openvino

导出模型为 yolov5s-seg_openvino_model

3 C++代码

YOLOv5-Seg模型对数据预处理的要求跟YOLOv5模型一样。YOLOv5-Seg模型的输出有两个张量，一个张量输出检测detect（1 x 25200 x 117），一个张量输出proto (1 x 32 x 160x160)，其中第一个张量detect中117个字段为 85 （cx、cy、w、h、confidence和80个类别分数）+ 32分割字段，detect 后32个字段与第二个张量的输出做矩阵乘法，可以获得尺寸为160x160的检测目标的掩码(mask)。

yolov5-seg_openvino.cpp

#pragma warning(disable:4996)#include "yolov5_seg.h"#include <iostream>
#include <string>#include <openvino/openvino.hpp> //include openvino runtime header files
#include <opencv2/opencv.hpp>    //opencv header fileusing namespace std;/* ---------  Please modify the path of yolov5 model and image -----------*/
std::string model_file = "E:/cpp_code/weights/yolov5s-seg_openvino_model/yolov5s-seg.xml";
std::string image_file = "E:/cpp_code/images/zidane.jpg";
std::vector<cv::Scalar> colors = { cv::Scalar(0, 0, 255) , cv::Scalar(0, 255, 0) , cv::Scalar(255, 0, 0) ,cv::Scalar(255, 255, 0) , cv::Scalar(0, 255, 255) , cv::Scalar(255, 0, 255) };const std::vector<std::string> class_names = {"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light","fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow","elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard","tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple","sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch","potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone","microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear","hair drier", "toothbrush" };cv::Mat letterbox(cv::Mat& img, std::vector<float>& paddings, std::vector<int> new_shape = { 640, 640 })
{// Get current image shape [height, width]// Refer to https://github.com/ultralytics/yolov5/blob/master/utils/augmentations.py#L111int img_h = img.rows;int img_w = img.cols;// Compute scale ratio(new / old) and target resized shapefloat scale = std::min(new_shape[1] * 1.0 / img_h, new_shape[0] * 1.0 / img_w);int resize_h = int(round(img_h * scale));int resize_w = int(round(img_w * scale));paddings[0] = scale;// Compute paddingint pad_h = new_shape[1] - resize_h;int pad_w = new_shape[0] - resize_w;// Resize and pad image while meeting stride-multiple constraintscv::Mat resized_img;cv::resize(img, resized_img, cv::Size(resize_w, resize_h));// divide padding into 2 sidesfloat half_h = pad_h * 1.0 / 2;float half_w = pad_w * 1.0 / 2;paddings[1] = half_h;paddings[2] = half_w;// Compute padding boarderint top = int(round(half_h - 0.1));int bottom = int(round(half_h + 0.1));int left = int(round(half_w - 0.1));int right = int(round(half_w + 0.1));// Add bordercv::copyMakeBorder(resized_img, resized_img, top, bottom, left, right, 0, cv::Scalar(114, 114, 114));return resized_img;
}float sigmoid_function(float a)
{float b = 1. / (1. + exp(-a));return b;
}int yolov5_seg_demo(){// -------- Get OpenVINO runtime version --------std::cout << ov::get_openvino_version().description << ':' << ov::get_openvino_version().buildNumber << std::endl;// -------- Step 1. Initialize OpenVINO Runtime Core --------ov::Core core;// -------- Step 2. Compile the Model --------auto compiled_model = core.compile_model(model_file, "CPU"); //GPU.1 is dGPU A770// -------- Step 3. Create an Inference Request --------ov::InferRequest infer_request = compiled_model.create_infer_request();// -------- Step 4. Read a picture file and do the preprocess --------cv::RNG rng;cv::Mat img = cv::imread(image_file); //Load a picture into memorycv::Mat masked_img;std::vector<float> paddings(3);       //scale, half_h, half_wcv::Mat resized_img = letterbox(img, paddings); //resize to (640,640) by letterbox// BGR->RGB, u8(0-255)->f32(0.0-1.0), HWC->NCHWcv::Mat blob = cv::dnn::blobFromImage(resized_img, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true);// -------- Step 5. Feed the blob into the input node of YOLOv5 -------// Get input port for model with one inputauto input_port = compiled_model.input();// Create tensor from external memoryov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));// Set input tensor for model with one inputinfer_request.set_input_tensor(input_tensor);// -------- Step 6. Start inference --------infer_request.infer();// -------- Step 7. Get the inference result --------auto detect = infer_request.get_output_tensor(0);auto detect_shape = detect.get_shape();std::cout << "The shape of Detection tensor:" << detect_shape << std::endl;auto proto = infer_request.get_output_tensor(1);auto proto_shape = proto.get_shape();std::cout << "The shape of Proto tensor:" << proto_shape << std::endl;// --------- Do the Post Process// Detect Matrix: 25200 x 117  cv::Mat detect_buffer(detect_shape[1], detect_shape[2], CV_32F, detect.data());// Proto Matrix:  1x32x160x160 => 32 x 25600cv::Mat proto_buffer(proto_shape[1], proto_shape[2] * proto_shape[3], CV_32F, proto.data());// -------- Step 8. Post-process the inference result -----------float conf_threshold = 0.25;float nms_threshold = 0.5;std::vector<cv::Rect> boxes;std::vector<int> class_ids;std::vector<float> class_scores;std::vector<float> confidences;std::vector<cv::Mat> masks;// cx,cy,w,h,confidence,c1,c2,...c80float scale = paddings[0];for (int i = 0; i < detect_buffer.rows; i++) {float confidence = detect_buffer.at<float>(i, 4);if (confidence < conf_threshold) {continue;}cv::Mat classes_scores = detect_buffer.row(i).colRange(5, 85);cv::Point class_id;double score;cv::minMaxLoc(classes_scores, NULL, &score, NULL, &class_id);// class score: 0~1if (score > 0.25){cv::Mat mask = detect_buffer.row(i).colRange(85, 117);float cx = detect_buffer.at<float>(i, 0);float cy = detect_buffer.at<float>(i, 1);float w = detect_buffer.at<float>(i, 2);float h = detect_buffer.at<float>(i, 3);int left = static_cast<int>((cx - 0.5 * w - paddings[2]) / scale);int top = static_cast<int>((cy - 0.5 * h - paddings[1]) / scale);int width = static_cast<int>(w / scale);int height = static_cast<int>(h / scale);cv::Rect box;box.x = left;box.y = top;box.width = width;box.height = height;boxes.push_back(box);class_ids.push_back(class_id.x);class_scores.push_back(score);confidences.push_back(confidence);masks.push_back(mask);}}// NMSstd::vector<int> indices;cv::dnn::NMSBoxes(boxes, confidences, conf_threshold, nms_threshold, indices);cv::Mat rgb_mask = cv::Mat::zeros(img.size(), img.type());// -------- Step 8. Visualize the detection results -----------for (size_t i = 0; i < indices.size(); i++) {int index = indices[i];int class_id = class_ids[index];cv::Rect box = boxes[index];int x1 = std::max(0, box.x);int y1 = std::max(0, box.y);int x2 = std::max(0, box.br().x);int y2 = std::max(0, box.br().y);cv::Mat m = masks[index] * proto_buffer;for (int col = 0; col < m.cols; col++) {m.at<float>(0, col) = sigmoid_function(m.at<float>(0, col));}cv::Mat m1 = m.reshape(1, 160); // 1x25600 -> 160x160int mx1 = std::max(0, int((x1 * scale + paddings[2]) * 0.25));int mx2 = std::max(0, int((x2 * scale + paddings[2]) * 0.25));int my1 = std::max(0, int((y1 * scale + paddings[1]) * 0.25));int my2 = std::max(0, int((y2 * scale + paddings[1]) * 0.25));cv::Mat mask_roi = m1(cv::Range(my1, my2), cv::Range(mx1, mx2));cv::Mat rm, det_mask;cv::resize(mask_roi, rm, cv::Size(x2 - x1, y2 - y1));for (int r = 0; r < rm.rows; r++) {for (int c = 0; c < rm.cols; c++) {float pv = rm.at<float>(r, c);if (pv > 0.5) {rm.at<float>(r, c) = 1.0;}else {rm.at<float>(r, c) = 0.0;}}}rm = rm * rng.uniform(0, 255);rm.convertTo(det_mask, CV_8UC1);if ((y1 + det_mask.rows) >= img.rows) {y2 = img.rows - 1;}if ((x1 + det_mask.cols) >= img.cols) {x2 = img.cols - 1;}cv::Mat mask = cv::Mat::zeros(cv::Size(img.cols, img.rows), CV_8UC1);det_mask(cv::Range(0, y2 - y1), cv::Range(0, x2 - x1)).copyTo(mask(cv::Range(y1, y2), cv::Range(x1, x2)));add(rgb_mask, cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)), rgb_mask, mask);cv::rectangle(img, boxes[index], colors[class_id % 6], 2, 8);std::string label = class_names[class_id] + ":" + std::to_string(class_scores[index]);cv::putText(img, label, cv::Point(boxes[index].tl().x, boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, colors[class_id % 6]);cv::addWeighted(img, 0.5, rgb_mask, 0.5, 0, masked_img);}cv::namedWindow("YOLOv5-Seg OpenVINO Inference C++ Demo");cv::imshow("YOLOv5-Seg OpenVINO Inference C++ Demo", masked_img);cv::waitKey(0);cv::destroyAllWindows();return 0;
}

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OpenVINO 2022.3实战二：Window 10 环境下用 OpenVINO 2022.3部署yolov5-seg 7.0

1 配置OpenVINO C++开发环境

2 下载并转换YOLOv5-Seg预训练模型

3 C++代码

OpenVINO 2022.3实战二：Window 10 环境下用 OpenVINO 2022.3部署yolov5-seg 7.0相关推荐

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