YOLOV5-LITE实时目标检测（onnxruntime部署+opencv获取摄像头+NCNN部署）python版本和C++版本

1.训练好的pt模型转换为onnx格式

使用yolov5-lite自带的export.py导出onnx格式，图像大小设置320，batch 1

之后可以使用 onnxsim对模型进一步简化

onnxsim参考链接：onnxsim-让导出的onnx模型更精简_alex1801的博客-CSDN博客

python export.py --weights weights/v5lite-e.pt --img 320 --batch 1
python -m onnxsim weights/v5lite-e.onnx weights/yolov5-lite-sim.onnx

2.使用onnxruntime调用onnx模型实时推理（python版本

这个版本的推理FPS能有11+FPS

这两处换成自己的模型和训练的类别即可：

    parser.add_argument('--modelpath', type=str, default="/media/xcy/dcd05f09-46df-4879-bfeb-3bab03a6cc3a/YOLOv5-Lite/weights/v5lite-e.onnx",

                        help="onnx filepath")

    parser.add_argument('--classfile', type=str, default='coco.names',

                        help="classname filepath")

参考github：GitHub - hpc203/yolov5-lite-onnxruntime: 使用ONNXRuntime部署yolov5-lite目标检测，包含C++和Python两个版本的程序

import cv2
import numpy as np
import argparse
import onnxruntime as ort
import time
class yolov5_lite():
    def __init__(self, model_pb_path, label_path, confThreshold=0.5, nmsThreshold=0.5, objThreshold=0.5):
        so = ort.SessionOptions()
        so.log_severity_level = 3
        self.net = ort.InferenceSession(model_pb_path, so)
        self.classes = list(map(lambda x: x.strip(), open(label_path, 'r').readlines()))
        self.num_classes = len(self.classes)
        anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
        self.nl = len(anchors)
        self.na = len(anchors[0]) // 2
        self.no = self.num_classes + 5
        self.grid = [np.zeros(1)] * self.nl
        self.stride = np.array([8., 16., 32.])
        self.anchor_grid = np.asarray(anchors, dtype=np.float32).reshape(self.nl, -1, 2)
        self.confThreshold = confThreshold
        self.nmsThreshold = nmsThreshold
        self.objThreshold = objThreshold
        self.input_shape = (self.net.get_inputs()[0].shape[2], self.net.get_inputs()[0].shape[3])
    def resize_image(self, srcimg, keep_ratio=True):
        top, left, newh, neww = 0, 0, self.input_shape[0], self.input_shape[1]
        if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
            hw_scale = srcimg.shape[0] / srcimg.shape[1]
            if hw_scale > 1:
                newh, neww = self.input_shape[0], int(self.input_shape[1] / hw_scale)
                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
                left = int((self.input_shape[1] - neww) * 0.5)
                img = cv2.copyMakeBorder(img, 0, 0, left, self.input_shape[1] - neww - left, cv2.BORDER_CONSTANT,
                                         value=0)  # add border
            else:
                newh, neww = int(self.input_shape[0] * hw_scale), self.input_shape[1]
                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
                top = int((self.input_shape[0] - newh) * 0.5)
                img = cv2.copyMakeBorder(img, top, self.input_shape[0] - newh - top, 0, 0, cv2.BORDER_CONSTANT, value=0)
        else:
            img = cv2.resize(srcimg, self.input_shape, interpolation=cv2.INTER_AREA)
        return img, newh, neww, top, left
    def _make_grid(self, nx=20, ny=20):
        xv, yv = np.meshgrid(np.arange(ny), np.arange(nx))
        return np.stack((xv, yv), 2).reshape((-1, 2)).astype(np.float32)
    def postprocess(self, frame, outs, pad_hw):
        newh, neww, padh, padw = pad_hw
        frameHeight = frame.shape[0]
        frameWidth = frame.shape[1]
        ratioh, ratiow = frameHeight / newh, frameWidth / neww
        # Scan through all the bounding boxes output from the network and keep only the
        # ones with high confidence scores. Assign the box's class label as the class with the highest score.
        classIds = []
        confidences = []
        boxes = []
        for detection in outs:
            scores = detection[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > self.confThreshold and detection[4] > self.objThreshold:
                center_x = int((detection[0] - padw) * ratiow)
                center_y = int((detection[1] - padh) * ratioh)
                width = int(detection[2] * ratiow)
                height = int(detection[3] * ratioh)
                left = int(center_x - width / 2)
                top = int(center_y - height / 2)
                classIds.append(classId)
                confidences.append(float(confidence))
                boxes.append([left, top, width, height])
        # Perform non maximum suppression to eliminate redundant overlapping boxes with
        # lower confidences.
        indices = cv2.dnn.NMSBoxes(boxes, confidences, self.confThreshold, self.nmsThreshold)
        for i in indices:
            i = i[0] if isinstance(i, (tuple, list)) else i
            box = boxes[i]
            left = box[0]
            top = box[1]
            width = box[2]
            height = box[3]
            frame = self.drawPred(frame, classIds[i], confidences[i], left, top, left + width, top + height)
        return frame
    def drawPred(self, frame, classId, conf, left, top, right, bottom):
        # Draw a bounding box.
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), thickness=4)
        label = '%.2f' % conf
        label = '%s:%s' % (self.classes[classId], label)
        # Display the label at the top of the bounding box
        labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
        top = max(top, labelSize[1])
        # cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), (255,255,255), cv.FILLED)
        cv2.putText(frame, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2)
        return frame
    def detect(self, srcimg):
        img, newh, neww, top, left = self.resize_image(srcimg)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = img.astype(np.float32) / 255.0
        blob = np.expand_dims(np.transpose(img, (2, 0, 1)), axis=0)
        outs = self.net.run(None, {self.net.get_inputs()[0].name: blob})[0].squeeze(axis=0)
        row_ind = 0
        for i in range(self.nl):
            h, w = int(self.input_shape[0] / self.stride[i]), int(self.input_shape[1] / self.stride[i])
            length = int(self.na * h * w)
            if self.grid[i].shape[2:4] != (h, w):
                self.grid[i] = self._make_grid(w, h)
            outs[row_ind:row_ind + length, 0:2] = (outs[row_ind:row_ind + length, 0:2] * 2. - 0.5 + np.tile(
                self.grid[i], (self.na, 1))) * int(self.stride[i])
            outs[row_ind:row_ind + length, 2:4] = (outs[row_ind:row_ind + length, 2:4] * 2) ** 2 * np.repeat(
                self.anchor_grid[i], h * w, axis=0)
            row_ind += length
        srcimg = self.postprocess(srcimg, outs, (newh, neww, top, left))
        #         cv2.imwrite('result.jpg', srcimg)
        return srcimg
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--imgpath', type=str, default="",
                        help="image path")
    parser.add_argument('--modelpath', type=str, default="/media/xcy/dcd05f09-46df-4879-bfeb-3bab03a6cc3a/YOLOv5-Lite/weights/v5lite-e.onnx",
                        help="onnx filepath")
    parser.add_argument('--classfile', type=str, default='coco.names',
                        help="classname filepath")
    parser.add_argument('--confThreshold', default=0.5, type=float, help='class confidence')
    parser.add_argument('--nmsThreshold', default=0.6, type=float, help='nms iou thresh')
    args = parser.parse_args()
    # srcimg = cv2.imread(args.imgpath)
    # print(args.imgpath,srcimg)
    net = yolov5_lite(args.modelpath, args.classfile, confThreshold=args.confThreshold, nmsThreshold=args.nmsThreshold)
    print(net)
    counter = 0
    start_time = time.time()
    # 1 加载视频文件
    capture = cv2.VideoCapture(0)
    # 2 读取视频
    ret, frame = capture.read()
    fps = capture.get(cv2.CAP_PROP_FPS)  # 视频平均帧率
    while ret:
        counter += 1  # 计算帧数
        if (time.time() - start_time) != 0:  # 实时显示帧数
            cv2.putText(frame, "FPS {0}".format(float('%.1f' % (counter / (time.time() - start_time)))), (30, 50),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255),
                        2)
            # 3 ret 是否读取到了帧，读取到了则为True
            cv2.imshow("video", frame)
            ret, frame = capture.read()
            print("FPS: ", counter / (time.time() - start_time))
            counter = 0
            start_time = time.time()
        srcimg = net.detect(frame)
        # winName = 'Deep learning object detection in onnxruntime'
        # cv2.namedWindow(winName, cv2.WINDOW_NORMAL)
        # cv2.imshow(winName, srcimg)
        # 4 若键盘按下q则退出播放
        if cv2.waitKey(20) & 0xff == ord('q'):
            break
    # 5 释放资源
    capture.release()
    # 6 关闭所有窗口
    cv2.destroyAllWindows()

3.使用NCNN+opencv来读取模型实时推理（C++版本

此版本能够在笔记本上达到33+FPS，正在整理代码。后续发

代码整理好了，如下：需要VS2019配置ncnn之后即可运行。

LINUX配置NCNN可以参考我的另一篇博客：Ubuntu20.04配置NCNN推理框架（转换yolov5 onnx格式到ncnn格式-CSDN博客

WINDOWS配置比较简单，大家搜一搜都能搜到。

#include "layer.h"
#include "net.h"
#if defined(USE_NCNN_SIMPLEOCV)
#include "simpleocv.h"
#else
#include 
#include 
#include 
#endif
#include 
#include 
#include 
#include
#include 
//#define YOLOV5_V60 1 //YOLOv5 v6.0
#define YOLOV5_V62 1 //YOLOv5 v6.2 export  onnx model method https://github.com/shaoshengsong/yolov5_62_export_ncnn
#if YOLOV5_V60 || YOLOV5_V62
#define MAX_STRIDE 64
#else
#define MAX_STRIDE 32
class YoloV5Focus : public ncnn::Layer
{
public:
    YoloV5Focus()
    {
        one_blob_only = true;
    }
    virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const
    {
        int w = bottom_blob.w;
        int h = bottom_blob.h;
        int channels = bottom_blob.c;
        int outw = w / 2;
        int outh = h / 2;
        int outc = channels * 4;
        top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);
        if (top_blob.empty())
            return -100;
#pragma omp parallel for num_threads(opt.num_threads)
        for (int p = 0; p  in_pad.h)
    {
        num_grid_x = in_pad.w / stride;
        num_grid_y = num_grid / num_grid_x;
    }
    else
    {
        num_grid_y = in_pad.h / stride;
        num_grid_x = num_grid / num_grid_y;
    }
    const int num_class = feat_blob.w - 5;  //特征图的w是85  是类别 80  +  xywh + 目标置信度
    const int num_anchors = anchors.w / 2;
    for (int q = 0; q = prob_threshold)
                {
                    // find class index with max class score
                    int class_index = 0;
                    float class_score = -FLT_MAX;
                    for (int k = 0; k  class_score)
                        {
                            class_index = k;
                            class_score = score;
                        }
                    }
                    float confidence = box_confidence * sigmoid(class_score);
                    if (confidence >= prob_threshold)
                    {
                        // yolov5/models/yolo.py Detect forward
                        // y = x[i].sigmoid()
                        // y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                        // y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                        float dx = sigmoid(featptr[0]);
                        float dy = sigmoid(featptr[1]);
                        float dw = sigmoid(featptr[2]);
                        float dh = sigmoid(featptr[3]);
                        float pb_cx = (dx * 2.f - 0.5f + j) * stride;
                        float pb_cy = (dy * 2.f - 0.5f + i) * stride;
                        float pb_w = pow(dw * 2.f, 2) * anchor_w;
                        float pb_h = pow(dh * 2.f, 2) * anchor_h;
                        float x0 = pb_cx - pb_w * 0.5f;
                        float y0 = pb_cy - pb_h * 0.5f;
                        float x1 = pb_cx + pb_w * 0.5f;
                        float y1 = pb_cy + pb_h * 0.5f;
                        Object obj;
                        obj.rect.x = x0;
                        obj.rect.y = y0;
                        obj.rect.width = x1 - x0;
                        obj.rect.height = y1 - y0;
                        obj.label = class_index;
                        obj.prob = confidence;
                        objects.push_back(obj);
                    }
                }
            }
        }
    }
}
static int detect_yolov5(const cv::Mat& bgr, std::vector& objects, ncnn::Extractor ex)
{
    const int target_size = 320;
    const float prob_threshold = 0.25f;
    const float nms_threshold = 0.45f;
    int img_w = bgr.cols;
    int img_h = bgr.rows;
    // letterbox pad to multiple of MAX_STRIDE
    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);
    // pad to target_size rectangle
    // yolov5/utils/datasets.py letterbox
    int wpad = (w + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w;
    int hpad = (h + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h;
    ncnn::Mat in_pad;
    ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);
    const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f };
    in_pad.substract_mean_normalize(0, norm_vals);
    /*ncnn::Extractor ex = yolov5.create_extractor();*/
    ex.input("images", in_pad);
    std::vector proposals;
    // anchor setting from yolov5/models/yolov5s.yaml
    // stride 8
    {
        ncnn::Mat out;
        ex.extract("output", out);
        ncnn::Mat anchors(6);
        anchors[0] = 10.f;
        anchors[1] = 13.f;
        anchors[2] = 16.f;
        anchors[3] = 30.f;
        anchors[4] = 33.f;
        anchors[5] = 23.f;
        std::vector objects8;
        generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);
        proposals.insert(proposals.end(), objects8.begin(), objects8.end());
    }
    // stride 16
    {
        ncnn::Mat out;
#if YOLOV5_V62
        ex.extract("1111", out);
#elif YOLOV5_V60
        ex.extract("376", out);
#else
        ex.extract("781", out);
#endif
        ncnn::Mat anchors(6);
        anchors[0] = 30.f;
        anchors[1] = 61.f;
        anchors[2] = 62.f;
        anchors[3] = 45.f;
        anchors[4] = 59.f;
        anchors[5] = 119.f;
        std::vector objects16;
        generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);
        proposals.insert(proposals.end(), objects16.begin(), objects16.end());
    }
    // stride 32
    {
        ncnn::Mat out;
#if YOLOV5_V62
        ex.extract("2222", out);
#elif YOLOV5_V60
        ex.extract("401", out);
#else
        ex.extract("801", out);
#endif
        ncnn::Mat anchors(6);
        anchors[0] = 116.f;
        anchors[1] = 90.f;
        anchors[2] = 156.f;
        anchors[3] = 198.f;
        anchors[4] = 373.f;
        anchors[5] = 326.f;
        std::vector objects32;
        generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);
        proposals.insert(proposals.end(), objects32.begin(), objects32.end());
    }
    // sort all proposals by score from highest to lowest
    qsort_descent_inplace(proposals);
    // apply nms with nms_threshold
    std::vector picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);
    int count = picked.size();
    objects.resize(count);
    for (int i = 0; i  image.cols)
            x = image.cols - label_size.width;
        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
            cv::Scalar(255, 255, 255), -1);
        cv::putText(image, text, cv::Point(x, y + label_size.height),
            cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
        cv::putText(image, "FPS: " + std::to_string(fps), cv::Point(30, 50),
            cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0, 0, 255), 2);
    }
    //cv::imshow("image", image);
    //cv::waitKey(1);
}
int main() {
    ncnn::Net yolov5;
    yolov5.opt.use_vulkan_compute = true;
    /*yolov5.opt.use_bf16_storage = true;*/
    yolov5.load_param("../model/v5lite-e.param");
    yolov5.load_model("../model/v5lite-e.bin");
    ncnn::Extractor ex = yolov5.create_extractor();
    /*ex.set_num_threads(4);*/
    cv::VideoCapture capture(0);  // 打开默认摄像头
    int frameCount = 0;
    double totalTime = 0.f;
    double fps = 0.f;
    cv::Mat frame;
    std::vector objects;
    while (true) {
        auto start = std::chrono::high_resolution_clock::now();
        capture >> frame;  // 读取摄像头的下一帧图像
        // yolo 检测 
        detect_yolov5(frame, objects,ex);
        draw_objects(frame, objects,fps);
        // 显示结果
        cv::imshow("YOLO检测e", frame);
        // 更新帧统计信息
        frameCount++;
        auto end = std::chrono::high_resolution_clock::now();
        double timeSec = std::chrono::duration(end - start).count();
        totalTime += timeSec;
        // 计算并显示帧率
        fps = frameCount / totalTime;
        std::cout