[AI智能摄像头]RV1126部署yolov5并加速

admin2024-05-15  0

导出onnx模型

yolov5官方地址 git clone https://github.com/ultralytics/yolov5

利用官方命令导出python export.py --weights yolov5n.pt --include onnx

利用代码导出

import os
import sys
os.chdir(sys.path[0])
import onnx
import torch
sys.path.append('..')
from models.common import DetectMultiBackend
from models.experimental import attempt_load
DEVICE='cuda' if torch.cuda.is_available else 'cpu'
def main():
    """create model """
    input = torch.randn(1, 3, 640, 640, requires_grad=False).float().to(torch.device(DEVICE))
    model = attempt_load('./model/yolov5n.pt', device=DEVICE, inplace=True, fuse=True)  # load FP32 model
    #model = DetectMultiBackend('./model/yolov5n.pt', data=input)
    model.to(DEVICE)

    torch.onnx.export(model,
            input,
            'yolov5n_self.onnx', # name of the exported onnx model
            export_params=True,
            opset_version=12,
            do_constant_folding=False, 
            input_names=["images"])
if __name__=="__main__":
    main()

onnx模型测试

import os
import sys
os.chdir(sys.path[0])
import onnxruntime
import torch
import torchvision
import numpy as np
import time
import cv2
sys.path.append('..')
from ultralytics.utils.plotting import Annotator, colors

ONNX_MODEL="./yolov5n.onnx"
DEVICE='cuda' if torch.cuda.is_available() else 'cpu'

def xywh2xyxy(x):
    """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
    return y

def box_iou(box1, box2, eps=1e-7):
    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
    """
    Return intersection-over-union (Jaccard index) of boxes.

    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
    Arguments:
        box1 (Tensor[N, 4])
        box2 (Tensor[M, 4])
    Returns:
        iou (Tensor[N, M]): the NxM matrix containing the pairwise
            IoU values for every element in boxes1 and boxes2
    """

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)

    # IoU = inter / (area1 + area2 - inter)
    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)

def non_max_suppression(
    prediction,
    conf_thres=0.25,
    iou_thres=0.45,
    classes=None,
    agnostic=False,
    multi_label=False,
    labels=(),
    max_det=300,
    nm=0,  # number of masks
):
    """
    Non-Maximum Suppression (NMS) on inference results to reject overlapping detections.

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """

    # Checks
    assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
    assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"

    device = prediction.device
    mps = "mps" in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    nc = prediction.shape[2] - nm - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    max_wh = 7680  # (pixels) maximum box width and height
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 0.5 + 0.05 * bs  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
    merge = False  # use merge-NMS

    t = time.time()
    mi = 5 + nc  # mask start index
    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            lb = labels[xi]
            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
            v[:, :4] = lb[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box/Mask
        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
        mask = x[:, mi:]  # zero columns if no masks

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
        else:  # best class only
            conf, j = x[:, 5:mi].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        i = i[:max_det]  # limit detections
        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if mps:
            output[xi] = output[xi].to(device)
        if (time.time() - t) > time_limit:
            break  # time limit exceeded

    return output

def draw_bbox(image, result, color=(0, 0, 255), thickness=2):
    # img_path = cv2.cvtColor(img_path, cv2.COLOR_BGR2RGB)
    image = image.copy()
    for point in result:
        x1,y1,x2,y2=point
        cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
    return image

def main():
    input=torch.load("input.pt").to('cpu')
    input_array=np.array(input)
    onnx_model = onnxruntime.InferenceSession(ONNX_MODEL)
    input_name = onnx_model.get_inputs()[0].name
    out = onnx_model.run(None, {input_name:input_array})
    out_tensor = torch.tensor(out).to(DEVICE)
    pred = non_max_suppression(out_tensor,0.25,0.45,classes=None,agnostic=False,max_det=1000)
    # Process predictions
    for i, det in enumerate(pred):  # per image
        im0_=cv2.imread('../data/images/bus.jpg')
        im0=im0_.reshape(1,3,640,640)
        names=torch.load('name.pt')
        annotator = Annotator(im0, line_width=3, example=str(names))
        coord=[]
        image=im0.reshape(640,640,3)
        if len(det):
            # Rescale boxes from img_size to im0 size
            #det[:, :4] = scale_boxes(im0.shape[2:], det[:, :4], im0.shape).round()
            # Write results
            for *xyxy, conf, cls in reversed(det):
                # Add bbox to image
                c = int(cls)  # integer class
                label = f"{names[c]} {conf:.2f}"
                # 创建两个顶点坐标子数组,并将它们组合成一个列表``
                coord.append([int(xyxy[0].item()), int(xyxy[1].item()),int(xyxy[2].item()), int(xyxy[3].item())])
        image=draw_bbox(image,coord)
        # Stream results
        save_success =cv2.imwrite('result.jpg', image)
        print(f"save image end {save_success}")
               
if __name__=="__main__":
    main()

测试结果

[AI智能摄像头]RV1126部署yolov5并加速,第1张

板端部署

环境准备

搭建好rknntoolkit以及rknpu环境

大致流程

[AI智能摄像头]RV1126部署yolov5并加速,第2张

模型转换

新建export_rknn.py用于将onnx模型转化为rknn模型

import os
import sys
os.chdir(sys.path[0])
import numpy as np
import cv2
from rknn.api import RKNN
import torchvision
import torch
import time

ONNX_MODEL = './model/yolov5n.onnx'
RKNN_MODEL = './model/yolov5n.rknn'

def main():
    """Create RKNN object"""
    rknn = RKNN()
    if not os.path.exists(ONNX_MODEL):
        print('model not exist')
        exit(-1)
        
    """pre-process config"""
    print('--> Config model')
    rknn.config(reorder_channel='0 1 2',
                mean_values=[[0, 0, 0]],
                std_values=[[255, 255, 255]],
                optimization_level=0,
                target_platform = ['rv1126'],
                output_optimize=1,
                quantize_input_node=True)
    print('done')
    
    """Load ONNX model"""
    print('--> Loading model')
    ret = rknn.load_onnx(model=ONNX_MODEL,
                        inputs=['images'],
                        input_size_list = [[3, 640, 640]],
                        outputs=['output0'])
    if ret != 0:
        print('Load yolov5 failed!')
        exit(ret)
    print('done')

    """Build model"""
    print('--> Building model')
    #ret = rknn.build(do_quantization=True,dataset='./data/data.txt')
    ret = rknn.build(do_quantization=False,pre_compile=True)
    if ret != 0:
        print('Build yolov5 failed!')
        exit(ret)
    print('done')

    """Export RKNN model"""
    print('--> Export RKNN model')
    ret = rknn.export_rknn(RKNN_MODEL)
    if ret != 0:
        print('Export yolov5rknn failed!')
        exit(ret)
    print('done')
    
if __name__=="__main__":
    main()

新建test_rknn.py用于测试rknn模型

import os
import sys
os.chdir(sys.path[0])
import numpy as np
import cv2
from rknn.api import RKNN
import torchvision
import torch
import time

RKNN_MODEL = './model/yolov5n.rknn'
DATA='./data/bus.jpg'

def xywh2xyxy(x):
    coord=[]
    for x_ in x:
        xl=x_[0]-x_[2]/2
        yl=x_[1]-x_[3]/2
        xr=x_[0]+x_[2]/2
        yr=x_[1]+x_[3]/2
        coord.append([xl,yl,xr,yr])
    coord=torch.tensor(coord).to(x.device)
    return coord
def box_iou(box1, box2, eps=1e-7):
    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
    # IoU = inter / (area1 + area2 - inter)
    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def non_max_suppression(
    prediction,
    conf_thres=0.25,
    iou_thres=0.45,
    classes=None,
    agnostic=False,
    multi_label=False,
    labels=(),
    max_det=300,
    nm=0,  # number of masks
):

    # Checks
    assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
    assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"

    device = prediction.device
    mps = "mps" in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    nc = prediction.shape[2] - nm - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates
    count_true = torch.sum(xc.type(torch.int))

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    max_wh = 7680  # (pixels) maximum box width and height
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 0.5 + 0.05 * bs  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
    merge = False  # use merge-NMS

    t = time.time()
    mi = 5 + nc  # mask start index
    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            lb = labels[xi]
            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
            v[:, :4] = lb[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box/Mask
        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
        mask = x[:, mi:]  # zero columns if no masks

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
        else:  # best class only
            conf, j = x[:, 5:mi].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        i = i[:max_det]  # limit detections
        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if mps:
            output[xi] = output[xi].to(device)
        if (time.time() - t) > time_limit:
            break  # time limit exceeded

    return output
def draw_bbox(image, result, color=(0, 0, 255), thickness=2):
    # img_path = cv2.cvtColor(img_path, cv2.COLOR_BGR2RGB)
    image = image.copy()
    for point in result:
        x1,y1,x2,y2=point
        cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
    return image

def main():
    # Create RKNN object
    rknn = RKNN()
    rknn.list_devices()
    #load rknn model
    ret = rknn.load_rknn(path=RKNN_MODEL)
    if ret != 0:
        print('load rknn failed')
        exit(ret)
    # init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime(target='rv1126', device_id='86d4fdeb7f3af5b1',perf_debug=True,eval_mem=True)
    if ret != 0:
        print('Init runtime environment failed')
        exit(ret)
    print('done')
    # Set inputs
    image=cv2.imread('./data/bus.jpg')
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    # Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[image])
    #post process
    out_tensor = torch.tensor(outputs)
    pred = non_max_suppression(out_tensor,0.25,0.45,classes=None,agnostic=False,max_det=1000)
    # Process predictions
    for i, det in enumerate(pred):  # per image
        im0_=cv2.imread(DATA)
        im0=im0_.reshape(1,3,640,640)
        coord=[]
        image=im0.reshape(640,640,3)
        if len(det):
            """Write results"""
            for *xyxy, conf, cls in reversed(det):
                c = int(cls)  # integer class
                coord.append([int(xyxy[0].item()), int(xyxy[1].item()),int(xyxy[2].item()), int(xyxy[3].item())])
                print(f"[{coord[0][0]},{coord[0][1]},{coord[0][2]},{coord[0][3]}]:ID is {c}")
        image=draw_bbox(image,coord)
        # Stream results
        save_success =cv2.imwrite('result.jpg', image)
        print(f"save image end {save_success}")
    
    rknn.release()

if __name__=="__main__":
    main()

板端cpp推理代码编写

拷贝一份template改名为yolov5,目录结构如下

[AI智能摄像头]RV1126部署yolov5并加速,第3张

前处理代码

void PreProcess(cv::Mat *image)
{
    cv::cvtColor(*image, *image, cv::COLOR_BGR2RGB);
}

后处理代码

1:输出维度为[1,25200,85],其中85的前四个为中心点的x,y以及框的宽和高,第五个为框的置信度,后面80个为类别的置信度(有80个类别);

2:25200=(80∗80+40∗40+20∗20)∗3,stride为8、16、32,640/8=80,640/16=40,640/32=20

3:NMS删除冗余候选框

1:IOU交并比:检测两个框重叠程度=交集面积/并集面积

2:主要步骤:

  1. 首先筛选出大于阈值的所有候选框(>0.4)
  2. 接着针对每一个种类将候选框进行分类
  3. 找到第n个类别进行循环操作
  4. 先找到置信度最大的框,放到保留区
  5. 和候选区的其他框计算交并比(IOU),若大于iou阈值则删除
  6. 再从候选区找到第二大的候选框放到保留区
  7. 重复4操作,直至候选区没有框
  8. 重复3操作,直至所有类别
float iou(Bbox box1, Bbox box2) {
    /*  
    iou=交并比
    */
    int x1 = max(box1.x, box2.x);
    int y1 = max(box1.y, box2.y);
    int x2 = min(box1.x + box1.w, box2.x + box2.w);
    int y2 = min(box1.y + box1.h, box2.y + box2.h);
    int w = max(0, x2 - x1);
    int h = max(0, y2 - y1);
    float over_area = w * h;
    return over_area / (box1.w * box1.h + box2.w * box2.h - over_area);
}

bool judge_in_lst(int index, vector<int> index_lst) {
    //若index在列表index_lst中则返回true,否则返回false
    if (index_lst.size() > 0) {
        for (int i = 0; i < int(index_lst.size()); i++) {
            if (index == index_lst.at(i)) {
                return true;
            }
        }
    }
    return false;
}

int get_max_index(vector<Detection> pre_detection) {
    //返回最大置信度值对应的索引值
    int index;
    float conf;
    if (pre_detection.size() > 0) {
        index = 0;
        conf = pre_detection.at(0).conf;
        for (int i = 0; i < int(pre_detection.size()); i++) {
            if (conf < pre_detection.at(i).conf) {
                index = i;
                conf = pre_detection.at(i).conf;
            }
        }
        return index;
    }
    else {
        return -1;
    }
}

vector<int> nms(vector<Detection> pre_detection, float iou_thr)
{
    /*
    返回需保存box的pre_detection对应位置索引值
    */
    int index;
    vector<Detection> pre_detection_new;
    //Detection det_best;
    Bbox box_best, box;
    float iou_value;
    vector<int> keep_index;
    vector<int> del_index;
    bool keep_bool;
    bool del_bool;

    if (pre_detection.size() > 0) {
        pre_detection_new.clear();
        // 循环将预测结果建立索引
        for (int i = 0; i < int(pre_detection.size()); i++) {
            pre_detection.at(i).index = i;
            pre_detection_new.push_back(pre_detection.at(i));
        }
        //循环遍历获得保留box位置索引-相对输入pre_detection位置
        while (pre_detection_new.size() > 0) {
            index = get_max_index(pre_detection_new);
            if (index >= 0) {
                keep_index.push_back(pre_detection_new.at(index).index); //保留索引位置

                // 更新最佳保留box
                box_best.x = pre_detection_new.at(index).bbox[0];
                box_best.y = pre_detection_new.at(index).bbox[1];
                box_best.w = pre_detection_new.at(index).bbox[2];
                box_best.h = pre_detection_new.at(index).bbox[3];

                for (int j = 0; j < int(pre_detection.size()); j++) {
                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
                    if ((!keep_bool) && (!del_bool)) { //不在keep_index与del_index才计算iou
                        box.x = pre_detection.at(j).bbox[0];
                        box.y = pre_detection.at(j).bbox[1];
                        box.w = pre_detection.at(j).bbox[2];
                        box.h = pre_detection.at(j).bbox[3];
                        iou_value = iou(box_best, box);
                        if (iou_value > iou_thr) {
                            del_index.push_back(j); //记录大于阈值将删除对应的位置
                        }
                    }

                }
                //更新pre_detection_new
                pre_detection_new.clear();
                for (int j = 0; j < int(pre_detection.size()); j++) {
                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
                    if ((!keep_bool) && (!del_bool)) {
                        pre_detection_new.push_back(pre_detection.at(j));
                    }
                }
            }
        }
    }

    del_index.clear();
    del_index.shrink_to_fit();
    pre_detection_new.clear();
    pre_detection_new.shrink_to_fit();

    return  keep_index;

}


vector<Detection> PostProcess(float* prob,float conf_thr=0.3,float nms_thr=0.5)
{
    vector<Detection> pre_results;
    vector<int> nms_keep_index;
    vector<Detection> results;
    bool keep_bool;
    Detection pre_res;
    float conf;
    int tmp_idx;
    float tmp_cls_score;
    for (int i = 0; i < 25200; i++) {
        tmp_idx = i * (CLSNUM + 5);
        pre_res.bbox[0] = prob[tmp_idx + 0];  //cx
        pre_res.bbox[1] = prob[tmp_idx + 1];  //cy
        pre_res.bbox[2] = prob[tmp_idx + 2];  //w
        pre_res.bbox[3] = prob[tmp_idx + 3];  //h
        conf = prob[tmp_idx + 4];  // 是为目标的置信度
        tmp_cls_score = prob[tmp_idx + 5] * conf; //conf_thr*nms_thr
        pre_res.class_id = 0;
        pre_res.conf = 0;
        // 这个过程相当于从除了前面5列,在后面的cla_num个数据中找出score最大的值作为pre_res.conf,对应的列作为类id
        for (int j = 1; j < CLSNUM; j++) {     
            tmp_idx = i * (CLSNUM + 5) + 5 + j; //获得对应类别索引
            if (tmp_cls_score < prob[tmp_idx] * conf){
                tmp_cls_score = prob[tmp_idx] * conf;
                pre_res.class_id = j;
                pre_res.conf = tmp_cls_score;
            }
        }
        if (conf >= conf_thr) {
            pre_results.push_back(pre_res);
        }
    }
    //使用nms,返回对应结果的索引
    nms_keep_index=nms(pre_results,nms_thr);
    // 茛据nms找到的索引,将结果取出来作为最终结果
    for (int i = 0; i < int(pre_results.size()); i++) {
        keep_bool = judge_in_lst(i, nms_keep_index);
        if (keep_bool) {
            results.push_back(pre_results.at(i));
        }
    }

    pre_results.clear();
    pre_results.shrink_to_fit();
    nms_keep_index.clear();
    nms_keep_index.shrink_to_fit();

    return results; 
}

结果展示

[AI智能摄像头]RV1126部署yolov5并加速,第4张

至此板端部署结束,接下来进行优化;

优化加速

可以看到模型推理的时间近2s,对于实时处理来说是远远不够的,因此需要对模型进行加速

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