树莓派部署YOLOv5模型

本文章是关于树莓派部署YOLOv5s模型，实际测试效果的FPS仅有0.15，不够满足实际检测需要，各位大佬可以参考参考。

1、在树莓派中安装opencv（默认安装好python3）

# 直接安装
# 安装依赖软件
sudo apt-get install -y libopencv-dev python3-opencv
sudo apt-get install libatlas-base-dev
sudo apt-get install libjasper-dev
sudo apt-get install libqtgui4
sudo apt-get install python3-pyqt5
sudo apt install libqt4-test
# 安装Python 包
pip3 install opencv-python

2、导出onnx模型

从YOLOv5官网下载源代码和YOLOv5s.pt文件

YOLOv5官网

YOLOv5s.pt下载

按照作者提示安装环境，使用它自带的export.py将YOLOv5s.pt转为YOLOv5s.onnx，安装好环境后，在终端输入以下命令即可自动生成。

python export.py --weights yolov5s.pt --include onnx

3.测试

        可以先在电脑上测试一下，使用如下代码测试上述转换的模型能否使用，假如成功即可将下述代码和上述生成的YOLOv5s.onnx模型直接移动到树莓派中进行测试。

# 图片检测
import cv2
import numpy as np
import time
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    """
    description: Plots one bounding box on image img,
                 this function comes from YoLov5 project.
    param: 
        x:      a box likes [x1,y1,x2,y2]
        img:    a opencv image object
        color:  color to draw rectangle, such as (0,255,0)
        label:  str
        line_thickness: int
    return:
        no return
    """
    tl = (
        line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
    )  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(
            img,
            label,
            (c1[0], c1[1] - 2),
            0,
            tl / 3,
            [225, 255, 255],
            thickness=tf,
            lineType=cv2.LINE_AA,
        )
 
def post_process_opencv(outputs,model_h,model_w,img_h,img_w,thred_nms,thred_cond):
    
    conf = outputs[:,4].tolist()
    c_x = outputs[:,0]/model_w*img_w
    c_y = outputs[:,1]/model_h*img_h
    w  = outputs[:,2]/model_w*img_w
    h  = outputs[:,3]/model_h*img_h
    p_cls = outputs[:,5:]
    if len(p_cls.shape)==1:
        p_cls = np.expand_dims(p_cls,1)
    cls_id = np.argmax(p_cls,axis=1)
 
    p_x1 = np.expand_dims(c_x-w/2,-