【详细记录】rv1126 跑通 yolov5

【详细记录】rv1126 跑通 yolov5 技术小宅 2024-01-06 09:02:38 752

在前面,已经将 onnx模型转为 rknn模型。

yolov5 onnx模型 转为 rknn模型

这里探讨的是:rknn模型在rv1126开发板上运行

1、rknn模型在PC端进行推理测试,评估模型精度

这里是在上面那个博客的提到的docker环境,即

2、模型预编译

执行过第一步,可以发现rknn模型推理的时候会很慢,所以需要对模型进行预编译。预编译的时候需要经过EASY EAI Nano主板的环境,开发板与Ubuntu必须保证adb连接稳定 。

但是Ubuntu环境与docker环境对adb设备资源是竞争关系,所以需要关掉ubuntu环境的adb服务,且在docker环境通过apt-get安装adb包

在Ubuntu环境关闭adb服务:

adb kill-server

在docker环境安装adb安装包:

apt-get install adb

adb service

adb devices

运行precompile_rknn.py脚本把模型执行预编译

python precompile_rknn.py

执行效果如下图所示,生成预编译模型yolov5_coco_rv1126_pre.rknn

3、推理代码编译

开发环境准备(交叉环境):
网盘链接:百度网盘 请输入提取码

提取码:i1ii

记得修改 run.sh中

VOL_SRC=你存放上面文件的目录you

--user=root

然后 在PC端Ubuntu系统中执行run脚本,进入EASY-EAI编译环境,具体如下所示。

cd ~/develop_environment
./run.sh

如此,Ubuntu 环境与 Docker 容器 建立映射关系, Docker 容器与开发板建立映射关系

推理代码编译:
百度网盘链接: (百度网盘 请输入提取码 提取码:1jfb)。

解压后,在交叉环境中对其进行编译操作:

./build.sh

注:

*由于依赖库部署在板卡上,因此交叉编译过程中必须保持adb连接。

复制编译结果到开发板

在交叉编译环境中,将编译结果以及预编译的模型复制到/mnt/userdata目录,而该目录映射到开发板

cp yolov5_detect_demo_release/ /mnt/userdata/ -rf

通过按键Ctrl+Shift+T创建一个新窗口,执行adb shell命令,进入板卡运行环境:

adb shell

进入板卡后,定位到例程上传的位置,如下所示:

cd /userdata/yolov5_detect_demo_release/

运行例程命令如下所示:

运行之前还要复制测试图片、预编译模型文件到该文件夹

./yolov5_detect_demo

执行结果如下图所示,算法执行时间约为50ms:

退出板卡环境,取回测试图片:

exit
adb pull /userdata/yolov5_detect_demo_release/result.jpg .

与onnx模型推理结果对比,rknn模型对远处的车、以及不明显的人不敏感,但是近处的物体识别效果很好与onnx模型一致!

以下是两份代码

rknn模型在PC端推理代码(python):


import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
import random
from rknn.api import RKNN


RKNN_MODEL = 'yolov5_coco_rv1126.rknn'
IMG_PATH = './test.jpg'
DATASET = './dataset.txt'


BOX_THRESH = 0.25
NMS_THRESH = 0.6
IMG_SIZE = 640


CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","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","sofa",
           "pottedplant","bed","diningtable","toilet ","tvmonitor","laptop","mouse","remote ","keyboard ","cell phone","microwave ",
           "oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush")



def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def xywh2xyxy(x):
    # Convert [x, y, w, h] to [x1, y1, x2, y2]
    y = 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 process(input, mask, anchors):

    anchors = [anchors[i] for i in mask]
    grid_h, grid_w = map(int, input.shape[0:2])

    box_confidence = sigmoid(input[..., 4])
    box_confidence = np.expand_dims(box_confidence, axis=-1)

    box_class_probs = sigmoid(input[..., 5:])

    box_xy = sigmoid(input[..., :2])*2 - 0.5

    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    grid = np.concatenate((col, row), axis=-1)
    box_xy += grid
    box_xy *= int(IMG_SIZE/grid_h)

    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
    box_wh = box_wh * anchors

    box = np.concatenate((box_xy, box_wh), axis=-1)

    return box, box_confidence, box_class_probs

def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.

    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """
    box_scores = box_confidences * box_class_probs
    box_classes = np.argmax(box_class_probs, axis=-1)
    box_class_scores = np.max(box_scores, axis=-1)
    pos = np.where(box_confidences[...,0] >= BOX_THRESH)


    boxes = boxes[pos]
    classes = box_classes[pos]
    scores = box_class_scores[pos]

    return boxes, classes, scores

def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.

    # Arguments
        boxes: ndarray, boxes of objects.
        scores: ndarray, scores of objects.

    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]

    areas = w * h
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])

        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1

        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    keep = np.array(keep)
    return keep


def yolov5_post_process(input_data):
    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
              [59, 119], [116, 90], [156, 198], [373, 326]]

    boxes, classes, scores = [], [], []
    for input,mask in zip(input_data, masks):
        b, c, s = process(input, mask, anchors)
        b, c, s = filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)

    boxes = np.concatenate(boxes)
    boxes = xywh2xyxy(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)

    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]

        keep = nms_boxes(b, s)

        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])

    if not nclasses and not nscores:
        return None, None, None

    boxes = np.concatenate(nboxes)
    classes = np.concatenate(nclasses)
    scores = np.concatenate(nscores)

    return boxes, classes, scores

def scale_coords(x1, y1, x2, y2, dst_width, dst_height):

    dst_top, dst_left, dst_right, dst_bottom = 0, 0, 0, 0
    gain = 0

    if dst_width > dst_height:
        image_max_len = dst_width
        gain = IMG_SIZE / image_max_len
        resized_height = dst_height * gain
        height_pading = (IMG_SIZE - resized_height)/2
        print("height_pading:", height_pading)
        y1 = (y1 - height_pading)
        y2 = (y2 - height_pading)

    print("gain:", gain)
    dst_x1 = int(x1 / gain)
    dst_y1 = int(y1 / gain)
    dst_x2 = int(x2 / gain)
    dst_y2 = int(y2 / gain)

    return dst_x1, dst_y1, dst_x2, dst_y2

def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    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 draw(image, boxes, scores, classes):
    """Draw the boxes on the image.
    # Argument:
        image: original image.
        boxes: ndarray, boxes of objects.
        classes: ndarray, classes of objects.
        scores: ndarray, scores of objects.
        all_classes: all classes name.
    """
    for box, score, cl in zip(boxes, scores, classes):

        x1, y1, x2, y2 = box
        print('class: {}, score: {}'.format(CLASSES[cl], score))
        print('box coordinate x1,y1,x2,y2: [{}, {}, {}, {}]'.format(x1, y1, x2, y2))
        x1 = int(x1)
        y1 = int(y1)
        x2 = int(x2)
        y2 = int(y2)

        dst_x1, dst_y1, dst_x2, dst_y2 = scale_coords(x1, y1, x2, y2, image.shape[1], image.shape[0])
        #print("img.cols:", image.cols)

        plot_one_box((dst_x1, dst_y1, dst_x2, dst_y2), image, label='{0} {1:.2f}'.format(CLASSES[cl], score))


        '''
        cv2.rectangle(image, (dst_x1, dst_y1), (dst_x2, dst_y2), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (dst_x1, dst_y1 - 6),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)
        '''


def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im, ratio, (dw, dh)


if __name__ == '__main__':

    # Create RKNN object
    rknn = RKNN()

    print('--> Loading model')
    ret = rknn.load_rknn(RKNN_MODEL)
    if ret != 0:
        print('load rknn model failed')
        exit(ret)
    print('done')

    # init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    # ret = rknn.init_runtime('rv1126', device_id='1126')
    if ret != 0:
        print('Init runtime environment failed')
        exit(ret)
    print('done')

    # Set inputs
    img = cv2.imread(IMG_PATH)
    letter_img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
    letter_img = cv2.cvtColor(letter_img, cv2.COLOR_BGR2RGB)


    # Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[letter_img])

    print('--> inference done')

    # post process
    input0_data = outputs[0]
    input1_data = outputs[1]
    input2_data = outputs[2]

    input0_data = input0_data.reshape([3,-1]+list(input0_data.shape[-2:]))
    input1_data = input1_data.reshape([3,-1]+list(input1_data.shape[-2:]))
    input2_data = input2_data.reshape([3,-1]+list(input2_data.shape[-2:]))

    input_data = list()
    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))

    print('--> transpose done')

    boxes, classes, scores = yolov5_post_process(input_data)

    print('--> get result done')

    img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    if boxes is not None:
        draw(img, boxes, scores, classes)

    cv2.imwrite('./result.jpg', img)
    cv2.imshow("post process result", img_1)
    cv2.waitKeyEx(0)

    rknn.release()

rknn模型预编译代码(Python):

import sys
import random
import os
import argparse

from rknn.api import RKNN


def precompile_file(fi, fo, target):
    print("precompile {} to {}".format(fi, fo))
    src_rknn_model_path = fi
    dst_rknn_model_path = fo

    rknn = RKNN(verbose=True)
    rknn.load_rknn(src_rknn_model_path)
    rknn.init_runtime(rknn2precompile=True, target=target)
    rknn.export_rknn_precompile_model(export_path=dst_rknn_model_path)


def precompile_dir(d, out_dir, target):
    """
    decrypt a directory assigned by <d>
    """
    file_list = os.listdir(d)
    file_count = len(file_list)
    for i in range(file_count):
        f = os.path.join(d, file_list[i])
        target_file_name = file_list[i]
        neof = os.path.join(out_dir, target_file_name)
        precompile_file(f, neof, target)
        print('Progress:%d/%d' % (i + 1, file_count))
    print('Directory <%s> has been decrypted.' % (d))


if __name__ == '__main__':

    precompile_file('./yolov5_coco_rv1126.rknn','./yolov5_coco_rv1126_pre.rknn', 'rv1126')

rknn模型C++推理代码—yolov5_detect.h

#ifndef _YOLOV5_DETECT_H_
#define _YOLOV5_DETECT_H_

#include "yolov5_detect_postprocess.h"
#include "rknn_api.h"
#include <opencv2/opencv.hpp>




/* 
 * COCO检测初始化函数
 * ctx:输入参数,rknn_context句柄
 * path:输入参数,算法模型路径
 */
int coco_detect_init(rknn_context *ctx, const char * path);


/* 
 * COCO检测执行函数
 * ctx:输入参数,rknn_context句柄
 * input_image:输入参数,图像数据输入(cv::Mat是Opencv的类型)
 * output_dets:输出参数,目标检测框输出
 */
int coco_detect_run(rknn_context ctx, cv::Mat input_image, coco_detect_result_group_t *detect_result_group);


/* 
 * COCO检测释放函数
 * ctx:输入参数,rknn_context句柄
 */
int coco_detect_release(rknn_context ctx);




#endif

yolov5_detect.cpp

#include <iostream>
#include <fstream>
#include <vector>
#include <cstdint>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <unistd.h>
#include <time.h>
#include <math.h>
#include <fcntl.h>
#include <opencv2/opencv.hpp>
#include "yolov5_detect.h"
#include "rknn_api.h"

#include <sys/time.h>

using namespace std;
using namespace cv;


//unsigned char *model;
//detection* dets;

static void printRKNNTensor(rknn_tensor_attr *attr)
{
    printf("index=%d name=%s n_dims=%d dims=[%d %d %d %d] n_elems=%d size=%d "
           "fmt=%d type=%d qnt_type=%d fl=%d zp=%d scale=%f\n",
           attr->index, attr->name, attr->n_dims, attr->dims[3], attr->dims[2],
           attr->dims[1], attr->dims[0], attr->n_elems, attr->size, 0, attr->type,
           attr->qnt_type, attr->fl, attr->zp, attr->scale);
}


// 调整图片尺寸达到模型输入尺寸要求
static int letter_box(cv::Mat input_image, cv::Mat *output_image, int model_input_size)
{

    // 计算缩放比
    int input_width, input_height;

    input_width = input_image.cols;
    input_height = input_image.rows;
    float ratio;
    ratio = min((float)model_input_size / input_width, (float)model_input_size / input_height); // 选择较小的缩放比


    // 计算缩放后的宽高尺寸
    int new_width, new_height;
    new_width = round(ratio * input_width );
    new_height = round(ratio * input_height);


    // 计算padding量
    // 长或者宽,至少有一个在缩放后满足模型需求了;另一个需要padding
    int height_padding = 0;
    int width_padding = 0;
    int top = 0;
    int bottom = 0;
    int left = 0;
    int right = 0;
    if( new_width >= new_height) // 宽已经满足要求了,高需要padding
    {
        height_padding = new_width - new_height; // 计算padding量
        if( (height_padding % 2) == 0 ) // 如果padding量是偶数
        {
            top = (int)((float)(height_padding/2)); // 直接除2就好
            bottom = (int)((float)(height_padding/2));
        }
        else // padding量是奇数
        {
            top = (int)((float)(height_padding/2)); 
            bottom = (int)((float)(height_padding/2))+1;    
        }
    }
    else   // 高已经满足要求了,宽需要padding
    {
        width_padding = new_height - new_width;
        if( (width_padding % 2) == 0 )
        {
            left = (int)((float)(width_padding/2));
            right = (int)((float)(width_padding/2));
        }
        else
        {
            left = (int)((float)(width_padding/2));
            right = (int)((float)(width_padding/2))+1;
        }

    }


    // 对长宽进行缩放
    cv::Mat resize_img;
    cv::resize(input_image, resize_img, cv::Size(new_width, new_height));

    // padding操作
    cv::copyMakeBorder(resize_img, *output_image, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));

    return 0;
}


// 模型为二进制格式存储,将其整个加载到内存中
int coco_detect_init(rknn_context *ctx, const char * path)
{
    int ret;

    // Load model
    FILE *fp = fopen(path, "rb"); // 打开指定路径的模型文件
    if(fp == NULL)
    {
        printf("fopen %s fail!\n", path);
        return -1;
    }
    fseek(fp, 0, SEEK_END);   // SEEK_EN为文件尾,文件指针移向文件的末尾
    int model_len = ftell(fp);   // 计算得到文件指针的偏移量
    unsigned char *model_data = (unsigned char*)malloc(model_len); // 分配与模型文件长度相等的内存块,用于存储模型数据

    fseek(fp, 0, SEEK_SET);   //SEEK_SET为文件头,文件指针重新移动到文件开头
    if(model_len != fread(model_data, 1, model_len, fp)) // 将模型文件中的数据读取到之前分配的内存块; 如果读取的数据长度与模型文件长度不一致,则
    {
        printf("fread %s fail!\n", path);
        free(model_data); // 释放内存块
        return -1;
    }
    fclose(fp); // 关闭文件

    //init
    ret = rknn_init(ctx, model_data, model_len, RKNN_FLAG_PRIOR_MEDIUM); // 初始化rknn模型上下文
    if(ret < 0)
    {
        printf("rknn_init fail! ret=%d\n", ret);
        return -1;
    }

    free(model_data);

    return 0;
}


// 目标框的坐标信息映射到原图上
static int scale_coords(coco_detect_result_group_t *detect_result_group, int img_width, int img_height, int model_size)
{
    for (int i = 0; i < detect_result_group->count; i++)
    {
        coco_detect_result_t *det_result = &(detect_result_group->results[i]);


        int x1 = det_result->box.left;
        int y1 = det_result->box.top;
        int x2 = det_result->box.right;
        int y2 = det_result->box.bottom;


        if( img_width >= img_height )
        {
            int image_max_len = img_width;
            float gain;
            gain = (float)model_size / image_max_len;
            int resized_height = img_height * gain;
            int height_pading = (model_size - resized_height)/2;
            y1 = (y1 - height_pading);
            y2 = (y2 - height_pading);
            x1 = int(x1 / gain);
            y1 = int(y1 / gain);
            x2 = int(x2 / gain);
            y2 = int(y2 / gain);

            det_result->box.left = x1;
            det_result->box.top = y1;
            det_result->box.right = x2;
            det_result->box.bottom = y2;
        }
        else
        {
            int image_max_len = img_height;
            float gain;
            gain = (float)model_size / image_max_len;
            int resized_width = img_width * gain;
            int width_pading = (model_size - resized_width)/2;
            x1 = (x1 - width_pading);
            x2 = (x2 - width_pading);
            x1 = int(x1 / gain);
            y1 = int(y1 / gain);
            x2 = int(x2 / gain);
            y2 = int(y2 / gain);

            det_result->box.left = x1;
            det_result->box.top = y1;
            det_result->box.right = x2;
            det_result->box.bottom = y2;    
        }

    }

    return 0;
}


int coco_detect_run(rknn_context ctx, cv::Mat input_image, coco_detect_result_group_t *detect_result_group)
{
    int img_width = 0;
    int img_height = 0;
    int img_channel = 0;

    size_t actual_size = 0;
    const float vis_threshold = 0.1;
    const float nms_threshold = 0.5;
    const float conf_threshold = 0.2;
    int ret;

    img_width = input_image.cols;
    img_height = input_image.rows;


    // 查询SDK版本、模型输入输出张量数量
    rknn_sdk_version version;
    ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version,
                     sizeof(rknn_sdk_version)); // 查询SDK版本
    if (ret < 0)
    {
        printf("rknn_init error ret=%d\n", ret);
        return -1;
    }
    /*
    printf("sdk version: %s driver version: %s\n", version.api_version,
           version.drv_version);
    */


    // 查询并保存输入和输出张量属性
    rknn_input_output_num io_num; // 用于存储查询到的输入和输出张量数量
    ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));  // 查询模型输入输出张量数量
    if (ret < 0)
    {
        printf("rknn_init error ret=%d\n", ret);
        return -1;
    }
    /*
    printf("model input num: %d, output num: %d\n", io_num.n_input,
           io_num.n_output);
    */

    rknn_tensor_attr input_attrs[io_num.n_input]; // 创建一个数组,用于存储输入张量的属性信息
    memset(input_attrs, 0, sizeof(input_attrs)); // 将数组的所有元素清零
    for (int i = 0; i < io_num.n_input; i++)
    {
        input_attrs[i].index = i;
        ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]),
                         sizeof(rknn_tensor_attr)); // 查询模型的输入张量属性,并保存在相应的结构体中
                                     // 包括 索引(index)、数据格式(fmt)、数据类型(type)、通道数(channel)、宽度(dims[0])和高度(dims[1])
        if (ret < 0)
        {
            printf("rknn_init error ret=%d\n", ret);
            return -1;
        }
        //printRKNNTensor(&(input_attrs[i]));
    }

    rknn_tensor_attr output_attrs[io_num.n_output];
    memset(output_attrs, 0, sizeof(output_attrs));
    for (int i = 0; i < io_num.n_output; i++)
    {
        output_attrs[i].index = i;
        ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), 
                         sizeof(rknn_tensor_attr));
        //printRKNNTensor(&(output_attrs[i]));
    }


    // 从输入张量属性中获取输入的高和宽
    int input_channel = 3;
    int input_width = 0;
    int input_height = 0;
    if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) // 检查图片通道顺序
    {
        //printf("model is NCHW input fmt\n");
        input_width = input_attrs[0].dims[0];
        input_height = input_attrs[0].dims[1];
    }
    else
    {
        //printf("model is NHWC input fmt\n");
        input_width = input_attrs[0].dims[1];
        input_height = input_attrs[0].dims[2];
    }

    /*
    printf("model input height=%d, width=%d, channel=%d\n", height, width,
           channel);
    */

    // 输入张量初始化
    /* Init input tensor */
    rknn_input inputs[1];
    memset(inputs, 0, sizeof(inputs));
    inputs[0].index = 0;
    inputs[0].type = RKNN_TENSOR_UINT8;
    inputs[0].size = input_width * input_height * input_channel;
    inputs[0].fmt = RKNN_TENSOR_NHWC;
    inputs[0].pass_through = 0;

    // 输出张量初始化
    /* Init output tensor */
    rknn_output outputs[io_num.n_output];
    memset(outputs, 0, sizeof(outputs));

    for (int i = 0; i < io_num.n_output; i++)
    {
        outputs[i].want_float = 0; // 输出张量的数据类型不需要转换为浮点数
    }

    // 对输入图像进行信封处理,将其调整为模型制定的输入尺寸
    cv::Mat letter_image;
    letter_box(input_image, &letter_image, input_width);
    inputs[0].buf = letter_image.data; // 预处理后的图像数据赋值给inputs[0].buf

    // 推理,获取模型输出
    rknn_inputs_set(ctx, io_num.n_input, inputs); //输入张量与ctx(RKNN模型上下文)关联起来
    ret = rknn_run(ctx, NULL); // 运行 RKNN 模型进行推理。此时模型会根据输入张量的数据进行前向传播,生成模型的输出结果
    ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); // 获取模型的输出结果; io_num.n_output表示输出张量的数量;outputs是输出张量数组

    // Post process

    // 获取模型输出的缩放因子和零点信息,用于后处理
    // 缩放因子是模型量化过程中得到的,会使用缩放因子和零点来确定如何将浮点数映射到整数范围内。
    // 缩放因子表示浮点数在量化后,映射到整数范围内所需的缩放比例;
    // 零点表示浮点数映射到整数范围内时的偏移量
    std::vector<float> out_scales; // 用于存储张量的缩放因子
    std::vector<uint8_t> out_zps; // 用于存储张量的零点
    for (int i = 0; i < io_num.n_output; ++i)
    {
        out_scales.push_back(output_attrs[i].scale); // 获取第i个输出张量的缩放因子;
        out_zps.push_back(output_attrs[i].zp); // 获取第i个输出张量的零点
    }


    // 后处理
    yolov5_post_process_u8((uint8_t *)outputs[0].buf, (uint8_t *)outputs[1].buf, (uint8_t *)outputs[2].buf, input_height, input_width,
                       conf_threshold, nms_threshold, out_zps, out_scales, detect_result_group);


    /*
    yolov5_post_process_fp((float *)outputs[0].buf, (float *)outputs[1].buf, (float *)outputs[2].buf, input_height, input_width,
                        conf_threshold, nms_threshold, &detect_result_group);
    */

    // 释放模型输出资源
    rknn_outputs_release(ctx, io_num.n_output, outputs);

    // 对后处理得到的目标框进行缩放,以适应原始图像的尺寸
    scale_coords(detect_result_group, img_width, img_height, input_width);

    return 0;
}

int coco_detect_release(rknn_context ctx)
{
    rknn_destroy(ctx);
    return 0;
}

yolov5_detect_postprocess.h

#ifndef _YOLOV5_DETECT_POSTPROCESS_H_
#define _YOLOV5_DETECT_POSTPROCESS_H_

#include <stdint.h>

#define COCO_NAME_MAX_SIZE 16
#define COCO_NUMB_MAX_SIZE 200
#define COCO_CLASS_NUM     80
#define COCO_PROP_BOX_SIZE     (5+COCO_CLASS_NUM)

typedef struct _COCO_BOX_RECT
{
    int left;
    int right;
    int top;
    int bottom;
} COCO_BOX_RECT;

typedef struct __coco_detect_result_t
{
    char name[COCO_NAME_MAX_SIZE];
    int class_index;
    COCO_BOX_RECT box;
    float prop;
} coco_detect_result_t;

typedef struct _detect_result_group_t
{
    int id;
    int count;
    coco_detect_result_t results[COCO_NUMB_MAX_SIZE];
} coco_detect_result_group_t;

int yolov5_post_process_u8(uint8_t *input0, uint8_t *input1, uint8_t *input2, int model_in_h, int model_in_w,
                 float conf_threshold, float nms_threshold,
                 std::vector<uint8_t> &qnt_zps, std::vector<float> &qnt_scales,
                 coco_detect_result_group_t *group);

int yolov5_post_process_fp(float *input0, float *input1, float *input2, int model_in_h, int model_in_w,
                 float conf_threshold, float nms_threshold, 
                 coco_detect_result_group_t *group);

#endif //_RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_

yolov5_detect_postprocess.cpp

// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <string.h>
#include <sys/time.h>
#include <vector>
#include "yolov5_detect_postprocess.h"
#include <stdint.h>


static char labels[COCO_CLASS_NUM][30] = {"person", "bicycle", "car","motorbike ","aeroplane ","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","sofa",
           "pottedplant","bed","diningtable","toilet ","tvmonitor","laptop","mouse","remote ","keyboard ","cell phone","microwave ",
           "oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush"};

const int anchor0[6] = {10, 13, 16, 30, 33, 23};
const int anchor1[6] = {30, 61, 62, 45, 59, 119};
const int anchor2[6] = {116, 90, 156, 198, 373, 326};


// 将一个浮点数val限制在一个指定的最小值min和最大值max的范围内
inline static int clamp(float val, int min, int max)
{
    return val > min ? (val < max ? val : max) : min;
}


// 计算两个矩形的重叠度 
// 在计算之前需要对x轴进行排序,左边那个矩形是0,右边那个是1
// yolov5输出结果,检测框已经经过排序处理,按照从左到右,从上到下的顺序排列
static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, float ymax1)
{
    float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0); // 重叠部分的宽
    float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0); // 重叠部分的高
    float i = w * h; // 重叠部分的面积,即两矩形交集
    float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i; // 两矩形并集面积
    return u <= 0.f ? 0.f : (i / u); // 交并比
}

// 
static int nms(int validCount, std::vector<float> &outputLocations, std::vector<int> &order, float threshold)
{
    for (int i = 0; i < validCount; ++i) // 遍历所有检测框
    {
        if (order[i] == -1)
        {
            continue;
        }
        int n = order[i]; // 检测框的索引顺序,即对应于outputLocations中检测框的顺序
        for (int j = i + 1; j < validCount; ++j) // 从 n+1 开始遍历剩余的检测框
        {
            int m = order[j];
            if (m == -1)
            {
                continue;
            }

            // 当前检测框
            float xmin0 = outputLocations[n * 4 + 0]; // xmin
            float ymin0 = outputLocations[n * 4 + 1]; // ymin
            float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2]; // xmin + w = xmax
            float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3]; // ymin + h = ymax

        // 剩余检测框
            float xmin1 = outputLocations[m * 4 + 0];
            float ymin1 = outputLocations[m * 4 + 1];
            float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2];
            float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3];

            float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1); // 计算交并比

            if (iou > threshold) // 交并比大于阈值,即认为是同一个目标的检测框
            {
                order[j] = -1;
            }
        }
    }
    return 0;
}

static int quick_sort_indice_inverse(
    std::vector<float> &input, // 待排序的向量
    int left, // 排序范围的左右边界
    int right,
    std::vector<int> &indices) // 与input相对应的索引向量
{
    float key;
    int key_index;
    int low = left;
    int high = right;
    if (left < right)
    {
        key_index = indices[left];
        key = input[left];
        while (low < high)
        {
            while (low < high && input[high] <= key)
            {
                high--;
            }
            input[low] = input[high];
            indices[low] = indices[high];
            while (low < high && input[low] >= key)
            {
                low++;
            }
            input[high] = input[low];
            indices[high] = indices[low];
        }
        input[low] = key;
        indices[low] = key_index;
        quick_sort_indice_inverse(input, left, low - 1, indices);
        quick_sort_indice_inverse(input, low + 1, right, indices);
    }
    return low;
}

static float sigmoid(float x)
{
    return 1.0 / (1.0 + expf(-x));
}

static float unsigmoid(float y)
{
    return -1.0 * logf((1.0 / y) - 1.0);
}

inline static int32_t __clip(float val, float min, float max)
{
    float f = val <= min ? min : (val >= max ? max : val);
    return f;
}

// 将一个浮点数(32)进行量化转换为一个固定范围内的整数值(uint8_t),并添加零点偏移(zp)和缩放因子(scale)的调整
static uint8_t qnt_f32_to_affine(float f32, uint8_t zp, float scale)
{
    float dst_val = (f32 / scale) + zp;
    uint8_t res = (uint8_t)__clip(dst_val, 0, 255); // 将结果限制到0~255之间,并转化为uint8_t
    return res;
}

// 一个经过量化转换和调整的整数值(qnt)反量化回浮点数
static float deqnt_affine_to_f32(uint8_t qnt, uint8_t zp, float scale)
{
    return ((float)qnt - (float)zp) * scale;
}


// 从输入数据中提取边界框,对边界框进行解码和筛选,并将结果存储到相应的向量中
static int process_u8(uint8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
                   std::vector<float> &boxes, std::vector<float> &boxScores, std::vector<int> &classId,
                   float threshold, uint8_t zp, float scale)
{

    int validCount = 0;
    int grid_len = grid_h * grid_w; // 网格的高度*宽度
    float thres = unsigmoid(threshold);
    uint8_t thres_u8 = qnt_f32_to_affine(thres, zp, scale); // 置信度量化为整数
    for (int a = 0; a < 3; a++) // 每个网格位置有三个锚框
    {
        for (int i = 0; i < grid_h; i++) // 当前网格的列索引,可以理解为图像的行数
        {
            for (int j = 0; j < grid_w; j++) // 当前网格的行索引,可以理解为图像的列数
            {
                uint8_t box_confidence = input[(COCO_PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j]; // dan
                // COCO_PROP_BOX_SIZE为常量,每个边界框的属性数量,这里应该是85;
                // +4是为了跳过边界框信息,以便直接获取边界框的置信度
                // (COCO_PROP_BOX_SIZE * a + 4) * grid_len
                // grid_len表示网格的总长度;
                // 不过这里我也看不懂rknn的输出,毕竟这句代码我看得太困惑,与onnx模型后处理的时候不一样

                if (box_confidence >= thres_u8) // 如果置信度大于阈值
                {
                    int offset = (COCO_PROP_BOX_SIZE * a) * grid_len + i * grid_w + j; // 计算偏移量
                    uint8_t *in_ptr = input + offset; // 获取输入指针

                    // 计算bounding box的x坐标
                    float box_x = sigmoid(deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5; 
                    float box_y = sigmoid(deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5;
                    float box_w = sigmoid(deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0;
                    float box_h = sigmoid(deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0;

                    // 根据当前点的位置和步长,缩放box的x和y坐标
                    box_x = (box_x + j) * (float)stride; 
                    box_y = (box_y + i) * (float)stride;

                    // 根据anchor的尺寸缩放box的宽度和高度
                    box_w = box_w * box_w * (float)anchor[a * 2]; 
                    box_h = box_h * box_h * (float)anchor[a * 2 + 1];

                    // 将box的坐标转换为左上角坐标和宽度、高度,并存储在boxes数组中
                    box_x -= (box_w / 2.0); 
                    box_y -= (box_h / 2.0);
                    boxes.push_back(box_x);
                    boxes.push_back(box_y);
                    boxes.push_back(box_w);
                    boxes.push_back(box_h);

            // 获取最大类别概率值和对应的类别ID
                    uint8_t maxClassProbs = in_ptr[5 * grid_len];
                    int maxClassId = 0;
                    for (int k = 1; k < COCO_CLASS_NUM; ++k)
                    {
                        uint8_t prob = in_ptr[(5 + k) * grid_len];
                        if (prob > maxClassProbs)
                        {
                            maxClassId = k;
                            maxClassProbs = prob;
                        }
                    }

                    // 将box_confidence和类别概率值进行逆量化并转换位浮点数
                    float box_conf_f32 = sigmoid(deqnt_affine_to_f32(box_confidence, zp, scale));
                    float class_prob_f32 = sigmoid(deqnt_affine_to_f32(maxClassProbs, zp, scale));

                    // 计算Box_scores,并存储在boxScores中
                    boxScores.push_back(box_conf_f32* class_prob_f32);

                    // 将最大类别的ID存储在classId数组中
                    classId.push_back(maxClassId);

                    // 增加有效目标框的数量
                    validCount++;
                }
            }
        }
    }
    return validCount;
}

static int process_fp(float *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
                   std::vector<float> &boxes, std::vector<float> &boxScores, std::vector<int> &classId,
                   float threshold)
{

    int validCount = 0;
    int grid_len = grid_h * grid_w;
    float thres_sigmoid = unsigmoid(threshold);
    for (int a = 0; a < 3; a++)
    {
        for (int i = 0; i < grid_h; i++)
        {
            for (int j = 0; j < grid_w; j++)
            {
                float box_confidence = input[(COCO_PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
                if (box_confidence >= thres_sigmoid)
                {
                    int offset = (COCO_PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
                    float *in_ptr = input + offset;
                    float box_x = sigmoid(*in_ptr) * 2.0 - 0.5;
                    float box_y = sigmoid(in_ptr[grid_len]) * 2.0 - 0.5;
                    float box_w = sigmoid(in_ptr[2 * grid_len]) * 2.0;
                    float box_h = sigmoid(in_ptr[3 * grid_len]) * 2.0;
                    box_x = (box_x + j) * (float)stride;
                    box_y = (box_y + i) * (float)stride;
                    box_w = box_w * box_w * (float)anchor[a * 2];
                    box_h = box_h * box_h * (float)anchor[a * 2 + 1];
                    box_x -= (box_w / 2.0);
                    box_y -= (box_h / 2.0);
                    boxes.push_back(box_x);
                    boxes.push_back(box_y);
                    boxes.push_back(box_w);
                    boxes.push_back(box_h);

                    float maxClassProbs = in_ptr[5 * grid_len];
                    int maxClassId = 0;
                    for (int k = 1; k < COCO_CLASS_NUM; ++k)
                    {
                        float prob = in_ptr[(5 + k) * grid_len];
                        if (prob > maxClassProbs)
                        {
                            maxClassId = k;
                            maxClassProbs = prob;
                        }
                    }
                    float box_conf_f32 = sigmoid(box_confidence);
                    float class_prob_f32 = sigmoid(maxClassProbs);
                    boxScores.push_back(box_conf_f32* class_prob_f32);
                    classId.push_back(maxClassId);
                    validCount++;
                }
            }
        }
    }
    return validCount;
}

int yolov5_post_process_u8(uint8_t *input0, uint8_t *input1, uint8_t *input2, int model_in_h, int model_in_w,
                 float conf_threshold, float nms_threshold,
                 std::vector<uint8_t> &qnt_zps, std::vector<float> &qnt_scales,
                 coco_detect_result_group_t *group)
{
    static int init = -1;
    if (init == -1)
    {
    /*
        int ret = 0;
        ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
        if (ret < 0)
        {
            return -1;
        }
    */
        init = 0;
    }

    // 初始化输出参数
    memset(group, 0, sizeof(coco_detect_result_group_t));

    // 定义储存结果的数组
    std::vector<float> filterBoxes;
    std::vector<float> boxesScore;
    std::vector<int> classId;

    // 第一个输入的步长和网格大小
    int stride0 = 8;
    int grid_h0 = model_in_h / stride0;
    int grid_w0 = model_in_w / stride0;
    int validCount0 = 0;

    // 处理第一个输入,提取bounding box和类别信息
    validCount0 = process_u8(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w,
                          stride0, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[0], qnt_scales[0]);


    // 第二个输入的步长和网格大小
    int stride1 = 16;
    int grid_h1 = model_in_h / stride1;
    int grid_w1 = model_in_w / stride1;
    int validCount1 = 0;

    // 处理第二个输入,提取bounding box和类别信息
    validCount1 = process_u8(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w,
                          stride1, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[1], qnt_scales[1]);

    // 第三个输入的步长和网格大小
    int stride2 = 32;
    int grid_h2 = model_in_h / stride2;
    int grid_w2 = model_in_w / stride2;
    int validCount2 = 0;

    // 处理第三个输入,提取bounding box和类别信息
    validCount2 = process_u8(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w,
                          stride2, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[2], qnt_scales[2]);

    // 计算有效目标框的总数
    int validCount = validCount0 + validCount1 + validCount2;

    // 没有检测到目标
    if (validCount <= 0)
    {
        return 0;
    }

    // 创建索引数组
    std::vector<int> indexArray;
    for (int i = 0; i < validCount; ++i)
    {
        indexArray.push_back(i);
    }


    // 对得分进行降序排序,并更新索引数组
    quick_sort_indice_inverse(boxesScore, 0, validCount - 1, indexArray);

    // 进行非极大值抑制,去除冗余框
    nms(validCount, filterBoxes, indexArray, nms_threshold);

    int last_count = 0;
    group->count = 0;
    /* 处理有效的检测目标框 */
    for (int i = 0; i < validCount; ++i)
    {

        if (indexArray[i] == -1 || boxesScore[i] < conf_threshold || last_count >= COCO_NUMB_MAX_SIZE)
        {
            continue;
        }
        int n = indexArray[i];

    // 计算每个目标框的坐标
        float x1 = filterBoxes[n * 4 + 0];
        float y1 = filterBoxes[n * 4 + 1];
        float x2 = x1 + filterBoxes[n * 4 + 2];
        float y2 = y1 + filterBoxes[n * 4 + 3];
        int id = classId[n];

    /*
        group->results[last_count].box.left = (int)((clamp(x1, 0, model_in_w) - w_offset) / resize_scale);
        group->results[last_count].box.top = (int)((clamp(y1, 0, model_in_h) - h_offset) / resize_scale);
        group->results[last_count].box.right = (int)((clamp(x2, 0, model_in_w) - w_offset) / resize_scale);
        group->results[last_count].box.bottom = (int)((clamp(y2, 0, model_in_h)  - h_offset) / resize_scale);
    */

    // 更新目标检测框的边界框坐标、置信度和类别信息
        group->results[last_count].box.left = (int) clamp(x1, 0, model_in_w);
        group->results[last_count].box.top = (int) clamp(y1, 0, model_in_h);
        group->results[last_count].box.right = (int) clamp(x2, 0, model_in_w);
        group->results[last_count].box.bottom = (int) clamp(y2, 0, model_in_h);

        group->results[last_count].prop = boxesScore[i];
        group->results[last_count].class_index = id;
        char *label = labels[id];
        strncpy(group->results[last_count].name, label, COCO_NAME_MAX_SIZE);

        // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, group->results[last_count].box.top,
        //        group->results[last_count].box.right, group->results[last_count].box.bottom, label);
        last_count++;
    }

    // 更新目标检结果的数量
    group->count = last_count;

    return 0;
}


int yolov5_post_process_fp(float *input0, float *input1, float *input2, int model_in_h, int model_in_w,
                 float conf_threshold, float nms_threshold,
                 coco_detect_result_group_t *group)
{
    static int init = -1;
    if (init == -1)
    {
    /*
        int ret = 0;
        ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
        if (ret < 0)
        {
            return -1;
        }
    */

        init = 0;
    }
    memset(group, 0, sizeof(coco_detect_result_group_t));

    std::vector<float> filterBoxes;
    std::vector<float> boxesScore;
    std::vector<int> classId;
    int stride0 = 8;
    int grid_h0 = model_in_h / stride0;
    int grid_w0 = model_in_w / stride0;
    int validCount0 = 0;
    validCount0 = process_fp(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w,
                          stride0, filterBoxes, boxesScore, classId, conf_threshold);

    int stride1 = 16;
    int grid_h1 = model_in_h / stride1;
    int grid_w1 = model_in_w / stride1;
    int validCount1 = 0;
    validCount1 = process_fp(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w,
                          stride1, filterBoxes, boxesScore, classId, conf_threshold);

    int stride2 = 32;
    int grid_h2 = model_in_h / stride2;
    int grid_w2 = model_in_w / stride2;
    int validCount2 = 0;
    validCount2 = process_fp(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w,
                          stride2, filterBoxes, boxesScore, classId, conf_threshold);

    int validCount = validCount0 + validCount1 + validCount2;
    // no object detect
    if (validCount <= 0)
    {
        return 0;
    }

    std::vector<int> indexArray;
    for (int i = 0; i < validCount; ++i)
    {
        indexArray.push_back(i);
    }

    quick_sort_indice_inverse(boxesScore, 0, validCount - 1, indexArray);

    nms(validCount, filterBoxes, indexArray, nms_threshold);

    int last_count = 0;
    group->count = 0;
    /* box valid detect target */
    for (int i = 0; i < validCount; ++i)
    {

        if (indexArray[i] == -1 || boxesScore[i] < conf_threshold || last_count >= COCO_NUMB_MAX_SIZE)
        {
            continue;
        }
        int n = indexArray[i];

        float x1 = filterBoxes[n * 4 + 0];
        float y1 = filterBoxes[n * 4 + 1];
        float x2 = x1 + filterBoxes[n * 4 + 2];
        float y2 = y1 + filterBoxes[n * 4 + 3];
        int id = classId[n];

    /*
        group->results[last_count].box.left = (int)((clamp(x1, 0, model_in_w) - w_offset) / resize_scale);
        group->results[last_count].box.top = (int)((clamp(y1, 0, model_in_h) - h_offset) / resize_scale);
        group->results[last_count].box.right = (int)((clamp(x2, 0, model_in_w) - w_offset) / resize_scale);
        group->results[last_count].box.bottom = (int)((clamp(y2, 0, model_in_h)  - h_offset) / resize_scale);
    */
        group->results[last_count].box.left = (int) clamp(x1, 0, model_in_w);
        group->results[last_count].box.top = (int) clamp(y1, 0, model_in_h);
        group->results[last_count].box.right = (int) clamp(x2, 0, model_in_w);
        group->results[last_count].box.bottom = (int) clamp(y2, 0, model_in_h);

        group->results[last_count].prop = boxesScore[i];
        group->results[last_count].class_index = id;
        char *label = labels[id];
        strncpy(group->results[last_count].name, label, COCO_NAME_MAX_SIZE);

        // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, group->results[last_count].box.top,
        //        group->results[last_count].box.right, group->results[last_count].box.bottom, label);
        last_count++;
    }
    group->count = last_count;

    return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8.4)

STRING(REGEX REPLACE ".*/(.*)" "\\1" CURRENT_FOLDER ${CMAKE_CURRENT_SOURCE_DIR} )
MESSAGE("current project: " ${CURRENT_FOLDER})

set(CMAKE_SYSTEM_NAME Linux)
set(CMAKE_CROSSCOMPILING TRUE)

cmake_host_system_information(RESULT arch_value QUERY OS_PLATFORM)

if(NOT "${arch_value}" STREQUAL "armv7l")
   include ($ENV{HOME}/configs/cross.cmake)
endif()

project(yolov5_detect_demo)

## 算法头文件
set(sdk_inc include/)

## 算法源码
file(GLOB file_source lib/*.cpp *.cpp)
set(source ${file_source})

find_package(OpenCV REQUIRED)

add_executable(yolov5_detect_demo ${source})
target_include_directories(yolov5_detect_demo PUBLIC ${sdk_inc} ${OpenCV_INCLUDE_DIRS})
target_link_libraries(yolov5_detect_demo pthread rknn_api ${OpenCV_LIBS})
声明:本文内容由易百纳平台入驻作者撰写,文章观点仅代表作者本人,不代表易百纳立场。如有内容侵权或者其他问题,请联系本站进行删除。
红包 2 1 评论 打赏
评论
0个
内容存在敏感词
手气红包
    易百纳技术社区暂无数据
相关专栏
置顶时间设置
结束时间
删除原因
  • 广告/SPAM
  • 恶意灌水
  • 违规内容
  • 文不对题
  • 重复发帖
打赏作者
易百纳技术社区
技术小宅
您的支持将鼓励我继续创作!
打赏金额:
¥1易百纳技术社区
¥5易百纳技术社区
¥10易百纳技术社区
¥50易百纳技术社区
¥100易百纳技术社区
支付方式:
微信支付
支付宝支付
易百纳技术社区微信支付
易百纳技术社区
打赏成功!

感谢您的打赏,如若您也想被打赏,可前往 发表专栏 哦~

举报反馈

举报类型

  • 内容涉黄/赌/毒
  • 内容侵权/抄袭
  • 政治相关
  • 涉嫌广告
  • 侮辱谩骂
  • 其他

详细说明

审核成功

发布时间设置
发布时间:
是否关联周任务-专栏模块

审核失败

失败原因
备注
拼手气红包 红包规则
祝福语
恭喜发财,大吉大利!
红包金额
红包最小金额不能低于5元
红包数量
红包数量范围10~50个
余额支付
当前余额:
可前往问答、专栏板块获取收益 去获取
取 消 确 定

小包子的红包

恭喜发财,大吉大利

已领取20/40,共1.6元 红包规则

    易百纳技术社区