物体检测（YOLOv8n）

前言

物体检测，是机器视觉里面非常典型的应用。要实现的就是将一幅图片里面的各种物体检测出来，然后跟已知模型做比较从而判断物体是什么。我们来学习一下如何通过MicroPython编程快速实现物体识别。

实验目的

编程实现物体检测识别并将识别结果画图指示。

实验讲解

本实验通过CanMV K230 AI视觉框架开发，详细说明参考 AI视觉开发框架 章节内容，这里不再重复。例程用到的模型已经存放在CanMV K230的文件系统，无需额外拷贝。

例程基于YOLOv8n, 支持识别80种物体。

["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"]

[“人”、“自行车”、“汽车”、“摩托车”、“飞机”、“公共汽车”、“火车”、“卡车”、“船”、“交通灯”、“消防栓”、“停车标志”、“停车收费表”、“长凳”、“鸟”、“猫”、“狗”、“马”、“羊”、“牛”、“大象”、“熊”、“斑马”、“长颈鹿”、“背包”、“雨伞”、“手提包”、“领带”、“手提箱”、“飞盘”、“滑雪板”、“滑雪板”、“运动球”、“风筝”、“棒球棒”、“棒球手套”、“滑板”、“冲浪板”、“网球拍”、“瓶子”、“酒杯”、“杯子”、“叉子”、“刀子”、“勺子”、“碗”、“香蕉”、“苹果”、“三明治”、“橙子”、“西兰花”、“胡萝卜”、“热狗”、“披萨”、“甜甜圈”、“蛋糕”、“椅子”、“沙发“盆栽”、“床”、“餐桌”、“马桶”、“电视”、“笔记本电脑”、“鼠标”、“遥控器”、“键盘”、“手机”、“微波炉”、“烤箱”、“烤面包机”、“水槽”、“冰箱”、“书”、“钟表”、“花瓶”、“剪刀”、“泰迪熊”、“吹风机”、“牙刷”]

具体编程思路如下：

参考代码

'''
实验名称：物体检测（基于yolov8n）
实验平台：01Studio CanMV K230
教程：wiki.01studio.cc
'''

from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import utime
import image
import random
import gc
import sys
import aidemo

# 自定义YOLOv8检测类
class ObjectDetectionApp(AIBase):
    def __init__(self,kmodel_path,labels,model_input_size,max_boxes_num,confidence_threshold=0.5,nms_threshold=0.2,rgb888p_size=[224,224],display_size=[1920,1080],debug_mode=0):
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        self.kmodel_path=kmodel_path
        self.labels=labels
        # 模型输入分辨率
        self.model_input_size=model_input_size
        # 阈值设置
        self.confidence_threshold=confidence_threshold
        self.nms_threshold=nms_threshold
        self.max_boxes_num=max_boxes_num
        # sensor给到AI的图像分辨率
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 显示分辨率
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        self.debug_mode=debug_mode
        # 检测框预置颜色值
        self.color_four=[(255, 220, 20, 60), (255, 119, 11, 32), (255, 0, 0, 142), (255, 0, 0, 230),
                         (255, 106, 0, 228), (255, 0, 60, 100), (255, 0, 80, 100), (255, 0, 0, 70),
                         (255, 0, 0, 192), (255, 250, 170, 30), (255, 100, 170, 30), (255, 220, 220, 0),
                         (255, 175, 116, 175), (255, 250, 0, 30), (255, 165, 42, 42), (255, 255, 77, 255),
                         (255, 0, 226, 252), (255, 182, 182, 255), (255, 0, 82, 0), (255, 120, 166, 157)]
        # 宽高缩放比例
        self.x_factor = float(self.rgb888p_size[0])/self.model_input_size[0]
        self.y_factor = float(self.rgb888p_size[1])/self.model_input_size[1]
        # Ai2d实例，用于实现模型预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置Ai2d的输入输出格式和类型
        self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)

    # 配置预处理操作，这里使用了resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
    def config_preprocess(self,input_image_size=None):
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            # 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，您可以通过设置input_image_size自行修改输入尺寸
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])

    # 自定义当前任务的后处理
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            result=results[0]
            result = result.reshape((result.shape[0] * result.shape[1], result.shape[2]))
            output_data = result.transpose()
            boxes_ori = output_data[:,0:4]
            scores_ori = output_data[:,4:]
            confs_ori = np.max(scores_ori,axis=-1)
            inds_ori = np.argmax(scores_ori,axis=-1)
            boxes,scores,inds = [],[],[]
            for i in range(len(boxes_ori)):
                if confs_ori[i] > confidence_threshold:
                    scores.append(confs_ori[i])
                    inds.append(inds_ori[i])
                    x = boxes_ori[i,0]
                    y = boxes_ori[i,1]
                    w = boxes_ori[i,2]
                    h = boxes_ori[i,3]
                    left = int((x - 0.5 * w) * self.x_factor)
                    top = int((y - 0.5 * h) * self.y_factor)
                    right = int((x + 0.5 * w) * self.x_factor)
                    bottom = int((y + 0.5 * h) * self.y_factor)
                    boxes.append([left,top,right,bottom])
            if len(boxes)==0:
                return []
            boxes = np.array(boxes)
            scores = np.array(scores)
            inds = np.array(inds)
            # NMS过程
            keep = self.nms(boxes,scores,nms_threshold)
            dets = np.concatenate((boxes, scores.reshape((len(boxes),1)), inds.reshape((len(boxes),1))), axis=1)
            dets_out = []
            for keep_i in keep:
                dets_out.append(dets[keep_i])
            dets_out = np.array(dets_out)
            dets_out = dets_out[:self.max_boxes_num, :]
            return dets_out

    # 绘制结果
    def draw_result(self,pl,dets):
        with ScopedTiming("display_draw",self.debug_mode >0):
            if dets:
                pl.osd_img.clear()
                for det in dets:
                    x1, y1, x2, y2 = map(lambda x: int(round(x, 0)), det[:4])
                    x= x1*self.display_size[0] // self.rgb888p_size[0]
                    y= y1*self.display_size[1] // self.rgb888p_size[1]
                    w = (x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
                    h = (y2 - y1) * self.display_size[1] // self.rgb888p_size[1]
                    pl.osd_img.draw_rectangle(x,y, w, h, color=self.get_color(int(det[5])),thickness=4)
                    pl.osd_img.draw_string_advanced( x , y-50,32," " + self.labels[int(det[5])] + " " + str(round(det[4],2)) , color=self.get_color(int(det[5])))
            else:
                pl.osd_img.clear()


    # 多目标检测 非最大值抑制方法实现
    def nms(self,boxes,scores,thresh):
        """Pure Python NMS baseline."""
        x1,y1,x2,y2 = boxes[:, 0],boxes[:, 1],boxes[:, 2],boxes[:, 3]
        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
        order = np.argsort(scores,axis = 0)[::-1]
        keep = []
        while order.size > 0:
            i = order[0]
            keep.append(i)
            new_x1,new_y1,new_x2,new_y2,new_areas = [],[],[],[],[]
            for order_i in order:
                new_x1.append(x1[order_i])
                new_x2.append(x2[order_i])
                new_y1.append(y1[order_i])
                new_y2.append(y2[order_i])
                new_areas.append(areas[order_i])
            new_x1 = np.array(new_x1)
            new_x2 = np.array(new_x2)
            new_y1 = np.array(new_y1)
            new_y2 = np.array(new_y2)
            xx1 = np.maximum(x1[i], new_x1)
            yy1 = np.maximum(y1[i], new_y1)
            xx2 = np.minimum(x2[i], new_x2)
            yy2 = np.minimum(y2[i], new_y2)
            w = np.maximum(0.0, xx2 - xx1 + 1)
            h = np.maximum(0.0, yy2 - yy1 + 1)
            inter = w * h
            new_areas = np.array(new_areas)
            ovr = inter / (areas[i] + new_areas - inter)
            new_order = []
            for ovr_i,ind in enumerate(ovr):
                if ind < thresh:
                    new_order.append(order[ovr_i])
            order = np.array(new_order,dtype=np.uint8)
        return keep

    # 根据当前类别索引获取框的颜色
    def get_color(self, x):
        idx=x%len(self.color_four)
        return self.color_four[idx]


if __name__=="__main__":
    # 显示模式，默认"hdmi",可以选择"hdmi"和"lcd"
    display_mode="lcd"
    if display_mode=="hdmi":
        display_size=[1920,1080]
    else:
        display_size=[800,480]
    # 模型路径
    kmodel_path="/sdcard/app/tests/kmodel/yolov8n_320.kmodel"
    labels = ["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"]
    # 其它参数设置
    confidence_threshold = 0.2
    nms_threshold = 0.2
    max_boxes_num = 50
    rgb888p_size=[320,320]

    # 初始化PipeLine
    pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
    pl.create()
    # 初始化自定义目标检测实例
    ob_det=ObjectDetectionApp(kmodel_path,labels=labels,model_input_size=[320,320],max_boxes_num=max_boxes_num,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)
    ob_det.config_preprocess()

    clock = time.clock()

    try:
        while True:

            os.exitpoint()

            clock.tick()

            img=pl.get_frame() # 获取当前帧数据
            res=ob_det.run(img) # 推理当前帧
            ob_det.draw_result(pl,res) # 绘制结果到PipeLine的osd图像
            print(res)  # 打印当前结果
            pl.show_image() # 显示当前的绘制结果
            gc.collect()

            print(clock.fps()) #打印帧率

    except Exception as e:
        sys.print_exception(e)
    finally:
        ob_det.deinit()
        pl.destroy()

这里对关键代码进行讲解：

• 主函数代码：

可以看到使用默认配置后只使用了4行代码便实现了获取当前帧图像、AI推理、绘制结果、显示结果 的识别流程。

代码中 res为识别结果, 包含目标位置和物体种类。

        ...
        while True:

            os.exitpoint()

            clock.tick()

            img=pl.get_frame() # 获取当前帧数据
            res=ob_det.run(img) # 推理当前帧
            ob_det.draw_result(pl,res) # 绘制结果到PipeLine的osd图像
            print(res)  # 打印当前结果
            pl.show_image() # 显示当前的绘制结果
            gc.collect()

            print(clock.fps()) #打印帧率
        ...

实验结果

在CanMV IDE中运行上述代码，将摄像头正对下方图像。

原图：

识别结果：