【CanMV K230 AI视觉】人体检测

人体检测

动态测试效果可以去下面网站自己看。

B站视频链接：已做成合集
抖音链接：已做成合集

人体检测

人体检测是判断摄像头画面中有无出现人体，常用于人体数量检测，人流量监控以及安防监控等。

在这里插入图片描述

'''
实验名称：人体检测
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 aicube# 自定义人体检测类
class PersonDetectionApp(AIBase):def __init__(self,kmodel_path,model_input_size,labels,anchors,confidence_threshold=0.2,nms_threshold=0.5,nms_option=False,strides=[8,16,32],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# 模型输入分辨率self.model_input_size=model_input_size# 标签self.labels=labels# 检测anchors设置self.anchors=anchors# 特征图降采样倍数self.strides=strides# 置信度阈值设置self.confidence_threshold=confidence_threshold# nms阈值设置self.nms_threshold=nms_thresholdself.nms_option=nms_option# 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# 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pad和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_sizetop,bottom,left,right=self.get_padding_param()self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [0,0,0])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):# 这里使用了aicube模型的后处理接口anchorbasedet_post_preocessdets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)return dets# 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_box in dets:x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]w = float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0]h = float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1]x1 = int(x1 * self.display_size[0] // self.rgb888p_size[0])y1 = int(y1 * self.display_size[1] // self.rgb888p_size[1])x2 = int(x2 * self.display_size[0] // self.rgb888p_size[0])y2 = int(y2 * self.display_size[1] // self.rgb888p_size[1])if (h<(0.1*self.display_size[0])):continueif (w<(0.25*self.display_size[0]) and ((x1<(0.03*self.display_size[0])) or (x2>(0.97*self.display_size[0])))):continueif (w<(0.15*self.display_size[0]) and ((x1<(0.01*self.display_size[0])) or (x2>(0.99*self.display_size[0])))):continuepl.osd_img.draw_rectangle(x1 , y1 , int(w) , int(h), color=(255, 0, 255, 0), thickness = 2)pl.osd_img.draw_string_advanced( x1 , y1-50,32, " " + self.labels[det_box[0]] + " " + str(round(det_box[1],2)), color=(255,0, 255, 0))else:pl.osd_img.clear()# 计算padding参数def get_padding_param(self):dst_w = self.model_input_size[0]dst_h = self.model_input_size[1]input_width = self.rgb888p_size[0]input_high = self.rgb888p_size[1]ratio_w = dst_w / input_widthratio_h = dst_h / input_highif ratio_w < ratio_h:ratio = ratio_welse:ratio = ratio_hnew_w = (int)(ratio * input_width)new_h = (int)(ratio * input_high)dw = (dst_w - new_w) / 2dh = (dst_h - new_h) / 2top = int(round(dh - 0.1))bottom = int(round(dh + 0.1))left = int(round(dw - 0.1))right = int(round(dw - 0.1))return  top, bottom, left, rightif __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/person_detect_yolov5n.kmodel"# 其它参数设置confidence_threshold = 0.2nms_threshold = 0.6rgb888p_size=[1920,1080]labels = ["person"]anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]# 初始化PipeLinepl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)pl.create()# 初始化自定义人体检测实例person_det=PersonDetectionApp(kmodel_path,model_input_size=[640,640],labels=labels,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,nms_option=False,strides=[8,16,32],rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)person_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=person_det.run(img)  # 推理当前帧person_det.draw_result(pl,res)  # 绘制结果到PipeLine的osd图像print(res) # 打印结果pl.show_image()  # 显示当前的绘制结果gc.collect()print(clock.fps()) #打印帧率#IDE中断注销相关对象，释放资源except Exception as e:sys.print_exception(e)finally:person_det.deinit()pl.destroy()