【CanMV K230 AI视觉】 人体关键点检测

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

人体关键点检测

人体关键点检测是指标注出人体关节等关键信息，分析人体姿态、运动轨迹、动作角度等。

'''
实验名称：人体关键点检测
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 aidemo

# 自定义人体关键点检测类
class PersonKeyPointApp(AIBase):
    def __init__(self,kmodel_path,model_input_size,confidence_threshold=0.2,nms_threshold=0.5,rgb888p_size=[1280,720],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
        # 置信度阈值设置
        self.confidence_threshold=confidence_threshold
        # nms阈值设置
        self.nms_threshold=nms_threshold
        # 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
        #骨骼信息
        self.SKELETON = [(16, 14),(14, 12),(17, 15),(15, 13),(12, 13),(6,  12),(7,  13),(6,  7),(6,  8),(7,  9),(8,  10),(9,  11),(2,  3),(1,  2),(1,  3),(2,  4),(3,  5),(4,  6),(5,  7)]
        #肢体颜色
        self.LIMB_COLORS = [(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 255, 51,  255),(255, 255, 51,  255),(255, 255, 51,  255),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0)]
        #关键点颜色，共17个
        self.KPS_COLORS = [(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255)]

        # 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_size
            top,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aidemo库的person_kp_postprocess接口
            results = aidemo.person_kp_postprocess(results[0],[self.rgb888p_size[1],self.rgb888p_size[0]],self.model_input_size,self.confidence_threshold,self.nms_threshold)
            return results

    #绘制结果，绘制人体关键点
    def draw_result(self,pl,res):
        with ScopedTiming("display_draw",self.debug_mode >0):
            if res[0]:
                pl.osd_img.clear()
                kpses = res[1]
                for i in range(len(res[0])):
                    for k in range(17+2):
                        if (k < 17):
                            kps_x,kps_y,kps_s = round(kpses[i][k][0]),round(kpses[i][k][1]),kpses[i][k][2]
                            kps_x1 = int(float(kps_x) * self.display_size[0] // self.rgb888p_size[0])
                            kps_y1 = int(float(kps_y) * self.display_size[1] // self.rgb888p_size[1])
                            if (kps_s > 0):
                                pl.osd_img.draw_circle(kps_x1,kps_y1,5,self.KPS_COLORS[k],4)
                        ske = self.SKELETON[k]
                        pos1_x,pos1_y= round(kpses[i][ske[0]-1][0]),round(kpses[i][ske[0]-1][1])
                        pos1_x_ = int(float(pos1_x) * self.display_size[0] // self.rgb888p_size[0])
                        pos1_y_ = int(float(pos1_y) * self.display_size[1] // self.rgb888p_size[1])

                        pos2_x,pos2_y = round(kpses[i][(ske[1] -1)][0]),round(kpses[i][(ske[1] -1)][1])
                        pos2_x_ = int(float(pos2_x) * self.display_size[0] // self.rgb888p_size[0])
                        pos2_y_ = int(float(pos2_y) * self.display_size[1] // self.rgb888p_size[1])

                        pos1_s,pos2_s = kpses[i][(ske[0] -1)][2],kpses[i][(ske[1] -1)][2]
                        if (pos1_s > 0.0 and pos2_s >0.0):
                            pl.osd_img.draw_line(pos1_x_,pos1_y_,pos2_x_,pos2_y_,self.LIMB_COLORS[k],4)
                    gc.collect()
            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_width
        ratio_h = dst_h / input_high
        if ratio_w < ratio_h:
            ratio = ratio_w
        else:
            ratio = ratio_h
        new_w = (int)(ratio * input_width)
        new_h = (int)(ratio * input_high)
        dw = (dst_w - new_w) / 2
        dh = (dst_h - new_h) / 2
        top = 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, right

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-pose.kmodel"
    # 其它参数设置
    confidence_threshold = 0.2
    nms_threshold = 0.5
    rgb888p_size=[1920,1080]
    # 初始化PipeLine
    pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
    pl.create()
    # 初始化自定义人体关键点检测实例
    person_kp=PersonKeyPointApp(kmodel_path,model_input_size=[320,320],confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)
    person_kp.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_kp.run(img)  # 推理当前帧
            person_kp.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中断释放相关资源
    except Exception as e:
        sys.print_exception(e)
    finally:
        person_kp.deinit()
        pl.destroy()