【CanMV K230 AI视觉】 跌倒检测
【CanMV K230 AI视觉】 跌倒检测
动态测试效果可以去下面网站自己看。
跌倒检测
跌倒检测主要根据人体姿态来判断，可以用于老人、小孩跌倒监护。
'''
实验名称：跌倒检测
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
# 自定义跌倒检测类，继承自AIBase基类
class FallDetectionApp(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) # 调用基类的构造函数
self.kmodel_path = kmodel_path # 模型文件路径
self.model_input_size = model_input_size # 模型输入分辨率
self.labels = labels # 分类标签
self.anchors = anchors # 锚点数据，用于跌倒检测
self.strides = strides # 步长设置
self.confidence_threshold = confidence_threshold # 置信度阈值
self.nms_threshold = nms_threshold # NMS（非极大值抑制）阈值
self.nms_option = nms_option # NMS选项
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0], 16), rgb888p_size[1]] # sensor给到AI的图像分辨率，并对宽度进行16的对齐
self.display_size = [ALIGN_UP(display_size[0], 16), display_size[1]] # 显示分辨率，并对宽度进行16的对齐
self.debug_mode = debug_mode # 是否开启调试模式
self.color = [(255,0, 0, 255), (255,0, 255, 0), (255,255,0, 0), (255,255,0, 255)] # 用于绘制不同类别的颜色
# 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): # 计时器，如果debug_mode大于0则开启
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size # 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
top, bottom, left, right = self.get_padding_param() # 获取padding参数
self.ai2d.pad([0, 0, 0, 0, top, bottom, left, right], 0, [0,0,0]) # 填充边缘
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) # 缩放图像
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]]) # 构建预处理流程
# 自定义当前任务的后处理，results是模型输出array的列表，这里使用了aicube库的anchorbasedet_post_process接口
def postprocess(self, results):
with ScopedTiming("postprocess", self.debug_mode > 0):
dets = 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
# 绘制检测结果到画面上
def draw_result(self, pl, dets):
with ScopedTiming("display_draw", self.debug_mode > 0):
if dets:
pl.osd_img.clear() # 清除OSD图像
for det_box in dets:
# 计算显示分辨率下的坐标
x1, y1, x2, y2 = det_box[2], det_box[3], det_box[4], det_box[5]
w = (x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
h = (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])
# 绘制矩形框和类别标签
pl.osd_img.draw_rectangle(x1, y1, int(w), int(h), color=self.color[det_box[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=self.color[det_box[0]])
else:
pl.osd_img.clear()
# 获取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]
# 设置模型路径和其他参数
kmodel_path = "/sdcard/app/tests/kmodel/yolov5n-falldown.kmodel"
confidence_threshold = 0.3
nms_threshold = 0.45
rgb888p_size = [1920, 1080]
labels = ["Fall","NoFall"] # 模型输出类别名称
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326] # anchor设置
# 初始化PipeLine，用于图像处理流程
pl = PipeLine(rgb888p_size=rgb888p_size, display_size=display_size, display_mode=display_mode)
pl.create()
# 初始化自定义跌倒检测实例
fall_det = FallDetectionApp(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)
fall_det.config_preprocess()
clock = time.clock()
try:
while True:
os.exitpoint() # 检查是否有退出信号
clock.tick()
img = pl.get_frame() # 获取当前帧数据
res = fall_det.run(img) # 推理当前帧
fall_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() # 垃圾回收
print(clock.fps()) #打印帧率
except Exception as e:
sys.print_exception(e) # 打印异常信息
finally:
fall_det.deinit() # 反初始化
pl.destroy() # 销毁PipeLine实例
| 使用类 | 说明 |
|---|---|
| FallDetectionApp | 跌倒检测类 |
原文地址：https://blog.csdn.net/weixin_45020839/article/details/142136963
免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！