k230人脸识别
from libs.PipeLine import PipeLine, ScopedTiming
# Pipeline是集成了摄像头、LCD屏幕显示等多媒体功能的模块，大大简化了调用摄像头和显示相关的操作
# Pipeline is a module that integrates multimedia functions like camera and LCD
display, greatly simplifying camera and display operations
# ScopedTiming是一个代码执行计时器，使用方法可以参考例程代码
# ScopedTiming is a code execution timer, refer to example code for usage
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
# AIBase和AI2D用来处理AI底层的逻辑
# AIBase is the base class for all AI functionality implementations
# AI2D用于处理图像
# AI2D is used for image processing
import os
import ujson
# os和ujson分别提供系统相关操作和JSON数据相关操作，不是每个例程都能用上
# os and ujson provide system operations and JSON data operations respectively,
not required for every example
from media.media import *
from time import *
import nncase_runtime as nn
# nncase是K230进行AI推理的核心模块，提供了便捷的方法供用户调用K230的KPU
# nncase is the core module for AI inference on K230, providing convenient
methods for users to call K230's KPU
import ulab.numpy as np
# ulab.numpy是从python的numpy中移植而来，用于进行一些AI运算中必要的矩阵操作
# ulab.numpy is ported from Python's numpy, used for necessary matrix operations
in AI computations
import time
import utime
import image
import random
import gc
import sys
import aidemo
# aidemo也是核心模块之一，K230固件中预制了非常多的AI玩法
# aidemo is another core module, K230 firmware includes many pre-configured AI
applications
# 通过aidemo模块可以快速简单的调用这些玩法中的复杂方法
# Complex methods in these applications can be easily called through the aidemo
module
import _thread
# _thread是线程模块
# _thread is the threading module, as detailed in previous chapters, no need to
elaborate further# 自定义人脸检测类，继承自AIBase基类
class FaceDetectionApp(AIBase):
def __init__(self, kmodel_path, model_input_size, anchors,
confidence_threshold=0.5, nms_threshold=0.2, rgb888p_size=[224,224],
display_size=[1920,1080], debug_mode=0):
# 调用基类的构造函数 / Call parent class constructor
super().__init__(kmodel_path, model_input_size, rgb888p_size,
debug_mode)
# 模型文件路径 / Path to the model file
self.kmodel_path = kmodel_path
# 模型输入分辨率 / Model input resolution
self.model_input_size = model_input_size
# 置信度阈值：检测结果的最小置信度要求 / Confidence threshold: minimum
confidence requirement for detection results
self.confidence_threshold = confidence_threshold
# NMS阈值：非极大值抑制的阈值 / NMS threshold: threshold for Non-Maximum
Suppression
self.nms_threshold = nms_threshold
# 锚点数据：用于目标检测的预定义框 / Anchor data: predefined boxes for object
detection
self.anchors = anchors
# sensor给到AI的图像分辨率，宽度16对齐 / Image resolution from sensor to AI,
width aligned to 16
self.rgb888p_size = [ALIGN_UP(rgb888p_size[0], 16), rgb888p_size[1]]
# 显示分辨率，宽度16对齐 / Display resolution, width aligned to 16
self.display_size = [ALIGN_UP(display_size[0], 16), display_size[1]]
# 调试模式标志 / Debug mode flag
self.debug_mode = debug_mode
# 实例化AI2D对象用于图像预处理 / Initialize AI2D object for image
preprocessing
self.ai2d = Ai2d(debug_mode)
# 设置AI2D的输入输出格式 / Set AI2D input/output format
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,
nn.ai2d_format.NCHW_FMT, np.uint8, np.uint8)
def config_preprocess(self, input_image_size=None):
with ScopedTiming("set preprocess config", self.debug_mode > 0):
# 获取AI2D输入尺寸 / Get AI2D input size
ai2d_input_size = input_image_size if input_image_size else
self.rgb888p_size
# 获取padding参数 / Get padding parameters
top, bottom, left, right = self.get_padding_param()
# 设置padding: [上,下,左,右], 填充值[104,117,123] / Set padding:
[top,bottom,left,right], padding value[104,117,123]
self.ai2d.pad([0, 0, 0, 0, top, bottom, left, right], 0, [104, 117,
123])
# 设置resize方法：双线性插值 / Set resize method: bilinear interpolation
self.ai2d.resize(nn.interp_method.tf_bilinear,
nn.interp_mode.half_pixel)
# 构建预处理流程 / Build preprocessing pipeline
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]])
def postprocess(self, results):
with ScopedTiming("postprocess", self.debug_mode > 0):
# 调用aidemo库进行人脸检测后处理 / Call aidemo library for face detection
post-processing
post_ret = aidemo.face_det_post_process(self.confidence_threshold,
self.nms_threshold,
self.model_input_size[1],
self.anchors,
self.rgb888p_size,
results)
return post_ret[0] if post_ret else post_ret
def draw_result(self, pl, dets):
with ScopedTiming("display_draw", self.debug_mode > 0):
if dets:
# 清除上一帧的OSD绘制 / Clear previous frame's OSD drawing
pl.osd_img.clear()
for det in dets:
# 转换检测框坐标到显示分辨率 / Convert detection box coordinates
to display resolution
x, y, w, h = map(lambda x: int(round(x, 0)), det[:4])
x = x * self.display_size[0] // self.rgb888p_size[0]
y = y * self.display_size[1] // self.rgb888p_size[1]
w = w * self.display_size[0] // self.rgb888p_size[0]
h = h * self.display_size[1] // self.rgb888p_size[1]
# 绘制黄色检测框 / Draw yellow detection box
pl.osd_img.draw_rectangle(x, y, w, h, color=(255, 255, 0,
255), thickness=2)
else:
pl.osd_img.clear()
def get_padding_param(self):
# 计算模型输入和实际图像的缩放比例 / Calculate scaling ratio between model
input and actual image
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
ratio_w = dst_w / self.rgb888p_size[0]
ratio_h = dst_h / self.rgb888p_size[1]
ratio = min(ratio_w, ratio_h)
# 计算缩放后的新尺寸 / Calculate new dimensions after scaling
new_w = int(ratio * self.rgb888p_size[0])
new_h = int(ratio * self.rgb888p_size[1])
# 计算padding值 / Calculate padding values
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
# 返回padding参数 / Return padding parameters
return (int(round(0)),
int(round(dh * 2 + 0.1)),
int(round(0)),
int(round(dw * 2 - 0.1)))def exce_demo(pl):
# 声明全局变量face_det / Declare global variable face_det
global face_det
# 获取显示相关参数 / Get display-related parameters
display_mode = pl.display_mode # 显示模式(如lcd) / Display mode (e.g.,
lcd)
rgb888p_size = pl.rgb888p_size # 原始图像分辨率 / Original image resolution
display_size = pl.display_size # 显示分辨率 / Display resolution
# 设置人脸检测模型路径 / Set face detection model path
kmodel_path = "/sdcard/kmodel/face_detection_320.kmodel"
# 设置模型参数 / Set model parameters
confidence_threshold = 0.5 # 置信度阈值 / Confidence threshold
nms_threshold = 0.2 # 非极大值抑制阈值 / Non-maximum suppression
threshold
anchor_len = 4200 # 锚框数量 / Number of anchor boxes
det_dim = 4 # 检测维度(x,y,w,h) / Detection dimensions
(x,y,w,h)
# 加载锚框数据 / Load anchor box data
anchors_path = "/sdcard/utils/prior_data_320.bin"
anchors = np.fromfile(anchors_path, dtype=np.float)
anchors = anchors.reshape((anchor_len, det_dim))
try:
# 初始化人脸检测应用实例 / Initialize face detection application instance
face_det = FaceDetectionApp(kmodel_path,
model_input_size=[320, 320],
anchors=anchors,
confidence_threshold=confidence_threshold,
nms_threshold=nms_threshold,
rgb888p_size=rgb888p_size,
display_size=display_size,
debug_mode=0)
# 配置图像预处理 / Configure image preprocessing
face_det.config_preprocess()
# 主循环 / Main loop
while True:
with ScopedTiming("total",0): # 计时器 / Timer
img = pl.get_frame() # 获取摄像头帧图像 / Get camera frame
res = face_det.run(img) # 执行人脸检测 / Run face detection
face_det.draw_result(pl, res) # 绘制检测结果 / Draw detection
results
pl.show_image() # 显示处理后的图像 / Display
processed image
gc.collect() # 垃圾回收 / Garbage collection
time.sleep_us(10) # 短暂延时 / Brief delay
except Exception as e:
print("人脸检测功能退出") # 异常退出提示 / Exception exit prompt
finally:
face_det.deinit() # 释放资源 / Release resources这是三个部分的代码