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STM32F103VE 开发板资料

这是一个从闲鱼购买的STM32F103VE开发板，原始资料缺失。本README记录了目前已还原的硬件接口信息和测试程序。

简化ImageDbHelper操作。

@Entity(tableName = "images")
public class ImageInfo {
    @PrimaryKey(autoGenerate = true)
    @ColumnInfo(name = "_id")

寄存器名称	偏移地址	R/W属性	功能描述
RTC_REG_CTL	0x0000	R/W	控制寄存器
RTC_REG_INIT	0x0004	WO	初始化触发寄存器
RTC_REG_IRQ_EN	0x0008	R/W	中断使能寄存器
RTC_REG_IRQ_STA	0x000C	R/W1C	中断状态寄存器
RTC_REG_TIME0	0x0020	R/W	时间设置寄存器0(低8位)
RTC_REG_TIME1	0x0024	R/W	时间设置寄存器1(次低8位)
RTC_REG_TIME2	0x0028	R/W	时间设置寄存器2(次高8位)

	/******************************** (C) COPYRIGHT *****************************
	* File Name : Main.c
	* Author : WCH
	* Version : V1.1
	* Date : 2022/01/25
	* Description : 模拟USB复合设备，键鼠，支持类命令
	*********************************************************************************
	* Copyright (c) 2021 Nanjing Qinheng Microelectronics Co., Ltd.
	* Attention: This software (modified or not) and binary are used for
	* microcontroller manufactured by Nanjing Qinheng Microelectronics.

	/******************************** (C) COPYRIGHT *****************************
	* File Name : Main.c
	* Author : WCH
	* Version : V1.1
	* Date : 2022/01/25
	* Description : 模拟兼容HID设备
	*********************************************************************************
	* Copyright (c) 2021 Nanjing Qinheng Microelectronics Co., Ltd.
	* Attention: This software (modified or not) and binary are used for
	* microcontroller manufactured by Nanjing Qinheng Microelectronics.

	package com.example.bleapplication;

	import android.annotation.SuppressLint;
	import android.bluetooth.BluetoothGattCharacteristic;
	import android.bluetooth.BluetoothGattDescriptor;
	import android.bluetooth.BluetoothGattService;
	import android.bluetooth.BluetoothManager;
	import android.content.Context;
	import android.os.Bundle;
	import android.util.Log;

	#include <stdio.h>

	#include "py/runtime.h"
	#include "py/mphal.h"

	STATIC mp_obj_t pytest_test(void)
	{
	printf("pytest module");
	return mp_obj_new_int(0);
	}

	import numpy as np
	import os
	from tensorflow import keras
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.callbacks import EarlyStopping, TensorBoard
	from scikeras.wrappers import KerasClassifier
	from sklearn.model_selection import GridSearchCV
	from datetime import datetime

	import cv2
	import numpy as np
	import platform
	from synset_label import labels
	from rknnlite.api import RKNNLite

	def preprocess_image(image_path):
	"""预处理单张图片"""
	img = cv2.imread(image_path)
	img = cv2.resize(img, (224, 224)) # 调整大小