我的一些可以直接用的Docker镜像

@[toc]

#  资料

依据这个进行训练：
https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn







```bash
 ├── backbone: 特征提取网络，可以根据自己的要求选择
  ├── network_files: Faster R-CNN网络（包括Fast R-CNN以及RPN等模块）
  ├── train_utils: 训练验证相关模块（包括cocotools）
  ├── my_dataset.py: 自定义dataset用于读取VOC数据集
  ├── train_mobilenet.py: 以MobileNetV2做为backbone进行训练
  ├── train_resnet50_fpn.py: 以resnet50+FPN做为backbone进行训练
  ├── train_multi_GPU.py: 针对使用多GPU的用户使用
  ├── predict.py: 简易的预测脚本，使用训练好的权重进行预测测试
  ├── validation.py: 利用训练好的权重验证/测试数据的COCO指标，并生成record_mAP.txt文件
  └── pascal_voc_classes.json: pascal_voc标签文件
```


![image.png](/static/img/9f10bfa210ff79c85e3f94559dbb20ab.image.webp)



# 环境

dockerfile：

```dart
FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
ENV DEBIAN_FRONTEND=noninteractive

# 安装基本软件包
RUN apt-get update && \
    apt-get upgrade -y && \
    apt-get -y --no-install-recommends install vim wget curl build-essential python3.10-dev python3.10 python3-pip sudo && \
    update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 && \
    apt-get install -y libgl1 libglib2.0-0 ffmpeg tzdata && \
    ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && \
    echo "Asia/Shanghai" > /etc/timezone


RUN apt-get -y --no-install-recommends install vim wget curl git build-essential python3.10 python3-pip python3.10-venv sudo

RUN apt-get install -y libgl1 libglib2.0-0 iputils-ping python3.10-dev libgoogle-perftools-dev nginx

# 更改默认Shell为bash
SHELL ["/bin/bash", "-c"]

```
python 环境：
```dart
git clone https://github.com/WZMIAOMIAO/deep-learning-for-image-processing.git
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
conda create -n py38 python=3.8 -y
conda activate py38
# CUDA 11.0
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch -y
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn
pip install -r requirements.txt


```

得到readme.md说的一些权重：

```dart
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/backbone

 wget https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
--2024-06-05 13:50:21--  https://download.pytorch.org/models/mobilenet_v2-b0353104.pth

mv mobilenet_v2-b0353104.pth mobilenet_v2.pth

wget https://download.pytorch.org/models/resnet50-0676ba61.pth
--2024-06-05 13:50:46--  https://download.pytorch.org/models/resnet50-0676ba61.pth

mv resnet50-0676ba61.pth resnet50.pth

wget https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

mv fasterrcnn_resnet50_fpn_coco-258fb6c6.pth fasterrcnn_resnet50_fpn_coco.pth

mkdir /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/data
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/data

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar


```



# 尝试训练


反向commit 镜像：

```dart
docker commit 74d9893ccb29 kevinchina/deeplearning:fasterrcnn_train_v1

docker push kevinchina/deeplearning:fasterrcnn_train_v1
```

重启容器：

```dart
docker run --gpus all -it -v $PWD:/wkp --shm-size=64g kevinchina/deeplearning:fasterrcnn_train_v1 bash
```

训练：
```dart
conda activate py38
cd /deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/
python train_mobilenetv2.py
```

启动成功：


![image.png](/static/img/81335fd1b2a0d7be58e5c43bc23db258.image.webp)

一轮训练完成后的验证：



![image.png](/static/img/f3399d9588ab11ec4fc0f2c1ec6cefa5.image.webp)


# 安全帽数据训练

安全帽佩戴检测
数据集：https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset

加入安全帽数据，小小修改一下源代码的一些小的东西：


![image.png](/static/img/1e06798ee54fdc375e7d5abffb8ef482.image.webp)


![image.png](/static/img/f05405c240b910cf06a56878cc0f84a8.image.webp)


![image.png](/static/img/20b3d4268a66b1ef323765cd716f1bb1.image.webp)


![image.png](/static/img/e96dc1cbd599c6cf04c8691312214207.image.webp)

启动训练：

```dart
python train_mobilenetv2.py
```

训练完一轮：


![image.png](/static/img/97791fc37173e5a3d01ff2d0a68dac8c.image.webp)

训练结束：

```dart
Test:  Total time: 0:00:52 (0.0858 s / it)
Averaged stats: model_time: 0.0590 (0.0440)  evaluator_time: 0.2436 (0.0344)
Accumulating evaluation results...
DONE (t=1.42s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.412
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.695
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.425
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.171
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.669
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.169
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.395
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.466
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.714
successful save loss curve!
successful save mAP curve!

```

# 测试

用test.txt中测试准确率

```dart
python validation.py
```


![image.png](/static/img/04ccf5406ccc6fbd8e073b675d2e2ee6.image.webp)




# 预测

```dart
(py38) root@f3661bce90f6:/deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn# python  predict.py
using cuda:0 device.
inference+NMS time: 0.018668174743652344
```


docker push kevinchina/deeplearning:fasterrcnn_train_v2


# 全部数据、代码、训练完的权重等资料见：

```dart
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
```

【深度学习】安全帽检测，目标检测，Faster RCNN训练

# 功能

DAC波形发生器

按键切换波形，正弦波、三角波、锯齿波、方波。





# 部分代码

```cpp

const unsigned int zhengxian[101] = { 32767, 34824, 36874, 38907, 40916, 42893,
		44830, 46719, 48553, 50325, 52027, 53654, 55198, 56653, 58015, 59276,
		60434, 61481, 62416, 63233, 63931, 64505, 64954, 65276, 65470, 65534,
		65470, 65276, 64954, 64505, 63931, 63233, 62416, 61481, 60434, 59276,
		58015, 56653, 55198, 53654, 52027, 50325, 48553, 46719, 44830, 42893,
		40916, 38907, 36874, 34824, 32767, 30710, 28660, 26627, 24618, 22641,
		20704, 18815, 16981, 15209, 13507, 11880, 10336, 8881, 7519, 6258, 5100,
		4053, 3118, 2301, 1603, 1029, 580, 258, 64, 0, 64, 258, 580, 1029, 1603,
		2301, 3118, 4053, 5100, 6258, 7519, 8881, 10336, 11880, 13507, 15209,
		16981, 18815, 20704, 22641, 24618, 26627, 28660, 30710 };
const unsigned int sanjiao[101] = { 0, 1310, 2621, 3932, 5242, 6553, 7864, 9174,
		10485, 11796, 13107, 14417, 15728, 17039, 18349, 19660, 20971, 22281,
		23592, 24903, 26214, 27524, 28835, 30146, 31456, 32767, 34078, 35388,
		36699, 38010, 39321, 40631, 41942, 43253, 44563, 45874, 47185, 48495,
		49806, 51117, 52428, 53738, 55049, 56360, 57670, 58981, 60292, 61602,
		62913, 64224, 65535, 64224, 62913, 61602, 60292, 58981, 57670, 56360,
		55049, 53738, 52428, 51117, 49806, 48495, 47185, 45874, 44563, 43253,
		41942, 40631, 39321, 38010, 36699, 35388, 34078, 32767, 31456, 30146,
		28835, 27524, 26214, 24903, 23592, 22281, 20971, 19660, 18349, 17039,
		15728, 14417, 13107, 11796, 10485, 9174, 7864, 6553, 5242, 3932, 2621,
		1310 };

const unsigned int juchi[101] = { 0, 655, 1310, 1966, 2621, 3276, 3932, 4587,
		5242, 5898, 6553, 7208, 7864, 8519, 9174, 9830, 10485, 11140, 11796,
		12451, 13107, 13762, 14417, 15073, 15728, 16383, 17039, 17694, 18349,
		19005, 19660, 20315, 20971, 21626, 22281, 22937, 23592, 24247, 24903,
		25558, 26214, 26869, 27524, 28180, 28835, 29490, 30146, 30801, 31456,
		32112, 32767, 33422, 34078, 34733, 35388, 36044, 36699, 37354, 38010,
		38665, 39321, 39976, 40631, 41287, 41942, 42597, 43253, 43908, 44563,
		45219, 45874, 46529, 47185, 47840, 48495, 49151, 49806, 50461, 51117,
		51772, 52428, 53083, 53738, 54394, 55049, 55704, 56360, 57015, 57670,
		58326, 58981, 59636, 60292, 60947, 61602, 62258, 62913, 63568, 64224,
		64879 };

const unsigned int fangbo[101] = { 65535, 65535, 65535, 65535, 65535, 65535,
		65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535,
		65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535,
		65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535,
		65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535, 65535,
		65535, 65535, 65535, 65535, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
		0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
		0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };



void main(void) {

	WDTCTL = WDTPW + WDTHOLD;                 // Stop WDT

	P1DIR = 0;
	P2DIR = 0;

	BCSCTL1 = CALBC1_16MHZ; /* Set DCO to16MHz */
	DCOCTL = CALDCO_16MHZ;

	TCA6416A_Init(); /* 初始化IO扩展口 */

	/* ----提示初始化成功---- */
	PinOUT(0, 1); /* 指定0号管脚输出为0 */
	PinOUT(1, 1); /* 指定1号管脚输出为0 */
	PinOUT(2, 1); /* 指定0号管脚输出为0 */
	PinOUT(3, 1); /* 指定1号管脚输出为0 */
	PinOUT(4, 1); /* 指定0号管脚输出为0 */
	PinOUT(5, 1); /* 指定1号管脚输出为0 */
	PinOUT(6, 1); /* 指定0号管脚输出为0 */
	PinOUT(7, 1); /* 指定1号管脚输出为0 */

	HT1621_init(); /* 初始化lcd_128 */

	LCD_Clear();

	/* 定时器0 检测按键的 */
	CCTL0 = CCIE;
	TACCR0 = 3276 / 5; /* 20MS */
	TACTL = TASSEL_1 + MC_1;

	/* 定时器0 检测按键的 */
//	TA1CCTL0 = CCIE;
//	TA1CCR0 = 32 * 5; /* 1  ms */
//	TA1CTL = TASSEL_1 + MC_1;                 //up to CCR0
	DAC8411_Init(); /* 蜂鸣器AGC */

	_EINT();

	while (1) {
		PinIN();
		I2C_IODect(); /*按键检测处理 */

		//TACTL = TASSEL_1 + MC_1 + TACLR; /* 打开定时器 */
		//LPM0;

		jishu++;
		if (jishu == 100) {
			jishu = 0;
		}

		//which 决定了是什么波形
		if (which == 0) {
			write2DAC8411(zhengxian[jishu]);
		} else if (which == 1) {
			write2DAC8411(sanjiao[jishu]);
		} else if (which == 2) {
			write2DAC8411(juchi[jishu]);
		} else if (which == 3) {
			write2DAC8411(fangbo[jishu]);
		}
	}
}

/* Timer A0 interrupt service routine */
#pragma vector=TIMER0_A0_VECTOR
__interrupt void Timer_A(void) {

	//TACTL = TASSEL_1 + MC_0 + TACLR; /* 停下时钟 */
	//LPM0_EXIT;
}

/* Timer A0 interrupt service routine */
#pragma vector=TIMER1_A0_VECTOR
__interrupt void Timer1_A(void) {

}

```

# 全部代码

```cpp
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
```

【TB作品】MSP430G2553 单片机口袋板，DAC波形发生器

寻求帮助请看这里：

```cpp
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
```




# 一、数据集
安全帽佩戴检测
数据集：https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset
基准模型：


![image.png](/static/img/f78244a8d84bd602d74a0b0ca37d5ae5.image.webp)

# 二、yolov10介绍

听说过yolov10吗：https://www.jiqizhixin.com/articles/2024-05-28-7


论文：

[https://arxiv.org/abs/2405.14458](https://arxiv.org/abs/2405.14458)

代码：

[https://github.com/THU-MIG/yolov10](https://github.com/THU-MIG/yolov10)


# 三、数据voc转换为yolo



调整一下，整成这样：

```cpp
VOC2028 # tree -L 1
.
├── images
├── labels
├── test.txt
├── train.txt
├── trainval.txt
└── val.txt

2 directories, 4 files

```

写为绝对路径：

```cpp
# 定义需要处理的文件名列表
file_names = ['test.txt', 'train.txt', 'trainval.txt', 'val.txt']

for file_name in file_names:
    # 打开文件用于读取
    with open(file_name, 'r') as file:
        # 读取所有行
        lines = file.readlines()
    
    # 打开（或创建）另一个文件用于写入修改后的内容，这里使用新的文件名表示已修改
    new_file_name = 'modified_' + file_name
    with open(new_file_name, 'w') as new_file:
        # 遍历每一行并进行修改
        for line in lines:
            # 删除行尾的换行符，添加'.jpg'和'images/'，然后再添加回换行符
            modified_line = '/ssd/xiedong/yolov10/VOC2028/images/' + line.strip() + '.jpg\n'
            # 将修改后的内容写入新文件
            new_file.write(modified_line)

print("所有文件处理完成。")
```

转yolo txt：

```python
import traceback
import xml.etree.ElementTree as ET
import os
import shutil
import random
import cv2
import numpy as np
from tqdm import tqdm


def convert_annotation_to_list(xml_filepath, size_width, size_height, classes):
    in_file = open(xml_filepath, encoding='UTF-8')
    tree = ET.parse(in_file)
    root = tree.getroot()
    # size = root.find('size')
    # size_width = int(size.find('width').text)
    # size_height = int(size.find('height').text)
    yolo_annotations = []
    # if size_width == 0 or size_height == 0:
    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        if cls not in classes:
            classes.append(cls)
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = [float(xmlbox.find('xmin').text),
             float(xmlbox.find('xmax').text),
             float(xmlbox.find('ymin').text),
             float(xmlbox.find('ymax').text)]

        # 标注越界修正
        if b[1] > size_width:
            b[1] = size_width
        if b[3] > size_height:
            b[3] = size_height

        txt_data = [((b[0] + b[1]) / 2.0) / size_width, ((b[2] + b[3]) / 2.0) / size_height,
                    (b[1] - b[0]) / size_width, (b[3] - b[2]) / size_height]
        # 标注越界修正
        if txt_data[0] > 1:
            txt_data[0] = 1
        if txt_data[1] > 1:
            txt_data[1] = 1
        if txt_data[2] > 1:
            txt_data[2] = 1
        if txt_data[3] > 1:
            txt_data[3] = 1
        yolo_annotations.append(f"{cls_id} {' '.join([str(round(a, 6)) for a in txt_data])}")

    in_file.close()
    return yolo_annotations


def main():
    classes = []

    root = r"/ssd/xiedong/yolov10/VOC2028"
    img_path_1 = os.path.join(root, "images")
    xml_path_1 = os.path.join(root, "labels")

    dst_yolo_root_txt = xml_path_1

    index = 0
    img_path_1_files = os.listdir(img_path_1)
    xml_path_1_files = os.listdir(xml_path_1)
    for img_id in tqdm(img_path_1_files):
        # 右边的.之前的部分
        xml_id = img_id.split(".")[0] + ".xml"
        if xml_id in xml_path_1_files:
            try:
                img = cv2.imdecode(np.fromfile(os.path.join(img_path_1, img_id), dtype=np.uint8), 1)  # img是矩阵
                new_txt_name = img_id.split(".")[0] + ".txt"
                yolo_annotations = convert_annotation_to_list(os.path.join(xml_path_1, img_id.split(".")[0] + ".xml"),
                                                              img.shape[1],
                                                              img.shape[0],
                                                              classes)
                with open(os.path.join(dst_yolo_root_txt, new_txt_name), 'w') as f:
                    f.write('\n'.join(yolo_annotations))
            except:
                traceback.print_exc()

    # classes
    print(f"我已经完成转换 {classes}")


if __name__ == '__main__':
    main()

```


vim voc2028x.yaml

```cpp
train: /ssd/xiedong/yolov10/VOC2028/modified_train.txt
val: /ssd/xiedong/yolov10/VOC2028/modified_val.txt
test: /ssd/xiedong/yolov10/VOC2028/modified_test.txt

# Classes
names:
  0: hat
  1: person
```

# 四、训练

环境：
```cpp
git clone https://github.com/THU-MIG/yolov10.git
cd yolov10
conda create -n yolov10 python=3.9 -y
conda activate yolov10
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple some-package
pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple some-package


```
训练

```cpp
yolo detect train data="/ssd/xiedong/yolov10/voc2028x.yaml" model=yolov10s.yaml epochs=200 batch=64 imgsz=640 device=1,3
```


![image.png](/static/img/f0a945d1730963f10dff2905a8075a8d.image.webp)

训练启动后：


![image.png](/static/img/00b4eb4ba0303d3c72ac5e93bc8cd723.image.webp)

训练完成后：


![image.png](/static/img/4a636c26fbe13ed2412c620e05fb8dcb.image.webp)

# 五、验证

```cpp
yolo val model="/ssd/xiedong/yolov10/runs/detect/train2/weights/best.pt" data="/ssd/xiedong/yolov10/voc2028x.yaml" batch=32 imgsz=640 device=1,3
```


![image.png](/static/img/b984512b5e67760e2dec0ac807148e2b.image.webp)

map50平均达到0.94，已超出基准很多了。


预测：
```cpp
yolo predict model=yolov10n/s/m/b/l/x.pt
```

导出：
```cpp
# End-to-End ONNX
yolo export model=yolov10n/s/m/b/l/x.pt format=onnx opset=13 simplify
# Predict with ONNX
yolo predict model=yolov10n/s/m/b/l/x.onnx

# End-to-End TensorRT
yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
# Or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine
```

demo：

```cpp
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
python app.py
# Please visit http://127.0.0.1:7860
```


![image.png](/static/img/bef28f2d5c5ed6576dc0eeff737de6de.image.webp)


![image.png](/static/img/490448ef7c3126992adf404d1b4acb50.image.webp)


# 六、数据、模型、训练后的所有文件


yolov10训练安全帽目标监测全部东西，下载看这里：

```cpp
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
```



![image.png](/static/img/00ad1b66631962fac892840a9630ca2b.image.webp)


![image.png](/static/img/4aa5ccd2b0a19b52f85326bbe7647741.image.webp)


# 七、更大、更强

用yolov10m训练了一个模型，1280输入。


![image.png](/static/img/62f915b3e942672e2366c709ad2c98d6.image.webp)

【深度学习】安全帽检测，目标检测，yolov10算法，yolov10训练

# 功能

硬件 
IIC OLED 0.96寸
无源蜂鸣器 低电平触发
DHT11 温湿度传感器
板子上的2个按键
板子上的2个灯





功能：
1 显示温湿度
2 按键单击双击识别
3 按键修改温湿度，双击选择某一个设置项目，单击进行加或者减
4 温湿度任意一个数值大于设定值后蜂鸣器报警，P1.0小灯亮
5 P4.7小灯一直闪烁 表示处于工作中

# 接线示意图
![在这里插入图片描述](https://i-blog.csdnimg.cn/blog_migrate/a9ab4e34a456a55e85f7d3556a8b5801.png)

# 效果图
![在这里插入图片描述](https://i-blog.csdnimg.cn/blog_migrate/84138a2b9a930440a628fe5cddb25ef9.jpeg)

# 原理

部分代码：

这段代码是一个温湿度报警仪系统的主程序，功能是通过连接DHT11温湿度传感器和OLED显示屏，实时监测环境的温度和湿度，并根据设定的温湿度上限进行报警。

代码中首先包含了一些引用的头文件，然后定义了一些全局变量和宏定义。接下来是一些函数的声明。

在main函数中，首先对时钟系统进行初始化，然后初始化OLED显示屏，显示初始界面。接着初始化LED灯和定时器，并开始主循环。

主循环中，首先检查是否到达定时时间，并进行相应的处理。然后读取按键的值，如果有按键按下，则根据按键的值进行操作，并更新显示屏上的内容。最后通过延时等待一段时间后继续循环。

其中，receive_init()函数用于初始化DHT11传感器，包括发出启动信号、接收温湿度数据并进行校验。receive()函数用于接收DHT11传感器发送的一个字节的数据。

其他的函数包括一些初始化操作，例如初始化LED灯、1秒定时器、按键等，以及一些辅助函数用于处理数据和更新显示屏内容。

总的来说，这段代码实现了一个简单的温湿度报警系统，通过DHT11传感器检测环境温湿度并根据设定的上限进行报警，同时通过OLED显示屏进行实时显示。

```c
/*
 * 主系统4MHZ smclk 1mhz aclk 32768HZ
 *
 * OLED  SCL--P3.0 SDA--P3.1 VCC--3.3V  GND--GND
 * beep DAT--P2.4 VCC--3.3V  GND--GND 无源 低电平触发
 * DHT11 DAT--P4.1  VCC--3.3 GND--GND
 * 注意 DHT11不是那种快速的传感器 是比较居家的那种
 *    读取DHT11的间隔时间要大于1s，不然这传感器工作不正常
 *    所以在程序里设定了2s钟 系统检测一次温湿度
 *    当程序下载后，需要断电系统重新上电，为了让DHT11重新上电工作。
 *
 *
 */
#include <msp430.h>
#include "OLED.h"
#include "key.h"

unsigned char warn_temp_h = 30, warn_humd_h = 50;
unsigned char disp[16];
char count = 0;
unsigned char time, key, setting = 0;

unsigned char over_flag = 0;

#define CPU_F ( (double) 4000000)
#define delay_us(x)   __delay_cycles( (long) (CPU_F * (double) x / 1000000.0) ) /* 微秒延时程序 */
#define delay_ms(x)   __delay_cycles( (long) (CPU_F * (double) x / 1000.0) )

#define HIGH    P4OUT   |= BIT1
#define LOW P4OUT   &= ~BIT1

unsigned char temph, templ, humdh, humdl, check, cal;


void main(void)
{
    WDTCTL = WDTPW | WDTHOLD; /* Stop watchdog timer */
    CLOCK_INIT();
    OLED_Init(); /* OLED初始化 */
    oled_init_disp();
    init_led();
    init_1s_timer();
    oled_disp();
    init_key();

    _EINT();
    while (1)
    {
        if (over_flag)
        {
            _DINT();
            over_flag = 0;
            /* 如果读取到了 并且校验成功 */
            if (receive_init() == 1)
            {
                oled_disp_dht11();
                if ((humdh > warn_humd_h) || (temph > warn_temp_h))
                {
                    P1OUT |= BIT0; /* 点亮 */
                    open_beep();
                }
                else
                {
                    P1OUT &= ~BIT0; /* 熄灭 */
                    close_beep();
                }
            }
            _EINT();
        }
        time++;
        if (time > 10)
        {
            time = 0;
            //p4.7
            P4OUT ^= BIT7;
        }
        delay_ms(10);
        key = get_key();
        if (key)
        {
            //显示按键
            count = 0;
            disp[count++] = key % 100 / 10 + '0';
            disp[count++] = key % 10 / 1 + '0';
            disp[count++] = 0;
            OLED_ShowString(0, 0, disp, 0);

            //处理按键
            do_key(key);
        }
    }
}

#pragma vector=TIMER1_A0_VECTOR
__interrupt void TIMER1_A0()
{
    over_flag = 1;
}


```



全部代码：dt2.8tupian.net/2/28880a20b16800.pg3

# 帮助、问询

```csharp
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tab=BB08J2
```

MSP430单片机，msp430f5529，DHT11 温湿度检测仪，上限报警，单击双击判定，OLED

我是一个专注技术分享的博主,尤其爱搞深度学习和单片机电子。你现在看到的是我的个人博客网站。