驾驶员困倦检测危险驾驶检测抽烟喝水打电话检测yolov5

驾驶员困倦检测危险驾驶检测抽烟喝水打电话检测yolov5

训练我的笔记本还是16年买的，GPU是Nvidia 950M，垃圾的很，用来跑这个不太行，所以我专门租了个云服务器，2.6元一小时，不过也没办法啦，总要跑跑看看效果。租的服务器GPU是1080Ti，16G16核的，然后就开始跑，运行train.py即可，默认epochs是50，我就设置跑了10轮就没跑了，跑10轮也花了一个多小时，loss到了10左右下降就很慢了，看有些博主跑了几百上千轮，跑了18个小时，最后loss下降到0.01，然后再用这个模型预测，效果很好。我没那条件，熟悉下整个过程就好。对了，它自动会给你保存之前每轮跑出的h5模型，放在logs文件夹下，h5模型就是训练出的模型，用来后面对测试的抽烟图片预测。

import os
from typing import List, Any
import numpy as np
import codecs
import json
from glob import glob
import cv2
import shutil
from sklearn.model_selection import train_test_split
 
# 1.标签路径
labelme_path = "annotations/"
#原始labelme标注数据路径
saved_path = "VOC2007/"
# 保存路径
isUseTest=True#是否创建test集
# 2.创建要求文件夹
if not os.path.exists(saved_path + "Annotations"):
    os.makedirs(saved_path + "Annotations")
if not os.path.exists(saved_path + "JPEGImages/"):
    os.makedirs(saved_path + "JPEGImages/")
if not os.path.exists(saved_path + "ImageSets/Main/"):
    os.makedirs(saved_path + "ImageSets/Main/")
# 3.获取待处理文件
files = glob(labelme_path + "*.json")
files = [i.replace("\\","/").split("/")[-1].split(".json")[0] for i in files]
print(files)
# 4.读取标注信息并写入 xml
for json_file_ in files:
    json_filename = labelme_path + json_file_ + ".json"
    json_file = json.load(open(json_filename, "r", encoding="utf-8"))
    height, width, channels = cv2.imread('jpeg/' + json_file_ + ".jpg").shape
    with codecs.open(saved_path + "Annotations/" + json_file_ + ".xml", "w", "utf-8") as xml:
 
        xml.write('\n')
        xml.write('\t' + 'WH_data' + '\n')
        xml.write('\t' + json_file_ + ".jpg" + '\n')
        xml.write('\t\n')
        xml.write('\t\tWH Data\n')
        xml.write('\t\tWH\n')
        xml.write('\t\tflickr\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\tWH\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\t' + str(width) + '\n')
        xml.write('\t\t' + str(height) + '\n')
        xml.write('\t\t' + str(channels) + '\n')
        xml.write('\t\n')
        xml.write('\t\t0\n')
        for multi in json_file["shapes"]:
            points = np.array(multi["points"])
            labelName=multi["label"]
            xmin = min(points[:, 0])
            xmax = max(points[:, 0])
            ymin = min(points[:, 1])
            ymax = max(points[:, 1])
            label = multi["label"]
            if xmax <= xmin:
                pass
            elif ymax <= ymin:
                pass
            else:
                xml.write('\t\n')
                print(json_filename, xmin, ymin, xmax, ymax, label)
        xml.write('')
# 5.复制图片到 VOC2007/JPEGImages/下
image_files = glob("jpeg/" + "*.jpg")
print("copy image files to VOC007/JPEGImages/")
for image in image_files:
    shutil.copy(image, saved_path + "JPEGImages/")
# 6.split files for txt
txtsavepath = saved_path + "ImageSets/Main/"
ftrainval = open(txtsavepath + '/trainval.txt', 'w')
ftest = open(txtsavepath + '/test.txt', 'w')
ftrain = open(txtsavepath + '/train.txt', 'w')
fval = open(txtsavepath + '/val.txt', 'w')
total_files = glob("./VOC2007/Annotations/*.xml")
total_files = [i.replace("\\","/").split("/")[-1].split(".xml")[0] for i in total_files]
trainval_files=[]
test_files=[] 
if isUseTest:
    trainval_files, test_files = train_test_split(total_files, test_size=0.15, random_state=55) 
else: 
    trainval_files=total_files 
for file in trainval_files: 
    ftrainval.write(file + "\n") 
# split 
train_files, val_files = train_test_split(trainval_files, test_size=0.15, random_state=55) 
# train
for file in train_files: 
    ftrain.write(file + "\n") 
# val 
for file in val_files: 
    fval.write(file + "\n")
for file in test_files:
    print(file)
    ftest.write(file + "\n")
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

 

效果图:

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驾驶员困倦检测危险驾驶检测抽烟喝水打电话检测yolov5-机器学习文档类资源-CSDN下载