pyqt5 yolov4实现车牌识别系统
一、pyqt5界面展示： 可支持图片和视频实时检测识别
1.对图片识别效果：
2. 对视频的识别：
二、环境配置
1.安装pyqt5
pip install PyQt52.安装opencv
Ubuntu下opencv4.4 带CUDA的编译安装_学术菜鸟小晨的博客-CSDN博客
3.yolov4的下载和编译（其他检测算法也可）
darknet下yolov4训练自己的数据集及其调参规则快速教程_学术菜鸟小晨的博客-CSDN博客_yolov4调参
三、训练
训练集：标注的是每个字符，我们将其分为70个类，分别为："plate", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "澳","川","鄂","甘","赣","港","贵","桂","黑","沪","吉","冀","津","晋","京","警","辽","鲁","蒙","闽","宁","青","琼","陕","苏","皖","湘","新","学","渝","豫","粤","云","浙","藏"。
将数据集放入下面文件夹中：
通过makeTxt.py
import os
import random
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = './VOC2008/Annotations'
txtsavepath = './VOC2008/ImageSets'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('./VOC2008/ImageSets/trainval.txt', 'w')
ftest = open('./VOC2008/ImageSets/test.txt', 'w')
ftrain = open('./VOC2008/ImageSets/train.txt', 'w')
fval = open('./VOC2008/ImageSets/val.txt', 'w')
for i in list:
name = total_xml[i][:-4] + '\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftest.write(name)
else:
fval.write(name)
else:
ftrain.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()和 voc_label.py
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2008', 'train'), ('2008', 'test'),('2008', 'val')]
classes = ["plate", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L",
"M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
"Y", "Z", "澳","川","鄂","甘","赣","港","贵","桂","黑","沪","吉","冀","津","晋","京","警","辽","鲁","蒙","闽","宁","青","琼","陕","苏","皖","湘","新","学","渝","豫","粤","云","浙","藏"]
#找到英文label名称在list中的位置
def convert(size, box):
dw = 1./(size[0])
dh = 1./(size[1])
x = (box[0] + box[1])/2.0 - 1
y = (box[2] + box[3])/2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(year, image_id):
in_file = open('VOC%s/Annotations/%s.xml'%(year, image_id))
out_file = open('VOC%s/labels/%s.txt'%(year, image_id), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
# difficult = obj.find('difficult').text
cls = obj.find('name').text
# if cls not in classes or int(difficult)==1:
if cls not in classes:
continue
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))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOC%s/labels/'%(year)):
os.makedirs('VOC%s/labels/'%(year))
image_ids = open('VOC%s/ImageSets/%s.txt'%(year, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
os.system("cat 2008_train.txt 2008_val.txt > train.txt")
#os.system("cat 2008_train.txt 2008_val.txt 2008_test.txt> train.txt")
#os.system("cat 2014_train.txt 2014_val.txt 2012_train.txt 2012_val.txt > train.txt")
#os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")参考上一步3中的训练规则训练，也可以加入自己的数据，优化检测结果。
将训练好的模型放入下面文件夹中
