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车牌识别

车牌检测+车牌识别看这里车牌检测识别

车牌颜色和车牌识别一起训练看这里：车牌识别+车牌颜色

训练的时候选择相应的cfg 即可选择模型的大小

train.py

 # construct face related neural networks
    #cfg =[8,8,16,16,'M',32,32,'M',48,48,'M',64,128] #small model
    # cfg =[16,16,32,32,'M',64,64,'M',96,96,'M',128,256]#medium model
    cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256] #big model
    model = myNet_ocr(num_classes=len(plate_chr),cfg=cfg)

环境配置

WIN 10 or Ubuntu 16.04
**PyTorch > 1.2.0 (may fix ctc loss)**🔥
yaml
easydict
tensorboardX

数据

车牌识别数据集CCPD+CRPD

从CCPD和CRPD截下来的车牌小图以及我自己收集的一部分车牌有需要的话加vx:we0091234

数据集打上标签,生成train.txt和val.txt

Image text

图片命名如上图：车牌号_序号.jpg 然后执行如下命令，得到train.txt和val.txt

python plateLabel.py --image_path your/train/img/path/ --label_file datasets/train.txt
python plateLabel.py --image_path your/val/img/path/ --label_file datasets/val.txt

数据格式如下：

train.txt

/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_ALL/冀BAJ731_3.jpg 5 53 52 60 49 45 43 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_ALL/冀BD387U_2454.jpg 5 53 55 45 50 49 70 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_ALL/冀BG150C_3.jpg 5 53 58 43 47 42 54 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_OTHER_ALL/皖A656V3_8090.jpg 13 52 48 47 48 71 45 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_OTHER_ALL/皖C91546_7979.jpg 13 54 51 43 47 46 48 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_OTHER_ALL/皖G88950_1540.jpg 13 58 50 50 51 47 42 
/mnt/Gu/trainData/plate/new_git_train/CCPD_CRPD_OTHER_ALL/皖GX9Y56_2113.jpg 13 58 73 51 74 47 48

将train.txt val.txt路径写入lib/config/360CC_config.yaml 中

DATASET:
  DATASET: 360CC
  ROOT: ""
  CHAR_FILE: 'lib/dataset/txt/plate2.txt'
  JSON_FILE: {'train': 'datasets/train.txt', 'val': 'datasets/val.txt'}

Train

python train.py --cfg lib/config/360CC_config.yaml

结果保存再output文件夹中

测试demo


python demo.py --model_path saved_model/best.pth --image_path images/test.jpg
                                   or your/model/path

Image text

结果是：

Image text

导出onnx


python export.py --weights saved_model/best.pth --save_path saved_model/best.onnx  --simplify

onnx 推理

python onnx_infer.py --onnx_file saved_model/best.onnx  --image_path images/test.jpg

双层车牌

双层车牌这里采用拼接成单层车牌的方式：

python:

def get_split_merge(img):
    h,w,c = img.shape
    img_upper = img[0:int(5/12*h),:]
    img_lower = img[int(1/3*h):,:]
    img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0]))
    new_img = np.hstack((img_upper,img_lower))
    return new_img

c++:

cv::Mat get_split_merge(cv::Mat &img)   //双层车牌 分割 拼接
{
    cv::Rect  upper_rect_area = cv::Rect(0,0,img.cols,int(5.0/12*img.rows));
    cv::Rect  lower_rect_area = cv::Rect(0,int(1.0/3*img.rows),img.cols,img.rows-int(1.0/3*img.rows));
    cv::Mat img_upper = img(upper_rect_area);
    cv::Mat img_lower =img(lower_rect_area);
    cv::resize(img_upper,img_upper,img_lower.size());
    cv::Mat out(img_lower.rows,img_lower.cols+img_upper.cols, CV_8UC3, cv::Scalar(114, 114, 114));
    img_upper.copyTo(out(cv::Rect(0,0,img_upper.cols,img_upper.rows)));
    img_lower.copyTo(out(cv::Rect(img_upper.cols,0,img_lower.cols,img_lower.rows)));
    return out;
}

Image text 通过变换得到

训练自己的数据集

修改alphabets.py，修改成你自己的字符集，plateName,plate_chr都要修改，plate_chr 多了一个空的占位符'#'
通过plateLabel.py 生成train.txt, val.txt
训练

数据增强

cd Text-Image-Augmentation-python-master

python demo1.py --src_path /mnt/Gu/trainData/test_aug --dst_path /mnt/Gu/trainData/result_aug/

src_path 是数据路径， dst_path是保存的数据路径

然后把两份数据放到一起进行训练，效果会好很多！

References

联系

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