Pytorch implementation of the CRNN model. In this repository I explain how to train a license plate-recognition model with pytorch-lightning.
pip install -r requirements.txt
Before training the model, it's a good practice to calculate the mean and std of the input dataset and therefore
normalize the model using proper values instead of merely normalizing with magical 0.5.
python creating_dataset.py
You can also use the images in the datasets.zip (Amirkabir University Plates Datasets) and put it in NR directory without the dataset configuration using creating_dataset.py code and List.csv labels. Make sure that the dataset has the following structure for training:
├── data-dir
│ ├── train
│ │ ├──<plate>.jpg
│ │ ├──<plate>.jpg
│ │ ├──...
│ ├── val
│ │ ├──<plate>.jpg
│ │ ├──<plate>.jpg
...
NOTE: Only .jpg, .png, and .jpeg extensions are supported!
Then checkout the alphabets.py module. It contains the alphabets characters that are required for training.
If the existing alphabets do not meet your requirements create a new dictionary containing your required alphabets and
then add it to the ALPHABETS variable with a specific name. You can get your character set using the following
command:
python get_character_sets.py --data_directory <path-to-dataset>
The output will be like the following:
[INFO] characters: +ابتثجدزسشصطعقلمنهوپگی۰۱۲۳۴۵۶۷۸۹
Run the following command to get the mean, std and n_classes of your input dataset:
python dataset.py --train_directory <your-train-dir> --alphabet_name FA_LPR --batch_size 128
or
python dataset.py --train_directory <your-train-dir> --alphabets +ابتثجدزسشصطعقلمنهوپگی۰۱۲۳۴۵۶۷۸۹ --batch_size 128
The output should be like below:
[INFO] MEAN: [0.4845], STD: [0.1884]
[INFO] N_CLASSES: 35 ---> ابپتشثجدزسصطعفقکگلمنوهی+۰۱۲۳۴۵۶۷۸۹
Run the following command to get the optimal img_w. For longer label length the img_w should be longer than usual, unless
the ctc-loss returns nan.
python get_optimum_img_w.py --alphabets ابپتشثجدزسصطعفقکگلمنوهی+۰۱۲۳۴۵۶۷۸۹ --data_directory <your-train-dir>
the output should be like below:
[INFO] max_length of this dataset is 8, optimal img_w is: 100
Or get all stats using the following command:
python get_all_stats.py --data_directory <your-dataset-dir>
Get the stats and replace them with values provided for img_w, MEAN, STD, and N_CLASSES in the settings.py module under
the BasicConfig class, or simply pass them as input arguments.
After modifying the aforementioned configs, run the following command to train the model:
python train.py
or
python train.py --img_w 100 --n_workers 8 --train_directory <your-trian-dir> --val_directory <your-val-dir> --mean 0.4845 --std 0.1884 --alphabets ابپتشثجدزسصطعفقکگلمنوهی+۰۱۲۳۴۵۶۷۸۹
- Note: For training in colab workspace you should update your pytorch-lightning pypi package then reinstall the 1.9.0 version.
To see all the configs:
python train.py -h
Output
optional arguments:
-h, --help show this help message and exit
--train_directory TRAIN_DIRECTORY
path to the dataset, default: ./dataset
--val_directory VAL_DIRECTORY
path to the dataset, default: ./dataset
--output_dir OUTPUT_DIR
path to the output directory, default: ./output
--epochs EPOCHS number of training epochs
--device DEVICE what should be the device for training, default is cuda
--mean MEAN [MEAN ...]
dataset channel-wise mean
--std STD [STD ...] dataset channel-wise std
--img_w IMG_W dataset img width size
--n_workers N_WORKERS
number of workers used for dataset collection
--batch_size BATCH_SIZE
batch size number
--alphabets ALPHABETS
alphabets used in the process
For inference run the following code:
python crnn_inference.py --model_path {path-to-your-output-dir}/best.ckpt --img_path sample_images/۱۴ق۹۱۸۱۱_7073.jpg
The output should be like the following:
۱۴ق۹۱۸۱۱
Image examples:
https://ceit.aut.ac.ir/~keyvanrad/download/ML971/project/
Password: ML971Data